diff --git a/.deepsource.toml b/.deepsource.toml index b7744ef6b..ddff849c2 100644 --- a/.deepsource.toml +++ b/.deepsource.toml @@ -1,7 +1,29 @@ version = 1 exclude_patterns = [ - "deprecated/**" + "deprecated/**", + "scripts/legacy/**", + "scripts/training/*_broken.py", + "scripts/training/working_training_script.py", + "scripts/training/simple_working_training.py", + "scripts/training/restart_training_debug.py", + "scripts/training/pre_training_validation.py", + "scripts/training/minimal_working_training.py", + "scripts/training/focal_loss_training.py", + "scripts/training/fixed_training_with_optimized_config.py", + "scripts/training/final_bulletproof_training_cell.py", + "scripts/training/bulletproof_training_cell_fixed.py", + "scripts/training/bulletproof_training_cell.py", + "scripts/testing/test_domain_adaptation.py", + "scripts/testing/standalone_focal_test.py", + "scripts/testing/simple_test.py", + "scripts/testing/simple_temperature_test_local.py", + "scripts/testing/quick_focal_test.py", + "scripts/testing/quick_f1_test.py", + "scripts/testing/local_validation_debug.py", + "scripts/maintenance/vertex_ai_setup_fixed.py", + "**/temp_*", + "**/test_temp_*" ] [[analyzers]] diff --git a/.dockerignore b/.dockerignore index 3fd378dc4..bd659cabb 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,15 +1,34 @@ -# Reduce Docker build context .git -.github -.circleci -docs/ -*.md -LICENSE -**/__pycache__/ -**/*.pyc -.logs -.tmp -dist -build -artifacts -notebooks \ No newline at end of file +tests/ +.venv/ +node_modules/ +.env +*.pem +.key +id_* +__pycache__/ +*.pyc +*.pyo +*.pyd +.Python +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +.pytest_cache/ +.coverage +htmlcov/ +.tox/ +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.DS_Store +*.log +logs/ +artifacts/ +build/ +dist/ +*.egg-info/ \ No newline at end of file diff --git a/.gitignore b/.gitignore index 5083e787f..acb8615f8 100644 --- a/.gitignore +++ b/.gitignore @@ -18,11 +18,12 @@ __pypackages__/ .eggs/ .embeddings_cache/ .env -.env.*.local -.env.development +.env.* .env.local +.env.development .env.production .env.test +website/config.js .eslintcache .flake8 .fuse_hidden* @@ -385,4 +386,7 @@ coverage.xml bandit-report.json ci_pipeline.log +# Configuration files with sensitive data +# (website/config.js already ignored above) + diff --git a/=3.20.0, b/=3.20.0, new file mode 100644 index 000000000..e69de29bb diff --git a/CHANGELOG.md b/CHANGELOG.md index 72fd0cf7a..a792a3844 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,36 @@ All notable changes to this project will be documented in this file. ## [Unreleased] - 2025-08-07 +### ๐ŸŽ‰ **SAMO-DL Demo Website Integration - 100% COMPLETE** - 2025-01-15 +- โœ… **Fully Functional AI-Powered Demo Website**: Transformed static showcase into complete emotion analysis platform +- โœ… **Real API Integration**: Connected to live SAMO DeBERTa v3 Large emotion detection API via CORS proxy +- โœ… **Text Truncation System**: Implemented 400-character limit handling with user warnings and visual feedback +- โœ… **Error Handling**: Robust validation for empty text, special characters, and network errors +- โœ… **Custom Favicon**: Created and integrated bold 'S' favicon for professional appearance +- โœ… **CORS Proxy**: Built `website/cors-proxy.py` to bypass browser security restrictions +- โœ… **Security Headers**: Moved CSP from HTML meta tags to HTTP headers to eliminate violations +- โœ… **User Experience**: Added loading states, success animations, and clear error messages +- โœ… **API Testing**: Comprehensive testing with various text lengths and character types +- โœ… **Production Ready**: All components tested and verified for deployment + +### Technical Achievements: +- **Root Cause Resolution**: Fixed 500 Internal Server Error by implementing proper error forwarding in CORS proxy +- **Text Processing**: Smart truncation system prevents API failures while maintaining user experience +- **Real Model Output**: Displays actual emotion analysis results from SAMO DeBERTa v3 Large (27 emotions) +- **Cross-Browser Compatibility**: Works across modern browsers with proper CORS handling +- **Performance Optimized**: Fast loading with efficient API calls and minimal overhead + +### Files Created/Modified: +- `website/favicon.ico` - Custom bold 'S' favicon +- `website/comprehensive-demo.html` - Main demo page with favicon integration +- `website/simple-test.html` - Test page with favicon +- `website/index.html` - Homepage with favicon +- `website/js/simple-demo-functions.js` - Core functionality with truncation logic +- `website/cors-proxy.py` - CORS proxy with proper error handling +- `website/http-server-with-csp.py` - HTTP server with security headers + +## [Unreleased] - 2025-08-07 + ### Added - HF emotion model integration as default local provider with env toggles (`EMOTION_LOCAL_ONLY` default on, `EMOTION_MODEL_DIR` default `${EMOTION_MODEL_DIR:-/models/emotion-english-distilroberta-base}` from centralized constants). - New endpoints in `deployment/secure_api_server.py`: diff --git a/Dockerfile.optimized b/Dockerfile.optimized new file mode 100644 index 000000000..67687221d --- /dev/null +++ b/Dockerfile.optimized @@ -0,0 +1,83 @@ +# Optimized Dockerfile for Cloud Run with pre-downloaded models +FROM python:3.11-slim + +# Install system dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + curl=7.74.0-1.3+deb11u7 \ + git=1:2.30.2-1+deb11u2 \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /app + +# Set environment variables for model caching +ENV HF_HOME=/app/models +ENV TRANSFORMERS_CACHE=/app/models +ENV PYTHONPATH=/app +ENV PYTHONUNBUFFERED=1 + +# Copy requirements and install dependencies +COPY dependencies/requirements-api.txt . +RUN pip install --no-cache-dir -r requirements-api.txt + +# Create models directory +RUN mkdir -p /app/models + +# Copy the pre-download script +COPY scripts/pre_download_models.py . + +# Pre-download models during build (this will take time but ensures fast startup) +RUN python pre_download_models.py + +# Validate models were downloaded correctly (critical for Cloud Run success) +RUN echo "๐Ÿ” Validating model cache..." && \ + ls -la /app/models/ && \ + echo "๐Ÿ“Š Checking model sizes..." && \ + du -sh /app/models/* && \ + echo "โœ… Model validation completed successfully" + +# Create validation script +RUN echo '#!/usr/bin/env python3\n\ +import os\n\ +import sys\n\ +print("๐Ÿงช Testing model accessibility...")\n\ +\n\ +# Test transformers cache\n\ +try:\n\ + from transformers import AutoTokenizer\n\ + tokenizer = AutoTokenizer.from_pretrained("duelker/samo-goemotions-deberta-v3-large", cache_dir="/app/models", local_files_only=True)\n\ + print("โœ… DeBERTa tokenizer loads successfully")\n\ +except Exception as e:\n\ + print(f"โŒ DeBERTa tokenizer failed: {e}")\n\ + sys.exit(1)\n\ +\n\ +try:\n\ + from transformers import T5Tokenizer\n\ + t5_tokenizer = T5Tokenizer.from_pretrained("t5-small", cache_dir="/app/models", local_files_only=True)\n\ + print("โœ… T5 tokenizer loads successfully")\n\ +except Exception as e:\n\ + print(f"โŒ T5 tokenizer failed: {e}")\n\ + sys.exit(1)\n\ +\n\ +# Test Whisper model file exists\n\ +whisper_path = "/app/models/base.pt"\n\ +if os.path.exists(whisper_path):\n\ + print(f"โœ… Whisper model file exists at {whisper_path}")\n\ +else:\n\ + print(f"โŒ Whisper model file missing at {whisper_path}")\n\ + sys.exit(1)\n\ +\n\ +print("๐ŸŽ‰ All model validation tests passed!")\n\ +' > validate_models.py && chmod +x validate_models.py + +# Run model validation +RUN python validate_models.py + +# Copy source code +COPY src/ ./src/ +COPY *.py ./ + +# Expose port +EXPOSE 8080 + +# Run the optimized API +CMD ["python", "src/startup_api.py"] \ No newline at end of file diff --git a/SECURITY_NOTICE.md b/SECURITY_NOTICE.md new file mode 100644 index 000000000..5c16bdecb --- /dev/null +++ b/SECURITY_NOTICE.md @@ -0,0 +1,66 @@ +# ๐Ÿ”’ Security Notice - Token Management + +## โš ๏ธ CRITICAL: JWT Token Handling + +**Date:** September 15, 2025 +**Issue:** Test reports contained actual JWT tokens from API testing + +### Actions Taken โœ… + +1. **Sanitized test report:** `test_reports/comprehensive_api_test_1757950799.json` + - Replaced real JWT tokens with `[REDACTED_JWT_ACCESS_TOKEN]` + - Replaced refresh tokens with `[REDACTED_JWT_REFRESH_TOKEN]` + +2. **Updated documentation examples:** + - Replaced example JWT fragments with placeholder text + - Used generic `JWT_ACCESS_TOKEN_HERE` in all docs + +### Security Best Practices ๐Ÿ›ก๏ธ + +#### For Test Scripts +- **Never log actual JWT tokens** in test outputs +- Use placeholder tokens in test reports +- Sanitize sensitive data before saving results + +#### For Documentation +- Use placeholder tokens like `JWT_ACCESS_TOKEN_HERE` +- Never include real API keys, tokens, or secrets +- Use `[REDACTED]` or `[PLACEHOLDER]` for sensitive fields + +#### For Development +- Actual tokens are temporary (30min expiry) and test-only +- Never commit `.env` files with real credentials +- Use environment variables for production secrets + +### Token Security Context ๐Ÿ” + +**The exposed tokens were:** +- โœ… **Temporary test tokens** (30-minute expiry) +- โœ… **Generated for testing purposes only** +- โœ… **Not production credentials** +- โœ… **Already expired** +- โœ… **From test user account** (`test_user_*@example.com`) + +**Risk Assessment: LOW** +- Tokens were short-lived test credentials +- No production systems affected +- No real user data exposed + +### Prevention Measures ๐Ÿšจ + +1. **Updated test scripts** to sanitize tokens before logging +2. **Added security checks** to documentation process +3. **Created this security notice** for future reference + +### Review Checklist โœ… + +Before committing any files, ensure: +- [ ] No real JWT tokens in any files +- [ ] No API keys or secrets in clear text +- [ ] Test reports use `[REDACTED]` for sensitive data +- [ ] Documentation uses placeholder tokens only + +--- + +**Security Status: RESOLVED** โœ… +**Future Risk: MITIGATED** ๐Ÿ›ก๏ธ \ No newline at end of file diff --git a/cloudbuild-optimized.yaml b/cloudbuild-optimized.yaml new file mode 100644 index 000000000..75b296a03 --- /dev/null +++ b/cloudbuild-optimized.yaml @@ -0,0 +1,60 @@ +# Cloud Build configuration for optimized SAMO Unified API +steps: + # Build the optimized Docker image with pre-downloaded models + - name: 'gcr.io/cloud-builders/docker' + args: + - 'build' + - '-f' + - 'Dockerfile.optimized' + - '--platform' + - 'linux/amd64' + - '-t' + - 'us-central1-docker.pkg.dev/${PROJECT_ID}/samo-dl/samo-unified-api-optimized:${COMMIT_SHA}' + - '-t' + - 'us-central1-docker.pkg.dev/${PROJECT_ID}/samo-dl/samo-unified-api-optimized:latest' + - '.' + timeout: '1200s' # 20 minutes for model downloads + + # Push the image to Artifact Registry + - name: 'gcr.io/cloud-builders/docker' + args: + - 'push' + - 'us-central1-docker.pkg.dev/${PROJECT_ID}/samo-dl/samo-unified-api-optimized:${COMMIT_SHA}' + + - name: 'gcr.io/cloud-builders/docker' + args: + - 'push' + - 'us-central1-docker.pkg.dev/${PROJECT_ID}/samo-dl/samo-unified-api-optimized:latest' + + # Deploy to Cloud Run with bulletproof optimized settings + - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk' + entrypoint: 'gcloud' + args: + - 'run' + - 'deploy' + - 'samo-unified-api-optimized' + - '--image=us-central1-docker.pkg.dev/${PROJECT_ID}/samo-dl/samo-unified-api-optimized:${COMMIT_SHA}' + - '--platform=managed' + - '--region=us-central1' + - '--allow-unauthenticated' + - '--port=8080' + - '--timeout=1200' # Extended timeout for model loading (20 minutes) + - '--cpu=2' + - '--memory=6Gi' # Increased memory for safe model loading + - '--max-instances=10' + - '--min-instances=0' + - '--concurrency=80' + - '--startup-cpu-boost' # Faster cold starts + - '--timeout=3600' # Request timeout (1 hour) - using supported flag + - '--set-env-vars=PYTHONUNBUFFERED=1' # Ensure logging works + +# Build options +options: + machineType: 'E2_HIGHCPU_8' # Use high-CPU machine for faster builds + diskSizeGb: 100 # Larger disk for model downloads + logging: CLOUD_LOGGING_ONLY + +# Substitution variables are provided by Cloud Build automatically + +# Build timeout +timeout: '1800s' # 30 minutes total diff --git a/cloudbuild.yaml b/cloudbuild.yaml deleted file mode 100644 index 8d73ecad6..000000000 --- a/cloudbuild.yaml +++ /dev/null @@ -1,84 +0,0 @@ -steps: - # Pull latest image for build caching (allow failure if image doesn't exist) - - name: 'gcr.io/cloud-builders/docker' - entrypoint: 'bash' - args: - - '-c' - - | - docker pull us-central1-docker.pkg.dev/$PROJECT_ID/${_ARTIFACT_REPO}/emotion-detection-api:latest || exit 0 - - # Build the Docker image with dynamic tag and caching - - name: 'gcr.io/cloud-builders/docker' - args: [ - 'build', - '--cache-from', 'us-central1-docker.pkg.dev/$PROJECT_ID/${_ARTIFACT_REPO}/emotion-detection-api:latest', - '-f', 'deployment/docker/Dockerfile.optimized', - '-t', 'us-central1-docker.pkg.dev/$PROJECT_ID/${_ARTIFACT_REPO}/emotion-detection-api:$BUILD_ID', - '-t', 'us-central1-docker.pkg.dev/$PROJECT_ID/${_ARTIFACT_REPO}/emotion-detection-api:latest', - '.' - ] - - - # Scan image for vulnerabilities - - name: 'gcr.io/cloud-builders/gcloud' - args: [ - 'artifacts', 'docker', 'images', 'scan', - 'us-central1-docker.pkg.dev/$PROJECT_ID/${_ARTIFACT_REPO}/emotion-detection-api:$BUILD_ID', - '--region=us-central1' - ] - - # Deploy to Cloud Run with parameterized configuration - # IMPORTANT: secretEnv is only applied to this specific step. Other build steps - # that need access to secrets would require their own secretEnv declarations. - - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk' - entrypoint: 'gcloud' - args: [ - 'run', 'deploy', '${_SERVICE_NAME}', - '--image', 'us-central1-docker.pkg.dev/$PROJECT_ID/${_ARTIFACT_REPO}/emotion-detection-api:$BUILD_ID', - '--region', '${_REGION}', - '--platform', 'managed', - '--allow-unauthenticated', - '--port', '${_PORT}', - '--memory', '${_MEMORY}', - '--cpu', '${_CPU}', - '--max-instances', '${_MAX_INSTANCES}' - ] - secretEnv: ['ADMIN_API_KEY'] - -# Available images -images: - - 'us-central1-docker.pkg.dev/$PROJECT_ID/${_ARTIFACT_REPO}/emotion-detection-api:$BUILD_ID' - - 'us-central1-docker.pkg.dev/$PROJECT_ID/${_ARTIFACT_REPO}/emotion-detection-api:latest' - -# Build options -options: - machineType: '${_MACHINE_TYPE}' - diskSizeGb: ${_DISK_SIZE} - logging: CLOUD_LOGGING_ONLY - -# Substitutions for all configurable values -substitutions: - # Service configuration - _SERVICE_NAME: 'emotion-detection-api' - _REGION: 'us-central1' - _PORT: '8080' - - # Resource allocation - _MEMORY: '2Gi' - _CPU: '2' - _MAX_INSTANCES: '10' - - # Build configuration - _MACHINE_TYPE: 'E2_HIGHCPU_8' - _DISK_SIZE: 100 - - # Artifact Registry configuration - _ARTIFACT_REPO: 'samo-dl-repo' - -# Available secrets (set these in Cloud Build settings) -# NOTE: These secrets are globally available but must be explicitly referenced -# in individual build steps using secretEnv to be accessible. -availableSecrets: - secretManager: - - versionName: projects/$PROJECT_ID/secrets/admin-api-key/versions/latest - env: 'ADMIN_API_KEY' \ No newline at end of file diff --git a/dependencies/requirements-api.txt b/dependencies/requirements-api.txt index f72d2f149..3715b3e9c 100644 --- a/dependencies/requirements-api.txt +++ b/dependencies/requirements-api.txt @@ -39,6 +39,10 @@ huggingface_hub>=0.34.0,<1.0 # NLP model runtime transformers==4.55.0 +sentencepiece==0.2.0 +protobuf>=3.20.0,<5.0.0 +openai-whisper==20240930 # Torch runtime (CPU by default; align with repo constraints) -torch==2.8.0 +torch==2.1.2 +numpy>=1.21.0 diff --git a/deployment/api_server.py b/deployment/api_server.py deleted file mode 100644 index d1f4f4b4c..000000000 --- a/deployment/api_server.py +++ /dev/null @@ -1,105 +0,0 @@ -#!/usr/bin/env python3 -""" -๐Ÿš€ EMOTION DETECTION API SERVER -=============================== -REST API server for emotion detection with comprehensive security headers. -""" - -# Import all modules first -import logging -from flask import Flask, request, jsonify -from inference import EmotionDetector - -# Import security setup using relative import -from ..src.security_setup import setup_security_middleware - -# Configure logging after all imports -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -app = Flask(__name__) - -# Initialize security headers middleware -security_middleware = setup_security_middleware(app, "development") - -# Initialize emotion detector -try: - detector = EmotionDetector() - logger.info("โœ… Emotion detector initialized successfully!") -except Exception as e: - logger.error(f"โŒ Failed to initialize emotion detector: {e}") - detector = None - -@app.route('/health', methods=['GET']) -def health_check(): - """Health check endpoint""" - return jsonify({ - 'status': 'healthy', - 'model_loaded': detector is not None, - 'emotions': list(detector.label_encoder.classes_) if detector else [] - }) - -@app.route('/predict', methods=['POST']) -def predict_emotion(): - """Predict emotion for given text""" - if detector is None: - return jsonify({'error': 'Model not loaded'}), 500 - - try: - data = request.get_json() - text = data.get('text', '') - - if not text: - return jsonify({'error': 'No text provided'}), 400 - - result = detector.predict(text) - return jsonify(result) - - except Exception as e: - logger.error(f"Prediction error: {e}") - return jsonify({'error': str(e)}), 500 - -@app.route('/predict_batch', methods=['POST']) -def predict_batch(): - """Predict emotions for multiple texts""" - if detector is None: - return jsonify({'error': 'Model not loaded'}), 500 - - try: - data = request.get_json() - texts = data.get('texts', []) - - if not texts: - return jsonify({'error': 'No texts provided'}), 400 - - results = detector.predict_batch(texts) - return jsonify({'results': results}) - - except Exception as e: - logger.error(f"Batch prediction error: {e}") - return jsonify({'error': str(e)}), 500 - -@app.route('/emotions', methods=['GET']) -def get_emotions(): - """Get list of supported emotions""" - if detector is None: - return jsonify({'error': 'Model not loaded'}), 500 - - return jsonify({ - 'emotions': list(detector.label_encoder.classes_), - 'count': len(detector.label_encoder.classes_) - }) - -if __name__ == '__main__': - print("๐Ÿš€ Starting Emotion Detection API Server") - print("=" * 50) - print("๐Ÿ“Š Model Performance: 99.48% F1 Score") - print("๐ŸŽฏ Supported Emotions:", list(detector.label_encoder.classes_) if detector else "None") - print("๐ŸŒ API Endpoints:") - print(" - GET /health - Health check") - print(" - POST /predict - Single text prediction") - print(" - POST /predict_batch - Batch prediction") - print(" - GET /emotions - List emotions") - print("=" * 50) - - app.run(host='0.0.0.0', port=5000, debug=False) diff --git a/deployment/cloud-run/CORS_CONFIGURATION.md b/deployment/cloud-run/CORS_CONFIGURATION.md new file mode 100644 index 000000000..f34f2e34f --- /dev/null +++ b/deployment/cloud-run/CORS_CONFIGURATION.md @@ -0,0 +1,155 @@ +# CORS Configuration Guide + +## Overview + +The SAMO-DL API uses a secure CORS (Cross-Origin Resource Sharing) configuration that prevents the unsafe combination of `allow_origins=["*"]` with `allow_credentials=True`. + +## Security Issue Fixed + +**Before**: The API used `allow_origins=["*"]` with `allow_credentials=True`, which is: +- Invalid for browsers (CORS spec violation) +- Unsafe (allows any origin to send credentials) + +**After**: The API now uses: +- Explicit allowed origins from environment configuration +- `allow_credentials=True` only when using explicit origins (not wildcard) +- `allow_credentials=False` when using wildcard origins for security + +## Environment Variables + +### CORS_ORIGINS +- **Description**: Comma-separated list of allowed origins +- **Default (Production)**: `https://samo-dl-demo.web.app,https://samo-dl-demo.firebaseapp.com` +- **Default (Staging)**: `https://samo-dl-demo-staging.web.app,https://samo-dl-demo-staging.firebaseapp.com` +- **Default (Development)**: `*` (wildcard - credentials disabled) + +### Examples + +#### Production Environment +```bash +export CORS_ORIGINS="https://yourdomain.com,https://app.yourdomain.com" +``` + +#### Staging Environment +```bash +export CORS_ORIGINS="https://staging.yourdomain.com,https://staging-app.yourdomain.com" +``` + +#### Development Environment +```bash +# Uses wildcard by default - credentials automatically disabled +export CORS_ORIGINS="*" +``` + +## Configuration Logic + +The API automatically determines whether to allow credentials based on the origins: + +1. **Explicit Origins**: If `CORS_ORIGINS` contains specific domains (not `*`), then `allow_credentials=True` +2. **Wildcard Origins**: If `CORS_ORIGINS` contains `*`, then `allow_credentials=False` for security + +## Deployment Configuration + +### Cloud Run Environment Variables + +Add the following to your Cloud Run service environment variables: + +```yaml +# Production +CORS_ORIGINS: "https://samo-dl-demo.web.app,https://samo-dl-demo.firebaseapp.com" + +# Staging +CORS_ORIGINS: "https://samo-dl-demo-staging.web.app,https://samo-dl-demo-staging.firebaseapp.com" +``` + +### Cloud Build Configuration + +Update your `cloudbuild.yaml` to include the CORS_ORIGINS environment variable: + +```yaml +- name: 'gcr.io/google.com/cloudsdktool/cloud-sdk' + entrypoint: 'gcloud' + args: [ + 'run', 'deploy', '${_SERVICE_NAME}', + '--image', 'us-central1-docker.pkg.dev/$PROJECT_ID/${_ARTIFACT_REPO}/emotion-detection-api:$BUILD_ID', + '--region', '${_REGION}', + '--platform', 'managed', + '--allow-unauthenticated', + '--port', '${_PORT}', + '--memory', '${_MEMORY}', + '--cpu', '${_CPU}', + '--max-instances', '${_MAX_INSTANCES}', + '--set-env-vars', 'CORS_ORIGINS=https://samo-dl-demo.web.app,https://samo-dl-demo.firebaseapp.com' + ] +``` + +## Security Benefits + +1. **Prevents CORS Violations**: No more browser CORS errors from invalid configurations +2. **Credential Security**: Credentials only sent to explicitly allowed origins +3. **Environment-Specific**: Different origins for dev/staging/production +4. **Configurable**: Easy to update allowed origins without code changes + +## Testing CORS Configuration + +### Check CORS Headers +```bash +curl -H "Origin: https://yourdomain.com" \ + -H "Access-Control-Request-Method: POST" \ + -H "Access-Control-Request-Headers: Content-Type" \ + -X OPTIONS \ + https://your-api-url.com/analyze/emotion +``` + +### Expected Response Headers +``` +Access-Control-Allow-Origin: https://yourdomain.com +Access-Control-Allow-Credentials: true +Access-Control-Allow-Methods: GET, POST, PUT, DELETE, OPTIONS +Access-Control-Allow-Headers: * +``` + +## Troubleshooting + +### Common Issues + +1. **CORS Error with Credentials**: Check that your origin is in the `CORS_ORIGINS` list +2. **Wildcard with Credentials**: The API automatically disables credentials when using `*` +3. **Missing Environment Variable**: Falls back to production defaults + +### Debug Mode + +Set `LOG_LEVEL=debug` to see CORS configuration in logs: + +```bash +export LOG_LEVEL=debug +``` + +## Migration Guide + +### From Old Configuration + +**Before**: +```python +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, # โŒ Unsafe with wildcard + allow_methods=["*"], + allow_headers=["*"], +) +``` + +**After**: +```python +# Automatically configured from environment +# allow_credentials=True only with explicit origins +# allow_credentials=False with wildcard origins +``` + +### Environment Setup + +1. Set `CORS_ORIGINS` environment variable +2. Deploy with updated configuration +3. Test CORS headers with your frontend domain +4. Update frontend to handle new CORS behavior if needed diff --git a/deployment/cloud-run/Dockerfile-full b/deployment/cloud-run/Dockerfile-full new file mode 100644 index 000000000..f39b4e39a --- /dev/null +++ b/deployment/cloud-run/Dockerfile-full @@ -0,0 +1,44 @@ +# SAMO Cloud Run Dockerfile - Full Features (All 3 Core Features) +# Supports: Emotion Detection + Text Summarization + Voice Transcription + +FROM python:3.11-slim + +# Install system dependencies for audio processing +RUN apt-get update && apt-get install -y \ + ffmpeg=7:4.3.6-0+deb11u1 \ + libsndfile1=1.0.31-2 \ + libsox-fmt-all=14.4.2+git20190427-2+deb11u1 \ + sox=14.4.2+git20190427-2+deb11u1 \ + curl=7.74.0-1.3+deb11u7 \ + && rm -rf /var/lib/apt/lists/* + +# Set working directory +WORKDIR /app + +# Copy requirements first for better Docker layer caching +COPY requirements-full.txt requirements.txt +RUN pip install --no-cache-dir -r requirements.txt + +# Copy source code +COPY src/ ./src/ +COPY deployment/cloud-run/secure_api_server.py ./ +COPY deployment/cloud-run/*.py ./ + +# Environment variables for production +ENV PYTHONPATH=/app +ENV PYTHONUNBUFFERED=1 +ENV MODEL_CACHE_DIR=/app/models +ENV TORCH_HOME=/app/models/torch + +# Create model cache directory +RUN mkdir -p /app/models/torch + +# Expose port +EXPOSE 8080 + +# Health check +HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \ + CMD curl -f http://localhost:8080/health || exit 1 + +# Run the unified API server +CMD ["python", "-m", "src.unified_ai_api"] \ No newline at end of file diff --git a/deployment/cloud-run/Dockerfile.unified b/deployment/cloud-run/Dockerfile.unified new file mode 100644 index 000000000..56465edea --- /dev/null +++ b/deployment/cloud-run/Dockerfile.unified @@ -0,0 +1,50 @@ +# Unified API Dockerfile for Cloud Run +# This deploys the real unified API with all 3 features (Whisper, T5, DeBERTa) +FROM --platform=linux/amd64 python:3.10-slim-bookworm + +# Set environment variables +ENV PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 \ + PYTHONHASHSEED=random \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + PORT=8080 + +# Set working directory +WORKDIR /app + +# Install system dependencies +ENV DEBIAN_FRONTEND=noninteractive +RUN apt-get update && apt-get install -y --no-install-recommends \ + ca-certificates \ + curl \ + ffmpeg \ + libsndfile1 \ + && rm -rf /var/lib/apt/lists/* \ + && apt-get clean + +# Copy requirements +COPY dependencies/requirements-api.txt . +COPY dependencies/requirements-ml.txt . + +# Install Python dependencies +RUN pip install --no-cache-dir -r requirements-api.txt -r requirements-ml.txt + +# Copy the unified API source code +COPY src/ ./src/ +COPY deployment/cloud-run/requirements.txt ./deployment/cloud-run/ + +# Create non-root user +RUN useradd -m -u 1000 appuser && \ + chown -R appuser:appuser /app +USER appuser + +# Expose port +EXPOSE 8080 + +# Health check +HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ + CMD curl -f http://localhost:8080/health || exit 1 + +# Start the unified API +CMD ["uvicorn", "src.unified_ai_api:app", "--host", "0.0.0.0", "--port", "8080"] diff --git a/deployment/cloud-run/api_config_production.py b/deployment/cloud-run/api_config_production.py new file mode 100644 index 000000000..544e264e1 --- /dev/null +++ b/deployment/cloud-run/api_config_production.py @@ -0,0 +1,121 @@ +#!/usr/bin/env python3 +""" +Production API Configuration for SAMO Cloud Run +Optimized rate limiting and model loading settings +""" + +import os +from typing import Any, ClassVar, Dict + + +class ProductionConfig: + """Production configuration for SAMO API.""" + + # Rate Limiting Configuration (More permissive for production) + RATE_LIMIT_CONFIG: ClassVar[Dict[str, Any]] = { + "requests_per_minute": 300, # Increased from 60 + "burst_size": 50, # Increased from 10 + "window_size_seconds": 60, + "block_duration_seconds": 120, # Reduced from 300 + "max_concurrent_requests": 20, # Increased from 5 + # Abuse detection (More lenient) + "rapid_fire_threshold": 30, # Increased from 10 + "sustained_rate_threshold": 600, # Increased from 200 + "rapid_fire_window": 1.0, + "sustained_rate_window": 60.0, + # Disable some strict checks for production + "enable_user_agent_analysis": False, + "enable_request_pattern_analysis": False, + } + + # Model Configuration + MODEL_CONFIG: ClassVar[Dict[str, Any]] = { + "emotion_model_id": os.getenv("EMOTION_MODEL_ID", "0xmnrv/samo"), + "voice_model_size": os.getenv("VOICE_MODEL_SIZE", "base"), + "text_model_size": os.getenv("TEXT_MODEL_SIZE", "t5-small"), + "enable_model_fallbacks": True, + "lazy_model_loading": True, + "model_timeout_seconds": 300, + } + + # Authentication Configuration + AUTH_CONFIG: ClassVar[Dict[str, Any]] = { + "jwt_secret": os.getenv("JWT_SECRET", "your-production-secret-key"), + "jwt_expiry_minutes": int(os.getenv("JWT_EXPIRY_MINUTES", "30")), + "enable_registration": os.getenv("ENABLE_REGISTRATION", "true").lower() == "true", + "require_email_verification": False, # Simplified for demo + } + + # Logging Configuration + LOGGING_CONFIG: ClassVar[Dict[str, Any]] = { + "level": "INFO", + "enable_access_logs": True, + "enable_performance_logs": True, + "log_format": "json", # Better for Cloud Run + } + + @classmethod + def get_rate_limit_config(cls) -> Dict[str, Any]: + """Get rate limiting configuration.""" + return cls.RATE_LIMIT_CONFIG.copy() + + @classmethod + def get_model_config(cls) -> Dict[str, Any]: + """Get model configuration.""" + return cls.MODEL_CONFIG.copy() + + @classmethod + def get_auth_config(cls) -> Dict[str, Any]: + """Get authentication configuration.""" + return cls.AUTH_CONFIG.copy() + + @classmethod + def get_logging_config(cls) -> Dict[str, Any]: + """Get logging configuration.""" + return cls.LOGGING_CONFIG.copy() + + +# Environment-specific overrides +def get_production_overrides() -> Dict[str, Any]: + """Get production-specific overrides.""" + overrides = {} + + # Cloud Run specific settings + if os.getenv("K_SERVICE"): # Running on Cloud Run + overrides.update( + { + "rate_limit_requests_per_minute": 500, # Higher for Cloud Run + "rate_limit_burst_size": 100, + "enable_health_check_bypass": True, + } + ) + + # Performance mode + if os.getenv("PERFORMANCE_MODE") == "high": + overrides.update( + { + "rate_limit_requests_per_minute": 1000, + "rate_limit_burst_size": 200, + "max_concurrent_requests": 50, + } + ) + + return overrides + + +# Usage example for API initialization +def configure_production_api(_app): + """Configure API with production settings.""" + config = ProductionConfig() + overrides = get_production_overrides() + + # Apply configurations + rate_config = config.get_rate_limit_config() + rate_config.update(overrides) + + return { + "rate_limiting": rate_config, + "models": config.get_model_config(), + "auth": config.get_auth_config(), + "logging": config.get_logging_config(), + } diff --git a/deployment/cloud-run/cloudbuild.yaml b/deployment/cloud-run/cloudbuild.yaml deleted file mode 100644 index 259e3b517..000000000 --- a/deployment/cloud-run/cloudbuild.yaml +++ /dev/null @@ -1,5 +0,0 @@ -steps: - - name: 'gcr.io/cloud-builders/docker' - args: ['build', '-t', 'us-central1-docker.pkg.dev/the-tendril-466607-n8/samo-dl/samo-emotion-api-secure', '-f', 'deployment/cloud-run/Dockerfile.secure', '.'] -images: - - 'us-central1-docker.pkg.dev/the-tendril-466607-n8/samo-dl/samo-emotion-api-secure' diff --git a/deployment/cloud-run/config.py b/deployment/cloud-run/config.py index d44221d89..aae928035 100644 --- a/deployment/cloud-run/config.py +++ b/deployment/cloud-run/config.py @@ -4,12 +4,14 @@ """ import os -from typing import Dict, Any, Optional, List from dataclasses import dataclass +from typing import Any, Dict, List, Optional + @dataclass class CloudRunConfig: """Cloud Run specific configuration""" + # Resource allocation memory_limit_mb: int = 2048 cpu_limit: int = 2 @@ -47,38 +49,46 @@ class CloudRunConfig: enable_rate_limiting: bool = True enable_input_sanitization: bool = True + class EnvironmentConfig: """Environment-specific configuration management""" def __init__(self, environment: str = None): - self.environment = environment or os.getenv('ENVIRONMENT', 'development') + self.environment = environment or os.getenv("ENVIRONMENT", "development") self.config = self._load_environment_config() def _load_environment_config(self) -> CloudRunConfig: """Load configuration based on environment""" - if self.environment == 'production': + if self.environment == "production": return CloudRunConfig( - memory_limit_mb=int(os.getenv('MEMORY_LIMIT_MB', '2048') or '2048'), - cpu_limit=int(os.getenv('CPU_LIMIT', '2') or '2'), - max_instances=int(os.getenv('MAX_INSTANCES', '10') or '10'), - min_instances=int(os.getenv('MIN_INSTANCES', '1') or '1'), - concurrency=int(os.getenv('CONCURRENCY', '80') or '80'), - timeout_seconds=int(os.getenv('TIMEOUT_SECONDS', '300') or '300'), - target_cpu_utilization=float(os.getenv('TARGET_CPU_UTILIZATION', '0.7') or '0.7'), - target_memory_utilization=float(os.getenv('TARGET_MEMORY_UTILIZATION', '0.8') or '0.8'), - health_check_interval_seconds=int(os.getenv('HEALTH_CHECK_INTERVAL', '30') or '30'), - graceful_shutdown_timeout_seconds=int(os.getenv('GRACEFUL_SHUTDOWN_TIMEOUT', '30') or '30'), - enable_monitoring=os.getenv('ENABLE_MONITORING', 'true').lower() == 'true', - enable_metrics=os.getenv('ENABLE_METRICS', 'true').lower() == 'true', - log_level=os.getenv('LOG_LEVEL', 'info'), - max_requests_per_minute=int(os.getenv('MAX_REQUESTS_PER_MINUTE', '1000') or '1000'), + memory_limit_mb=int(os.getenv("MEMORY_LIMIT_MB", "2048") or "2048"), + cpu_limit=int(os.getenv("CPU_LIMIT", "2") or "2"), + max_instances=int(os.getenv("MAX_INSTANCES", "10") or "10"), + min_instances=int(os.getenv("MIN_INSTANCES", "1") or "1"), + concurrency=int(os.getenv("CONCURRENCY", "80") or "80"), + timeout_seconds=int(os.getenv("TIMEOUT_SECONDS", "300") or "300"), + target_cpu_utilization=float(os.getenv("TARGET_CPU_UTILIZATION", "0.7") or "0.7"), + target_memory_utilization=float( + os.getenv("TARGET_MEMORY_UTILIZATION", "0.8") or "0.8" + ), + health_check_interval_seconds=int(os.getenv("HEALTH_CHECK_INTERVAL", "30") or "30"), + graceful_shutdown_timeout_seconds=int( + os.getenv("GRACEFUL_SHUTDOWN_TIMEOUT", "30") or "30" + ), + enable_monitoring=os.getenv("ENABLE_MONITORING", "true").lower() == "true", + enable_metrics=os.getenv("ENABLE_METRICS", "true").lower() == "true", + log_level=os.getenv("LOG_LEVEL", "info"), + max_requests_per_minute=int(os.getenv("MAX_REQUESTS_PER_MINUTE", "1000") or "1000"), enable_cors=True, - cors_origins=os.getenv('CORS_ORIGINS', '*').split(','), + cors_origins=os.getenv( + "CORS_ORIGINS", + "https://samo-dl-demo.web.app,https://samo-dl-demo.firebaseapp.com", + ).split(","), enable_rate_limiting=True, - enable_input_sanitization=True + enable_input_sanitization=True, ) - if self.environment == 'staging': + if self.environment == "staging": return CloudRunConfig( memory_limit_mb=1024, cpu_limit=1, @@ -92,12 +102,15 @@ def _load_environment_config(self) -> CloudRunConfig: graceful_shutdown_timeout_seconds=15, enable_monitoring=True, enable_metrics=True, - log_level='debug', + log_level="debug", max_requests_per_minute=500, enable_cors=True, - cors_origins=['*'], + cors_origins=[ + "https://samo-dl-demo-staging.web.app", + "https://samo-dl-demo-staging.firebaseapp.com", + ], enable_rate_limiting=True, - enable_input_sanitization=True + enable_input_sanitization=True, ) return CloudRunConfig( memory_limit_mb=512, @@ -112,59 +125,59 @@ def _load_environment_config(self) -> CloudRunConfig: graceful_shutdown_timeout_seconds=10, enable_monitoring=False, enable_metrics=False, - log_level='debug', + log_level="debug", max_requests_per_minute=100, enable_cors=True, - cors_origins=['*'], + cors_origins=["*"], enable_rate_limiting=False, - enable_input_sanitization=False + enable_input_sanitization=False, ) def get_gunicorn_config(self) -> Dict[str, Any]: """Get Gunicorn configuration for Cloud Run""" return { - 'bind': f':{os.getenv("PORT", "8080")}', - 'workers': 1, # Cloud Run best practice - 'threads': 8, - 'timeout': 0, # Cloud Run handles timeouts - 'keepalive': 5, - 'max_requests': 1000, - 'max_requests_jitter': 100, - 'access_logfile': '-', - 'error_logfile': '-', - 'loglevel': self.config.log_level, - 'preload_app': True, - 'worker_class': 'sync', - 'worker_connections': self.config.concurrency + "bind": f':{os.getenv("PORT", "8080")}', + "workers": 1, # Cloud Run best practice + "threads": 8, + "timeout": 0, # Cloud Run handles timeouts + "keepalive": 5, + "max_requests": 1000, + "max_requests_jitter": 100, + "access_logfile": "-", + "error_logfile": "-", + "loglevel": self.config.log_level, + "preload_app": True, + "worker_class": "sync", + "worker_connections": self.config.concurrency, } def get_health_check_config(self) -> Dict[str, Any]: """Get health check configuration""" return { - 'interval_seconds': self.config.health_check_interval_seconds, - 'timeout_seconds': self.config.health_check_timeout_seconds, - 'retries': self.config.health_check_retries, - 'graceful_shutdown_timeout': self.config.graceful_shutdown_timeout_seconds + "interval_seconds": self.config.health_check_interval_seconds, + "timeout_seconds": self.config.health_check_timeout_seconds, + "retries": self.config.health_check_retries, + "graceful_shutdown_timeout": self.config.graceful_shutdown_timeout_seconds, } def get_monitoring_config(self) -> Dict[str, Any]: """Get monitoring configuration""" return { - 'enabled': self.config.enable_monitoring, - 'metrics_enabled': self.config.enable_metrics, - 'log_level': self.config.log_level, - 'target_cpu_utilization': self.config.target_cpu_utilization, - 'target_memory_utilization': self.config.target_memory_utilization + "enabled": self.config.enable_monitoring, + "metrics_enabled": self.config.enable_metrics, + "log_level": self.config.log_level, + "target_cpu_utilization": self.config.target_cpu_utilization, + "target_memory_utilization": self.config.target_memory_utilization, } def get_security_config(self) -> Dict[str, Any]: """Get security configuration""" return { - 'enable_cors': self.config.enable_cors, - 'cors_origins': self.config.cors_origins, - 'enable_rate_limiting': self.config.enable_rate_limiting, - 'enable_input_sanitization': self.config.enable_input_sanitization, - 'max_requests_per_minute': self.config.max_requests_per_minute + "enable_cors": self.config.enable_cors, + "cors_origins": self.config.cors_origins, + "enable_rate_limiting": self.config.enable_rate_limiting, + "enable_input_sanitization": self.config.enable_input_sanitization, + "max_requests_per_minute": self.config.max_requests_per_minute, } def validate_config(self) -> None: @@ -194,26 +207,28 @@ def validate_config(self) -> None: def to_dict(self) -> Dict[str, Any]: """Convert configuration to dictionary""" return { - 'environment': self.environment, - 'cloud_run': { - 'memory_limit_mb': self.config.memory_limit_mb, - 'cpu_limit': self.config.cpu_limit, - 'max_instances': self.config.max_instances, - 'min_instances': self.config.min_instances, - 'concurrency': self.config.concurrency, - 'timeout_seconds': self.config.timeout_seconds, - 'target_cpu_utilization': self.config.target_cpu_utilization, - 'target_memory_utilization': self.config.target_memory_utilization, - 'health_check_interval_seconds': self.config.health_check_interval_seconds, - 'graceful_shutdown_timeout_seconds': self.config.graceful_shutdown_timeout_seconds + "environment": self.environment, + "cloud_run": { + "memory_limit_mb": self.config.memory_limit_mb, + "cpu_limit": self.config.cpu_limit, + "max_instances": self.config.max_instances, + "min_instances": self.config.min_instances, + "concurrency": self.config.concurrency, + "timeout_seconds": self.config.timeout_seconds, + "target_cpu_utilization": self.config.target_cpu_utilization, + "target_memory_utilization": self.config.target_memory_utilization, + "health_check_interval_seconds": self.config.health_check_interval_seconds, + "graceful_shutdown_timeout_seconds": self.config.graceful_shutdown_timeout_seconds, }, - 'monitoring': self.get_monitoring_config(), - 'security': self.get_security_config() + "monitoring": self.get_monitoring_config(), + "security": self.get_security_config(), } + # Global configuration instance config = EnvironmentConfig() + def get_config() -> EnvironmentConfig: """Get the global configuration instance""" - return config + return config diff --git a/deployment/cloud-run/debug_api_import.py b/deployment/cloud-run/debug_api_import.py deleted file mode 100644 index 9ceee410d..000000000 --- a/deployment/cloud-run/debug_api_import.py +++ /dev/null @@ -1,97 +0,0 @@ -#!/usr/bin/env python3 -""" -Debug script to isolate the 'int' object is not callable error -""" - -import sys -import os - -# Add current directory to path -sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) - -print("๐Ÿ” Starting API import debug...") - -try: - print("1. Importing Flask...") - from flask import Flask - print("โœ… Flask imported successfully") -except Exception as e: - print(f"โŒ Flask import failed: {e}") - sys.exit(1) - -try: - print("2. Importing Flask-RESTX...") - from flask_restx import Api, Resource, fields, Namespace - print("โœ… Flask-RESTX imported successfully") -except Exception as e: - print(f"โŒ Flask-RESTX import failed: {e}") - sys.exit(1) - -try: - print("3. Creating Flask app...") - app = Flask(__name__) - print("โœ… Flask app created successfully") -except Exception as e: - print(f"โŒ Flask app creation failed: {e}") - sys.exit(1) - -try: - print("4. Creating API object...") - api = Api( - app, - version='1.0.0', - title='Test API', - description='Test API for debugging' - ) - print(f"โœ… API object created successfully: {type(api)}") - print(f"API object: {api}") -except Exception as e: - print(f"โŒ API creation failed: {e}") - sys.exit(1) - -try: - print("5. Testing API decorator...") - @api.errorhandler(429) - def test_handler(error): - return {"error": "test"}, 429 - print("โœ… API decorator test successful") -except Exception as e: - print(f"โŒ API decorator test failed: {e}") - print(f"API type at this point: {type(api)}") - print(f"API value at this point: {api}") - sys.exit(1) - -try: - print("6. Testing namespace creation...") - test_ns = Namespace('test', description='Test namespace') - api.add_namespace(test_ns) - print("โœ… Namespace test successful") -except Exception as e: - print(f"โŒ Namespace test failed: {e}") - sys.exit(1) - -print("๐ŸŽ‰ All tests passed! The issue is not with basic Flask-RESTX functionality.") - -# Now let's test the actual imports from secure_api_server.py -try: - print("\n7. Testing security_headers import...") - from security_headers import add_security_headers - print("โœ… security_headers imported successfully") -except Exception as e: - print(f"โŒ security_headers import failed: {e}") - -try: - print("8. Testing rate_limiter import...") - from rate_limiter import rate_limit - print("โœ… rate_limiter imported successfully") -except Exception as e: - print(f"โŒ rate_limiter import failed: {e}") - -try: - print("9. Testing model_utils import...") - from model_utils import ensure_model_loaded, predict_emotions, get_model_status, validate_text_input - print("โœ… model_utils imported successfully") -except Exception as e: - print(f"โŒ model_utils import failed: {e}") - -print("\n๐Ÿ” Debug complete. Check above for any import issues.") # noqa: T201 diff --git a/deployment/cloud-run/debug_errorhandler.py b/deployment/cloud-run/debug_errorhandler.py deleted file mode 100644 index 1e78cfe2f..000000000 --- a/deployment/cloud-run/debug_errorhandler.py +++ /dev/null @@ -1,70 +0,0 @@ -#!/usr/bin/env python3 -""" -Debug script to investigate the errorhandler issue -""" - -import sys -import os - -# Add current directory to path -sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) - -print("๐Ÿ” Starting errorhandler debug...") - -try: - from flask import Flask - from flask_restx import Api, Resource, fields, Namespace - print("โœ… Imports successful") -except Exception as e: - print(f"โŒ Import failed: {e}") - sys.exit(1) - -try: - app = Flask(__name__) - api = Api( - app, - version='1.0.0', - title='Test API', - description='Test API for debugging' - ) - print("โœ… API object created successfully") -except Exception as e: - print(f"โŒ API creation failed: {e}") - sys.exit(1) - -# Let's inspect the API object in detail -print(f"\n๐Ÿ” API object details:") -print(f"Type: {type(api)}") -print(f"Dir: {[attr for attr in dir(api) if not attr.startswith('_')]}") -print(f"Has errorhandler: {'errorhandler' in dir(api)}") - -try: - errorhandler_method = getattr(api, 'errorhandler') - print(f"โœ… errorhandler method found: {type(errorhandler_method)}") - print(f"errorhandler callable: {callable(errorhandler_method)}") -except Exception as e: - print(f"โŒ errorhandler method access failed: {e}") - -# Let's check if there are any global variables that might be interfering -print(f"\n๐Ÿ” Checking for global variable conflicts...") -print(f"Built-in errorhandler: {getattr(__builtins__, 'errorhandler', 'Not found')}") -print(f"Global errorhandler: {globals().get('errorhandler', 'Not found')}") - -# Let's try to call errorhandler directly -try: - print(f"\n๐Ÿ” Testing errorhandler call...") - result = api.errorhandler(429) - print(f"โœ… errorhandler(429) call successful: {type(result)}") -except Exception as e: - print(f"โŒ errorhandler(429) call failed: {e}") - print(f"Error type: {type(e)}") - print(f"Error details: {e}") - -# Let's check if there's a version issue -try: - import flask_restx - print(f"\n๐Ÿ” Flask-RESTX version: {flask_restx.__version__}") -except Exception as e: - print(f"โŒ Could not get Flask-RESTX version: {e}") - -print("\n๐Ÿ” Debug complete.") \ No newline at end of file diff --git a/deployment/cloud-run/debug_errorhandler_detailed.py b/deployment/cloud-run/debug_errorhandler_detailed.py deleted file mode 100644 index 2aecdcb8d..000000000 --- a/deployment/cloud-run/debug_errorhandler_detailed.py +++ /dev/null @@ -1,79 +0,0 @@ -#!/usr/bin/env python3 -""" -Detailed debug script to understand the errorhandler issue -""" - -import os -os.environ['ADMIN_API_KEY'] = 'test123' - -print("๐Ÿ” Starting detailed errorhandler debug...") - -try: - from flask import Flask - from flask_restx import Api - print("โœ… Imports successful") -except Exception as e: - print(f"โŒ Import failed: {e}") - exit(1) - -try: - app = Flask(__name__) - api = Api(app, version='1.0.0', title='Test') - print("โœ… API object created") -except Exception as e: - print(f"โŒ API creation failed: {e}") - exit(1) - -# Let's inspect the API object in detail -print(f"\n๐Ÿ” API object details:") -print(f"Type: {type(api)}") -print(f"Dir: {[attr for attr in dir(api) if not attr.startswith('_')]}") -print(f"Has errorhandler: {'errorhandler' in dir(api)}") - -try: - errorhandler_method = getattr(api, 'errorhandler') - print(f"โœ… errorhandler method found: {type(errorhandler_method)}") - print(f"errorhandler callable: {callable(errorhandler_method)}") - print(f"errorhandler bound: {errorhandler_method.__self__ if hasattr(errorhandler_method, '__self__') else 'Not bound'}") -except Exception as e: - print(f"โŒ errorhandler method access failed: {e}") - -# Let's try to understand what happens when we call errorhandler -try: - print(f"\n๐Ÿ” Testing errorhandler call step by step...") - - # First, let's see what the method looks like - print(f"errorhandler method: {errorhandler_method}") - print(f"errorhandler method type: {type(errorhandler_method)}") - - # Let's try calling it with different approaches - print(f"\nTrying direct call...") - result = errorhandler_method(429) - print(f"Direct call result: {type(result)} - {result}") - - print(f"\nTrying bound call...") - result2 = api.errorhandler(429) - print(f"Bound call result: {type(result2)} - {result2}") - - # Let's check if there's a difference - print(f"\nResults are the same: {result == result2}") - -except Exception as e: - print(f"โŒ errorhandler testing failed: {e}") - print(f"Error type: {type(e)}") - print(f"Error details: {e}") - -# Let's check if there are any global variables that might be interfering -print(f"\n๐Ÿ” Checking for global variable conflicts...") -print(f"Built-in errorhandler: {getattr(__builtins__, 'errorhandler', 'Not found')}") -print(f"Global errorhandler: {globals().get('errorhandler', 'Not found')}") - -# Let's check if there's a version issue -try: - import flask_restx - print(f"\n๐Ÿ” Flask-RESTX version: {flask_restx.__version__}") - print(f"Flask version: {flask.__version__}") -except Exception as e: - print(f"โŒ Could not get versions: {e}") - -print("\n๐Ÿ” Debug complete.") \ No newline at end of file diff --git a/deployment/cloud-run/docs_blueprint.py b/deployment/cloud-run/docs_blueprint.py index 169a6a289..e9895b6fa 100644 --- a/deployment/cloud-run/docs_blueprint.py +++ b/deployment/cloud-run/docs_blueprint.py @@ -1,41 +1,42 @@ from __future__ import annotations import os -from flask import Blueprint, Response, jsonify, render_template, g +from flask import Blueprint, Response, g, jsonify, render_template -docs_bp = Blueprint('docs', __name__, template_folder='templates') +docs_bp = Blueprint("docs", __name__, template_folder="templates") -@docs_bp.route('/openapi.yaml', methods=['GET']) +@docs_bp.route("/openapi.yaml", methods=["GET"]) def serve_openapi_spec(): """Serve OpenAPI spec for Swagger UI with safe path validation.""" # Restrict spec path to a safe directory - allowed_dir = os.path.abspath(os.environ.get('OPENAPI_ALLOWED_DIR', '/app')) - spec_path = os.environ.get('OPENAPI_SPEC_PATH', '/app/openapi.yaml') + allowed_dir = os.path.abspath(os.environ.get("OPENAPI_ALLOWED_DIR", "/app")) + spec_path = os.environ.get("OPENAPI_SPEC_PATH", "/app/openapi.yaml") abs_spec_path = os.path.abspath(spec_path) try: # Validate that the spec path is within the allowed directory if os.path.commonpath([abs_spec_path, allowed_dir]) != allowed_dir: - return jsonify({'error': 'Invalid OpenAPI spec path'}), 400 + return jsonify({"error": "Invalid OpenAPI spec path"}), 400 - with open(abs_spec_path, 'r', encoding='utf-8') as f: + with open(abs_spec_path, "r", encoding="utf-8") as f: content = f.read() # Use a standard YAML mimetype - return Response(content, mimetype='application/x-yaml') + return Response(content, mimetype="application/x-yaml") except Exception as e: # Avoid leaking exact path in error; log on server side only if needed - return jsonify({'error': 'OpenAPI spec not found'}), 404 + return jsonify({"error": "OpenAPI spec not found"}), 404 -@docs_bp.route('/docs', methods=['GET'], strict_slashes=False) +@docs_bp.route("/docs", methods=["GET"], strict_slashes=False) def swagger_ui(): """Render Swagger UI that loads the OpenAPI spec from /openapi.yaml.""" # Allow overriding the spec URL (e.g., behind a proxy) but default to local - spec_url = os.environ.get('OPENAPI_SPEC_URL', '/openapi.yaml') + spec_url = os.environ.get("OPENAPI_SPEC_URL", "/openapi.yaml") # Generate per-request nonce for CSP and pass to template import secrets + nonce = secrets.token_urlsafe(16) g.csp_nonce = nonce - return render_template('docs.html', spec_url=spec_url, csp_nonce=nonce) + return render_template("docs.html", spec_url=spec_url, csp_nonce=nonce) diff --git a/deployment/cloud-run/health_monitor.py b/deployment/cloud-run/health_monitor.py index 8f681a028..64b6e1937 100644 --- a/deployment/cloud-run/health_monitor.py +++ b/deployment/cloud-run/health_monitor.py @@ -3,23 +3,26 @@ Provides comprehensive health checks, graceful shutdown, and monitoring """ +import logging import os +import signal import sys import time -import signal -import logging -from typing import Dict, Any, Optional from dataclasses import dataclass from datetime import datetime +from typing import Any, Dict, Optional + import psutil # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) + @dataclass class HealthMetrics: """Health check metrics""" + status: str response_time_ms: float memory_usage_mb: float @@ -28,6 +31,7 @@ class HealthMetrics: timestamp: datetime error_message: Optional[str] = None + class HealthMonitor: """Comprehensive health monitoring for Cloud Run""" @@ -36,7 +40,7 @@ def __init__(self): self.is_shutting_down = False self.active_requests = 0 self.health_metrics: Dict[str, HealthMetrics] = {} - self.shutdown_timeout = int(os.getenv('GRACEFUL_SHUTDOWN_TIMEOUT', '30') or '30') + self.shutdown_timeout = int(os.getenv("GRACEFUL_SHUTDOWN_TIMEOUT", "30") or "30") # Register graceful shutdown handlers signal.signal(signal.SIGTERM, self._graceful_shutdown) @@ -56,7 +60,9 @@ def _graceful_shutdown(self, signum, frame): time.sleep(1) if self.active_requests > 0: - logger.warning(f"Force shutdown after {self.shutdown_timeout}s timeout with {self.active_requests} active requests") + logger.warning( + f"Force shutdown after {self.shutdown_timeout}s timeout with {self.active_requests} active requests" + ) else: logger.info("Graceful shutdown completed successfully") @@ -69,18 +75,18 @@ def get_system_metrics(self) -> Dict[str, float]: memory_info = process.memory_info() return { - 'memory_usage_mb': memory_info.rss / 1024 / 1024, - 'cpu_usage_percent': process.cpu_percent(), - 'memory_percent': process.memory_percent(), - 'uptime_seconds': (datetime.now() - self.start_time).total_seconds() + "memory_usage_mb": memory_info.rss / 1024 / 1024, + "cpu_usage_percent": process.cpu_percent(), + "memory_percent": process.memory_percent(), + "uptime_seconds": (datetime.now() - self.start_time).total_seconds(), } except Exception as e: logger.error(f"Error getting system metrics: {e}") return { - 'memory_usage_mb': 0.0, - 'cpu_usage_percent': 0.0, - 'memory_percent': 0.0, - 'uptime_seconds': 0.0 + "memory_usage_mb": 0.0, + "cpu_usage_percent": 0.0, + "memory_percent": 0.0, + "uptime_seconds": 0.0, } @staticmethod @@ -88,17 +94,18 @@ def check_model_health() -> Dict[str, Any]: """Check if ML models are loaded and responding""" try: # Import models (this will fail if models aren't loaded) - from secure_api_server import app + pass # Test model loading start_time = time.time() # Simple health check - try to import key components import importlib + modules_to_check = [ - 'src.models.emotion_detection.bert_classifier', - 'src.models.summarization.t5_summarizer', - 'src.models.voice_processing.whisper_transcriber' + "src.models.emotion_detection.bert_classifier", + "src.models.summarization.t5_summarizer", + "src.models.voice_processing.whisper_transcriber", ] for module_name in modules_to_check: @@ -106,22 +113,22 @@ def check_model_health() -> Dict[str, Any]: importlib.import_module(module_name) except ImportError as e: return { - 'status': 'unhealthy', - 'error': f'Model module {module_name} not available: {e}', - 'response_time_ms': (time.time() - start_time) * 1000 + "status": "unhealthy", + "error": f"Model module {module_name} not available: {e}", + "response_time_ms": (time.time() - start_time) * 1000, } return { - 'status': 'healthy', - 'response_time_ms': (time.time() - start_time) * 1000, - 'models_loaded': len(modules_to_check) + "status": "healthy", + "response_time_ms": (time.time() - start_time) * 1000, + "models_loaded": len(modules_to_check), } except Exception as e: return { - 'status': 'unhealthy', - 'error': f'Model health check failed: {e}', - 'response_time_ms': 0 + "status": "unhealthy", + "error": f"Model health check failed: {e}", + "response_time_ms": 0, } @staticmethod @@ -141,31 +148,31 @@ def check_api_health() -> Dict[str, Any]: if response.status_code == 200: return { - 'status': 'healthy', - 'response_time_ms': response_time, - 'status_code': response.status_code + "status": "healthy", + "response_time_ms": response_time, + "status_code": response.status_code, } return { - 'status': 'unhealthy', - 'error': f'Health endpoint returned {response.status_code}', - 'response_time_ms': response_time, - 'status_code': response.status_code + "status": "unhealthy", + "error": f"Health endpoint returned {response.status_code}", + "response_time_ms": response_time, + "status_code": response.status_code, } except Exception as e: return { - 'status': 'unhealthy', - 'error': f'API health check failed: {e}', - 'response_time_ms': 0 + "status": "unhealthy", + "error": f"API health check failed: {e}", + "response_time_ms": 0, } def get_comprehensive_health(self) -> Dict[str, Any]: """Get comprehensive health status""" if self.is_shutting_down: return { - 'status': 'shutting_down', - 'message': 'Service is shutting down gracefully', - 'active_requests': self.active_requests, - 'timestamp': datetime.now().isoformat() + "status": "shutting_down", + "message": "Service is shutting down gracefully", + "active_requests": self.active_requests, + "timestamp": datetime.now().isoformat(), } # Get system metrics @@ -178,43 +185,43 @@ def get_comprehensive_health(self) -> Dict[str, Any]: api_health = self.check_api_health() # Determine overall health - overall_status = 'healthy' - if model_health['status'] != 'healthy' or api_health['status'] != 'healthy': - overall_status = 'unhealthy' + overall_status = "healthy" + if model_health["status"] != "healthy" or api_health["status"] != "healthy": + overall_status = "unhealthy" # Check resource thresholds - if system_metrics['memory_usage_mb'] > 1500: # 1.5GB threshold - overall_status = 'degraded' + if system_metrics["memory_usage_mb"] > 1500: # 1.5GB threshold + overall_status = "degraded" - if system_metrics['cpu_usage_percent'] > 80: # 80% CPU threshold - overall_status = 'degraded' + if system_metrics["cpu_usage_percent"] > 80: # 80% CPU threshold + overall_status = "degraded" health_data = { - 'status': overall_status, - 'timestamp': datetime.now().isoformat(), - 'uptime_seconds': system_metrics['uptime_seconds'], - 'system': { - 'memory_usage_mb': round(system_metrics['memory_usage_mb'], 2), - 'cpu_usage_percent': round(system_metrics['cpu_usage_percent'], 2), - 'memory_percent': round(system_metrics['memory_percent'], 2) + "status": overall_status, + "timestamp": datetime.now().isoformat(), + "uptime_seconds": system_metrics["uptime_seconds"], + "system": { + "memory_usage_mb": round(system_metrics["memory_usage_mb"], 2), + "cpu_usage_percent": round(system_metrics["cpu_usage_percent"], 2), + "memory_percent": round(system_metrics["memory_percent"], 2), + }, + "models": model_health, + "api": api_health, + "requests": { + "active": self.active_requests, + "total_processed": len(self.health_metrics), }, - 'models': model_health, - 'api': api_health, - 'requests': { - 'active': self.active_requests, - 'total_processed': len(self.health_metrics) - } } # Store metrics for trend analysis self.health_metrics[datetime.now().isoformat()] = HealthMetrics( status=overall_status, - response_time_ms=api_health.get('response_time_ms', 0), - memory_usage_mb=system_metrics['memory_usage_mb'], - cpu_usage_percent=system_metrics['cpu_usage_percent'], + response_time_ms=api_health.get("response_time_ms", 0), + memory_usage_mb=system_metrics["memory_usage_mb"], + cpu_usage_percent=system_metrics["cpu_usage_percent"], active_requests=self.active_requests, timestamp=datetime.now(), - error_message=model_health.get('error') or api_health.get('error') + error_message=model_health.get("error") or api_health.get("error"), ) # Keep only last 100 metrics @@ -234,9 +241,11 @@ def request_completed(self): with self.lock: self.active_requests = max(0, self.active_requests - 1) + # Global health monitor instance health_monitor = HealthMonitor() + def get_health_monitor() -> HealthMonitor: """Get the global health monitor instance""" - return health_monitor + return health_monitor diff --git a/deployment/cloud-run/minimal_api_server.py b/deployment/cloud-run/minimal_api_server.py deleted file mode 100644 index 5f90bc504..000000000 --- a/deployment/cloud-run/minimal_api_server.py +++ /dev/null @@ -1,158 +0,0 @@ -#!/usr/bin/env python3 -""" -Minimal Emotion Detection API Server -Uses known working PyTorch/transformers combination -Matches the actual model architecture: RoBERTa with 12 emotion classes -""" - -import logging -import os -import time -import os - -from flask import Flask, request, jsonify -import psutil -from prometheus_client import Counter, Histogram, generate_latest, CONTENT_TYPE_LATEST - -# Import shared model utilities -from model_utils import ( - ensure_model_loaded, predict_emotions, get_model_status, - MAX_TEXT_LENGTH -) - -# Configure logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -# Initialize Flask app -app = Flask(__name__) - -# Register shared docs blueprint -from docs_blueprint import docs_bp -app.register_blueprint(docs_bp) - -# Prometheus metrics -REQUEST_COUNT = Counter('emotion_api_requests_total', 'Total requests', ['endpoint', 'status']) -REQUEST_DURATION = Histogram('emotion_api_request_duration_seconds', 'Request duration', ['endpoint']) -MODEL_LOAD_TIME = Histogram('emotion_model_load_time_seconds', 'Model load time') - - -def initialize_model(): - """Initialize model using shared utilities.""" - logger.info("๐Ÿ”„ Initializing model...") - success = ensure_model_loaded() - if success: - logger.info("โœ… Model initialized successfully") - else: - logger.error("โŒ Model initialization failed") - - -@app.route('/health', methods=['GET']) -def health_check(): - """Health check endpoint.""" - try: - # Check model status using shared utilities - model_status_info = get_model_status() - model_status = "ready" if model_status_info.get('model_loaded', False) else "loading" - - # System metrics - cpu_percent = psutil.cpu_percent() - memory = psutil.virtual_memory() - - health_data = { - 'status': 'healthy', - 'model_status': model_status, - 'timestamp': time.time(), - 'system': { - 'cpu_percent': cpu_percent, - 'memory_percent': memory.percent, - 'memory_available': memory.available - } - } - - REQUEST_COUNT.labels(endpoint='/health', status='success').inc() - return jsonify(health_data), 200 - - except Exception as e: - logger.error(f"โŒ Health check failed: {e}") - REQUEST_COUNT.labels(endpoint='/health', status='error').inc() - return jsonify({'status': 'unhealthy', 'error': str(e)}), 500 - - -@app.route('/predict', methods=['POST']) -def predict(): - """Predict emotions from text.""" - start_time = time.time() - - try: - # Validate request - if not request.is_json: - REQUEST_COUNT.labels(endpoint='/predict', status='error').inc() - return jsonify({'error': 'Content-Type must be application/json'}), 400 - - data = request.get_json() - text = data.get('text', '').strip() - - if not text: - REQUEST_COUNT.labels(endpoint='/predict', status='error').inc() - return jsonify({'error': 'Text field is required'}), 400 - - if len(text) > MAX_TEXT_LENGTH: - REQUEST_COUNT.labels(endpoint='/predict', status='error').inc() - return jsonify({'error': f'Text too long (max {MAX_TEXT_LENGTH} characters)'}), 400 - - # Ensure model is loaded - initialize_model() - - # Make prediction using shared utilities - result = predict_emotions(text) - - # Record metrics - duration = time.time() - start_time - REQUEST_DURATION.labels(endpoint='/predict').observe(duration) - REQUEST_COUNT.labels(endpoint='/predict', status='success').inc() - - return jsonify(result), 200 - - except Exception as e: - logger.error(f"โŒ Prediction endpoint error: {e}") - duration = time.time() - start_time - REQUEST_DURATION.labels(endpoint='/predict').observe(duration) - REQUEST_COUNT.labels(endpoint='/predict', status='error').inc() - return jsonify({'error': 'Internal server error'}), 500 - - -@app.route('/metrics', methods=['GET']) -def metrics(): - """Prometheus metrics endpoint.""" - return generate_latest(), 200, {'Content-Type': CONTENT_TYPE_LATEST} - - -@app.route('/', methods=['GET']) -def root(): - """Root endpoint with API information.""" - # Get model status from shared utilities - model_status = get_model_status() - - return jsonify({ - 'service': 'SAMO Emotion Detection API (Minimal)', - 'version': '2.0.0', - 'status': 'operational', - 'endpoints': { - 'health': '/health', - 'predict': '/predict', - 'metrics': '/metrics' - }, - 'model_type': 'roberta_single_label', - 'emotions_supported': len(model_status.get('emotion_labels', [])), - 'emotions': model_status.get('emotion_labels', []) - }), 200 - - -if __name__ == '__main__': - # Initialize model on startup - initialize_model() - - # Start server - port = int(os.getenv('PORT', '8080')) - app.run(host='0.0.0.0', port=port, debug=False, threaded=True) diff --git a/deployment/cloud-run/minimal_test.py b/deployment/cloud-run/minimal_test.py index dffdddac6..dbb269c1b 100644 --- a/deployment/cloud-run/minimal_test.py +++ b/deployment/cloud-run/minimal_test.py @@ -4,14 +4,16 @@ """ import os -os.environ['ADMIN_API_KEY'] = 'test123' + +os.environ["ADMIN_API_KEY"] = "test123" print("๐Ÿ” Starting minimal API setup test...") try: print("1. Importing modules...") from flask import Flask - from flask_restx import Api, Resource, fields, Namespace + from flask_restx import Api, fields, Namespace + print("โœ… Imports successful") except Exception as e: print(f"โŒ Imports failed: {e}") @@ -27,12 +29,7 @@ try: print("3. Creating API object...") - api = Api( - app, - version='1.0.0', - title='Test API', - description='Test API' - ) + api = Api(app, version="1.0.0", title="Test API", description="Test API") print(f"โœ… API object created: {type(api)}") except Exception as e: print(f"โŒ API creation failed: {e}") @@ -40,7 +37,7 @@ try: print("4. Creating namespace...") - test_ns = Namespace('test', description='Test namespace') + test_ns = Namespace("test", description="Test namespace") api.add_namespace(test_ns) print("โœ… Namespace added") except Exception as e: @@ -49,9 +46,7 @@ try: print("5. Creating model...") - test_model = api.model('Test', { - 'message': fields.String(description='Test message') - }) + test_model = api.model("Test", {"message": fields.String(description="Test message")}) print("โœ… Model created") except Exception as e: print(f"โŒ Model creation failed: {e}") @@ -59,9 +54,11 @@ try: print("6. Testing errorhandler...") + @api.errorhandler(429) def test_handler(error): return {"error": "test"}, 429 + print("โœ… Error handler created") except Exception as e: print(f"โŒ Error handler creation failed: {e}") @@ -69,4 +66,4 @@ def test_handler(error): print(f"API errorhandler type: {type(api.errorhandler)}") exit(1) -print("๐ŸŽ‰ All tests passed!") \ No newline at end of file +print("๐ŸŽ‰ All tests passed!") diff --git a/deployment/cloud-run/model_utils.py b/deployment/cloud-run/model_utils.py index 0156e08d8..c60ef727f 100644 --- a/deployment/cloud-run/model_utils.py +++ b/deployment/cloud-run/model_utils.py @@ -9,10 +9,10 @@ import os import threading import time -from typing import Dict, List, Optional, Tuple, Any +from typing import Any, Dict, List, Optional, Tuple import torch -from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification +from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline from transformers.pipelines import TextClassificationPipeline # Import centralized constants with fallback for non-package environments @@ -20,8 +20,7 @@ from src.constants import EMOTION_MODEL_DIR # single source of truth except ImportError: EMOTION_MODEL_DIR = os.getenv( - 'EMOTION_MODEL_DIR', - '/app/models/emotion-english-distilroberta-base' + "EMOTION_MODEL_DIR", "/app/models/emotion-english-distilroberta-base" ) logger = logging.getLogger(__name__) @@ -34,16 +33,12 @@ model_ready_event = threading.Event() # Configuration -EMOTION_PROVIDER = os.getenv('EMOTION_PROVIDER', 'hf') -EMOTION_LOCAL_ONLY = os.getenv('EMOTION_LOCAL_ONLY', '1').lower() in ( - '1', 'true', 'yes' -) -MAX_TEXT_LENGTH = int(os.getenv('MAX_TEXT_LENGTH', '1000')) +EMOTION_PROVIDER = os.getenv("EMOTION_PROVIDER", "hf") +EMOTION_LOCAL_ONLY = os.getenv("EMOTION_LOCAL_ONLY", "1").lower() in ("1", "true", "yes") +MAX_TEXT_LENGTH = int(os.getenv("MAX_TEXT_LENGTH", "1000")) # Emotion labels for the HF emotion model (6 classes) -EMOTION_LABELS = [ - 'anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise' -] +EMOTION_LABELS = ["anger", "disgust", "fear", "joy", "neutral", "sadness", "surprise"] # Runtime emotion labels emotion_labels_runtime: List[str] = EMOTION_LABELS.copy() @@ -64,12 +59,12 @@ def _create_emotion_pipeline(tokenizer, model) -> TextClassificationPipeline: model=model, tokenizer=tokenizer, return_all_scores=True, - device=0 if torch.cuda.is_available() else -1 + device=0 if torch.cuda.is_available() else -1, ) def _validate_and_prepare_texts( - texts: List[str] + texts: List[str], ) -> Tuple[List[Optional[Dict[str, Any]]], List[str], List[int]]: """Validate input texts and prepare them for batch processing. @@ -86,21 +81,21 @@ def _validate_and_prepare_texts( for i, text in enumerate(texts): if not isinstance(text, str): results[i] = { - 'error': 'Text must be a non-empty string', - 'emotions': [], - 'confidence': 0.0 + "error": "Text must be a non-empty string", + "emotions": [], + "confidence": 0.0, } elif not text.strip(): results[i] = { - 'error': 'Text must be a non-empty string', - 'emotions': [], - 'confidence': 0.0 + "error": "Text must be a non-empty string", + "emotions": [], + "confidence": 0.0, } elif len(text) > MAX_TEXT_LENGTH: results[i] = { - 'error': f'Text too long (max {MAX_TEXT_LENGTH} characters)', - 'emotions': [], - 'confidence': 0.0 + "error": f"Text too long (max {MAX_TEXT_LENGTH} characters)", + "emotions": [], + "confidence": 0.0, } else: valid_texts.append(text) @@ -137,11 +132,8 @@ def ensure_model_loaded() -> bool: # Check if local model directory exists if EMOTION_LOCAL_ONLY and os.path.isdir(EMOTION_MODEL_DIR): # Load from local directory - logger.info("๐Ÿ“ Loading from local model directory: %s", - EMOTION_MODEL_DIR) - tokenizer = AutoTokenizer.from_pretrained( - EMOTION_MODEL_DIR, local_files_only=True - ) + logger.info("๐Ÿ“ Loading from local model directory: %s", EMOTION_MODEL_DIR) + tokenizer = AutoTokenizer.from_pretrained(EMOTION_MODEL_DIR, local_files_only=True) model = AutoModelForSequenceClassification.from_pretrained( EMOTION_MODEL_DIR, local_files_only=True ) @@ -155,25 +147,23 @@ def ensure_model_loaded() -> bool: task="text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True, - device=0 if torch.cuda.is_available() else -1 + device=0 if torch.cuda.is_available() else -1, ) logger.info("โœ… Emotion model loaded from Hugging Face Hub") except Exception as download_error: - logger.warning("Failed to load from cache, downloading model: %s", - download_error) + logger.warning("Failed to load from cache, downloading model: %s", download_error) # Force download the model from huggingface_hub import snapshot_download + model_path = snapshot_download( repo_id="j-hartmann/emotion-english-distilroberta-base", local_dir=EMOTION_MODEL_DIR, - local_dir_use_symlinks=False + local_dir_use_symlinks=False, ) logger.info("๐Ÿ“ฅ Model downloaded to: %s", model_path) # Load from downloaded directory - tokenizer = AutoTokenizer.from_pretrained( - EMOTION_MODEL_DIR, local_files_only=True - ) + tokenizer = AutoTokenizer.from_pretrained(EMOTION_MODEL_DIR, local_files_only=True) model = AutoModelForSequenceClassification.from_pretrained( EMOTION_MODEL_DIR, local_files_only=True ) @@ -183,9 +173,7 @@ def ensure_model_loaded() -> bool: # Update runtime labels from loaded model if available try: id2label = emotion_pipeline.model.config.id2label - emotion_labels_runtime = [ - id2label[i] for i in range(len(id2label)) - ] + emotion_labels_runtime = [id2label[i] for i in range(len(id2label))] except Exception as label_err: logger.debug("Unable to derive runtime labels from model config: %s", label_err) with model_lock: @@ -217,14 +205,10 @@ def predict_emotions(text: str) -> Dict[str, Any]: # Validate input first ok, err = validate_text_input(text) if not ok: - return {'error': err, 'emotions': [], 'confidence': 0.0} + return {"error": err, "emotions": [], "confidence": 0.0} if not ensure_model_loaded(): - return { - 'error': 'Emotion model not available', - 'emotions': [], - 'confidence': 0.0 - } + return {"error": "Emotion model not available", "emotions": [], "confidence": 0.0} try: @@ -234,31 +218,24 @@ def predict_emotions(text: str) -> Dict[str, Any]: # Format results to match expected output emotions = [] for result in results[0]: # results is a list with one item for single text - emotions.append({ - 'emotion': result['label'], - 'confidence': result['score'] - }) + emotions.append({"emotion": result["label"], "confidence": result["score"]}) # Sort by confidence (highest first) - emotions.sort(key=lambda x: x['confidence'], reverse=True) + emotions.sort(key=lambda x: x["confidence"], reverse=True) # Overall confidence is the highest confidence score - overall_confidence = emotions[0]['confidence'] if emotions else 0.0 + overall_confidence = emotions[0]["confidence"] if emotions else 0.0 return { - 'text': text, - 'emotions': emotions, - 'confidence': overall_confidence, - 'timestamp': time.time() + "text": text, + "emotions": emotions, + "confidence": overall_confidence, + "timestamp": time.time(), } except Exception as e: logger.exception("โŒ Emotion prediction failed: %s", e) - return { - 'error': 'Emotion prediction failed', - 'emotions': [], - 'confidence': 0.0 - } + return {"error": "Emotion prediction failed", "emotions": [], "confidence": 0.0} def get_model_status() -> Dict[str, Any]: @@ -268,14 +245,14 @@ def get_model_status() -> Dict[str, Any]: Dict[str, Any]: Model status information """ return { - 'model_loaded': model_loaded, - 'model_loading': model_loading, - 'model_dir': EMOTION_MODEL_DIR, - 'model_provider': EMOTION_PROVIDER, - 'local_only': EMOTION_LOCAL_ONLY, - 'max_text_length': MAX_TEXT_LENGTH, - 'emotion_labels': emotion_labels_runtime, - 'timestamp': time.time() + "model_loaded": model_loaded, + "model_loading": model_loading, + "model_dir": EMOTION_MODEL_DIR, + "model_provider": EMOTION_PROVIDER, + "local_only": EMOTION_LOCAL_ONLY, + "max_text_length": MAX_TEXT_LENGTH, + "emotion_labels": emotion_labels_runtime, + "timestamp": time.time(), } @@ -289,16 +266,14 @@ def predict_emotions_batch(texts: List[str]) -> List[Dict[str, Any]]: List[Dict[str, Any]]: List of prediction results for each text """ if not ensure_model_loaded(): - return [{ - 'error': 'Emotion model not available', - 'emotions': [], - 'confidence': 0.0 - } for _ in texts] + return [ + {"error": "Emotion model not available", "emotions": [], "confidence": 0.0} + for _ in texts + ] try: # Validate and prepare texts for processing - results, valid_texts_to_process, valid_indices = \ - _validate_and_prepare_texts(texts) + results, valid_texts_to_process, valid_indices = _validate_and_prepare_texts(texts) # Only run pipeline if there are valid texts if valid_texts_to_process: @@ -312,35 +287,31 @@ def predict_emotions_batch(texts: List[str]) -> List[Dict[str, Any]]: # Convert emotion results to list comprehension emotions = [ - { - 'emotion': emotion_result['label'], - 'confidence': emotion_result['score'] - } + {"emotion": emotion_result["label"], "confidence": emotion_result["score"]} for emotion_result in result ] # Sort by confidence (highest first) - emotions.sort(key=lambda x: x['confidence'], reverse=True) + emotions.sort(key=lambda x: x["confidence"], reverse=True) # Overall confidence is the highest confidence score - overall_confidence = emotions[0]['confidence'] if emotions else 0.0 + overall_confidence = emotions[0]["confidence"] if emotions else 0.0 results[original_idx] = { - 'text': text, - 'emotions': emotions, - 'confidence': overall_confidence, - 'timestamp': time.time() + "text": text, + "emotions": emotions, + "confidence": overall_confidence, + "timestamp": time.time(), } return results except Exception as e: logger.exception("โŒ Batch emotion prediction failed: %s", e) - return [{ - 'error': 'Batch emotion prediction failed', - 'emotions': [], - 'confidence': 0.0 - } for _ in texts] + return [ + {"error": "Batch emotion prediction failed", "emotions": [], "confidence": 0.0} + for _ in texts + ] def validate_text_input(text: str) -> Tuple[bool, str]: @@ -354,7 +325,7 @@ def validate_text_input(text: str) -> Tuple[bool, str]: Tuple[bool, str]: (is_valid, error_message) """ if not isinstance(text, str) or not text.strip(): - return False, 'Text must be a non-empty string' + return False, "Text must be a non-empty string" if len(text) > MAX_TEXT_LENGTH: - return False, f'Text too long (max {MAX_TEXT_LENGTH} characters)' - return True, '' + return False, f"Text too long (max {MAX_TEXT_LENGTH} characters)" + return True, "" diff --git a/deployment/cloud-run/onnx_api_server.py b/deployment/cloud-run/onnx_api_server.py deleted file mode 100644 index 7354c35fc..000000000 --- a/deployment/cloud-run/onnx_api_server.py +++ /dev/null @@ -1,365 +0,0 @@ -#!/usr/bin/env python3 -""" -Simplified ONNX-Based Emotion Detection API Server -Uses simple string tokenization - no complex dependencies -""" -import logging -import os -import time -import re -from typing import Dict, List, Optional, Tuple -import threading - -import numpy as np -import onnxruntime as ort -from flask import Flask, request, jsonify -import psutil -from prometheus_client import Counter, Histogram, generate_latest, CONTENT_TYPE_LATEST - -# Configure logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -# Initialize Flask app -app = Flask(__name__) - -# Global variables -model_session = None -vocab = None -model_loading = False -model_lock = threading.Lock() - -# Prometheus metrics -REQUEST_COUNT = Counter('emotion_api_requests_total', 'Total requests', ['endpoint', 'status']) -REQUEST_DURATION = Histogram('emotion_api_request_duration_seconds', 'Request duration', ['endpoint']) -MODEL_LOAD_TIME = Histogram('emotion_model_load_time_seconds', 'Model load time') - -# Emotion labels (immutable tuple) -EMOTION_LABELS = ( - 'admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', - 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', - 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', - 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', - 'relief', 'remorse', 'sadness', 'surprise', 'neutral' -) - -# Configuration -MODEL_PATH = os.getenv('MODEL_PATH', '/app/model/bert_emotion_classifier.onnx') -VOCAB_PATH = os.getenv('VOCAB_PATH', '/app/model/vocab.txt') -MAX_LENGTH = int(os.getenv('MAX_LENGTH', '128') or '128') -TEMPERATURE = float(os.getenv('TEMPERATURE', '1.0') or '1.0') -THRESHOLD = float(os.getenv('THRESHOLD', '0.6') or '0.6') - -# Simple vocabulary (fallback if no vocab file) -SIMPLE_VOCAB = { - '': 0, '': 1, '': 2, '': 3, - 'the': 4, 'a': 5, 'and': 6, 'is': 7, 'in': 8, 'to': 9, 'of': 10, - 'i': 11, 'you': 12, 'he': 13, 'she': 14, 'it': 15, 'we': 16, 'they': 17, - 'am': 18, 'are': 19, 'was': 20, 'were': 21, 'be': 22, 'been': 23, 'being': 24, - 'have': 25, 'has': 26, 'had': 27, 'do': 28, 'does': 29, 'did': 30, - 'will': 31, 'would': 32, 'could': 33, 'should': 34, 'may': 35, 'might': 36, - 'can': 37, 'must': 38, 'shall': 39, 'this': 40, 'that': 41, 'these': 42, 'those': 43, - 'my': 44, 'your': 45, 'his': 46, 'her': 47, 'its': 48, 'our': 49, 'their': 50, - 'me': 51, 'him': 52, 'us': 53, 'them': 54, 'myself': 55, 'yourself': 56, 'himself': 57, - 'herself': 58, 'itself': 59, 'ourselves': 60, 'yourselves': 61, 'themselves': 62, - 'what': 63, 'which': 64, 'who': 65, 'whom': 66, 'whose': 67, 'all': 72, 'any': 73, 'both': 74, 'each': 75, 'few': 76, - 'more': 77, 'most': 78, 'other': 79, 'some': 80, 'such': 81, 'no': 82, 'nor': 83, - 'not': 84, 'only': 85, 'own': 86, 'same': 87, 'so': 88, 'than': 89, 'too': 90, - 'very': 91, 'just': 92, 'now': 93, 'then': 94, 'here': 95, 'there': 96, 'when': 97, - 'where': 98, 'why': 99, 'how': 100 -} - - -def load_vocab() -> Dict[str, int]: - """Load vocabulary from file or use simple fallback.""" - try: - if os.path.exists(VOCAB_PATH): - vocab_dict = {} - with open(VOCAB_PATH, 'r', encoding='utf-8') as f: - for i, line in enumerate(f): - word = line.strip() - if word: - vocab_dict[word] = i - logger.info(f"โœ… Vocabulary loaded from file: {len(vocab)} words") - else: - vocab_dict = SIMPLE_VOCAB.copy() - logger.info(f"โœ… Using simple vocabulary: {len(vocab_dict)} words") - return vocab_dict - except Exception as e: - logger.error(f"โŒ Failed to load vocabulary: {e}") - logger.info("โœ… Using fallback simple vocabulary") - return SIMPLE_VOCAB.copy() - - -def simple_tokenize(text: str) -> List[int]: - """Simple tokenization using word splitting and vocabulary lookup.""" - # Clean and normalize text - text = text.lower().strip() - text = re.sub(r'[^\w\s]', ' ', text) - - # Split into words - words = text.split() - - # Convert to token IDs - tokens = [vocab.get('', 2)] # Start token - - for word in words[:MAX_LENGTH-2]: # Leave room for CLS and SEP - token_id = vocab.get(word, vocab.get('', 1)) - tokens.append(token_id) - - tokens.append(vocab.get('', 3)) # End token - - return tokens - - -def preprocess_text(text: str) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: - """Preprocess text using simple tokenization.""" - # Tokenize - tokens = simple_tokenize(text) - - # Pad or truncate to MAX_LENGTH - if len(tokens) < MAX_LENGTH: - tokens.extend([vocab.get('', 0)] * (MAX_LENGTH - len(tokens))) - else: - tokens = tokens[:MAX_LENGTH] - - # Convert to numpy arrays - input_ids = np.array(tokens, dtype=np.int64).reshape(1, -1) - attention_mask = np.ones_like(input_ids, dtype=np.int64) - token_type_ids = np.zeros_like(input_ids, dtype=np.int64) - - return input_ids, attention_mask, token_type_ids - - -def load_onnx_model() -> ort.InferenceSession: - """Load ONNX model with optimized settings.""" - try: - start_time = time.time() - - # Optimized session options - session_options = ort.SessionOptions() - session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL - session_options.intra_op_num_threads = 1 - session_options.inter_op_num_threads = 1 - - # Load model - session = ort.InferenceSession(MODEL_PATH, session_options) - - load_time = time.time() - start_time - MODEL_LOAD_TIME.observe(load_time) - - logger.info(f"โœ… ONNX model loaded successfully in {load_time:.2f}s") - logger.info(f"๐Ÿ“Š Model input names: {session.get_inputs()}") - logger.info(f"๐Ÿ“Š Model output names: {session.get_outputs()}") - - return session - except Exception as e: - logger.error(f"โŒ Failed to load ONNX model: {e}") - raise - - -def postprocess_predictions(logits: np.ndarray) -> List[Dict[str, float]]: - """Postprocess ONNX model outputs.""" - # Apply temperature scaling - logits = logits / TEMPERATURE - - # Apply softmax - exp_logits = np.exp(logits - np.max(logits, axis=-1, keepdims=True)) - probabilities = exp_logits / np.sum(exp_logits, axis=-1, keepdims=True) - - # Filter by threshold and create results - results = [] - for i, prob in enumerate(probabilities[0]): - if prob >= THRESHOLD: - results.append({ - 'emotion': EMOTION_LABELS[i], - 'confidence': float(prob) - }) - - # Sort by confidence - results.sort(key=lambda x: x['confidence'], reverse=True) - - return results - - -def predict_emotions(text: str) -> Dict[str, any]: - """Predict emotions using ONNX model.""" - try: - # Preprocess - input_ids, attention_mask, token_type_ids = preprocess_text(text) - - # Prepare inputs for ONNX - onnx_inputs = { - 'input_ids': input_ids, - 'attention_mask': attention_mask, - 'token_type_ids': token_type_ids - } - - # Run inference - start_time = time.time() - outputs = model_session.run(None, onnx_inputs) - inference_time = time.time() - start_time - - # Postprocess - logits = outputs[0] - emotions = postprocess_predictions(logits) - - return { - 'emotions': emotions, - 'inference_time': inference_time, - 'text_length': len(text), - 'model_type': 'onnx_simple' - } - - except Exception as e: - logger.error(f"โŒ Prediction failed: {e}") - raise - - -def initialize_model(): - """Initialize model and vocabulary.""" - global model_session, vocab, model_loading - - with model_lock: - if model_session is None and not model_loading: - model_loading = True - try: - logger.info("๐Ÿš€ Initializing model and vocabulary...") - model_session = load_onnx_model() - vocab = load_vocab() - logger.info("โœ… Model initialization complete") - except Exception as e: - logger.error(f"โŒ Model initialization failed: {e}") - model_session = None - vocab = None - finally: - model_loading = False - - -# Initialize on startup -initialize_model() - - -@app.route('/health', methods=['GET']) -def health_check(): - """Health check endpoint.""" - try: - # Check model status - model_status = "ready" if model_session is not None else "loading" - - # System metrics - cpu_percent = psutil.cpu_percent() - memory = psutil.virtual_memory() - - health_data = { - 'status': 'healthy', - 'model_status': model_status, - 'timestamp': time.time(), - 'system': { - 'cpu_percent': cpu_percent, - 'memory_percent': memory.percent, - 'memory_available': memory.available - } - } - - REQUEST_COUNT.labels(endpoint='/health', status='success').inc() - return jsonify(health_data), 200 - - except Exception as e: - logger.error(f"โŒ Health check failed: {e}") - REQUEST_COUNT.labels(endpoint='/health', status='error').inc() - return jsonify({'error': str(e)}), 500 - - -@app.route('/predict', methods=['POST']) -def predict(): - """Predict emotions from text.""" - start_time = time.time() - - try: - # Get request data - data = request.get_json() - if not data or 'text' not in data: - return jsonify({'error': 'Missing text field'}), 400 - - text = data['text'].strip() - if not text: - return jsonify({'error': 'Text cannot be empty'}), 400 - - # Predict emotions - result = predict_emotions(text) - - # Record metrics - duration = time.time() - start_time - REQUEST_DURATION.labels(endpoint='/predict').observe(duration) - REQUEST_COUNT.labels(endpoint='/predict', status='success').inc() - - return jsonify(result), 200 - - except Exception as e: - logger.error(f"โŒ Prediction failed: {e}") - duration = time.time() - start_time - REQUEST_DURATION.labels(endpoint='/predict').observe(duration) - REQUEST_COUNT.labels(endpoint='/predict', status='error').inc() - return jsonify({'error': str(e)}), 500 - - -@app.route('/metrics', methods=['GET']) -def metrics(): - """Prometheus metrics endpoint.""" - return generate_latest(), 200, {'Content-Type': CONTENT_TYPE_LATEST} - - -@app.route('/', methods=['GET']) -def root(): - """Root endpoint with API information.""" - return jsonify({ - 'service': 'SAMO Emotion Detection API', - 'version': '2.0.0', - 'model_type': 'ONNX Simple Tokenizer', - 'endpoints': { - '/health': 'Health check', - '/predict': 'Emotion prediction (POST)', - '/metrics': 'Prometheus metrics' - } - }), 200 - - -if __name__ == '__main__': - # Production WSGI server - try: - import gunicorn.app.base - - class StandaloneApplication(gunicorn.app.base.BaseApplication): - def init(self, parser, opts, args): - """Initialize the application (abstract method override).""" - raise NotImplementedError() - def __init__(self, flask_app, gunicorn_options=None): - self.options = gunicorn_options or {} - self.application = flask_app - super().__init__() - - def load_config(self): - for key, value in self.options.items(): - self.cfg.set(key, value) - - def load(self): - return self.application - - # Production configuration - options = { - 'bind': '127.0.0.1:8080', - 'workers': 1, - 'worker_class': 'sync', - 'timeout': 120, - 'keepalive': 2, - 'max_requests': 1000, - 'max_requests_jitter': 50, - 'preload_app': True - } - - StandaloneApplication(flask_app=app, gunicorn_options=options).run() - - except ImportError: - # Development server - app.run(host='127.0.0.1', port=8080, debug=False) diff --git a/deployment/cloud-run/rate_limiter.py b/deployment/cloud-run/rate_limiter.py index 96f040232..172832be6 100644 --- a/deployment/cloud-run/rate_limiter.py +++ b/deployment/cloud-run/rate_limiter.py @@ -1,12 +1,14 @@ #!/usr/bin/env python3 """Rate Limiter for Flask API""" -import time import threading +import time from collections import defaultdict, deque -from flask import request, jsonify from functools import wraps +from flask import jsonify, request + + class RateLimiter: def __init__(self, requests_per_minute: int = 100): self.requests_per_minute = requests_per_minute @@ -19,8 +21,7 @@ def is_allowed(self, client_id: str) -> bool: with self.lock: # Clean old requests (older than 1 minute) - while (self.requests[client_id] and - current_time - self.requests[client_id][0] > 60): + while self.requests[client_id] and current_time - self.requests[client_id][0] > 60: self.requests[client_id].popleft() # Check if under limit @@ -34,13 +35,14 @@ def is_allowed(self, client_id: str) -> bool: def get_client_id(request) -> str: """Get client identifier""" # Try API key first - api_key = request.headers.get('X-API-Key') + api_key = request.headers.get("X-API-Key") if api_key: return f"api_key:{api_key}" # Fall back to IP address return f"ip:{request.remote_addr}" + def rate_limit(requests_per_minute: int = 100): """Rate limiting decorator""" limiter = RateLimiter(requests_per_minute) @@ -51,11 +53,10 @@ def decorated_function(*args, **kwargs): client_id = limiter.get_client_id(request) if not limiter.is_allowed(client_id): - return jsonify({ - 'error': 'Rate limit exceeded', - 'retry_after': 60 - }), 429 + return jsonify({"error": "Rate limit exceeded", "retry_after": 60}), 429 return f(*args, **kwargs) + return decorated_function + return decorator diff --git a/deployment/cloud-run/requirements.txt b/deployment/cloud-run/requirements.txt index a98531c17..b7247eec9 100644 --- a/deployment/cloud-run/requirements.txt +++ b/deployment/cloud-run/requirements.txt @@ -1,8 +1,38 @@ +# SAMO Cloud Run Deployment - Complete Requirements +# Includes all dependencies for Emotion Detection, Text Summarization, AND Voice Transcription + +# Core API Framework flask>=3.1.1,<4.0.0 flask-restx>=1.3.0,<2.0.0 +gunicorn>=23.0.0,<24.0.0 +requests==2.32.4 + +# Machine Learning Core torch>=2.7.1,<2.9.0 transformers>=4.55.0,<5.0.0 -gunicorn>=23.0.0,<24.0.0 numpy>=1.24.0,<2.0.0 scikit-learn>=1.5.0,<2.0.0 -requests==2.32.4 + +# Voice Processing Dependencies (MISSING FROM ORIGINAL) +openai-whisper>=20231117 +pydub>=0.25.1 +ffmpeg-python>=0.2.0 + +# Text Processing +sentencepiece>=0.1.99 + +# Authentication and Security +pyjwt>=2.8.0 +cryptography>=41.0.0 +bcrypt>=4.0.0 + +# Rate Limiting and Caching +flask-limiter>=3.5.0 +redis>=4.5.0 + +# Monitoring and Logging +prometheus-client>=0.17.0 +psutil>=5.9.0 + +# Audio Processing System Dependencies +# Note: ffmpeg must be installed at system level in Docker \ No newline at end of file diff --git a/deployment/cloud-run/robust_predict.py b/deployment/cloud-run/robust_predict.py index 713de8542..ea2949fcf 100644 --- a/deployment/cloud-run/robust_predict.py +++ b/deployment/cloud-run/robust_predict.py @@ -5,20 +5,20 @@ Robust Flask API optimized for Cloud Run deployment. """ +import logging import os +import threading import time -import logging import uuid -import threading -from flask import Flask, request, jsonify -import torch -from transformers import AutoTokenizer, AutoModelForSequenceClassification from pathlib import Path +import torch +from flask import Flask, jsonify, request +from transformers import AutoModelForSequenceClassification, AutoTokenizer + # Configure logging for Cloud Run logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' + level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" ) logger = logging.getLogger(__name__) @@ -33,50 +33,64 @@ model_lock = threading.Lock() # Emotion mapping based on training order -EMOTION_MAPPING = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] +EMOTION_MAPPING = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", +] # Constants MAX_INPUT_LENGTH = 512 + def load_model(): """Load the emotion detection model""" global model, tokenizer, emotion_mapping, model_loading, model_loaded, model_lock - + with model_lock: if model_loading or model_loaded: return - + model_loading = True logger.info("๐Ÿ”„ Starting model loading...") - + try: # Get model path model_path = Path("/app/model") logger.info(f"๐Ÿ“ Loading model from: {model_path}") - + # Check if model files exist if not model_path.exists(): raise FileNotFoundError(f"Model directory not found: {model_path}") - + # Load tokenizer and model logger.info("๐Ÿ“ฅ Loading tokenizer...") tokenizer = AutoTokenizer.from_pretrained("roberta-base") - + logger.info("๐Ÿ“ฅ Loading model...") model = AutoModelForSequenceClassification.from_pretrained(str(model_path)) - + # Set device (CPU for Cloud Run) - device = torch.device('cpu') + device = torch.device("cpu") model.to(device) model.eval() - + emotion_mapping = EMOTION_MAPPING model_loaded = True model_loading = False - + logger.info(f"โœ… Model loaded successfully on {device}") logger.info(f"๐ŸŽฏ Supported emotions: {emotion_mapping}") - + except Exception: model_loading = False logger.exception("โŒ Failed to load model") @@ -84,10 +98,11 @@ def load_model(): finally: model_loading = False + def predict_emotion(text): """Predict emotion for given text""" global model, tokenizer, emotion_mapping - + if not model_loaded: raise RuntimeError("Model not loaded") @@ -98,143 +113,150 @@ def predict_emotion(text): raise ValueError(f"Input text too long (>{MAX_INPUT_LENGTH} characters).") # Tokenize - inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=MAX_INPUT_LENGTH, padding=True) - + inputs = tokenizer( + text, return_tensors="pt", truncation=True, max_length=MAX_INPUT_LENGTH, padding=True + ) + # Predict with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + # Map to emotion name emotion = emotion_mapping[predicted_class] - - return { - "emotion": emotion, - "confidence": confidence, - "text": text - } + + return {"emotion": emotion, "confidence": confidence, "text": text} + def ensure_model_loaded(): """Ensure model is loaded before processing requests""" if not model_loaded and not model_loading: load_model() - + if not model_loaded: raise RuntimeError("Model not loaded") + def create_error_response(message, status_code=500): """Create standardized error response with request ID for debugging""" request_id = str(uuid.uuid4()) logger.exception(f"{message} [request_id={request_id}]") - return jsonify({ - 'error': message, - 'request_id': request_id - }), status_code + return jsonify({"error": message, "request_id": request_id}), status_code + -@app.route('/', methods=['GET']) +@app.route("/", methods=["GET"]) def root(): """Root endpoint""" - return jsonify({ - "message": "Hello from SAMO Emotion Detection API!", - "status": "running", - "timestamp": time.time() - }) + return jsonify( + { + "message": "Hello from SAMO Emotion Detection API!", + "status": "running", + "timestamp": time.time(), + } + ) + -@app.route('/health', methods=['GET']) +@app.route("/health", methods=["GET"]) def health_check(): """Health check endpoint""" - return jsonify({ - 'status': 'healthy', - 'model_loaded': model_loaded, - 'model_loading': model_loading, - 'port': os.environ.get('PORT', '8080'), - 'timestamp': time.time() - }) - -@app.route('/predict', methods=['POST']) + return jsonify( + { + "status": "healthy", + "model_loaded": model_loaded, + "model_loading": model_loading, + "port": os.environ.get("PORT", "8080"), + "timestamp": time.time(), + } + ) + + +@app.route("/predict", methods=["POST"]) def predict(): """Predict emotion for given text""" try: # Ensure model is loaded ensure_model_loaded() - + # Content-type validation if not request.is_json: - return jsonify({'error': 'Content-Type must be application/json'}), 400 - + return jsonify({"error": "Content-Type must be application/json"}), 400 + try: data = request.get_json() except Exception: - return jsonify({'error': 'Invalid JSON data'}), 400 - + return jsonify({"error": "Invalid JSON data"}), 400 + if not data: - return jsonify({'error': 'No JSON data provided'}), 400 - - text = data.get('text', '') + return jsonify({"error": "No JSON data provided"}), 400 + + text = data.get("text", "") if not text: - return jsonify({'error': 'No text provided'}), 400 - + return jsonify({"error": "No text provided"}), 400 + # Make prediction result = predict_emotion(text) return jsonify(result) - + except Exception: - return create_error_response('Prediction processing failed. Please try again later.') + return create_error_response("Prediction processing failed. Please try again later.") + -@app.route('/predict_batch', methods=['POST']) +@app.route("/predict_batch", methods=["POST"]) def predict_batch(): """Predict emotions for multiple texts""" try: # Ensure model is loaded ensure_model_loaded() - + # Content-type validation if not request.is_json: - return jsonify({'error': 'Content-Type must be application/json'}), 400 - + return jsonify({"error": "Content-Type must be application/json"}), 400 + try: data = request.get_json() except Exception: - return jsonify({'error': 'Invalid JSON data'}), 400 - + return jsonify({"error": "Invalid JSON data"}), 400 + if not data: - return jsonify({'error': 'No JSON data provided'}), 400 - - texts = data.get('texts', []) + return jsonify({"error": "No JSON data provided"}), 400 + + texts = data.get("texts", []) if not texts: - return jsonify({'error': 'No texts provided'}), 400 - + return jsonify({"error": "No texts provided"}), 400 + # Make predictions results = [] for text in texts: result = predict_emotion(text) results.append(result) - - return jsonify({'results': results}) - + + return jsonify({"results": results}) + except Exception: - return create_error_response('Batch prediction processing failed. Please try again later.') + return create_error_response("Batch prediction processing failed. Please try again later.") -@app.route('/emotions', methods=['GET']) + +@app.route("/emotions", methods=["GET"]) def get_emotions(): """Get list of supported emotions""" - return jsonify({ - 'emotions': EMOTION_MAPPING, - 'count': len(EMOTION_MAPPING) - }) + return jsonify({"emotions": EMOTION_MAPPING, "count": len(EMOTION_MAPPING)}) + -@app.route('/model_status', methods=['GET']) +@app.route("/model_status", methods=["GET"]) def model_status(): """Get detailed model status""" - return jsonify({ - 'model_loaded': model_loaded, - 'model_loading': model_loading, - 'emotions': EMOTION_MAPPING if model_loaded else [], - 'device': 'cpu', - 'timestamp': time.time() - }) + return jsonify( + { + "model_loaded": model_loaded, + "model_loading": model_loading, + "emotions": EMOTION_MAPPING if model_loaded else [], + "device": "cpu", + "timestamp": time.time(), + } + ) + # Load model on startup def initialize_model(): @@ -244,10 +266,11 @@ def initialize_model(): except Exception: logger.exception("Failed to initialize model") + # Initialize model when module is imported initialize_model() -if __name__ == '__main__': +if __name__ == "__main__": logger.info("๐Ÿš€ Starting SAMO Emotion Detection API") logger.info("=" * 50) logger.info("๐Ÿ“Š Model Performance: 99.48% F1 Score") @@ -260,45 +283,48 @@ def initialize_model(): logger.info(" - GET /emotions - List emotions") logger.info(" - GET /model_status - Model status") logger.info("=" * 50) - + # Load model immediately try: load_model() except Exception: logger.exception("Failed to load model on startup") - + # Get port from environment (Cloud Run requirement) - port = int(os.environ.get('PORT', '8080')) - + port = int(os.environ.get("PORT", "8080")) + # Use production WSGI server for better performance and reliability import gunicorn.app.base - + class StandaloneApplication(gunicorn.app.base.BaseApplication): def __init__(self, app, options=None): self.options = options or {} self.application = app super().__init__() - + def load_config(self): - config = {key: value for key, value in self.options.items() - if key in self.cfg.settings and value is not None} + config = { + key: value + for key, value in self.options.items() + if key in self.cfg.settings and value is not None + } for key, value in config.items(): self.cfg.set(key.lower(), value) - + def load(self): return self.application - + options = { - 'bind': f'0.0.0.0:{port}', - 'workers': 1, # Single worker for Cloud Run - 'threads': 8, - 'timeout': 0, # No timeout for Cloud Run - 'keepalive': 5, - 'max_requests': 1000, - 'max_requests_jitter': 100, - 'access_logfile': '-', - 'error_logfile': '-', - 'loglevel': 'info' + "bind": f"0.0.0.0:{port}", + "workers": 1, # Single worker for Cloud Run + "threads": 8, + "timeout": 0, # No timeout for Cloud Run + "keepalive": 5, + "max_requests": 1000, + "max_requests_jitter": 100, + "access_logfile": "-", + "error_logfile": "-", + "loglevel": "info", } - - StandaloneApplication(app, options).run() \ No newline at end of file + + StandaloneApplication(app, options).run() diff --git a/deployment/cloud-run/secure_api_server.py b/deployment/cloud-run/secure_api_server.py deleted file mode 100644 index beca133e2..000000000 --- a/deployment/cloud-run/secure_api_server.py +++ /dev/null @@ -1,513 +0,0 @@ -#!/usr/bin/env python3 -""" -๐Ÿš€ SECURE EMOTION DETECTION API FOR CLOUD RUN -============================================ -Production-ready Flask API with comprehensive security features and Swagger documentation. -""" - -import os -import time -import logging -import uuid -import threading -import hmac -from flask import Flask, request, jsonify, g -from flask_restx import Api, Resource, fields, Namespace -from functools import wraps - -# Import security modules -from security_headers import add_security_headers -from rate_limiter import rate_limit - -# Import shared model utilities -from model_utils import ( - ensure_model_loaded, predict_emotions, get_model_status, - validate_text_input, -) - -# Configure logging for Cloud Run -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' -) -logger = logging.getLogger(__name__) - -app = Flask(__name__) - -# Add security headers -add_security_headers(app) - -# Register root endpoint BEFORE Flask-RESTX initialization to avoid conflicts -@app.route('/') -def home(): # Changed from api_root to home to avoid conflict with Flask-RESTX's root - """Get API status and information""" - try: - logger.info(f"Root endpoint accessed from {request.remote_addr}") - return jsonify({ - 'service': 'SAMO Emotion Detection API', - 'status': 'operational', - 'version': '2.0.0-secure', - 'security': 'enabled', - 'rate_limit': RATE_LIMIT_PER_MINUTE, - 'timestamp': time.time() - }) - except Exception as e: - logger.error(f"Root endpoint error for {request.remote_addr}: {str(e)}") - return create_error_response('Internal server error', 500) - -# Initialize Flask-RESTX API without Swagger to avoid 500 errors -api = Api( - app, - version='2.0.0', - title='SAMO Emotion Detection API', - description='Secure, production-ready emotion detection API with comprehensive security features', - # Temporarily disable Swagger docs to avoid 500 errors - # doc='/docs', - authorizations={ - 'apikey': { - 'type': 'apiKey', - 'in': 'header', - 'name': 'X-API-Key' - } - }, - security='apikey' -) - -# Create namespaces for better organization -main_ns = Namespace('api', description='Main API operations') # Removed leading slash to avoid double slashes -admin_ns = Namespace('/admin', description='Admin operations', authorizations={ - 'apikey': { - 'type': 'apiKey', - 'in': 'header', - 'name': 'X-API-Key' - } -}) - -# Add namespaces to API -api.add_namespace(main_ns) -api.add_namespace(admin_ns) - -# Define request/response models for Swagger -text_input_model = api.model('TextInput', { - 'text': fields.String(required=True, description='Text to analyze for emotion', example='I am feeling happy today!') -}) - -emotion_response_model = api.model('EmotionResponse', { - 'text': fields.String(description='Input text'), - 'emotions': fields.List(fields.Nested(api.model('Emotion', { - 'emotion': fields.String(description='Emotion label'), - 'confidence': fields.Float(description='Confidence score') - }))), - 'confidence': fields.Float(description='Overall confidence'), - 'request_id': fields.String(description='Unique request identifier'), - 'timestamp': fields.Float(description='Unix timestamp') -}) - -batch_input_model = api.model('BatchInput', { - 'texts': fields.List(fields.String, required=True, description='List of texts to analyze', example=['I am happy', 'I am sad']) -}) - -batch_response_model = api.model('BatchResponse', { - 'results': fields.List(fields.Nested(emotion_response_model)) -}) - -error_model = api.model('Error', { - 'error': fields.String(description='Error message'), - 'status_code': fields.Integer(description='HTTP status code'), - 'request_id': fields.String(description='Unique request identifier'), - 'timestamp': fields.Float(description='Unix timestamp') -}) - -# Security configuration from environment variables -ADMIN_API_KEY = os.environ.get("ADMIN_API_KEY") -if not ADMIN_API_KEY: - raise ValueError("ADMIN_API_KEY environment variable must be set") -MAX_INPUT_LENGTH = int(os.environ.get("MAX_INPUT_LENGTH", "512")) -RATE_LIMIT_PER_MINUTE = int(os.environ.get("RATE_LIMIT_PER_MINUTE", "100")) -MODEL_PATH = os.environ.get("MODEL_PATH", "/app/model") -PORT = int(os.environ.get("PORT", "8080")) - -# Global variables for model state (thread-safe with locks) -model = None -tokenizer = None -emotion_mapping = None -model_loading = False -model_loaded = False -model_lock = threading.Lock() - -# Emotion mapping based on training order -EMOTION_MAPPING = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] - -def require_api_key(f): - """Decorator to require API key via X-API-Key header""" - @wraps(f) - def decorated_function(*args, **kwargs): - api_key = request.headers.get('X-API-Key') - if not verify_api_key(api_key): - logger.warning(f"Invalid API key attempt from {request.remote_addr}") - return create_error_response('Unauthorized - Invalid API key', 401) - return f(*args, **kwargs) - return decorated_function - -def verify_api_key(api_key: str) -> bool: - """Verify API key using constant-time comparison""" - if not api_key: - return False - return hmac.compare_digest(api_key, ADMIN_API_KEY) - -def sanitize_input(text: str) -> str: - """Sanitize input text""" - if not isinstance(text, str): - raise ValueError("Input must be a string") - - # Remove potentially dangerous characters - dangerous_chars = ['<', '>', '"', "'", '&', ';', '|', '`', '$', '(', ')', '{{', '}}'] - for char in dangerous_chars: - text = text.replace(char, '') - - # Limit length - if len(text) > MAX_INPUT_LENGTH: - text = text[:MAX_INPUT_LENGTH] - - return text.strip() - -def load_model(): - """Load the emotion detection model using shared utilities""" - # Use the shared model loading function - success = ensure_model_loaded() - if not success: - logger.error("โŒ Model loading failed") - raise RuntimeError("Model loading failed - check logs for details") - -def predict_emotion(text: str) -> dict: - """Predict emotion for given text using shared utilities""" - # Use shared prediction function - result = predict_emotions(text) - - # Add request ID for tracking - result['request_id'] = str(uuid.uuid4()) - - return result - -def check_model_loaded(): - """Ensure model is loaded before processing requests""" - # Use shared model loading function - return ensure_model_loaded() - -def create_error_response(error_message: str, status_code: int): - """Create a properly formatted error response for Flask-RESTX""" - error_response = { - 'error': error_message, - 'status_code': status_code, - 'request_id': str(uuid.uuid4()), - 'timestamp': time.time() - } - return error_response, status_code - -def handle_rate_limit_exceeded(): - """Handle rate limit exceeded - return proper error response""" - logger.warning(f"Rate limit exceeded for {request.remote_addr}") - return create_error_response('Rate limit exceeded - too many requests', 429) - -def log_rate_limit_info(): - """Log rate limiting information for debugging""" - logger.debug(f"Rate limiting configured: {RATE_LIMIT_PER_MINUTE} requests per minute") - logger.debug(f"Current request from: {request.remote_addr}") - -@app.before_request -def before_request(): - """Add request ID and timing to all requests""" - g.start_time = time.time() - g.request_id = str(uuid.uuid4()) - - # Lazy model initialization on first request - if not check_model_loaded(): - logger.info("๐Ÿ”„ Lazy initializing model on first request...") - initialize_model() - - # Log incoming requests for debugging - logger.info(f"๐Ÿ“ฅ Request: {request.method} {request.path} from {request.remote_addr} (ID: {g.request_id})") - - # Log request headers for debugging (excluding sensitive ones) - headers_to_log = {k: v for k, v in request.headers.items() - if k.lower() not in ['authorization', 'x-api-key', 'cookie']} - logger.debug(f"๐Ÿ“‹ Request headers: {headers_to_log}") - -@app.after_request -def after_request(response): - """Add request tracking headers""" - if hasattr(g, 'start_time'): - duration = time.time() - g.start_time - response.headers['X-Request-Duration'] = str(duration) - if hasattr(g, 'request_id'): - response.headers['X-Request-ID'] = g.request_id - - # Log response for debugging - logger.info(f"๐Ÿ“ค Response: {response.status_code} for {request.method} {request.path} " - f"from {request.remote_addr} (ID: {g.request_id}, Duration: {duration:.3f}s)") - - return response - - - -@main_ns.route('/health') -class Health(Resource): - @api.doc('get_health') - @api.response(200, 'Success') - @api.response(503, 'Service Unavailable', error_model) - @api.response(500, 'Internal Server Error', error_model) - def get(self): - """Get API health status""" - try: - logger.info(f"Health check from {request.remote_addr}") - model_status = check_model_loaded() - - if model_status: - logger.info("Health check passed - model is ready") - return { - 'status': 'healthy', - 'model_loaded': model_status, - 'model_loading': False, - 'port': PORT, - 'timestamp': time.time() - } - else: - logger.warning("Health check failed - model not ready") - return create_error_response('Service unavailable - model not ready', 503) - - except Exception as e: - logger.error(f"Health check error for {request.remote_addr}: {str(e)}") - return create_error_response('Internal server error', 500) - -@main_ns.route('/predict') -class Predict(Resource): - @api.doc('post_predict', security='apikey') - @api.expect(text_input_model, validate=True) - @api.response(200, 'Success', emotion_response_model) - @api.response(400, 'Bad Request', error_model) - @api.response(401, 'Unauthorized', error_model) - @api.response(429, 'Too Many Requests', error_model) - @api.response(503, 'Service Unavailable', error_model) - @rate_limit(RATE_LIMIT_PER_MINUTE) - @require_api_key - def post(self): - """Predict emotion for a single text input""" - try: - # Log rate limiting info for debugging - log_rate_limit_info() - - # Get and validate input - data = request.get_json() - if not data or 'text' not in data: - logger.warning(f"Missing text field in request from {request.remote_addr}") - return create_error_response('Missing text field', 400) - - text = data['text'] - if not text or not isinstance(text, str): - logger.warning(f"Invalid text input from {request.remote_addr}: {type(text)}") - return create_error_response('Text must be a non-empty string', 400) - - # Sanitize input - try: - text = sanitize_input(text) - except ValueError as e: - logger.warning(f"Input sanitization failed for {request.remote_addr}: {str(e)}") - return create_error_response(str(e), 400) - - # Ensure model is loaded - if not check_model_loaded(): - logger.error("Model not ready for prediction request") - return create_error_response('Model not ready', 503) - - # Predict emotion - logger.info(f"Processing prediction request for {request.remote_addr}") - result = predict_emotion(text) - return result - - except Exception as e: - logger.error(f"Prediction error for {request.remote_addr}: {str(e)}") - return create_error_response('Internal server error', 500) - -@main_ns.route('/predict_batch') -class PredictBatch(Resource): - @api.doc('post_predict_batch', security='apikey') - @api.expect(batch_input_model, validate=True) - @api.response(200, 'Success', batch_response_model) - @api.response(400, 'Bad Request', error_model) - @api.response(401, 'Unauthorized', error_model) - @api.response(429, 'Too Many Requests', error_model) - @api.response(503, 'Service Unavailable', error_model) - @rate_limit(RATE_LIMIT_PER_MINUTE) - @require_api_key - def post(self): - """Predict emotions for multiple text inputs""" - try: - # Log rate limiting info for debugging - log_rate_limit_info() - - # Get and validate input - data = request.get_json() - if not data or 'texts' not in data: - logger.warning(f"Missing texts field in batch request from {request.remote_addr}") - return create_error_response('Missing texts field', 400) - - texts = data['texts'] - if not isinstance(texts, list) or len(texts) == 0: - logger.warning(f"Invalid texts input from {request.remote_addr}: {type(texts)}") - return create_error_response('Texts must be a non-empty list', 400) - - if len(texts) > 100: # Limit batch size - logger.warning(f"Batch size too large from {request.remote_addr}: {len(texts)}") - return create_error_response('Batch size too large (max 100)', 400) - - # Ensure model is loaded - if not check_model_loaded(): - logger.error("Model not ready for batch prediction request") - return create_error_response('Model not ready', 503) - - # Process each text - logger.info(f"Processing batch prediction request for {request.remote_addr} with {len(texts)} texts") - results = [] - for text in texts: - if not text or not isinstance(text, str): - continue - - try: - text = sanitize_input(text) - result = predict_emotion(text) - results.append(result) - except Exception as e: - logger.warning(f"Failed to process text in batch from {request.remote_addr}: {str(e)}") - continue - - return {'results': results} - - except Exception as e: - logger.error(f"Batch prediction error for {request.remote_addr}: {str(e)}") - return create_error_response('Internal server error', 500) - -@main_ns.route('/emotions') -class Emotions(Resource): - @api.doc('get_emotions') - @api.response(200, 'Success') - @api.response(500, 'Internal Server Error', error_model) - def get(self): - """Get list of supported emotions""" - try: - logger.info(f"Emotions list requested from {request.remote_addr}") - return { - 'emotions': EMOTION_MAPPING, - 'count': len(EMOTION_MAPPING), - 'timestamp': time.time() - } - except Exception as e: - logger.error(f"Emotions endpoint error for {request.remote_addr}: {str(e)}") - return create_error_response('Internal server error', 500) - -# Admin endpoints -@admin_ns.route('/model_status') -class ModelStatus(Resource): - @api.doc('get_model_status', security='apikey') - @api.response(200, 'Success') - @api.response(401, 'Unauthorized', error_model) - @api.response(500, 'Internal Server Error', error_model) - @require_api_key - def get(self): - """Get detailed model status (admin only)""" - try: - # Get model status from shared utilities - logger.info(f"Admin model status request from {request.remote_addr}") - status = get_model_status() - return status - except Exception as e: - logger.error(f"Model status error for {request.remote_addr}: {str(e)}") - return create_error_response('Internal server error', 500) - -@admin_ns.route('/security_status') -class SecurityStatus(Resource): - @api.doc('get_security_status', security='apikey') - @api.response(200, 'Success') - @api.response(401, 'Unauthorized', error_model) - @api.response(500, 'Internal Server Error', error_model) - @require_api_key - def get(self): - """Get security configuration status (admin only)""" - try: - logger.info(f"Admin security status request from {request.remote_addr}") - return { - 'api_key_protection': True, - 'input_sanitization': True, - 'rate_limiting': True, - 'request_tracking': True, - 'security_headers': True, - 'timestamp': time.time() - } - except Exception as e: - logger.error(f"Security status error for {request.remote_addr}: {str(e)}") - return create_error_response('Internal server error', 500) - -# Error handlers for Flask-RESTX - using direct registration due to decorator compatibility issue -def rate_limit_exceeded(error): - """Handle rate limit exceeded errors""" - logger.warning(f"Rate limit exceeded for {request.remote_addr}") - return create_error_response('Rate limit exceeded - too many requests', 429) - -def internal_error(error): - """Handle internal server errors""" - logger.error(f"Internal server error for {request.remote_addr}: {str(error)}") - return create_error_response('Internal server error', 500) - -def not_found(error): - """Handle not found errors""" - logger.warning(f"Endpoint not found for {request.remote_addr}: {request.url}") - return create_error_response('Endpoint not found', 404) - -def method_not_allowed(error): - """Handle method not allowed errors""" - logger.warning(f"Method not allowed for {request.remote_addr}: {request.method} {request.url}") - return create_error_response('Method not allowed', 405) - -def handle_unexpected_error(error): - """Handle any unexpected errors""" - logger.error(f"Unexpected error for {request.remote_addr}: {str(error)}") - return create_error_response('An unexpected error occurred', 500) - -# Register error handlers directly -api.error_handlers[429] = rate_limit_exceeded -api.error_handlers[500] = internal_error -api.error_handlers[404] = not_found -api.error_handlers[405] = method_not_allowed -api.error_handlers[Exception] = handle_unexpected_error - -def initialize_model(): - """Initialize the emotion detection model""" - try: - logger.info("๐Ÿš€ Initializing emotion detection API server...") - logger.info(f"๐Ÿ“Š Configuration: MAX_INPUT_LENGTH={MAX_INPUT_LENGTH}, RATE_LIMIT={RATE_LIMIT_PER_MINUTE}/min") - logger.info(f"๐Ÿ” Security: API key protection enabled, Admin API key configured") - logger.info(f"๐ŸŒ Server: Port {PORT}, Model path: {MODEL_PATH}") - logger.info(f"๐Ÿ”„ Rate limiting: {RATE_LIMIT_PER_MINUTE} requests per minute") - - # Load the emotion detection model - logger.info("๐Ÿ”„ Loading emotion detection model...") - load_model() - logger.info("โœ… Model initialization completed successfully") - logger.info("๐Ÿš€ API server ready to handle requests") - - except Exception as e: - logger.error(f"โŒ Failed to initialize API server: {str(e)}") - raise - -# Initialize model when the application starts -if __name__ == '__main__': - initialize_model() - logger.info(f"๐ŸŒ Starting Flask development server on port {PORT}") - app.run(host='0.0.0.0', port=PORT, debug=False) -else: - # For production deployment - don't initialize during import - # Model will be initialized when the app actually starts - logger.info("๐Ÿš€ Production deployment detected - model will be initialized on first request") - -# Root endpoint is now registered BEFORE Flask-RESTX initialization to avoid conflicts - -# Make Flask app available to Gunicorn diff --git a/deployment/cloud-run/security_headers.py b/deployment/cloud-run/security_headers.py index 0aebcc545..0e3faf62d 100644 --- a/deployment/cloud-run/security_headers.py +++ b/deployment/cloud-run/security_headers.py @@ -1,8 +1,9 @@ #!/usr/bin/env python3 """Security Headers Module for Cloud Run API""" -from flask import Flask, request, g -from typing import Dict, Any + +from flask import Flask, g, request + def add_security_headers(app: Flask) -> None: """Add comprehensive security headers to Flask app""" @@ -19,8 +20,8 @@ def add_headers(response): "connect-src 'self'; " "frame-ancestors 'none';" ) - if request.path.startswith('/docs'): - nonce = getattr(g, 'csp_nonce', None) + if request.path.startswith("/docs"): + nonce = getattr(g, "csp_nonce", None) if nonce: csp_docs = ( "default-src 'self'; " @@ -34,19 +35,19 @@ def add_headers(response): else: # Reject request if no nonce is available for docs return "Content Security Policy violation: nonce required for /docs", 403 - response.headers['Content-Security-Policy'] = csp_docs + response.headers["Content-Security-Policy"] = csp_docs else: - response.headers['Content-Security-Policy'] = csp_base + response.headers["Content-Security-Policy"] = csp_base # Security headers - response.headers['X-Content-Type-Options'] = 'nosniff' - response.headers['X-Frame-Options'] = 'DENY' - response.headers['X-XSS-Protection'] = '1; mode=block' - response.headers['Referrer-Policy'] = 'strict-origin-when-cross-origin' - response.headers['Permissions-Policy'] = 'geolocation=(), microphone=(), camera=()' - response.headers['Strict-Transport-Security'] = 'max-age=31536000; includeSubDomains' + response.headers["X-Content-Type-Options"] = "nosniff" + response.headers["X-Frame-Options"] = "DENY" + response.headers["X-XSS-Protection"] = "1; mode=block" + response.headers["Referrer-Policy"] = "strict-origin-when-cross-origin" + response.headers["Permissions-Policy"] = "geolocation=(), microphone=(), camera=()" + response.headers["Strict-Transport-Security"] = "max-age=31536000; includeSubDomains" # Remove server information - response.headers.pop('Server', None) + response.headers.pop("Server", None) return response diff --git a/deployment/cloud-run/test_direct_errorhandler.py b/deployment/cloud-run/test_direct_errorhandler.py index 00f16200a..5e1d5522f 100644 --- a/deployment/cloud-run/test_direct_errorhandler.py +++ b/deployment/cloud-run/test_direct_errorhandler.py @@ -4,13 +4,15 @@ """ import os -os.environ['ADMIN_API_KEY'] = 'test123' + +os.environ["ADMIN_API_KEY"] = "test123" print("๐Ÿ” Testing direct error handler registration...") try: from flask import Flask from flask_restx import Api + print("โœ… Imports successful") except Exception as e: print(f"โŒ Import failed: {e}") @@ -18,7 +20,7 @@ try: app = Flask(__name__) - api = Api(app, version='1.0.0', title='Test') + api = Api(app, version="1.0.0", title="Test") print("โœ… API object created") except Exception as e: print(f"โŒ API creation failed: {e}") @@ -27,38 +29,38 @@ # Let's try to register error handlers directly try: print("1. Testing direct error handler registration...") - + def rate_limit_handler(error): return {"error": "Rate limit exceeded"}, 429 - + def internal_error_handler(error): return {"error": "Internal server error"}, 500 - + # Try to register directly api.error_handlers[429] = rate_limit_handler api.error_handlers[500] = internal_error_handler - + print("โœ… Direct registration successful") print(f"Error handlers: {api.error_handlers}") - + except Exception as e: print(f"โŒ Direct registration failed: {e}") # Let's also try using the Flask app's error handler try: print("\n2. Testing Flask app error handler...") - + @app.errorhandler(429) def flask_rate_limit_handler(error): return {"error": "Rate limit exceeded"}, 429 - + @app.errorhandler(500) def flask_internal_error_handler(error): return {"error": "Internal server error"}, 500 - + print("โœ… Flask app error handlers registered") - + except Exception as e: print(f"โŒ Flask app error handler failed: {e}") -print("\n๏ฟฝ๏ฟฝ Test complete.") \ No newline at end of file +print("\n๏ฟฝ๏ฟฝ Test complete.") diff --git a/deployment/cloud-run/test_docs_error.py b/deployment/cloud-run/test_docs_error.py index ab387bab1..45648fde0 100644 --- a/deployment/cloud-run/test_docs_error.py +++ b/deployment/cloud-run/test_docs_error.py @@ -4,38 +4,41 @@ """ import os + import requests # Set required environment variables -os.environ['ADMIN_API_KEY'] = 'test-key-123' -os.environ['MAX_INPUT_LENGTH'] = '512' -os.environ['RATE_LIMIT_PER_MINUTE'] = '100' -os.environ['MODEL_PATH'] = '/app/model' -os.environ['PORT'] = '8082' # Different port +os.environ["ADMIN_API_KEY"] = "test-key-123" +os.environ["MAX_INPUT_LENGTH"] = "512" +os.environ["RATE_LIMIT_PER_MINUTE"] = "100" +os.environ["MODEL_PATH"] = "/app/model" +os.environ["PORT"] = "8082" # Different port try: from secure_api_server import app - + print("โœ… Successfully imported secure_api_server") - + # Start server in background import threading + def run_server(): - app.run(host='0.0.0.0', port=8082, debug=False) - + app.run(host="0.0.0.0", port=8082, debug=False) + server_thread = threading.Thread(target=run_server, daemon=True) server_thread.start() - + # Wait for server to start import time + print("๐Ÿ”„ Starting server...") time.sleep(3) - + # Test docs endpoint specifically base_url = "http://localhost:8082" - + print("\n=== Testing Docs Endpoint ===") - + try: response = requests.get(f"{base_url}/docs", timeout=10) print(f"Status Code: {response.status_code}") @@ -43,17 +46,18 @@ def run_server(): print(f"Content Type: {response.headers.get('content-type', 'unknown')}") print(f"Content Length: {len(response.text)}") print(f"Response Text (first 500 chars): {response.text[:500]}") - + if response.status_code == 500: print("\nโŒ 500 Error Details:") print(f"Full Response: {response.text}") - + except Exception as e: print(f"โŒ Request failed: {e}") - + print("\nโœ… Docs test completed!") - + except Exception as e: print(f"โŒ Error: {e}") import traceback - traceback.print_exc() \ No newline at end of file + + traceback.print_exc() diff --git a/deployment/cloud-run/test_minimal_import.py b/deployment/cloud-run/test_minimal_import.py index 1bd62f110..b872cc230 100644 --- a/deployment/cloud-run/test_minimal_import.py +++ b/deployment/cloud-run/test_minimal_import.py @@ -4,7 +4,8 @@ """ import os -os.environ['ADMIN_API_KEY'] = 'test123' + +os.environ["ADMIN_API_KEY"] = "test123" print("๐Ÿ” Starting minimal import test...") @@ -12,6 +13,7 @@ print("1. Importing Flask and Flask-RESTX...") from flask import Flask from flask_restx import Api + print("โœ… Basic imports successful") except Exception as e: print(f"โŒ Basic imports failed: {e}") @@ -27,7 +29,7 @@ try: print("3. Creating API object...") - api = Api(app, version='1.0.0', title='Test') + api = Api(app, version="1.0.0", title="Test") print(f"โœ… API object created: {type(api)}") except Exception as e: print(f"โŒ API creation failed: {e}") @@ -52,4 +54,4 @@ print(f"Error type: {type(e)}") exit(1) -print("๐ŸŽ‰ All tests passed!") \ No newline at end of file +print("๐ŸŽ‰ All tests passed!") diff --git a/deployment/cloud-run/test_minimal_swagger.py b/deployment/cloud-run/test_minimal_swagger.py index a372cc6c7..511b57db7 100644 --- a/deployment/cloud-run/test_minimal_swagger.py +++ b/deployment/cloud-run/test_minimal_swagger.py @@ -4,45 +4,50 @@ """ import os + from flask import Flask, jsonify -from flask_restx import Api, Resource, Namespace + +from flask_restx import Api, Namespace, Resource # Create Flask app app = Flask(__name__) + # Register root endpoint first -@app.route('/') +@app.route("/") def root(): - return jsonify({'message': 'Root endpoint'}) + return jsonify({"message": "Root endpoint"}) + # Initialize Flask-RESTX API api = Api( - app, - version='1.0.0', - title='Test API', - description='Minimal test for Swagger docs', - doc='/docs' + app, version="1.0.0", title="Test API", description="Minimal test for Swagger docs", doc="/docs" ) # Create namespace -main_ns = Namespace('api', description='Main operations') +main_ns = Namespace("api", description="Main operations") api.add_namespace(main_ns) + # Test endpoint -@main_ns.route('/health') +@main_ns.route("/health") class Health(Resource): - def get(self): - return {'status': 'healthy'} + @staticmethod + def get(): + return {"status": "healthy"} + -if __name__ == '__main__': +if __name__ == "__main__": print("=== Routes ===") for rule in app.url_map.iter_rules(): print(f"{rule.rule} -> {rule.endpoint}") - + print("\n=== Starting test server ===") print("Test these endpoints:") print("- http://localhost:5003/ (should work)") print("- http://localhost:5003/docs (should work)") print("- http://localhost:5003/api/health (should work)") - - app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 5003)), debug=False) # Debug mode disabled for security \ No newline at end of file + + app.run( + host="0.0.0.0", port=int(os.environ.get("PORT", 5003)), debug=False + ) # Debug mode disabled for security diff --git a/deployment/cloud-run/test_routing_debug.py b/deployment/cloud-run/test_routing_debug.py index a7a53a252..d5c1dc500 100644 --- a/deployment/cloud-run/test_routing_debug.py +++ b/deployment/cloud-run/test_routing_debug.py @@ -4,7 +4,8 @@ """ from flask import Flask, jsonify -from flask_restx import Api, Resource, Namespace + +from flask_restx import Api, Namespace, Resource # Create Flask app app = Flask(__name__) @@ -15,35 +16,40 @@ # Initialize Flask-RESTX API api = Api( app, - version='1.0.0', - title='Test API', - description='Minimal test to isolate routing issues', - doc='/docs' + version="1.0.0", + title="Test API", + description="Minimal test to isolate routing issues", + doc="/docs", ) print("\n=== After API creation ===") print("App routes:", [rule.rule for rule in app.url_map.iter_rules()]) # Create namespace -main_ns = Namespace('/api', description='Main operations') +main_ns = Namespace("/api", description="Main operations") api.add_namespace(main_ns) print("\n=== After adding namespace ===") print("App routes:", [rule.rule for rule in app.url_map.iter_rules()]) + # Test endpoint in namespace -@main_ns.route('/health') +@main_ns.route("/health") class Health(Resource): - def get(self): - return {'status': 'healthy'} + @staticmethod + def get(): + return {"status": "healthy"} + print("\n=== After adding namespace route ===") print("App routes:", [rule.rule for rule in app.url_map.iter_rules()]) + # Test direct Flask route -@app.route('/test') +@app.route("/test") def test(): - return jsonify({'message': 'Test route'}) + return jsonify({"message": "Test route"}) + print("\n=== After adding Flask route ===") print("App routes:", [rule.rule for rule in app.url_map.iter_rules()]) @@ -51,9 +57,11 @@ def test(): # Now try to add root endpoint print("\n=== Trying to add root endpoint ===") try: - @app.route('/') + + @app.route("/") def root(): - return jsonify({'message': 'Root endpoint'}) + return jsonify({"message": "Root endpoint"}) + print("โœ… Root endpoint added successfully") except Exception as e: print(f"โŒ Failed to add root endpoint: {e}") @@ -78,7 +86,7 @@ def root(): # Check what Flask-RESTX created for the root route print("\n=== Flask-RESTX root route details ===") for rule in app.url_map.iter_rules(): - if rule.rule == '/': + if rule.rule == "/": print(f"Root route: {rule.rule} -> {rule.endpoint}") print(f" Methods: {rule.methods}") - print(f" View function: {rule.endpoint}") \ No newline at end of file + print(f" View function: {rule.endpoint}") diff --git a/deployment/cloud-run/test_routing_fixed.py b/deployment/cloud-run/test_routing_fixed.py index dc3e579f5..572a1be49 100644 --- a/deployment/cloud-run/test_routing_fixed.py +++ b/deployment/cloud-run/test_routing_fixed.py @@ -6,52 +6,54 @@ import os # Set required environment variables -os.environ['ADMIN_API_KEY'] = 'test-key-123' -os.environ['MAX_INPUT_LENGTH'] = '512' -os.environ['RATE_LIMIT_PER_MINUTE'] = '100' -os.environ['MODEL_PATH'] = '/app/model' -os.environ['PORT'] = '8080' +os.environ["ADMIN_API_KEY"] = "test-key-123" +os.environ["MAX_INPUT_LENGTH"] = "512" +os.environ["RATE_LIMIT_PER_MINUTE"] = "100" +os.environ["MODEL_PATH"] = "/app/model" +os.environ["PORT"] = "8080" try: from secure_api_server import app + print("โœ… Successfully imported secure_api_server") - + print("\n=== All Routes ===") for rule in app.url_map.iter_rules(): print(f"{rule.rule} -> {rule.endpoint}") - + print("\n=== Testing specific endpoints ===") - + # Check if root endpoint exists - root_routes = [rule for rule in app.url_map.iter_rules() if rule.rule == '/'] + root_routes = [rule for rule in app.url_map.iter_rules() if rule.rule == "/"] if root_routes: print("โœ… Root endpoint (/) exists") for route in root_routes: print(f" - {route.endpoint} (methods: {route.methods})") else: print("โŒ Root endpoint (/) missing") - + # Check if health endpoint exists - health_routes = [rule for rule in app.url_map.iter_rules() if '/health' in rule.rule] + health_routes = [rule for rule in app.url_map.iter_rules() if "/health" in rule.rule] if health_routes: print("โœ… Health endpoint exists") for route in health_routes: print(f" - {route.rule} -> {route.endpoint}") else: print("โŒ Health endpoint missing") - + # Check if docs endpoint exists - docs_routes = [rule for rule in app.url_map.iter_rules() if rule.rule == '/docs'] + docs_routes = [rule for rule in app.url_map.iter_rules() if rule.rule == "/docs"] if docs_routes: print("โœ… Docs endpoint (/docs) exists") for route in docs_routes: print(f" - {route.endpoint} (methods: {route.methods})") else: print("โŒ Docs endpoint (/docs) missing") - + print("\nโœ… Routing test completed successfully!") - + except Exception as e: print(f"โŒ Error testing routing: {e}") import traceback - traceback.print_exc() \ No newline at end of file + + traceback.print_exc() diff --git a/deployment/cloud-run/test_routing_minimal.py b/deployment/cloud-run/test_routing_minimal.py index 73f2ea03e..9bef1bdd8 100644 --- a/deployment/cloud-run/test_routing_minimal.py +++ b/deployment/cloud-run/test_routing_minimal.py @@ -4,8 +4,10 @@ """ import os + from flask import Flask, jsonify -from flask_restx import Api, Resource, Namespace + +from flask_restx import Api, Namespace, Resource # Create Flask app app = Flask(__name__) @@ -13,45 +15,53 @@ # Initialize Flask-RESTX API api = Api( app, - version='1.0.0', - title='Test API', - description='Minimal test to isolate routing issues', - doc='/docs' + version="1.0.0", + title="Test API", + description="Minimal test to isolate routing issues", + doc="/docs", ) # Create namespace with a different path to avoid conflicts -main_ns = Namespace('/api', description='Main operations') # Changed from '/' to '/api' +main_ns = Namespace("/api", description="Main operations") # Changed from '/' to '/api' api.add_namespace(main_ns) + # Test endpoint in namespace -@main_ns.route('/health') +@main_ns.route("/health") class Health(Resource): - def get(self): - return {'status': 'healthy'} + @staticmethod + def get(): + return {"status": "healthy"} + # Test direct Flask route BEFORE API setup -@app.route('/test_before') +@app.route("/test_before") def test_before(): - return jsonify({'message': 'This route was added before API setup'}) + return jsonify({"message": "This route was added before API setup"}) + # Test direct Flask route AFTER API setup -@app.route('/test_after') +@app.route("/test_after") def test_after(): - return jsonify({'message': 'This route was added after API setup'}) + return jsonify({"message": "This route was added after API setup"}) + # Test root endpoint - this should work now -@app.route('/') +@app.route("/") def root(): - return jsonify({'message': 'Root endpoint'}) + return jsonify({"message": "Root endpoint"}) + -if __name__ == '__main__': +if __name__ == "__main__": print("=== Flask App Routes ===") for rule in app.url_map.iter_rules(): print(f"App: {rule.rule} -> {rule.endpoint}") - + print("\n=== Flask-RESTX API Routes ===") for rule in api.url_map.iter_rules(): print(f"API: {rule.rule} -> {rule.endpoint}") - + print("\n=== Starting test server ===") - app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 5000)), debug=False) # Debug mode disabled for security \ No newline at end of file + app.run( + host="0.0.0.0", port=int(os.environ.get("PORT", 5000)), debug=False + ) # Debug mode disabled for security diff --git a/deployment/cloud-run/test_server_start.py b/deployment/cloud-run/test_server_start.py index 19eb6edd1..d2d2005b1 100644 --- a/deployment/cloud-run/test_server_start.py +++ b/deployment/cloud-run/test_server_start.py @@ -5,61 +5,64 @@ import os import time + import requests # Set required environment variables -os.environ['ADMIN_API_KEY'] = 'test-key-123' -os.environ['MAX_INPUT_LENGTH'] = '512' -os.environ['RATE_LIMIT_PER_MINUTE'] = '100' -os.environ['MODEL_PATH'] = '/app/model' -os.environ['PORT'] = '8081' # Different port to avoid conflicts +os.environ["ADMIN_API_KEY"] = "test-key-123" +os.environ["MAX_INPUT_LENGTH"] = "512" +os.environ["RATE_LIMIT_PER_MINUTE"] = "100" +os.environ["MODEL_PATH"] = "/app/model" +os.environ["PORT"] = "8081" # Different port to avoid conflicts try: from secure_api_server import app - + print("โœ… Successfully imported secure_api_server") - + # Start server in background import threading + def run_server(): - app.run(host='0.0.0.0', port=8081, debug=False) - + app.run(host="0.0.0.0", port=8081, debug=False) + server_thread = threading.Thread(target=run_server, daemon=True) server_thread.start() - + # Wait for server to start print("๐Ÿ”„ Starting server...") time.sleep(3) - + # Test endpoints base_url = "http://localhost:8081" - + print("\n=== Testing Endpoints ===") - + # Test root endpoint try: response = requests.get(f"{base_url}/", timeout=5) print(f"โœ… Root endpoint: {response.status_code} - {response.json()}") except Exception as e: print(f"โŒ Root endpoint failed: {e}") - + # Test health endpoint try: response = requests.get(f"{base_url}/api/health", timeout=5) print(f"โœ… Health endpoint: {response.status_code} - {response.json()}") except Exception as e: print(f"โŒ Health endpoint failed: {e}") - + # Test docs endpoint try: response = requests.get(f"{base_url}/docs", timeout=5) print(f"โœ… Docs endpoint: {response.status_code} - Content length: {len(response.text)}") except Exception as e: print(f"โŒ Docs endpoint failed: {e}") - + print("\nโœ… Server test completed!") - + except Exception as e: print(f"โŒ Error testing server: {e}") import traceback - traceback.print_exc() \ No newline at end of file + + traceback.print_exc() diff --git a/deployment/cloud-run/test_swagger_debug.py b/deployment/cloud-run/test_swagger_debug.py index fdb5b3f40..73e251fbd 100644 --- a/deployment/cloud-run/test_swagger_debug.py +++ b/deployment/cloud-run/test_swagger_debug.py @@ -4,8 +4,10 @@ """ import os + from flask import Flask, jsonify -from flask_restx import Api, Resource, Namespace + +from flask_restx import Api, Namespace, Resource # Create Flask app app = Flask(__name__) @@ -13,36 +15,42 @@ # Initialize Flask-RESTX API api = Api( app, - version='1.0.0', - title='Test API', - description='Minimal test to isolate routing issues', - doc='/docs' + version="1.0.0", + title="Test API", + description="Minimal test to isolate routing issues", + doc="/docs", ) # Create namespace -main_ns = Namespace('/api', description='Main operations') +main_ns = Namespace("/api", description="Main operations") api.add_namespace(main_ns) + # Test endpoint in namespace -@main_ns.route('/health') +@main_ns.route("/health") class Health(Resource): - def get(self): - return {'status': 'healthy'} + @staticmethod + def get(): + return {"status": "healthy"} + # Override the root route with a different endpoint name -@app.route('/') +@app.route("/") def api_root(): # Different function name to avoid conflict - return jsonify({'message': 'Root endpoint'}) + return jsonify({"message": "Root endpoint"}) -if __name__ == '__main__': + +if __name__ == "__main__": print("=== Routes ===") for rule in app.url_map.iter_rules(): print(f"{rule.rule} -> {rule.endpoint}") - + print("\n=== Starting test server ===") print("Test these endpoints:") print("- http://localhost:5001/ (should work)") print("- http://localhost:5001/docs (should work)") print("- http://localhost:5001/api/health (should work)") - - app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 5001)), debug=False) # Debug mode disabled for security \ No newline at end of file + + app.run( + host="0.0.0.0", port=int(os.environ.get("PORT", 5001)), debug=False + ) # Debug mode disabled for security diff --git a/deployment/cloud-run/test_swagger_debug_detailed.py b/deployment/cloud-run/test_swagger_debug_detailed.py index 0cb467f87..c698a33d3 100644 --- a/deployment/cloud-run/test_swagger_debug_detailed.py +++ b/deployment/cloud-run/test_swagger_debug_detailed.py @@ -4,81 +4,82 @@ """ import os -import requests import traceback +import requests + # Set required environment variables -os.environ['ADMIN_API_KEY'] = 'test-key-123' -os.environ['MAX_INPUT_LENGTH'] = '512' -os.environ['RATE_LIMIT_PER_MINUTE'] = '100' -os.environ['MODEL_PATH'] = '/app/model' -os.environ['PORT'] = '8084' +os.environ["ADMIN_API_KEY"] = "test-key-123" +os.environ["MAX_INPUT_LENGTH"] = "512" +os.environ["RATE_LIMIT_PER_MINUTE"] = "100" +os.environ["MODEL_PATH"] = "/app/model" +os.environ["PORT"] = "8084" try: from secure_api_server import app - + print("โœ… Successfully imported secure_api_server") - + # Start server in background with error capture import threading import time - + def run_server(): try: - app.run(host='0.0.0.0', port=8084, debug=False) + app.run(host="0.0.0.0", port=8084, debug=False) except Exception as e: print(f"โŒ Server error: {e}") traceback.print_exc() - + server_thread = threading.Thread(target=run_server, daemon=True) server_thread.start() - + # Wait for server to start print("๐Ÿ”„ Starting server...") time.sleep(3) - + # Test docs endpoint with detailed error capture base_url = "http://localhost:8084" - + print("\n=== Testing Docs Endpoint with Error Capture ===") - + try: # First test if server is responding response = requests.get(f"{base_url}/", timeout=5) print(f"โœ… Root endpoint: {response.status_code}") - + # Test health endpoint response = requests.get(f"{base_url}/api/health", timeout=5) print(f"โœ… Health endpoint: {response.status_code}") - + # Now test docs endpoint print("\n๐Ÿ”„ Testing /docs endpoint...") response = requests.get(f"{base_url}/docs", timeout=10) - + print(f"Status Code: {response.status_code}") print(f"Headers: {dict(response.headers)}") print(f"Content Type: {response.headers.get('content-type', 'unknown')}") print(f"Content Length: {len(response.text)}") - + if response.status_code == 500: print("\nโŒ 500 Error Details:") print(f"Full Response: {response.text}") - + # Try to get more info by checking if it's a Flask error page if "Internal Server Error" in response.text: print("๐Ÿ” This is a Flask internal server error page") print("๐Ÿ” The actual error is likely in the server logs") - + elif response.status_code == 200: print("โœ… Docs endpoint working!") print(f"Content preview: {response.text[:200]}...") - + except Exception as e: print(f"โŒ Request failed: {e}") traceback.print_exc() - + print("\nโœ… Docs test completed!") - + except Exception as e: print(f"โŒ Error: {e}") - traceback.print_exc() \ No newline at end of file + traceback.print_exc() diff --git a/deployment/cloud-run/test_swagger_no_model.py b/deployment/cloud-run/test_swagger_no_model.py index 09b350a00..f6c67b40e 100644 --- a/deployment/cloud-run/test_swagger_no_model.py +++ b/deployment/cloud-run/test_swagger_no_model.py @@ -4,52 +4,57 @@ """ import os + from flask import Flask, jsonify -from flask_restx import Api, Resource, Namespace + +from flask_restx import Api, Namespace, Resource # Set required environment variables -os.environ['ADMIN_API_KEY'] = 'test-key-123' -os.environ['MAX_INPUT_LENGTH'] = '512' -os.environ['RATE_LIMIT_PER_MINUTE'] = '100' -os.environ['MODEL_PATH'] = '/app/model' -os.environ['PORT'] = '8083' +os.environ["ADMIN_API_KEY"] = "test-key-123" +os.environ["MAX_INPUT_LENGTH"] = "512" +os.environ["RATE_LIMIT_PER_MINUTE"] = "100" +os.environ["MODEL_PATH"] = "/app/model" +os.environ["PORT"] = "8083" # Create Flask app app = Flask(__name__) + # Register root endpoint first -@app.route('/') +@app.route("/") def home(): - return jsonify({'message': 'Root endpoint'}) + return jsonify({"message": "Root endpoint"}) + # Initialize Flask-RESTX API api = Api( - app, - version='1.0.0', - title='Test API', - description='Test for Swagger docs issue', - doc='/docs' + app, version="1.0.0", title="Test API", description="Test for Swagger docs issue", doc="/docs" ) # Create namespace -main_ns = Namespace('api', description='Main operations') +main_ns = Namespace("api", description="Main operations") api.add_namespace(main_ns) + # Test endpoint -@main_ns.route('/health') +@main_ns.route("/health") class Health(Resource): - def get(self): - return {'status': 'healthy'} + @staticmethod + def get(): + return {"status": "healthy"} + -if __name__ == '__main__': +if __name__ == "__main__": print("=== Routes ===") for rule in app.url_map.iter_rules(): print(f"{rule.rule} -> {rule.endpoint}") - + print("\n=== Starting test server ===") print("Test these endpoints:") print("- http://localhost:8083/ (should work)") print("- http://localhost:8083/docs (should work)") print("- http://localhost:8083/api/health (should work)") - - app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 8083)), debug=False) # Debug mode disabled for security \ No newline at end of file + + app.run( + host="0.0.0.0", port=int(os.environ.get("PORT", 8083)), debug=False + ) # Debug mode disabled for security diff --git a/deployment/gcp/predict.py b/deployment/gcp/predict.py index 73fc60bff..d2cc352fb 100644 --- a/deployment/gcp/predict.py +++ b/deployment/gcp/predict.py @@ -7,55 +7,72 @@ """ import os + import torch -from transformers import AutoTokenizer, AutoModelForSequenceClassification -from flask import Flask, request, jsonify +from flask import Flask, jsonify, request +from transformers import AutoModelForSequenceClassification, AutoTokenizer app = Flask(__name__) + class EmotionDetectionModel: def __init__(self): """Initialize the model.""" self.model_path = os.path.join(os.getcwd(), "model") print(f"Loading model from: {self.model_path}") - + try: self.tokenizer = AutoTokenizer.from_pretrained(self.model_path) self.model = AutoModelForSequenceClassification.from_pretrained(self.model_path) - + # Move to GPU if available if torch.cuda.is_available(): - self.model = self.model.to('cuda') + self.model = self.model.to("cuda") print("โœ… Model moved to GPU") else: print("โš ๏ธ CUDA not available, using CPU") - - self.emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] + + self.emotions = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", + ] print("โœ… Model loaded successfully") - + except Exception as e: print(f"โŒ Failed to load model: {str(e)}") raise - + def predict(self, text): """Make a prediction.""" try: # Tokenize input - inputs = self.tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=512) - + inputs = self.tokenizer( + text, return_tensors="pt", truncation=True, padding=True, max_length=512 + ) + if torch.cuda.is_available(): - inputs = {k: v.to('cuda') for k, v in inputs.items()} - + inputs = {k: v.to("cuda") for k, v in inputs.items()} + # Get prediction with torch.no_grad(): outputs = self.model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_label = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_label].item() - + # Get all probabilities all_probs = probabilities[0].cpu().numpy() - + # Get predicted emotion if predicted_label in self.model.config.id2label: predicted_emotion = self.model.config.id2label[predicted_label] @@ -63,87 +80,96 @@ def predict(self, text): predicted_emotion = self.model.config.id2label[str(predicted_label)] else: predicted_emotion = f"unknown_{predicted_label}" - + # Create response response = { - 'text': text, - 'predicted_emotion': predicted_emotion, - 'confidence': float(confidence), - 'probabilities': { + "text": text, + "predicted_emotion": predicted_emotion, + "confidence": float(confidence), + "probabilities": { emotion: float(prob) for emotion, prob in zip(self.emotions, all_probs) }, - 'model_version': '2.0', - 'model_type': 'comprehensive_emotion_detection', - 'performance': { - 'basic_accuracy': '100.00%', - 'real_world_accuracy': '93.75%', - 'average_confidence': '83.9%' - } + "model_version": "2.0", + "model_type": "comprehensive_emotion_detection", + "performance": { + "basic_accuracy": "100.00%", + "real_world_accuracy": "93.75%", + "average_confidence": "83.9%", + }, } - + return response - + except Exception as e: print(f"Prediction error: {str(e)}") raise + # Initialize model print("๐Ÿ”ง Loading emotion detection model...") model = EmotionDetectionModel() -@app.route('/health', methods=['GET']) + +@app.route("/health", methods=["GET"]) def health_check(): """Health check endpoint.""" - return jsonify({ - 'status': 'healthy', - 'model_version': '2.0', - 'model_type': 'comprehensive_emotion_detection' - }) + return jsonify( + { + "status": "healthy", + "model_version": "2.0", + "model_type": "comprehensive_emotion_detection", + } + ) + -@app.route('/predict', methods=['POST']) +@app.route("/predict", methods=["POST"]) def predict(): """Prediction endpoint.""" try: data = request.get_json() - - if not data or 'text' not in data: - return jsonify({'error': 'No text provided'}), 400 - - text = data['text'] + + if not data or "text" not in data: + return jsonify({"error": "No text provided"}), 400 + + text = data["text"] if not text.strip(): - return jsonify({'error': 'Empty text provided'}), 400 - + return jsonify({"error": "Empty text provided"}), 400 + # Make prediction result = model.predict(text) - + return jsonify(result) - + except Exception as e: print(f"Prediction endpoint error: {str(e)}") - return jsonify({'error': str(e)}), 500 + return jsonify({"error": str(e)}), 500 -@app.route('/', methods=['GET']) + +@app.route("/", methods=["GET"]) def home(): """Home endpoint.""" - return jsonify({ - 'message': 'Comprehensive Emotion Detection API', - 'version': '2.0', - 'endpoints': { - 'GET /': 'This documentation', - 'GET /health': 'Health check', - 'POST /predict': 'Single prediction (send {"text": "your text"})' - }, - 'model_info': { - 'emotions': model.emotions, - 'performance': { - 'basic_accuracy': '100.00%', - 'real_world_accuracy': '93.75%', - 'average_confidence': '83.9%' - } + return jsonify( + { + "message": "Comprehensive Emotion Detection API", + "version": "2.0", + "endpoints": { + "GET /": "This documentation", + "GET /health": "Health check", + "POST /predict": 'Single prediction (send {"text": "your text"})', + }, + "model_info": { + "emotions": model.emotions, + "performance": { + "basic_accuracy": "100.00%", + "real_world_accuracy": "93.75%", + "average_confidence": "83.9%", + }, + }, } - }) + ) + -if __name__ == '__main__': +if __name__ == "__main__": print("๐ŸŒ Starting Vertex AI prediction server...") print("๐Ÿ“‹ Available endpoints:") print(" GET / - API documentation") @@ -152,6 +178,8 @@ def home(): print("") print("๐Ÿš€ Server starting on http://0.0.0.0:8080") print("") - - # Run the Flask app - app.run(host='0.0.0.0', port=8080, debug=False) + + # Run the Flask app - use environment variable for host binding + host = os.getenv("API_HOST", "127.0.0.1") + port = int(os.getenv("API_PORT", "8080")) + app.run(host=host, port=port, debug=False) diff --git a/deployment/inference.py b/deployment/inference.py index 430f45042..9c5743806 100644 --- a/deployment/inference.py +++ b/deployment/inference.py @@ -5,54 +5,67 @@ Standalone script to run emotion detection on text. """ -import torch -from transformers import AutoTokenizer, AutoModelForSequenceClassification from pathlib import Path +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + + class EmotionDetector: def __init__(self, model_path=None): """Initialize the emotion detector""" if model_path is None: # Use the model directory relative to this script model_path = Path(__file__).parent / "model" - - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - + + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"๐Ÿ”ง Loading model from: {model_path}") - + # Load model and tokenizer self.tokenizer = AutoTokenizer.from_pretrained("roberta-base") self.model = AutoModelForSequenceClassification.from_pretrained(str(model_path)) self.model.to(self.device) self.model.eval() - + # Define emotion mapping based on training order - self.emotion_mapping = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] - + self.emotion_mapping = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", + ] + print(f"โœ… Model loaded successfully on {self.device}") - + def predict(self, text): """Predict emotion for given text""" # Tokenize - inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True) + inputs = self.tokenizer( + text, return_tensors="pt", truncation=True, max_length=512, padding=True + ) inputs = {k: v.to(self.device) for k, v in inputs.items()} - + # Predict with torch.no_grad(): outputs = self.model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + # Map to emotion name emotion = self.emotion_mapping[predicted_class] - - return { - "emotion": emotion, - "confidence": confidence, - "text": text - } - + + return {"emotion": emotion, "confidence": confidence, "text": text} + def predict_batch(self, texts): """Predict emotions for multiple texts""" results = [] @@ -61,28 +74,30 @@ def predict_batch(self, texts): results.append(result) return results + def main(): """Main function for command line usage""" import sys - + if len(sys.argv) < 2: print("Usage: python inference.py 'Your text here'") print("Example: python inference.py 'I am feeling happy today!'") return - + text = sys.argv[1] - + # Initialize detector detector = EmotionDetector() - + # Make prediction result = detector.predict(text) - + print(f"\n๐ŸŽฏ EMOTION DETECTION RESULT") print(f"=" * 40) print(f"Text: {result['text']}") print(f"Emotion: {result['emotion']}") print(f"Confidence: {result['confidence']:.3f}") + if __name__ == "__main__": main() diff --git a/deployment/local/api_server.py b/deployment/local/api_server.py index 56224e566..0334f28a0 100644 --- a/deployment/local/api_server.py +++ b/deployment/local/api_server.py @@ -6,39 +6,35 @@ A production-ready Flask API server with monitoring, logging, rate limiting, and comprehensive security headers. """ - -# Import all modules first -import os import logging -import time +import os import threading +import time from collections import defaultdict, deque from datetime import datetime from functools import wraps import torch import werkzeug -from flask import Flask, request, jsonify -from transformers import AutoTokenizer, AutoModelForSequenceClassification +from flask import Flask, jsonify, request +from transformers import AutoModelForSequenceClassification, AutoTokenizer -# Import security setup using relative import -from ...src.security_setup import setup_security_middleware +# Import security setup using absolute import +# TODO: Package src/ as a proper module and depend on it explicitly. +# from src.security_setup import setup_security_middleware # Configure logging after all imports logging.basicConfig( level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', - handlers=[ - logging.FileHandler('api_server.log'), - logging.StreamHandler() - ] + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + handlers=[logging.FileHandler("api_server.log"), logging.StreamHandler()], ) logger = logging.getLogger(__name__) app = Flask(__name__) # Initialize security headers middleware -security_middleware = setup_security_middleware(app, "development") +security_middleware = setup_security_middleware(app, os.getenv("ENVIRONMENT", "development")) # Rate limiting configuration RATE_LIMIT_WINDOW = 60 # seconds @@ -48,108 +44,144 @@ # Monitoring metrics metrics = { - 'total_requests': 0, - 'successful_requests': 0, - 'failed_requests': 0, - 'average_response_time': 0.0, - 'response_times': deque(maxlen=1000), - 'emotion_distribution': defaultdict(int), - 'error_counts': defaultdict(int), - 'start_time': datetime.now() + "total_requests": 0, + "successful_requests": 0, + "failed_requests": 0, + "average_response_time": 0.0, + "response_times": deque(maxlen=1000), + "emotion_distribution": defaultdict(int), + "error_counts": defaultdict(int), + "start_time": datetime.now(), } metrics_lock = threading.Lock() + def rate_limit(f): """Rate limiting decorator.""" + @wraps(f) def decorated_function(*args, **kwargs): client_ip = request.remote_addr current_time = time.time() - + with rate_limit_lock: # Clean old requests - while rate_limit_data[client_ip] and current_time - rate_limit_data[client_ip][0] > RATE_LIMIT_WINDOW: + while ( + rate_limit_data[client_ip] + and current_time - rate_limit_data[client_ip][0] > RATE_LIMIT_WINDOW + ): rate_limit_data[client_ip].popleft() - + # Check rate limit if len(rate_limit_data[client_ip]) >= RATE_LIMIT_MAX_REQUESTS: logger.warning(f"Rate limit exceeded for IP: {client_ip}") - return jsonify({ - 'error': 'Rate limit exceeded', - 'message': f'Maximum {RATE_LIMIT_MAX_REQUESTS} requests per {RATE_LIMIT_WINDOW} seconds' - }), 429 - + return ( + jsonify( + { + "error": "Rate limit exceeded", + "message": f"Maximum {RATE_LIMIT_MAX_REQUESTS} requests per {RATE_LIMIT_WINDOW} seconds", + } + ), + 429, + ) + # Add current request rate_limit_data[client_ip].append(current_time) - + return f(*args, **kwargs) + return decorated_function + def update_metrics(response_time, success=True, emotion=None, error_type=None): """Update monitoring metrics.""" with metrics_lock: - metrics['total_requests'] += 1 - metrics['response_times'].append(response_time) - + metrics["total_requests"] += 1 + metrics["response_times"].append(response_time) + if success: - metrics['successful_requests'] += 1 + metrics["successful_requests"] += 1 if emotion: - metrics['emotion_distribution'][emotion] += 1 + metrics["emotion_distribution"][emotion] += 1 else: - metrics['failed_requests'] += 1 + metrics["failed_requests"] += 1 if error_type: - metrics['error_counts'][error_type] += 1 - + metrics["error_counts"][error_type] += 1 + # Update average response time - if metrics['response_times']: - metrics['average_response_time'] = sum(metrics['response_times']) / len(metrics['response_times']) + if metrics["response_times"]: + metrics["average_response_time"] = sum(metrics["response_times"]) / len( + metrics["response_times"] + ) + class EmotionDetectionModel: def __init__(self): """Initialize the model.""" - self.model_path = os.path.join(os.getcwd(), "model") + self.model_path = os.getenv( + "MODEL_PATH", + os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "model")), + ) logger.info(f"Loading model from: {self.model_path}") - + try: - self.tokenizer = AutoTokenizer.from_pretrained(self.model_path) - self.model = AutoModelForSequenceClassification.from_pretrained(self.model_path) - + self.tokenizer = AutoTokenizer.from_pretrained(self.model_path, local_files_only=True) + self.model = AutoModelForSequenceClassification.from_pretrained( + self.model_path, local_files_only=True + ) + self.model.eval() + # Move to GPU if available if torch.cuda.is_available(): - self.model = self.model.to('cuda') + self.model = self.model.to("cuda") logger.info("โœ… Model moved to GPU") else: logger.info("โš ๏ธ CUDA not available, using CPU") - - self.emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] + + self.emotions = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", + ] logger.info("โœ… Model loaded successfully") - + except Exception as e: logger.error(f"โŒ Failed to load model: {str(e)}") raise - + def predict(self, text): """Make a prediction.""" start_time = time.time() - + try: # Tokenize input - inputs = self.tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=512) - + inputs = self.tokenizer( + text, return_tensors="pt", truncation=True, padding=True, max_length=512 + ) + if torch.cuda.is_available(): - inputs = {k: v.to('cuda') for k, v in inputs.items()} - + inputs = {k: v.to("cuda") for k, v in inputs.items()} + # Get prediction with torch.no_grad(): outputs = self.model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_label = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_label].item() - + # Get all probabilities all_probs = probabilities[0].cpu().numpy() - + # Get predicted emotion if predicted_label in self.model.config.id2label: predicted_emotion = self.model.config.id2label[predicted_label] @@ -157,240 +189,252 @@ def predict(self, text): predicted_emotion = self.model.config.id2label[str(predicted_label)] else: predicted_emotion = f"unknown_{predicted_label}" - + prediction_time = time.time() - start_time - logger.info(f"Prediction completed in {prediction_time:.3f}s: '{text[:50]}...' โ†’ {predicted_emotion} (conf: {confidence:.3f})") - + snippet = ( + (text[:50] + "...") if os.getenv("LOG_INPUT_SNIPPETS", "0") == "1" else "" + ) + logger.info( + "Prediction completed in %.3fs: '%s' โ†’ %s (conf: %.3f)", + prediction_time, + snippet, + predicted_emotion, + confidence, + ) + # Create response response = { - 'text': text, - 'predicted_emotion': predicted_emotion, - 'confidence': float(confidence), - 'probabilities': { + "text": text, + "predicted_emotion": predicted_emotion, + "confidence": float(confidence), + "probabilities": { emotion: float(prob) for emotion, prob in zip(self.emotions, all_probs) }, - 'model_version': '2.0', - 'model_type': 'comprehensive_emotion_detection', - 'performance': { - 'basic_accuracy': '100.00%', - 'real_world_accuracy': '93.75%', - 'average_confidence': '83.9%' + "model_version": "2.0", + "model_type": "comprehensive_emotion_detection", + "performance": { + "basic_accuracy": "100.00%", + "real_world_accuracy": "93.75%", + "average_confidence": "83.9%", }, - 'prediction_time_ms': round(prediction_time * 1000, 2) + "prediction_time_ms": round(prediction_time * 1000, 2), } - + return response - + except Exception as e: prediction_time = time.time() - start_time logger.error(f"Prediction failed after {prediction_time:.3f}s: {str(e)}") raise + # Initialize model logger.info("๐Ÿ”ง Loading emotion detection model...") model = EmotionDetectionModel() -@app.route('/health', methods=['GET']) + +@app.route("/health", methods=["GET"]) @rate_limit def health_check(): """Health check endpoint.""" start_time = time.time() - + try: response = { - 'status': 'healthy', - 'model_loaded': True, - 'model_version': '2.0', - 'emotions': model.emotions, - 'uptime_seconds': (datetime.now() - metrics['start_time']).total_seconds(), - 'metrics': { - 'total_requests': metrics['total_requests'], - 'successful_requests': metrics['successful_requests'], - 'failed_requests': metrics['failed_requests'], - 'average_response_time_ms': round(metrics['average_response_time'] * 1000, 2) - } + "status": "healthy", + "model_loaded": True, + "model_version": "2.0", + "emotions": model.emotions, + "uptime_seconds": (datetime.now() - metrics["start_time"]).total_seconds(), + "metrics": { + "total_requests": metrics["total_requests"], + "successful_requests": metrics["successful_requests"], + "failed_requests": metrics["failed_requests"], + "average_response_time_ms": round(metrics["average_response_time"] * 1000, 2), + }, } - + response_time = time.time() - start_time update_metrics(response_time, success=True) - + return jsonify(response) - + except Exception as e: response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='health_check_error') + update_metrics(response_time, success=False, error_type="health_check_error") logger.error(f"Health check failed: {str(e)}") - return jsonify({'error': str(e)}), 500 + return jsonify({"error": str(e)}), 500 + -@app.route('/predict', methods=['POST']) +@app.route("/predict", methods=["POST"]) @rate_limit def predict(): """Prediction endpoint.""" start_time = time.time() - + try: data = request.get_json() - - if not data or 'text' not in data: + + if not data or "text" not in data: response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='missing_text') - return jsonify({'error': 'No text provided'}), 400 - - text = data['text'] + update_metrics(response_time, success=False, error_type="missing_text") + return jsonify({"error": "No text provided"}), 400 + + text = data["text"] if not text.strip(): response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='empty_text') - return jsonify({'error': 'Empty text provided'}), 400 - + update_metrics(response_time, success=False, error_type="empty_text") + return jsonify({"error": "Empty text provided"}), 400 + # Make prediction result = model.predict(text) - + response_time = time.time() - start_time - update_metrics(response_time, success=True, emotion=result['predicted_emotion']) - + update_metrics(response_time, success=True, emotion=result["predicted_emotion"]) + return jsonify(result) - - except werkzeug.exceptions.BadRequest: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='invalid_json') - logger.error(f"Invalid JSON in request") - return jsonify({'error': 'Invalid JSON format'}), 400 + except Exception as e: response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='prediction_error') + update_metrics(response_time, success=False, error_type="prediction_error") logger.error(f"Prediction endpoint error: {str(e)}") - return jsonify({'error': str(e)}), 500 + return jsonify({"error": str(e)}), 500 + -@app.route('/predict_batch', methods=['POST']) +@app.route("/predict_batch", methods=["POST"]) @rate_limit def predict_batch(): """Batch prediction endpoint.""" start_time = time.time() - + try: data = request.get_json() - - if not data or 'texts' not in data: + + if not data or "texts" not in data: response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='missing_texts') - return jsonify({'error': 'No texts provided'}), 400 - - texts = data['texts'] + update_metrics(response_time, success=False, error_type="missing_texts") + return jsonify({"error": "No texts provided"}), 400 + + texts = data["texts"] if not isinstance(texts, list): response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='invalid_texts_format') - return jsonify({'error': 'Texts must be a list'}), 400 - + update_metrics(response_time, success=False, error_type="invalid_texts_format") + return jsonify({"error": "Texts must be a list"}), 400 + results = [] for text in texts: if text.strip(): result = model.predict(text) results.append(result) - + response_time = time.time() - start_time update_metrics(response_time, success=True) - - return jsonify({ - 'predictions': results, - 'count': len(results), - 'batch_processing_time_ms': round(response_time * 1000, 2) - }) - - except werkzeug.exceptions.BadRequest: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='invalid_json') - logger.error(f"Invalid JSON in batch request") - return jsonify({'error': 'Invalid JSON format'}), 400 + + return jsonify( + { + "predictions": results, + "count": len(results), + "batch_processing_time_ms": round(response_time * 1000, 2), + } + ) + except Exception as e: response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='batch_prediction_error') + update_metrics(response_time, success=False, error_type="batch_prediction_error") logger.error(f"Batch prediction endpoint error: {str(e)}") - return jsonify({'error': str(e)}), 500 + return jsonify({"error": str(e)}), 500 + -@app.route('/metrics', methods=['GET']) +@app.route("/metrics", methods=["GET"]) def get_metrics(): """Get detailed metrics endpoint.""" with metrics_lock: - return jsonify({ - 'server_metrics': { - 'uptime_seconds': (datetime.now() - metrics['start_time']).total_seconds(), - 'total_requests': metrics['total_requests'], - 'successful_requests': metrics['successful_requests'], - 'failed_requests': metrics['failed_requests'], - 'success_rate': f"{(metrics['successful_requests'] / max(metrics['total_requests'], 1)) * 100:.2f}%", - 'average_response_time_ms': round(metrics['average_response_time'] * 1000, 2), - 'requests_per_minute': metrics['total_requests'] / max((datetime.now() - metrics['start_time']).total_seconds() / 60, 1) - }, - 'emotion_distribution': dict(metrics['emotion_distribution']), - 'error_counts': dict(metrics['error_counts']), - 'rate_limiting': { - 'window_seconds': RATE_LIMIT_WINDOW, - 'max_requests': RATE_LIMIT_MAX_REQUESTS + return jsonify( + { + "server_metrics": { + "uptime_seconds": (datetime.now() - metrics["start_time"]).total_seconds(), + "total_requests": metrics["total_requests"], + "successful_requests": metrics["successful_requests"], + "failed_requests": metrics["failed_requests"], + "success_rate": f"{(metrics['successful_requests'] / max(metrics['total_requests'], 1)) * 100:.2f}%", + "average_response_time_ms": round(metrics["average_response_time"] * 1000, 2), + "requests_per_minute": metrics["total_requests"] + / max((datetime.now() - metrics["start_time"]).total_seconds() / 60, 1), + }, + "emotion_distribution": dict(metrics["emotion_distribution"]), + "error_counts": dict(metrics["error_counts"]), + "rate_limiting": { + "window_seconds": RATE_LIMIT_WINDOW, + "max_requests": RATE_LIMIT_MAX_REQUESTS, + }, } - }) + ) + -@app.route('/', methods=['GET']) +@app.route("/", methods=["GET"]) @rate_limit def home(): """Home endpoint with API documentation.""" start_time = time.time() - + try: response = { - 'message': 'Comprehensive Emotion Detection API', - 'version': '2.0', - 'endpoints': { - 'GET /': 'This documentation', - 'GET /health': 'Health check with basic metrics', - 'GET /metrics': 'Detailed server metrics', - 'POST /predict': 'Single prediction (send {"text": "your text"})', - 'POST /predict_batch': 'Batch prediction (send {"texts": ["text1", "text2"]})' + "message": "Comprehensive Emotion Detection API", + "version": "2.0", + "endpoints": { + "GET /": "This documentation", + "GET /health": "Health check with basic metrics", + "GET /metrics": "Detailed server metrics", + "POST /predict": 'Single prediction (send {"text": "your text"})', + "POST /predict_batch": 'Batch prediction (send {"texts": ["text1", "text2"]})', }, - 'model_info': { - 'emotions': model.emotions, - 'performance': { - 'basic_accuracy': '100.00%', - 'real_world_accuracy': '93.75%', - 'average_confidence': '83.9%' - } + "model_info": { + "emotions": model.emotions, + "performance": { + "basic_accuracy": "100.00%", + "real_world_accuracy": "93.75%", + "average_confidence": "83.9%", + }, }, - 'features': { - 'rate_limiting': f'{RATE_LIMIT_MAX_REQUESTS} requests per {RATE_LIMIT_WINDOW} seconds', - 'monitoring': 'Comprehensive metrics and logging', - 'batch_processing': 'Efficient batch predictions', - 'error_handling': 'Robust error handling and reporting' + "features": { + "rate_limiting": f"{RATE_LIMIT_MAX_REQUESTS} requests per {RATE_LIMIT_WINDOW} seconds", + "monitoring": "Comprehensive metrics and logging", + "batch_processing": "Efficient batch predictions", + "error_handling": "Robust error handling and reporting", }, - 'example_usage': { - 'single_prediction': { - 'url': 'POST /predict', - 'body': '{"text": "I am feeling happy today!"}' + "example_usage": { + "single_prediction": { + "url": "POST /predict", + "body": '{"text": "I am feeling happy today!"}', }, - 'batch_prediction': { - 'url': 'POST /predict_batch', - 'body': '{"texts": ["I am happy", "I feel sad", "I am excited"]}' - } - } + "batch_prediction": { + "url": "POST /predict_batch", + "body": '{"texts": ["I am happy", "I feel sad", "I am excited"]}', + }, + }, } - + response_time = time.time() - start_time update_metrics(response_time, success=True) - + return jsonify(response) - + except Exception as e: response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='documentation_error') + update_metrics(response_time, success=False, error_type="documentation_error") logger.error(f"Documentation endpoint error: {str(e)}") - return jsonify({'error': str(e)}), 500 + return jsonify({"error": str(e)}), 500 + @app.errorhandler(werkzeug.exceptions.BadRequest) def handle_bad_request(e): """Handle BadRequest exceptions (invalid JSON, etc.).""" - logger.error(f"BadRequest error: {str(e)}") - update_metrics(0.0, success=False, error_type='invalid_json') - return jsonify({'error': 'Invalid JSON format'}), 400 + logger.exception("BadRequest error") + update_metrics(0.0, success=False, error_type="invalid_json") + return jsonify({"error": "Invalid JSON format"}), 400 -if __name__ == '__main__': + +if __name__ == "__main__": logger.info("๐ŸŒ Starting enhanced local API server...") logger.info("๐Ÿ“‹ Available endpoints:") logger.info(" GET / - API documentation") @@ -403,10 +447,15 @@ def handle_bad_request(e): logger.info("๐Ÿ“ Example usage:") logger.info(" curl -X POST http://localhost:8000/predict \\") logger.info(" -H 'Content-Type: application/json' \\") - logger.info(" -d '{\"text\": \"I am feeling happy today!\"}'") + logger.info(' -d \'{"text": "I am feeling happy today!"}\'') logger.info("") - logger.info(f"๐Ÿ”’ Rate limiting: {RATE_LIMIT_MAX_REQUESTS} requests per {RATE_LIMIT_WINDOW} seconds") + logger.info( + f"๐Ÿ”’ Rate limiting: {RATE_LIMIT_MAX_REQUESTS} requests per {RATE_LIMIT_WINDOW} seconds" + ) logger.info("๐Ÿ“Š Monitoring: Comprehensive metrics and logging enabled") logger.info("") - - app.run(host='0.0.0.0', port=8000, debug=False) + + # Use environment variable for host binding, default to localhost for security + host = os.getenv("API_HOST", "127.0.0.1") + port = int(os.getenv("API_PORT", "8000")) + app.run(host=host, port=port, debug=False) diff --git a/deployment/local/requirements-simple.txt b/deployment/local/requirements-simple.txt new file mode 100644 index 000000000..248c14852 --- /dev/null +++ b/deployment/local/requirements-simple.txt @@ -0,0 +1,3 @@ +flask>=2.0.0 +flask-cors>=3.0.0 +requests>=2.25.0 \ No newline at end of file diff --git a/deployment/local/requirements.txt b/deployment/local/requirements.txt index bade617cb..8228eb523 100644 --- a/deployment/local/requirements.txt +++ b/deployment/local/requirements.txt @@ -1,6 +1,7 @@ flask>=2.0.0 torch>=2.0.0 -transformers>=4.55.0 +transformers>=4.46.0,<4.47.0 numpy>=1.21.0 requests==2.32.4 httpx>=0.25.0,<0.29.0 +pyyaml>=6.0 diff --git a/deployment/local/simple_server.py b/deployment/local/simple_server.py new file mode 100644 index 000000000..09ef9ffef --- /dev/null +++ b/deployment/local/simple_server.py @@ -0,0 +1,115 @@ +# (shebang removed; run via `python deployment/local/simple_server.py`) +""" +Simple Local API Server for Development +======================================== + +A lightweight Flask server for local development testing. +Serves static files and provides basic CORS support. +""" + +import argparse +import logging +import os + +import requests +from flask import Flask, jsonify, request, send_from_directory +from flask_cors import CORS + +app = Flask(__name__) +CORS(app) # Enable CORS for all domains on all routes + +# Configure logging +logging.basicConfig(level=logging.INFO) + +# Resolve once +WEBSITE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "website")) + +# Environment-configurable upstream settings +UPSTREAM_BASE = os.getenv( + "SAMO_UNIFIED_API_BASE", "https://samo-unified-api-optimized-frrnetyhfa-uc.a.run.app" +) +API_KEY = os.getenv("SAMO_API_KEY") # optional +COMMON_HEADERS = {"Authorization": f"Bearer {API_KEY}"} if API_KEY else {} + + +# Serve static files from website directory +@app.route("/") +def index(): + return send_from_directory(WEBSITE_DIR, "comprehensive-demo.html") + + +@app.route("/") +def static_files(filename): + return send_from_directory(WEBSITE_DIR, filename) + + +# CORS Proxy for Real API +@app.route("/api/emotion", methods=["POST"]) +def proxy_emotion(): + try: + # Accept JSON body or query param + data = request.get_json(silent=True) or {} + text = (data.get("text") or request.args.get("text", "")).strip() + if not text: + return jsonify({"error": "No text provided"}), 400 + + # Call real API (requests will encode params) + api_url = f"{UPSTREAM_BASE}/analyze/emotion" + response = requests.post(api_url, params={"text": text}, headers=COMMON_HEADERS, timeout=30) + + if response.ok: + return jsonify(response.json()) + return jsonify({"error": f"API error: {response.status_code}"}), response.status_code + + except Exception: + logging.exception("Unhandled exception in /api/emotion") + return jsonify({"error": "Internal server error"}), 500 + + +@app.route("/api/summarize", methods=["POST"]) +def proxy_summarize(): + try: + # Accept JSON body or query param + data = request.get_json(silent=True) or {} + text = (data.get("text") or request.args.get("text", "")).strip() + if not text: + return jsonify({"error": "No text provided"}), 400 + + # Call real API (requests will encode params) + api_url = f"{UPSTREAM_BASE}/analyze/summarize" + response = requests.post(api_url, params={"text": text}, headers=COMMON_HEADERS, timeout=30) + + if response.ok: + return jsonify(response.json()) + return jsonify({"error": f"API error: {response.status_code}"}), response.status_code + + except Exception: + logging.exception("Unhandled exception in /api/summarize") + return jsonify({"error": "Internal server error"}), 500 + + +@app.route("/api/health", methods=["GET"]) +def health(): + return jsonify({"status": "healthy", "server": "simple_local_dev"}) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Simple Local Development Server") + parser.add_argument( + "--port", + type=int, + default=int(os.getenv("PORT", 8000)), + help="Port to run the server on (default: 8000)", + ) + parser.add_argument("--host", default="127.0.0.1", help="Host to bind to (default: 127.0.0.1)") + args = parser.parse_args() + + print("๐Ÿš€ SIMPLE LOCAL DEVELOPMENT SERVER") + print("==================================") + print(f"๐ŸŒ Server starting at: http://{args.host}:{args.port}") + print("๐Ÿ“ Serving website files with CORS enabled") + print("๐Ÿ”ง Proxy AI endpoints available for testing") + print("Press Ctrl+C to stop the server") + print("") + + app.run(host=args.host, port=args.port, debug=False) diff --git a/deployment/local/start-simple.sh b/deployment/local/start-simple.sh new file mode 100755 index 000000000..89e2b31fc --- /dev/null +++ b/deployment/local/start-simple.sh @@ -0,0 +1,29 @@ +#!/usr/bin/env bash +# Start simple local development server + +# Enable strict bash options for fail-fast behavior +set -euo pipefail +IFS=$'\n\t' + +# Change to script's directory for location independence +cd "$(dirname "$0")" + +echo "๐Ÿš€ STARTING SIMPLE LOCAL DEVELOPMENT SERVER" +echo "===========================================" + +# Install minimal dependencies +echo "๐Ÿ“ฆ Installing minimal dependencies..." +command -v python3 >/dev/null || { echo "python3 not found in PATH" >&2; exit 127; } +[ -f requirements-simple.txt ] || { echo "requirements-simple.txt not found next to script" >&2; exit 1; } +if [ -z "${VIRTUAL_ENV:-}" ]; then USER_FLAG="--user"; else USER_FLAG=""; fi +python3 -m pip install $USER_FLAG -r requirements-simple.txt + +# Start simple server +echo "๐ŸŒ Starting simple development server..." +PORT="${PORT:-8000}" +echo "Server will be available at: http://localhost:${PORT}" +echo "Website files served with CORS enabled" +echo "Press Ctrl+C to stop the server" +echo "" + +exec python3 simple_server.py --port "${PORT}" \ No newline at end of file diff --git a/deployment/local/start.sh b/deployment/local/start.sh index a7cf19c86..b82446124 100755 --- a/deployment/local/start.sh +++ b/deployment/local/start.sh @@ -6,7 +6,7 @@ echo "============================" # Install dependencies echo "๐Ÿ“ฆ Installing dependencies..." -pip install -r requirements-api.txt +pip install -r requirements.txt # Start API server echo "๐ŸŒ Starting API server..." @@ -14,4 +14,6 @@ echo "Server will be available at: http://localhost:5000" echo "Press Ctrl+C to stop the server" echo "" +# Set PYTHONPATH to include the project root +export PYTHONPATH="${PYTHONPATH}:$(pwd)/../.." python api_server.py diff --git a/deployment/local/test_api.py b/deployment/local/test_api.py index fb3c415c6..d4a1141ed 100644 --- a/deployment/local/test_api.py +++ b/deployment/local/test_api.py @@ -7,10 +7,11 @@ logging, and rate limiting features. """ -import requests +import sys import time from concurrent.futures import ThreadPoolExecutor, as_completed -import sys + +import requests # Configuration BASE_URL = "http://localhost:8000" @@ -26,9 +27,10 @@ "I feel overwhelmed by all the work", "I am hopeful for the future", "I feel content with my life", - "I am tired after a long day" + "I am tired after a long day", ] + def test_health_check(): """Test the enhanced health check endpoint.""" print("1. Testing enhanced health check...") @@ -41,7 +43,9 @@ def test_health_check(): print(f" Model Version: {data['model_version']}") print(f" Uptime: {data['uptime_seconds']:.1f} seconds") print(f" Total Requests: {data['metrics']['total_requests']}") - print(f" Success Rate: {data['metrics']['successful_requests']}/{data['metrics']['total_requests']}") + print( + f" Success Rate: {data['metrics']['successful_requests']}/{data['metrics']['total_requests']}" + ) print(f" Avg Response Time: {data['metrics']['average_response_time_ms']}ms") return True else: @@ -51,6 +55,7 @@ def test_health_check(): print(f"โŒ Health check error: {str(e)}") return False + def test_metrics_endpoint(): """Test the new metrics endpoint.""" print("\n2. Testing metrics endpoint...") @@ -61,7 +66,9 @@ def test_metrics_endpoint(): print(f"โœ… Metrics endpoint working") print(f" Success Rate: {data['server_metrics']['success_rate']}") print(f" Requests/Minute: {data['server_metrics']['requests_per_minute']:.2f}") - print(f" Rate Limiting: {data['rate_limiting']['max_requests']} req/{data['rate_limiting']['window_seconds']}s") + print( + f" Rate Limiting: {data['rate_limiting']['max_requests']} req/{data['rate_limiting']['window_seconds']}s" + ) return True else: print(f"โŒ Metrics endpoint failed: {response.status_code}") @@ -70,52 +77,58 @@ def test_metrics_endpoint(): print(f"โŒ Metrics endpoint error: {str(e)}") return False + def test_single_predictions(): """Test single predictions with timing.""" print("\n3. Testing single predictions...") results = [] - + for i, text in enumerate(TEST_TEXTS[:5], 1): try: start_time = time.time() response = requests.post( f"{BASE_URL}/predict", json={"text": text}, - headers={"Content-Type": "application/json"} + headers={"Content-Type": "application/json"}, ) end_time = time.time() - + if response.status_code == 200: data = response.json() - emotion = data['predicted_emotion'] - confidence = data['confidence'] - prediction_time = data.get('prediction_time_ms', 0) + emotion = data["predicted_emotion"] + confidence = data["confidence"] + prediction_time = data.get("prediction_time_ms", 0) total_time = (end_time - start_time) * 1000 - - print(f"โœ… Test {i}: '{text[:30]}...' โ†’ {emotion} (conf: {confidence:.3f}, time: {prediction_time}ms)") - results.append({ - 'text': text, - 'emotion': emotion, - 'confidence': confidence, - 'prediction_time_ms': prediction_time, - 'total_time_ms': total_time - }) + + print( + f"โœ… Test {i}: '{text[:30]}...' โ†’ {emotion} (conf: {confidence:.3f}, time: {prediction_time}ms)" + ) + results.append( + { + "text": text, + "emotion": emotion, + "confidence": confidence, + "prediction_time_ms": prediction_time, + "total_time_ms": total_time, + } + ) else: print(f"โŒ Test {i} failed: {response.status_code}") return False - + except Exception as e: print(f"โŒ Test {i} error: {str(e)}") return False - + # Calculate average performance - avg_confidence = sum(r['confidence'] for r in results) / len(results) - avg_prediction_time = sum(r['prediction_time_ms'] for r in results) / len(results) + avg_confidence = sum(r["confidence"] for r in results) / len(results) + avg_prediction_time = sum(r["prediction_time_ms"] for r in results) / len(results) print(f" ๐Ÿ“Š Average confidence: {avg_confidence:.3f}") print(f" ๐Ÿ“Š Average prediction time: {avg_prediction_time:.1f}ms") - + return True + def test_batch_predictions(): """Test batch predictions.""" print("\n4. Testing batch predictions...") @@ -124,67 +137,68 @@ def test_batch_predictions(): response = requests.post( f"{BASE_URL}/predict_batch", json={"texts": TEST_TEXTS[:5]}, - headers={"Content-Type": "application/json"} + headers={"Content-Type": "application/json"}, ) end_time = time.time() - + if response.status_code == 200: data = response.json() - predictions = data['predictions'] - batch_time = data.get('batch_processing_time_ms', 0) + predictions = data["predictions"] + batch_time = data.get("batch_processing_time_ms", 0) total_time = (end_time - start_time) * 1000 - + print(f"โœ… Batch prediction successful: {len(predictions)} predictions") print(f" Batch processing time: {batch_time}ms") print(f" Total time: {total_time:.1f}ms") - + for i, pred in enumerate(predictions, 1): - emotion = pred['predicted_emotion'] - confidence = pred['confidence'] - text = pred['text'][:30] + "..." if len(pred['text']) > 30 else pred['text'] + emotion = pred["predicted_emotion"] + confidence = pred["confidence"] + text = pred["text"][:30] + "..." if len(pred["text"]) > 30 else pred["text"] print(f" {i}. '{text}' โ†’ {emotion} (conf: {confidence:.3f})") - + return True else: print(f"โŒ Batch prediction failed: {response.status_code}") return False - + except Exception as e: print(f"โŒ Batch prediction error: {str(e)}") return False + def test_rate_limiting(): """Test rate limiting functionality.""" print("\n5. Testing rate limiting...") - + def make_request(): try: response = requests.post( f"{BASE_URL}/predict", json={"text": "Test rate limiting"}, - headers={"Content-Type": "application/json"} + headers={"Content-Type": "application/json"}, ) return response.status_code except: return 0 - + # Make rapid requests to test rate limiting print(" Making rapid requests to test rate limiting...") start_time = time.time() - + with ThreadPoolExecutor(max_workers=10) as executor: futures = [executor.submit(make_request) for _ in range(50)] results = [future.result() for future in as_completed(futures)] - + end_time = time.time() - + successful = sum(1 for code in results if code == 200) rate_limited = sum(1 for code in results if code == 429) failed = sum(1 for code in results if code not in [200, 429]) - + print(f" โœ… Rate limiting test completed in {end_time - start_time:.2f}s") print(f" ๐Ÿ“Š Successful: {successful}, Rate limited: {rate_limited}, Failed: {failed}") - + if rate_limited > 0: print(f" โœ… Rate limiting is working (blocked {rate_limited} requests)") return True @@ -192,16 +206,15 @@ def make_request(): print(f" โš ๏ธ No rate limiting detected (may need more requests)") return True + def test_error_handling(): """Test error handling.""" print("\n6. Testing error handling...") - + # Test missing text try: response = requests.post( - f"{BASE_URL}/predict", - json={}, - headers={"Content-Type": "application/json"} + f"{BASE_URL}/predict", json={}, headers={"Content-Type": "application/json"} ) if response.status_code == 400: print("โœ… Missing text error handled correctly") @@ -211,13 +224,11 @@ def test_error_handling(): except Exception as e: print(f"โŒ Missing text test error: {str(e)}") return False - + # Test empty text try: response = requests.post( - f"{BASE_URL}/predict", - json={"text": ""}, - headers={"Content-Type": "application/json"} + f"{BASE_URL}/predict", json={"text": ""}, headers={"Content-Type": "application/json"} ) if response.status_code == 400: print("โœ… Empty text error handled correctly") @@ -227,13 +238,11 @@ def test_error_handling(): except Exception as e: print(f"โŒ Empty text test error: {str(e)}") return False - + # Test invalid JSON try: response = requests.post( - f"{BASE_URL}/predict", - data="invalid json", - headers={"Content-Type": "application/json"} + f"{BASE_URL}/predict", data="invalid json", headers={"Content-Type": "application/json"} ) if response.status_code == 400: print("โœ… Invalid JSON error handled correctly") @@ -243,52 +252,53 @@ def test_error_handling(): except Exception as e: print(f"โŒ Invalid JSON test error: {str(e)}") return False - + return True + def test_performance(): """Test performance under load.""" print("\n7. Testing performance under load...") - + def make_prediction_request(): try: start_time = time.time() response = requests.post( f"{BASE_URL}/predict", json={"text": "Performance test"}, - headers={"Content-Type": "application/json"} + headers={"Content-Type": "application/json"}, ) end_time = time.time() return { - 'status_code': response.status_code, - 'response_time': (end_time - start_time) * 1000 + "status_code": response.status_code, + "response_time": (end_time - start_time) * 1000, } except Exception as e: - return {'status_code': 0, 'response_time': 0, 'error': str(e)} - + return {"status_code": 0, "response_time": 0, "error": str(e)} + # Test with concurrent requests print(" Testing with 20 concurrent requests...") start_time = time.time() - + with ThreadPoolExecutor(max_workers=5) as executor: futures = [executor.submit(make_prediction_request) for _ in range(20)] results = [future.result() for future in as_completed(futures)] - + end_time = time.time() - - successful = [r for r in results if r['status_code'] == 200] - failed = [r for r in results if r['status_code'] != 200] - + + successful = [r for r in results if r["status_code"] == 200] + failed = [r for r in results if r["status_code"] != 200] + if successful: - avg_response_time = sum(r['response_time'] for r in successful) / len(successful) - min_response_time = min(r['response_time'] for r in successful) - max_response_time = max(r['response_time'] for r in successful) - + avg_response_time = sum(r["response_time"] for r in successful) / len(successful) + min_response_time = min(r["response_time"] for r in successful) + max_response_time = max(r["response_time"] for r in successful) + print(f" โœ… Performance test completed in {end_time - start_time:.2f}s") print(f" ๐Ÿ“Š Successful requests: {len(successful)}/{len(results)}") print(f" ๐Ÿ“Š Average response time: {avg_response_time:.1f}ms") print(f" ๐Ÿ“Š Response time range: {min_response_time:.1f}ms - {max_response_time:.1f}ms") - + if avg_response_time < 1000: # Less than 1 second print(" โœ… Performance is acceptable") return True @@ -299,15 +309,16 @@ def make_prediction_request(): print(" โŒ No successful requests in performance test") return False + def main(): """Run all tests.""" print("๐Ÿงช ENHANCED API TESTING") print("=" * 50) - + # Wait for server to start print("โณ Waiting for server to start...") time.sleep(2) - + tests = [ ("Health Check", test_health_check), ("Metrics Endpoint", test_metrics_endpoint), @@ -315,12 +326,12 @@ def main(): ("Batch Predictions", test_batch_predictions), ("Rate Limiting", test_rate_limiting), ("Error Handling", test_error_handling), - ("Performance", test_performance) + ("Performance", test_performance), ] - + passed = 0 total = len(tests) - + for test_name, test_func in tests: try: if test_func(): @@ -329,11 +340,11 @@ def main(): print(f"โŒ {test_name} failed") except Exception as e: print(f"โŒ {test_name} error: {str(e)}") - + print("\n" + "=" * 50) print(f"๐ŸŽ‰ ENHANCED API TESTING COMPLETED!") print(f"๐Ÿ“Š Results: {passed}/{total} tests passed") - + if passed == total: print("โœ… All tests passed! Enhanced API is working correctly.") print("\n๐Ÿ“‹ Enhanced Features Verified:") @@ -348,5 +359,6 @@ def main(): print(f"โŒ {total - passed} tests failed. Please check the implementation.") return 1 + if __name__ == "__main__": sys.exit(main()) diff --git a/deployment/secure_api_server.py b/deployment/secure_api_server.py deleted file mode 100644 index 8c78347ad..000000000 --- a/deployment/secure_api_server.py +++ /dev/null @@ -1,1073 +0,0 @@ -#!/usr/bin/env python3 -""" -๐Ÿ”’ SECURE EMOTION DETECTION API SERVER -====================================== -Production-ready Flask API server with comprehensive security features. - -Security Features: -- Rate limiting with token bucket algorithm -- Input sanitization and validation -- Security headers (CSP, HSTS, X-Frame-Options, etc.) -- Request/response logging and monitoring -- IP whitelist/blacklist support -- Abuse detection and automatic blocking -- Request correlation and tracing -""" - -# Import all modules first -import os -from flask import Flask, request, jsonify, g -import werkzeug -import logging -from pathlib import Path -import time -from datetime import datetime -from collections import defaultdict, deque -import threading -from functools import wraps, lru_cache -from typing import List, Tuple, Any, Dict - -# Import security components using relative imports -from ..src.api_rate_limiter import TokenBucketRateLimiter, RateLimitConfig -from ..src.input_sanitizer import InputSanitizer, SanitizationConfig -from ..src.security_setup import setup_security_middleware, get_environment -from ..src.inference.text_emotion_service import HFEmotionService # type: ignore - -# Import centralized constants with fallback for non-package environments -try: - from src.constants import EMOTION_MODEL_DIR # single source of truth -except ImportError: - EMOTION_MODEL_DIR = os.getenv( - 'EMOTION_MODEL_DIR', - '/app/models/emotion-english-distilroberta-base' - ) - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', - handlers=[ - logging.FileHandler('secure_api_server.log'), - logging.StreamHandler() - ] -) -logger = logging.getLogger(__name__) - -# Initialize Flask app -app = Flask(__name__) - -# Security configurations -rate_limit_config = RateLimitConfig( - requests_per_minute=60, - burst_size=10, - window_size_seconds=60, - block_duration_seconds=300, - max_concurrent_requests=5, - enable_ip_whitelist=False, - whitelisted_ips=set(), - enable_ip_blacklist=True, - blacklisted_ips=set() -) - -sanitization_config = SanitizationConfig( - max_text_length=10000, - max_batch_size=100, - enable_xss_protection=True, - enable_sql_injection_protection=True, - enable_path_traversal_protection=True, - enable_command_injection_protection=True, - enable_unicode_normalization=True, - enable_content_type_validation=True -) - -# Initialize security components -rate_limiter = TokenBucketRateLimiter(rate_limit_config) -input_sanitizer = InputSanitizer(sanitization_config) -security_middleware = setup_security_middleware(app, get_environment()) - -# Monitoring metrics -metrics = { - 'total_requests': 0, - 'successful_requests': 0, - 'failed_requests': 0, - 'rate_limited_requests': 0, - 'sanitization_warnings': 0, - 'security_violations': 0, - 'average_response_time': 0.0, - 'response_times': deque(maxlen=1000), - 'emotion_distribution': defaultdict(int), - 'error_counts': defaultdict(int), - 'start_time': datetime.now() -} - -metrics_lock = threading.Lock() - -def update_metrics(response_time, success=True, emotion=None, error_type=None, rate_limited=False, sanitization_warnings=0): - """Update monitoring metrics.""" - with metrics_lock: - metrics['total_requests'] += 1 - metrics['response_times'].append(response_time) - - if rate_limited: - metrics['rate_limited_requests'] += 1 - elif success: - metrics['successful_requests'] += 1 - if emotion: - metrics['emotion_distribution'][emotion] += 1 - else: - metrics['failed_requests'] += 1 - if error_type: - metrics['error_counts'][error_type] += 1 - - if sanitization_warnings > 0: - metrics['sanitization_warnings'] += sanitization_warnings - - # Update average response time - if metrics['response_times']: - metrics['average_response_time'] = sum(metrics['response_times']) / len(metrics['response_times']) - -def secure_endpoint(f): - """Decorator for secure endpoint handling.""" - @wraps(f) - def decorated_function(*args, **kwargs): - start_time = time.time() - client_ip = request.remote_addr - user_agent = request.headers.get('User-Agent', '') - - try: - # Rate limiting - allowed, reason, rate_limit_meta = rate_limiter.allow_request(client_ip, user_agent) - if not allowed: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='rate_limited', rate_limited=True) - logger.warning(f"Rate limit exceeded: {reason} from {client_ip}") - return jsonify({ - 'error': 'Rate limit exceeded', - 'message': reason, - 'retry_after': rate_limit_config.window_size_seconds - }), 429 - - # Content type validation - if request.method == 'POST': - content_type = request.headers.get('Content-Type', '') - if not input_sanitizer.validate_content_type(content_type): - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='invalid_content_type') - logger.warning(f"Invalid content type: {content_type} from {client_ip}") - return jsonify({ - 'error': 'Invalid content type', - 'message': 'Content-Type must be application/json' - }), 400 - - # Process request - result = f(*args, **kwargs) - - # Release rate limit slot - rate_limiter.release_request(client_ip, user_agent) - - return result - - except Exception as e: - # Release rate limit slot on error - rate_limiter.release_request(client_ip, user_agent) - - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='endpoint_error') - logger.error(f"Endpoint error: {str(e)}") - return jsonify({'error': str(e)}), 500 - - return decorated_function - - -class _ClientError(Exception): - """Lightweight exception with HTTP status and error type for client faults.""" - - def __init__(self, message: str, status_code: int, error_type: str) -> None: - super().__init__(message) - self.message = message - self.status_code = status_code - self.error_type = error_type - - -class SecureEmotionDetectionModel: - def __init__(self): - """Initialize the secure emotion detection model.""" - # Resolve model directory (allow override via env var for tests/dev) - default_model_dir = Path(__file__).resolve().parent.parent / 'model' - env_model_dir = os.environ.get("SECURE_MODEL_DIR") - self.model_path = Path(env_model_dir).expanduser().resolve() if env_model_dir else default_model_dir - logger.info(f"Loading secure model from: {self.model_path}") - - # Default emotions list available even if model isn't loaded - self.emotions = [ - 'anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', - 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired' - ] - self.loaded = False - - # In CI/TESTING, or when model directory is missing/invalid, run in stub mode - if os.environ.get("TESTING") or os.environ.get("CI"): - logger.warning("TEST/CI environment detected. Running secure model in stub mode.") - self.tokenizer = None - self.model = None - self.loaded = False - return - - # If the local model directory is missing, skip heavy loading to keep imports working - if not self.model_path.exists() or not self.model_path.is_dir(): - logger.warning( - "Secure model directory not found. Running in stub mode (no HF model will be loaded)." - ) - self.tokenizer = None - self.model = None - self.loaded = False - return - - # If directory exists but lacks required files, also stub to avoid HF hub lookups - required_all = [ - self.model_path / 'config.json', - self.model_path / 'tokenizer.json', - self.model_path / 'tokenizer_config.json', - ] - if not all(p.exists() for p in required_all): - logger.warning( - "Secure model directory lacks expected files. Running in stub mode." - ) - self.tokenizer = None - self.model = None - self.loaded = False - return - - try: - # Lazy import heavy deps only when not in stub mode and path checks passed - from transformers import AutoTokenizer, AutoModelForSequenceClassification # type: ignore - import torch # type: ignore - - self.tokenizer = AutoTokenizer.from_pretrained(str(self.model_path), local_files_only=True) - self.model = AutoModelForSequenceClassification.from_pretrained(str(self.model_path), local_files_only=True) - - # Move to GPU if available - try: - if torch.cuda.is_available(): - self.model = self.model.to('cuda') - logger.info("โœ… Model moved to GPU") - else: - logger.info("โš ๏ธ CUDA not available, using CPU") - except Exception: - # If torch is absent at runtime, remain on CPU - logger.info("โš ๏ธ Torch not available, using CPU") - - self.loaded = True - logger.info("โœ… Secure model loaded successfully") - - except Exception as e: - logger.error(f"โŒ Failed to load secure model: {str(e)}. Falling back to stub mode.") - self.tokenizer = None - self.model = None - self.loaded = False - - def predict(self, text, confidence_threshold=None): - """Make a secure prediction.""" - start_time = time.time() - - try: - if not getattr(self, 'loaded', False): - raise RuntimeError("SecureEmotionDetectionModel is not loaded; prediction unavailable.") - # Ensure torch is available within function scope for linter/runtime - try: - import torch # type: ignore - except Exception as e: # pragma: no cover - logger.error("Torch import failed during prediction: %s", e) - raise - # Sanitize input text - sanitized_text, warnings = input_sanitizer.sanitize_text(text, "emotion") - if warnings: - logger.warning(f"Sanitization warnings: {warnings}") - - # Tokenize input - inputs = self.tokenizer(sanitized_text, return_tensors='pt', truncation=True, padding=True, max_length=512) - - if torch.cuda.is_available(): - inputs = {k: v.to('cuda') for k, v in inputs.items()} - - # Get prediction - with torch.no_grad(): - outputs = self.model(**inputs) - probabilities = torch.softmax(outputs.logits, dim=1) - predicted_label = torch.argmax(probabilities, dim=1).item() - confidence = probabilities[0][predicted_label].item() - - # Apply confidence threshold if specified - if confidence_threshold and confidence < confidence_threshold: - predicted_emotion = "uncertain" - confidence = 0.0 - elif predicted_label in self.model.config.id2label: - predicted_emotion = self.model.config.id2label[predicted_label] - elif str(predicted_label) in self.model.config.id2label: - predicted_emotion = self.model.config.id2label[str(predicted_label)] - else: - predicted_emotion = f"unknown_{predicted_label}" - - # Get all probabilities - all_probs = probabilities[0].cpu().numpy() - - prediction_time = time.time() - start_time - logger.info(f"Secure prediction completed in {prediction_time:.3f}s: '{sanitized_text[:50]}...' โ†’ {predicted_emotion} (conf: {confidence:.3f})") - - # Create secure response - return { - 'text': sanitized_text, - 'predicted_emotion': predicted_emotion, - 'confidence': float(confidence), - 'probabilities': { - emotion: float(prob) for emotion, prob in zip(self.emotions, all_probs) - }, - 'model_version': '2.0', - 'model_type': 'secure_emotion_detection', - 'performance': { - 'basic_accuracy': '100.00%', - 'real_world_accuracy': '93.75%', - 'average_confidence': '83.9%' - }, - 'prediction_time_ms': round(prediction_time * 1000, 2), - 'security': { - 'sanitization_warnings': warnings, - 'request_id': getattr(g, 'request_id', None), - 'correlation_id': getattr(g, 'correlation_id', None) - } - } - - except Exception as e: - prediction_time = time.time() - start_time - logger.error(f"Secure prediction failed after {prediction_time:.3f}s: {str(e)}") - raise - -# Secure model factory for explicit creation and testability -logger.info("๐Ÿ”’ Secure model will be created via factory function") - -def create_secure_model(): - """Factory function to create a SecureEmotionDetectionModel or a stub in CI/TEST. - - This avoids implicit global state and makes the creation path explicit and mockable in tests. - """ - if os.environ.get("TESTING") or os.environ.get("CI"): - class _Stub: - emotions = [ - 'anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', - 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired' - ] - loaded = False - return _Stub() - return SecureEmotionDetectionModel() - - -@lru_cache(maxsize=1) -def get_secure_model(): - """Return a cached secure model instance created via the factory. - - Using an LRU cache (size=1) avoids global mutable state and ensures a single - instance per process. Tests can clear the cache with - get_secure_model.cache_clear(). - """ - return create_secure_model() - - -# Provider selection for text emotion (simple registry/factory) -EMOTION_PROVIDER = os.environ.get("EMOTION_PROVIDER", "hf").lower() -_provider_registry = {} - - -def register_provider(name, factory): - """Register a provider factory by name for emotion services.""" - _provider_registry[name] = factory - - -def get_emotion_service(): - """Return an emotion service instance for the configured provider.""" - name = EMOTION_PROVIDER - factory = _provider_registry.get(name) - if not factory: - raise ValueError(f"Unsupported EMOTION_PROVIDER: {name}") - return factory() - - -# Register default providers -register_provider("hf", HFEmotionService) - - -def _parse_single_text_payload(data: dict) -> str: - """Validate and extract 'text' from request payload.""" - text = data.get('text') if isinstance(data, dict) else None - if not isinstance(text, str) or not text.strip(): - raise ValueError('Field "text" must be a non-empty string') - return text - - -def _sanitize_texts_batch(texts: List[str]) -> Tuple[List[str], int]: - """Sanitize batch texts and return (sanitized_texts, total_warnings).""" - sanitized: List[str] = [] - total_warnings = 0 - for t in texts: - s, warnings = input_sanitizer.sanitize_text(t, "emotion") - sanitized.append(s) - total_warnings += len(warnings) - return sanitized, total_warnings - - -def _build_provider_info() -> dict: - """Build provider info dict reflecting local-only mode and model_dir.""" - local_only_env = str(os.environ.get('EMOTION_LOCAL_ONLY', '')).strip().lower() - return { - 'local_only': local_only_env in ('1', 'true', 'yes', 'on'), - 'model_dir': os.environ.get('EMOTION_MODEL_DIR', '') or EMOTION_MODEL_DIR, - } - -# Read admin API key per-request to reflect environment changes during tests -def get_admin_api_key() -> str | None: - """Fetch the admin API key from the environment on each call. - - This function intentionally does not cache the key to support dynamic - updates (e.g., during tests or runtime reconfiguration). Be aware this - per-request read may introduce race conditions if the environment variable - changes mid-request; callers should treat the value as ephemeral per call. - """ - return os.environ.get("ADMIN_API_KEY") - -def require_admin_api_key(f): - """Decorator to require admin API key via X-Admin-API-Key header. - - Reads the expected key via ``get_admin_api_key()`` for each request and - does not cache it. See ``get_admin_api_key`` for concurrency considerations. - """ - @wraps(f) - def decorated_function(*args, **kwargs): - api_key = request.headers.get("X-Admin-API-Key") - expected_key = get_admin_api_key() - if not expected_key or api_key != expected_key: - logger.warning(f"Unauthorized admin access attempt from {request.remote_addr}") - return jsonify({"error": "Unauthorized: admin API key required"}), 403 - return f(*args, **kwargs) - return decorated_function - - -def _get_json_payload_or_raise() -> Dict[str, Any]: - """Return JSON payload or raise _ClientError for invalid JSON.""" - data = request.get_json(silent=True) - if data is None: - raise _ClientError('Invalid JSON format', 400, 'invalid_json') - return data - - -def _extract_and_filter_texts_or_raise( - data: Dict[str, Any] -) -> Tuple[List[str], List[str], int]: - """Extract 'texts' list, filter invalid entries, and return tuple. - - Returns (original_texts, filtered_texts, num_filtered). - """ - if not data or 'texts' not in data or not isinstance(data['texts'], list): - raise _ClientError( - 'Field "texts" must be a list of strings', 400, 'validation_error' - ) - original_texts = data['texts'] - texts = [t for t in original_texts if isinstance(t, str) and t.strip()] - num_filtered = len(original_texts) - len(texts) - if not texts: - raise _ClientError('No valid texts provided', 400, 'validation_error') - return original_texts, texts, num_filtered - - -def _validate_alignment_count_or_raise( - results: Any, expected_count: int -) -> bool: - """Ensure provider results match expected count or raise _ClientError.""" - if (not isinstance(results, list)) or (len(results) != expected_count): - raise _ClientError( - 'Provider returned mismatched result count', 502, 'provider_misalignment' - ) - return True - - -def _validate_single_results_or_raise(results: Any) -> List[Dict[str, Any]]: - """Validate single-input provider results shape and return the distribution. - - Expects results to be List[List[Dict[str, Any]]], with len(results) == 1. - Raises _ClientError(502) on invalid shape. - """ - if ( - (not isinstance(results, list)) - or (len(results) != 1) - or (not isinstance(results[0], list)) - ): - outer_type = type(results).__name__ - outer_len = ( - len(results) if isinstance(results, list) else 'N/A' - ) - inner_type = ( - type(results[0]).__name__ - if isinstance(results, list) and results - else 'N/A' - ) - logger.error( - "Provider returned invalid shape for single input: " - "type=%s len=%s inner_type=%s", - outer_type, - outer_len, - inner_type, - ) - raise _ClientError( - 'Provider returned mismatched result count', - 502, - 'provider_misalignment' - ) - dist = results[0] - if dist and not ( - isinstance(dist[0], dict) - and 'label' in dist[0] - and 'score' in dist[0] - ): - inner_first_type = ( - type(dist[0]).__name__ if dist else 'N/A' - ) - inner_keys = ( - list(dist[0].keys()) if isinstance(dist[0], dict) else 'N/A' - ) - logger.error( - "Provider returned invalid inner element: " - "inner_first_type=%s keys=%s", - inner_first_type, - inner_keys, - ) - raise _ClientError( - 'Provider returned mismatched result count', - 502, - 'provider_misalignment' - ) - return dist - - -def _build_single_response( - sanitized_text: str, - dist: List[Dict[str, Any]], - warnings: List[Any] -) -> Dict[str, Any]: - """Build JSON response payload for the single-input endpoint.""" - return { - 'text': sanitized_text, - 'scores': dist, - 'provider': os.environ.get("EMOTION_PROVIDER", EMOTION_PROVIDER).lower(), - 'provider_info': _build_provider_info(), - 'timestamp': time.time(), - 'security': { - 'sanitization_warnings': warnings, - 'request_id': getattr(g, 'request_id', None), - 'correlation_id': getattr(g, 'correlation_id', None) - } - } - -@app.route('/health', methods=['GET']) -@secure_endpoint -def health_check(): - """Secure health check endpoint.""" - start_time = time.time() - - try: - mdl = get_secure_model() - response = { - 'status': 'healthy', - 'model_loaded': getattr(mdl, 'loaded', False), - 'model_version': '2.0', - 'emotions': getattr(mdl, 'emotions', []), - 'uptime_seconds': (datetime.now() - metrics['start_time']).total_seconds(), - 'security': { - 'rate_limiting': rate_limiter.get_stats(), - 'sanitization': input_sanitizer.get_sanitization_stats(), - 'security_headers': security_middleware.get_security_stats() - }, - 'metrics': { - 'total_requests': metrics['total_requests'], - 'successful_requests': metrics['successful_requests'], - 'failed_requests': metrics['failed_requests'], - 'rate_limited_requests': metrics['rate_limited_requests'], - 'sanitization_warnings': metrics['sanitization_warnings'], - 'average_response_time_ms': round(metrics['average_response_time'] * 1000, 2) - } - } - - response_time = time.time() - start_time - update_metrics(response_time, success=True) - - return jsonify(response) - - except Exception as e: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='health_check_error') - logger.error(f"Health check failed: {str(e)}") - return jsonify({'error': str(e)}), 500 - -@app.route('/predict', methods=['POST']) -@secure_endpoint -def predict(): - """Secure prediction endpoint.""" - start_time = time.time() - - try: - # Parse and validate request data - try: - data = request.get_json() - except werkzeug.exceptions.BadRequest: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='invalid_json') - logger.error(f"Invalid JSON in request from {request.remote_addr}") - return jsonify({'error': 'Invalid JSON format'}), 400 - - if not data: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='missing_data') - return jsonify({'error': 'No data provided'}), 400 - - # Sanitize and validate request - try: - sanitized_data, warnings = input_sanitizer.validate_emotion_request(data) - except ValueError as e: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='validation_error') - logger.warning(f"Validation error: {str(e)} from {request.remote_addr}") - return jsonify({'error': str(e)}), 400 - - # Detect anomalies - anomalies = input_sanitizer.detect_anomalies(data) - if anomalies: - logger.warning(f"Security anomalies detected: {anomalies}") - with metrics_lock: - metrics['security_violations'] += 1 - - # Make secure prediction - model_instance = get_secure_model() - if not getattr(model_instance, 'loaded', False): - return jsonify({'error': 'Secure model not loaded'}), 503 - result = model_instance.predict( - sanitized_data['text'], - confidence_threshold=sanitized_data.get('confidence_threshold') - ) - - # Add sanitization warnings to response - if warnings: - result['security']['sanitization_warnings'] = warnings - - response_time = time.time() - start_time - update_metrics( - response_time, - success=True, - emotion=result['predicted_emotion'], - sanitization_warnings=len(warnings) - ) - - return jsonify(result) - - except Exception as e: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='prediction_error') - logger.error(f"Secure prediction endpoint error: {str(e)}") - return jsonify({'error': str(e)}), 500 - -@app.route('/predict_batch', methods=['POST']) -@secure_endpoint -def predict_batch(): - """Secure batch prediction endpoint.""" - start_time = time.time() - - try: - # Parse and validate request data - try: - data = request.get_json() - except werkzeug.exceptions.BadRequest: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='invalid_json') - logger.error(f"Invalid JSON in batch request from {request.remote_addr}") - return jsonify({'error': 'Invalid JSON format'}), 400 - - if not data: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='missing_data') - return jsonify({'error': 'No data provided'}), 400 - - # Sanitize and validate request - try: - sanitized_data, warnings = input_sanitizer.validate_batch_request(data) - except ValueError as e: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='validation_error') - logger.warning(f"Batch validation error: {str(e)} from {request.remote_addr}") - return jsonify({'error': str(e)}), 400 - - # Detect anomalies - anomalies = input_sanitizer.detect_anomalies(data) - if anomalies: - logger.warning(f"Security anomalies detected in batch: {anomalies}") - with metrics_lock: - metrics['security_violations'] += 1 - - # Make secure batch predictions - results = [] - model_instance = get_secure_model() - if not getattr(model_instance, 'loaded', False): - return jsonify({'error': 'Secure model not loaded'}), 503 - for text in sanitized_data['texts']: - if text.strip(): - result = model_instance.predict( - text, - confidence_threshold=sanitized_data.get('confidence_threshold') - ) - results.append(result) - - response_time = time.time() - start_time - update_metrics( - response_time, - success=True, - sanitization_warnings=len(warnings) - ) - - return jsonify({ - 'predictions': results, - 'count': len(results), - 'batch_processing_time_ms': round(response_time * 1000, 2), - 'security': { - 'sanitization_warnings': warnings, - 'request_id': getattr(g, 'request_id', None), - 'correlation_id': getattr(g, 'correlation_id', None) - } - }) - - except Exception as e: - response_time = time.time() - start_time - update_metrics( - response_time, success=False, error_type='batch_prediction_error' - ) - logger.error("NLP emotion batch error: %s", e) - return jsonify({'error': 'An internal server error occurred.'}), 500 - - -@app.route('/nlp/emotion', methods=['POST']) -@secure_endpoint -def nlp_emotion(): - """Classify emotion distribution for a single input text.""" - start_time = time.time() - try: - # Parse and validate JSON - data = _get_json_payload_or_raise() - text = _parse_single_text_payload(data) - - # Sanitize and classify - sanitized_text, warnings = input_sanitizer.sanitize_text(text, "emotion") - try: - service = get_emotion_service() - except (ImportError, ValueError): - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='provider_error') - logger.exception("Emotion provider misconfiguration") - return jsonify({'error': 'Emotion provider misconfiguration.'}), 503 - except Exception: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='provider_error') - logger.exception("Unknown provider error in /nlp/emotion") - return jsonify({'error': 'Internal server error'}), 500 - - results = service.classify(sanitized_text) - dist = _validate_single_results_or_raise(results) - - response = _build_single_response(sanitized_text, dist, warnings) - - # Update distribution metric by top label - try: - top = max(dist, key=lambda x: x.get('score', 0.0)) if dist else None - update_metrics( - time.time() - start_time, - success=True, - emotion=(top.get('label') if top else None), - sanitization_warnings=len(warnings) - ) - except Exception: - update_metrics( - time.time() - start_time, - success=True, - sanitization_warnings=len(warnings) - ) - - return jsonify(response) - except Exception: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='prediction_error') - logger.exception("NLP emotion error") - return jsonify({'error': 'An internal error occurred.'}), 500 - - -@app.route('/nlp/emotion/batch', methods=['POST']) -@secure_endpoint -def nlp_emotion_batch(): - """Classify emotion distributions for a batch of input texts.""" - start_time = time.time() - try: - data = _get_json_payload_or_raise() - _original_texts, texts, num_filtered = _extract_and_filter_texts_or_raise(data) - if num_filtered > 0: - logger.warning( - "%s invalid texts filtered out from input batch.", num_filtered - ) - - sanitized, total_warnings = _sanitize_texts_batch(texts) - - try: - service = get_emotion_service() - except (ImportError, ValueError): - response_time = time.time() - start_time - update_metrics( - response_time, success=False, error_type='provider_error' - ) - logger.exception("Emotion provider misconfiguration") - return jsonify({'error': 'Emotion provider misconfiguration.'}), 503 - except Exception: - response_time = time.time() - start_time - update_metrics( - response_time, success=False, error_type='provider_error' - ) - logger.exception("Unknown provider error in /nlp/emotion/batch") - return jsonify({'error': 'Internal server error'}), 500 - - results = service.classify(sanitized) - _validate_alignment_count_or_raise(results, len(sanitized)) - - responses = [] - for text, dist in zip(sanitized, results): - dist = dist if isinstance(dist, list) else [] - top = ( - max(dist, key=lambda x: x.get('score', 0.0)) - if dist else {'label': 'unknown', 'score': 0.0} - ) - responses.append({ - 'text': text, - 'scores': dist, - 'top_label': top.get('label'), - 'top_score': top.get('score') - }) - - response_time = time.time() - start_time - try: - first_top = ( - max(results[0], key=lambda x: x.get('score', 0.0)) - if results and results[0] else None - ) - update_metrics( - response_time, - success=True, - emotion=(first_top.get('label') if first_top else None), - sanitization_warnings=total_warnings - ) - except Exception: - update_metrics( - response_time, success=True, sanitization_warnings=total_warnings - ) - - return jsonify({ - 'results': responses, - 'count': len(responses), - 'provider': os.environ.get("EMOTION_PROVIDER", EMOTION_PROVIDER).lower(), - 'provider_info': _build_provider_info(), - 'batch_processing_time_ms': round(response_time * 1000, 2), - 'security': { - 'sanitization_warnings': total_warnings, - 'request_id': getattr(g, 'request_id', None), - 'correlation_id': getattr(g, 'correlation_id', None) - } - }) - except _ClientError as ce: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type=ce.error_type) - if ce.error_type == 'provider_misalignment': - logger.error(ce.message) - else: - logger.warning(ce.message) - return jsonify({'error': ce.message}), ce.status_code - except Exception as e: - response_time = time.time() - start_time - update_metrics( - response_time, success=False, error_type='batch_prediction_error' - ) - logger.error("NLP emotion batch error: %s", e) - return jsonify({'error': "An internal error has occurred."}), 500 - -@app.route('/metrics', methods=['GET']) -def get_metrics(): - """Get detailed security metrics endpoint.""" - with metrics_lock: - return jsonify({ - 'server_metrics': { - 'uptime_seconds': (datetime.now() - metrics['start_time']).total_seconds(), - 'total_requests': metrics['total_requests'], - 'successful_requests': metrics['successful_requests'], - 'failed_requests': metrics['failed_requests'], - 'rate_limited_requests': metrics['rate_limited_requests'], - 'sanitization_warnings': metrics['sanitization_warnings'], - 'security_violations': metrics['security_violations'], - 'success_rate': f"{(metrics['successful_requests'] / max(metrics['total_requests'], 1)) * 100:.2f}%", - 'average_response_time_ms': round(metrics['average_response_time'] * 1000, 2), - 'requests_per_minute': metrics['total_requests'] / max((datetime.now() - metrics['start_time']).total_seconds() / 60, 1) - }, - 'emotion_distribution': dict(metrics['emotion_distribution']), - 'error_counts': dict(metrics['error_counts']), - 'security': { - 'rate_limiting': rate_limiter.get_stats(), - 'sanitization': input_sanitizer.get_sanitization_stats(), - 'security_headers': security_middleware.get_security_stats() - } - }) - -@app.route('/security/blacklist', methods=['POST']) -@require_admin_api_key -def add_to_blacklist(): - """Add IP to blacklist (admin endpoint).""" - try: - data = request.get_json() - if not data or 'ip' not in data: - return jsonify({'error': 'IP address required'}), 400 - - ip = data['ip'] - rate_limiter.add_to_blacklist(ip) - logger.info(f"Added {ip} to blacklist") - return jsonify({'message': f'Added {ip} to blacklist'}) - except Exception as e: - logger.error(f"Blacklist error: {str(e)}") - return jsonify({'error': str(e)}), 500 - -@app.route('/security/whitelist', methods=['POST']) -@require_admin_api_key -def add_to_whitelist(): - """Add IP to whitelist (admin endpoint).""" - try: - data = request.get_json() - if not data or 'ip' not in data: - return jsonify({'error': 'IP address required'}), 400 - - ip = data['ip'] - rate_limiter.add_to_whitelist(ip) - logger.info(f"Added {ip} to whitelist") - return jsonify({'message': f'Added {ip} to whitelist'}) - except Exception as e: - logger.error(f"Whitelist error: {str(e)}") - return jsonify({'error': str(e)}), 500 - -@app.route('/', methods=['GET']) -@secure_endpoint -def home(): - """Secure home endpoint with API documentation.""" - start_time = time.time() - - try: - response = { - 'message': 'Secure Emotion Detection API', - 'version': '2.0', - 'security_features': { - 'rate_limiting': f'{rate_limit_config.requests_per_minute} requests per minute', - 'input_sanitization': 'XSS, SQL injection, and command injection protection', - 'security_headers': 'CSP, HSTS, X-Frame-Options, and more', - 'abuse_detection': 'Automatic blocking of abusive clients', - 'request_correlation': 'Request ID and correlation ID tracking', - 'audit_logging': 'Comprehensive security event logging' - }, - 'endpoints': { - 'GET /': 'This documentation', - 'GET /health': 'Health check with security metrics', - 'GET /metrics': 'Detailed security metrics', - 'POST /predict': 'Secure single prediction', - 'POST /predict_batch': 'Secure batch prediction', - 'POST /nlp/emotion': 'HF-backed text emotion classification', - 'POST /nlp/emotion/batch': ( - 'HF-backed batch text emotion classification' - ), - 'POST /security/blacklist': 'Add IP to blacklist (admin)', - 'POST /security/whitelist': 'Add IP to whitelist (admin)' - }, - 'model_info': { - 'emotions': getattr(get_secure_model(), 'emotions', []), - 'performance': { - 'basic_accuracy': '100.00%', - 'real_world_accuracy': '93.75%', - 'average_confidence': '83.9%' - } - }, - 'example_usage': { - 'single_prediction': { - 'url': 'POST /predict', - 'body': '{"text": "I am feeling happy today!"}', - 'headers': '{"Content-Type": "application/json"}' - }, - 'batch_prediction': { - 'url': 'POST /predict_batch', - 'body': '{"texts": ["I am happy", "I feel sad", "I am excited"]}', - 'headers': '{"Content-Type": "application/json"}' - } - } - } - - response_time = time.time() - start_time - update_metrics(response_time, success=True) - - return jsonify(response) - - except Exception as e: - response_time = time.time() - start_time - update_metrics(response_time, success=False, error_type='documentation_error') - logger.error(f"Documentation endpoint error: {str(e)}") - return jsonify({'error': str(e)}), 500 - -@app.errorhandler(werkzeug.exceptions.BadRequest) -def handle_bad_request(e): - """Handle BadRequest exceptions (invalid JSON, etc.).""" - logger.error(f"BadRequest error: {str(e)}") - update_metrics(0.0, success=False, error_type='invalid_json') - return jsonify({'error': 'Invalid JSON format'}), 400 - -@app.errorhandler(404) -def handle_not_found(e): - """Handle 404 errors.""" - logger.warning(f"404 error: {request.path} from {request.remote_addr}") - return jsonify({'error': 'Endpoint not found'}), 404 - -@app.errorhandler(500) -def handle_internal_error(e): - """Handle 500 errors.""" - logger.error(f"Internal server error: {str(e)}") - return jsonify({'error': 'Internal server error'}), 500 - -if __name__ == '__main__': - logger.info("๐Ÿ”’ Starting Secure Emotion Detection API Server") - logger.info("=" * 60) - logger.info("๐Ÿ›ก๏ธ Security Features Enabled:") - logger.info(" โœ… Rate limiting with token bucket algorithm") - logger.info(" โœ… Input sanitization and validation") - logger.info(" โœ… Security headers (CSP, HSTS, X-Frame-Options)") - logger.info(" โœ… Request/response logging and monitoring") - logger.info(" โœ… IP whitelist/blacklist support") - logger.info(" โœ… Abuse detection and automatic blocking") - logger.info(" โœ… Request correlation and tracing") - logger.info("") - logger.info("๐Ÿ“‹ Available endpoints:") - logger.info(" GET / - API documentation") - logger.info(" GET /health - Health check with security metrics") - logger.info(" GET /metrics - Detailed security metrics") - logger.info(" POST /predict - Secure single prediction") - logger.info(" POST /predict_batch - Secure batch prediction") - logger.info(" POST /security/blacklist - Add IP to blacklist (admin)") - logger.info(" POST /security/whitelist - Add IP to whitelist (admin)") - logger.info("") - logger.info("๐Ÿš€ Server starting on http://localhost:8000") - logger.info("๐Ÿ“ Example usage:") - logger.info(" curl -X POST http://localhost:8000/predict \\") - logger.info(" -H 'Content-Type: application/json' \\") - logger.info(" -d '{\"text\": \"I am feeling happy today!\"}'") - logger.info("") - logger.info(f"๐Ÿ”’ Rate limiting: {rate_limit_config.requests_per_minute} requests per minute") - logger.info("๐Ÿ›ก๏ธ Security monitoring: Comprehensive logging and metrics enabled") - logger.info("=" * 60) - - app.run(host='0.0.0.0', port=8000, debug=False) \ No newline at end of file diff --git a/deployment/test_examples.py b/deployment/test_examples.py index fa1cb949f..e8d611a8a 100644 --- a/deployment/test_examples.py +++ b/deployment/test_examples.py @@ -7,11 +7,12 @@ from inference import EmotionDetector + def test_model(): """Test the emotion detection model""" print("๐Ÿงช EMOTION DETECTION MODEL TESTING") print("=" * 50) - + # Initialize detector try: detector = EmotionDetector() @@ -19,7 +20,7 @@ def test_model(): except Exception as e: print(f"โŒ Failed to load model: {e}") return - + # Test cases test_cases = [ # Happy emotions @@ -27,39 +28,42 @@ def test_model(): "I'm excited about the new opportunities ahead.", "I'm grateful for all the support I've received.", "I'm proud of what I've accomplished so far.", - # Negative emotions "I'm so frustrated with this project. Nothing is working.", "I feel anxious about the upcoming presentation.", "I'm feeling sad and lonely today.", "I'm feeling overwhelmed with all these tasks.", - # Neutral emotions "I feel calm and peaceful right now.", "I'm content with how things are going.", "I'm hopeful that things will get better.", - "I'm tired and need some rest." + "I'm tired and need some rest.", ] - + print("\n๐Ÿ“Š Testing Results:") print("=" * 50) - + correct_predictions = 0 total_predictions = len(test_cases) - + for i, text in enumerate(test_cases, 1): result = detector.predict(text) - + print(f"{i:2d}. Text: {text}") print(f" Predicted: {result['emotion']} (confidence: {result['confidence']:.3f})") - + # Show top 3 predictions - sorted_probs = sorted(result['probabilities'].items(), key=lambda x: x[1], reverse=True) - print(f" Top 3: {', '.join([f'{emotion}({prob:.3f})' for emotion, prob in sorted_probs[:3]])}") + sorted_probs = sorted(result["probabilities"].items(), key=lambda x: x[1], reverse=True) + print( + f" Top 3: {', '.join([f'{emotion}({prob:.3f})' for emotion, prob in sorted_probs[:3]])}" + ) print() - + print("๐ŸŽ‰ Testing completed!") - print(f"๐Ÿ“Š Model confidence range: {min([detector.predict(text)['confidence'] for text in test_cases]):.3f} - {max([detector.predict(text)['confidence'] for text in test_cases]):.3f}") + print( + f"๐Ÿ“Š Model confidence range: {min([detector.predict(text)['confidence'] for text in test_cases]):.3f} - {max([detector.predict(text)['confidence'] for text in test_cases]):.3f}" + ) + if __name__ == "__main__": test_model() diff --git a/docs/api/API_DOCUMENTATION.md b/docs/api/API_DOCUMENTATION.md index 25af658e0..36ac7018b 100644 --- a/docs/api/API_DOCUMENTATION.md +++ b/docs/api/API_DOCUMENTATION.md @@ -51,19 +51,24 @@ curl -X GET https://samo-emotion-api-xxxxx-ew.a.run.app/health ### 2. Emotion Detection -**Endpoint**: `POST /predict` +**Endpoint**: `POST /analyze/journal` **Description**: Analyze text and return detected emotions with confidence scores +**Authentication**: Required (JWT Bearer token) + **Request Headers**: ``` +Authorization: Bearer YOUR_TOKEN Content-Type: application/json ``` **Request Body**: ```json { - "text": "I am feeling really happy today!" + "text": "I am feeling really happy today!", + "generate_summary": true, + "emotion_threshold": 0.1 } ``` @@ -109,12 +114,156 @@ EMOTIONS = [ **Example Usage**: ```bash -curl -X POST https://samo-emotion-api-xxxxx-ew.a.run.app/predict \ +curl -X POST https://samo-unified-api-frrnetyhfa-uc.a.run.app/analyze/journal \ + -H "Authorization: Bearer YOUR_TOKEN" \ -H "Content-Type: application/json" \ - -d '{"text": "I am feeling really happy today!"}' + -d '{"text": "I am feeling really happy today!", "generate_summary": true}' +``` + +### 3. Voice Transcription + +**Endpoint**: `POST /transcribe/voice` + +**Description**: Transcribe audio files to text with detailed analysis + +**Authentication**: Required (JWT Bearer token) + +**Request Format**: `multipart/form-data` (NOT JSON) + +**Request Headers**: +``` +Authorization: Bearer YOUR_TOKEN +``` + +**Request Parameters**: +- `audio_file` (file, required): Audio file to transcribe +- `language` (form data, optional): Language code (e.g., "en") +- `model_size` (form data, optional): Whisper model size ("base", "small", "medium") +- `timestamp` (form data, optional): Include timestamps ("true"/"false") + +**Example Usage**: +```bash +curl -X POST https://samo-unified-api-frrnetyhfa-uc.a.run.app/transcribe/voice \ + -H "Authorization: Bearer YOUR_TOKEN" \ + -F "audio_file=@audio.wav" \ + -F "language=en" \ + -F "model_size=base" +``` + +**Response**: +```json +{ + "text": "Transcribed audio content", + "language": "en", + "confidence": 0.85, + "duration": 15.4, + "word_count": 12, + "speaking_rate": 120.5, + "audio_quality": "good" +} +``` + +### 4. Text Summarization + +**Endpoint**: `POST /summarize/text` + +**Description**: Generate summaries from text input + +**Authentication**: Required (JWT Bearer token) + +**Request Format**: `application/x-www-form-urlencoded` (NOT JSON) + +**Request Headers**: +``` +Authorization: Bearer YOUR_TOKEN +Content-Type: application/x-www-form-urlencoded +``` + +**Request Parameters**: +- `text` (form data, required): Text to summarize +- `model` (form data, optional): Model to use ("t5-small", "t5-base") +- `max_length` (form data, optional): Maximum summary length +- `min_length` (form data, optional): Minimum summary length + +**Example Usage**: +```bash +curl -X POST https://samo-unified-api-frrnetyhfa-uc.a.run.app/summarize/text \ + -H "Authorization: Bearer YOUR_TOKEN" \ + -d "text=Long text to summarize..." \ + -d "model=t5-small" \ + -d "max_length=150" +``` + +**Response**: +```json +{ + "summary": "Generated summary text", + "key_emotions": ["neutral"], + "compression_ratio": 0.7, + "emotional_tone": "neutral" +} +``` + +### 5. Voice Journal Analysis + +**Endpoint**: `POST /analyze/voice-journal` + +**Description**: Complete pipeline - transcribe audio and analyze emotions + +**Authentication**: Required (JWT Bearer token) + +**Request Format**: `multipart/form-data` (NOT JSON) + +**Request Parameters**: +- `audio_file` (file, required): Audio file to process +- `language` (form data, optional): Language for transcription +- `generate_summary` (form data, optional): Generate summary ("true"/"false") +- `emotion_threshold` (form data, optional): Emotion detection threshold + +**Example Usage**: +```bash +curl -X POST https://samo-unified-api-frrnetyhfa-uc.a.run.app/analyze/voice-journal \ + -H "Authorization: Bearer YOUR_TOKEN" \ + -F "audio_file=@journal.wav" \ + -F "language=en" \ + -F "generate_summary=true" +``` + +### 6. Authentication + +**Registration Endpoint**: `POST /auth/register` + +**Request Body**: +```json +{ + "username": "user@example.com", + "email": "user@example.com", + "password": "YourPassword123!", + "full_name": "Your Name" +} +``` + +**Login Endpoint**: `POST /auth/login` + +**Request Body**: +```json +{ + "username": "user@example.com", + "password": "YourPassword123!" +} +``` + +**Response** (both endpoints): +```json +{ + "access_token": "JWT_ACCESS_TOKEN_HERE", + "refresh_token": "JWT_REFRESH_TOKEN_HERE", + "token_type": "bearer", + "expires_in": 1800 +} ``` -### 3. Metrics +### 7. Metrics **Endpoint**: `GET /metrics` diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000..3e2c17cfa --- /dev/null +++ b/requirements.txt @@ -0,0 +1,12 @@ +fastapi==0.104.1 +uvicorn[standard]==0.24.0 +pydantic==2.5.0 +python-multipart==0.0.6 +python-jose[cryptography]==3.3.0 +passlib[bcrypt]==1.7.4 +transformers==4.35.2 +torch==2.1.2 +numpy==1.24.4 +scikit-learn==1.3.2 +requests==2.31.0 +openai-whisper==20240930 \ No newline at end of file diff --git a/scripts/__pycache__/secure_model_loader.cpython-311.pyc b/scripts/__pycache__/secure_model_loader.cpython-311.pyc deleted file mode 100644 index debb3c526..000000000 Binary files a/scripts/__pycache__/secure_model_loader.cpython-311.pyc and /dev/null differ diff --git a/scripts/ci/api_health_check.py b/scripts/ci/api_health_check.py index 1cab3ac57..3352aaeaf 100755 --- a/scripts/ci/api_health_check.py +++ b/scripts/ci/api_health_check.py @@ -10,12 +10,14 @@ import sys from pathlib import Path +from pydantic import BaseModel, Field, ValidationError + +# Test imports +from api_rate_limiter import RateLimitConfig, TokenBucketRateLimiter + # Add src to path sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) -# Test imports -from api_rate_limiter import TokenBucketRateLimiter, RateLimitConfig -from pydantic import BaseModel, ValidationError, Field # Configure logging logging.basicConfig(level=logging.INFO) diff --git a/scripts/ci/bert_model_test.py b/scripts/ci/bert_model_test.py index 230fe4ea3..01a629847 100755 --- a/scripts/ci/bert_model_test.py +++ b/scripts/ci/bert_model_test.py @@ -8,9 +8,10 @@ import logging import sys -import torch from pathlib import Path +import torch + # Add src to path sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) diff --git a/scripts/ci/model_calibration_test.py b/scripts/ci/model_calibration_test.py index f1d0fd5fe..bf6d97f75 100644 --- a/scripts/ci/model_calibration_test.py +++ b/scripts/ci/model_calibration_test.py @@ -15,9 +15,10 @@ import logging import sys + import torch from sklearn.metrics import f1_score -from transformers import AutoTokenizer, AutoModel +from transformers import AutoModel, AutoTokenizer # Configure logging logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") @@ -40,7 +41,9 @@ def __init__(self, model_name="bert-base-uncased", num_emotions=28): self.temperature = torch.nn.Parameter(torch.ones(1)) def forward(self, input_ids, attention_mask, token_type_ids=None): - outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) + outputs = self.bert( + input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids + ) pooled_output = outputs.pooler_output logits = self.classifier(pooled_output) return logits @@ -88,10 +91,10 @@ def create_test_data(): # Create tokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") - + # Basic validation assert len(test_texts) == len(emotions), "Texts and emotions must have same length" - + return test_texts, emotions, emotion_to_idx, tokenizer @@ -102,7 +105,7 @@ def test_model_calibration(): # Create test data test_texts, emotions, emotion_to_idx, tokenizer = create_test_data() - + # Create model model = SimpleBERTClassifier("bert-base-uncased", num_emotions=28) model.eval() @@ -113,17 +116,13 @@ def test_model_calibration(): with torch.no_grad(): # Tokenize inputs = tokenizer( - test_texts[0], - return_tensors="pt", - padding=True, - truncation=True, - max_length=512 + test_texts[0], return_tensors="pt", padding=True, truncation=True, max_length=512 ) - + # Get predictions (only pass required arguments) outputs = model(inputs["input_ids"], inputs["attention_mask"]) probabilities = torch.sigmoid(outputs) - + logger.info(f"โœ… Model inference successful, output shape: {outputs.shape}") # Test temperature setting @@ -141,9 +140,9 @@ def test_model_calibration(): labels = torch.zeros(1, 28) # Match the single prediction shape if emotions[0] in emotion_to_idx: labels[0, emotion_to_idx[emotions[0]]] = 1.0 - + # Calculate F1 score - f1 = f1_score(labels.flatten(), predictions.flatten(), average='micro') + f1 = f1_score(labels.flatten(), predictions.flatten(), average="micro") logger.info(f"โœ… Metrics calculation successful, F1: {f1:.3f}") logger.info("โœ… Model calibration test passed") diff --git a/scripts/ci/model_compression_test.py b/scripts/ci/model_compression_test.py index a33b8e7c8..e3c059e15 100644 --- a/scripts/ci/model_compression_test.py +++ b/scripts/ci/model_compression_test.py @@ -1,25 +1,28 @@ - # Calculate compression ratio - # Create a simple model for testing - # Create dummy input - # Create simple model - # Get compressed model size and performance - # Get original model size and performance - # Simple forward pass for testing - # Test quantization - # Test saving compressed model - # Validate compression -# Add src to path -# Configure logging -#!/usr/bin/env python3 -from pathlib import Path -from torch import nn +# Calculate compression ratio +# Create a simple model for testing +# Create dummy input +# Create simple model +# Get compressed model size and performance +# Get original model size and performance +# Simple forward pass for testing +# Test quantization +# Test saving compressed model +# Validate compression + import logging import sys import tempfile -import torch +# Configure logging +#!/usr/bin/env python3 +from pathlib import Path + +import torch +# Add src to path +from torch import nn +from .validation_utils import ensure """ Model Compression Test for CI/CD Pipeline. @@ -32,7 +35,6 @@ logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) -from .validation_utils import ensure class SimpleBERTClassifier(nn.Module): @@ -110,9 +112,7 @@ def test_model_compression(): logger.info("Original inference time: {original_time:.2f} ms") logger.info("Testing quantization...") - quantized_model = torch.quantization.quantize_dynamic( - model, {nn.Linear}, dtype=torch.qint8 - ) + quantized_model = torch.quantization.quantize_dynamic(model, {nn.Linear}, dtype=torch.qint8) compressed_size = get_model_size(quantized_model) benchmark_inference(quantized_model, dummy_input) diff --git a/scripts/ci/model_monitoring_test.py b/scripts/ci/model_monitoring_test.py index 14616b03c..bc5b31747 100644 --- a/scripts/ci/model_monitoring_test.py +++ b/scripts/ci/model_monitoring_test.py @@ -1,38 +1,40 @@ - # Calculate baseline and current metrics - # Calculate drift (simplified) - # Calculate metrics - # Create a simple model for testing - # Create baseline data - # Create current data (simulate drift) - # Create model - # Create model and data - # Create monitoring log entry - # Get baseline predictions - # Get predictions - # Simple forward pass for testing - # Simulate logging - # Validate drift detection - # Validate log entry - # Validate metrics - # Calculate F1 score - # Calculate accuracy - # Calculate precision and recall (simplified) - # Convert predictions to binary - # Create synthetic input data - # Create synthetic labels (multi-label) -# Add src to path +# Calculate baseline and current metrics +# Calculate drift (simplified) +# Calculate metrics +# Create a simple model for testing +# Create baseline data +# Create current data (simulate drift) +# Create model +# Create model and data +# Create monitoring log entry +# Get baseline predictions +# Get predictions +# Simple forward pass for testing +# Simulate logging +# Validate drift detection +# Validate log entry +# Validate metrics +# Calculate F1 score +# Calculate accuracy +# Calculate precision and recall (simplified) +# Convert predictions to binary +# Create synthetic input data +# Create synthetic labels (multi-label) + +import logging +import sys + # Configure logging #!/usr/bin/env python3 from datetime import datetime, timezone -from pathlib import Path -from torch import nn -import logging -import sys -import torch -from .validation_utils import validate_metric_ranges, validate_required_keys, ensure +# Add src to path +from pathlib import Path +import torch +from torch import nn +from .validation_utils import ensure, validate_metric_ranges, validate_required_keys """ Model Monitoring Test for CI/CD Pipeline. @@ -94,10 +96,10 @@ def calculate_metrics(predictions, labels, threshold=0.5): f1_score = 2 * (precision * recall) / (precision + recall + 1e-8) return { - 'accuracy': accuracy.item(), - 'precision': precision.item(), - 'recall': recall.item(), - 'f1_score': f1_score.item() + "accuracy": accuracy.item(), + "precision": precision.item(), + "recall": recall.item(), + "f1_score": f1_score.item(), } @@ -140,7 +142,9 @@ def test_model_drift_detection(): model = SimpleBERTClassifier(num_emotions=28) model.eval() - baseline_input_ids, baseline_attention_mask, baseline_labels = create_synthetic_data(100, 28) + baseline_input_ids, baseline_attention_mask, baseline_labels = create_synthetic_data( + 100, 28 + ) current_input_ids, current_attention_mask, current_labels = create_synthetic_data(100, 28) @@ -154,8 +158,8 @@ def test_model_drift_detection(): baseline_metrics = calculate_metrics(baseline_probabilities, baseline_labels) current_metrics = calculate_metrics(current_probabilities, current_labels) - accuracy_drift = abs(current_metrics['accuracy'] - baseline_metrics['accuracy']) - f1_drift = abs(current_metrics['f1_score'] - baseline_metrics['f1_score']) + accuracy_drift = abs(current_metrics["accuracy"] - baseline_metrics["accuracy"]) + f1_drift = abs(current_metrics["f1_score"] - baseline_metrics["f1_score"]) logger.info("Accuracy drift: {accuracy_drift:.4f}") logger.info("F1 score drift: {f1_drift:.4f}") @@ -178,23 +182,20 @@ def test_monitoring_logging(): timestamp = datetime.now(timezone.utc) model_version = "test-v1.0.0" - metrics = { - 'accuracy': 0.85, - 'precision': 0.82, - 'recall': 0.88, - 'f1_score': 0.85 - } + metrics = {"accuracy": 0.85, "precision": 0.82, "recall": 0.88, "f1_score": 0.85} log_entry = { - 'timestamp': timestamp.isoformat(), - 'model_version': model_version, - 'metrics': metrics, - 'status': 'healthy' + "timestamp": timestamp.isoformat(), + "model_version": model_version, + "metrics": metrics, + "status": "healthy", } logger.info("Monitoring log entry: {log_entry}") - validate_required_keys(log_entry, ["timestamp", "model_version", "metrics", "status"], label="Log entry") + validate_required_keys( + log_entry, ["timestamp", "model_version", "metrics", "status"], label="Log entry" + ) logger.info("โœ… Monitoring logging test passed") return True diff --git a/scripts/ci/onnx_conversion_test.py b/scripts/ci/onnx_conversion_test.py index 62eba7e7b..f1372f172 100644 --- a/scripts/ci/onnx_conversion_test.py +++ b/scripts/ci/onnx_conversion_test.py @@ -7,19 +7,21 @@ """ import logging -import numpy as np import os import sys import tempfile +import numpy as np + +import onnx +import onnxruntime as ort + # Test imports try: from onnx import helper except ImportError: print("ONNX not available, skipping ONNX conversion test") sys.exit(0) -import onnx -import onnxruntime as ort logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) @@ -50,25 +52,18 @@ def test_onnx_dependencies(): input_shape = [1, 768] input_tensor = helper.make_tensor_value_info( - 'input_ids', onnx.TensorProto.FLOAT, input_shape + "input_ids", onnx.TensorProto.FLOAT, input_shape ) output_shape = [1, 28] output_tensor = helper.make_tensor_value_info( - 'logits', onnx.TensorProto.FLOAT, output_shape + "logits", onnx.TensorProto.FLOAT, output_shape ) - identity_node = helper.make_node( - 'Identity', - inputs=['input_ids'], - outputs=['logits'] - ) + identity_node = helper.make_node("Identity", inputs=["input_ids"], outputs=["logits"]) graph = helper.make_graph( - [identity_node], - 'test-model', - [input_tensor], - [output_tensor] + [identity_node], "test-model", [input_tensor], [output_tensor] ) onnx_model = helper.make_model(graph) @@ -87,11 +82,14 @@ def test_onnx_dependencies(): logger.info("โœ… ONNX Runtime session created") test_input = np.random.default_rng().standard_normal((1, 768)).astype(np.float32) - outputs = session.run(None, {'input_ids': test_input}) - logger.info(f"โœ… ONNX Runtime inference successful, output shape: {outputs[0].shape}") + outputs = session.run(None, {"input_ids": test_input}) + logger.info( + f"โœ… ONNX Runtime inference successful, output shape: {outputs[0].shape}" + ) finally: from contextlib import suppress + with suppress(BaseException): os.unlink(temp_path) diff --git a/scripts/ci/pre_warm_models.py b/scripts/ci/pre_warm_models.py index dd1eee916..79e42f76e 100644 --- a/scripts/ci/pre_warm_models.py +++ b/scripts/ci/pre_warm_models.py @@ -9,6 +9,7 @@ # Add src to path for imports sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) + def pre_warm_models(): """Pre-download and cache models for faster CI execution.""" print("Pre-warming models for CI pipeline...") @@ -16,10 +17,10 @@ def pre_warm_models(): try: import os from src.common.env import is_truthy + # Respect offline mode in CI to avoid failing when network is unavailable. - offline = ( - is_truthy(os.getenv("HF_HUB_OFFLINE")) - or is_truthy(os.getenv("TRANSFORMERS_OFFLINE")) + offline = is_truthy(os.getenv("HF_HUB_OFFLINE")) or is_truthy( + os.getenv("TRANSFORMERS_OFFLINE") ) if offline: print("Offline mode detected. Skipping pre-warm.") @@ -28,13 +29,13 @@ def pre_warm_models(): # Pre-download BERT models print("Downloading BERT base...") - AutoTokenizer.from_pretrained('bert-base-uncased') - AutoModel.from_pretrained('bert-base-uncased') + AutoTokenizer.from_pretrained("bert-base-uncased") + AutoModel.from_pretrained("bert-base-uncased") # Pre-download T5 models print("Downloading T5 small...") - AutoTokenizer.from_pretrained('t5-small') - AutoModelForSeq2SeqLM.from_pretrained('t5-small') + AutoTokenizer.from_pretrained("t5-small") + AutoModelForSeq2SeqLM.from_pretrained("t5-small") print("Models pre-warmed successfully!") return True @@ -43,6 +44,7 @@ def pre_warm_models(): print(f"Error pre-warming models: {e}") return False + if __name__ == "__main__": success = pre_warm_models() - sys.exit(0 if success else 1) + sys.exit(0 if success else 1) diff --git a/scripts/ci/run_full_ci_pipeline.py b/scripts/ci/run_full_ci_pipeline.py index d2c900023..e9d803ebf 100644 --- a/scripts/ci/run_full_ci_pipeline.py +++ b/scripts/ci/run_full_ci_pipeline.py @@ -14,40 +14,39 @@ import logging import os +import subprocess import sys import time -import subprocess from pathlib import Path -from typing import Dict, List, Tuple +from typing import Dict, Tuple # Use shared truthy parsing try: from src.common.env import is_truthy except Exception: # Fallback to local helper if import path not available + def is_truthy(value: str | None) -> bool: return bool(value) and value.strip().lower() in {"1", "true", "yes"} + # Configure logging logging.basicConfig( level=logging.INFO, - format='%(asctime)s - %(levelname)s - %(message)s', - handlers=[ - logging.StreamHandler(sys.stdout), - logging.FileHandler('ci_pipeline.log') - ] + format="%(asctime)s - %(levelname)s - %(message)s", + handlers=[logging.StreamHandler(sys.stdout), logging.FileHandler("ci_pipeline.log")], ) logger = logging.getLogger(__name__) class CIPipelineRunner: """Comprehensive CI Pipeline Runner.""" - + def __init__(self): self.results = {} self.start_time = time.time() self.ci_scripts = [ "scripts/ci/api_health_check.py", - "scripts/ci/bert_model_test.py", + "scripts/ci/bert_model_test.py", "scripts/ci/t5_summarization_test.py", "scripts/ci/whisper_transcription_test.py", "scripts/ci/model_calibration_test.py", @@ -56,14 +55,12 @@ def __init__(self): def _get_test_stats(self) -> tuple[dict, int, int]: """Calculate statistics on test results. - + Returns: tuple: (test_results dict, total_tests, passed_tests) """ test_results = { - name: result - for name, result in self.results.items() - if isinstance(result, bool) + name: result for name, result in self.results.items() if isinstance(result, bool) } total_tests = len(test_results) # Booleans can be summed directly (True=1, False=0) @@ -73,7 +70,7 @@ def _get_test_stats(self) -> tuple[dict, int, int]: def detect_environment(self) -> Dict[str, str]: """Detect the current environment (local vs Colab).""" logger.info("๐Ÿ” Detecting environment...") - + env_info = { "platform": sys.platform, "python_version": sys.version, @@ -81,36 +78,43 @@ def detect_environment(self) -> Dict[str, str]: "gpu_available": False, "conda_env": os.environ.get("CONDA_DEFAULT_ENV", "unknown"), } - + # Check for GPU try: import torch + env_info["gpu_available"] = torch.cuda.is_available() if env_info["gpu_available"]: env_info["gpu_count"] = torch.cuda.device_count() env_info["gpu_name"] = torch.cuda.get_device_name(0) except ImportError: logger.warning("โš ๏ธ PyTorch not available for GPU detection") - + # Check for Colab if env_info["is_colab"]: logger.info("๐ŸŽฏ Running in Google Colab environment") env_info["colab_gpu"] = os.environ.get("COLAB_GPU", "unknown") else: logger.info("๐Ÿ’ป Running in local environment") - + logger.info(f"๐Ÿ“Š Environment: {env_info}") return env_info - + def validate_dependencies(self) -> bool: """Validate that all required dependencies are available.""" logger.info("๐Ÿ“ฆ Validating dependencies...") - + required_packages = [ - "torch", "transformers", "fastapi", "pydantic", - "datasets", "tokenizers", "numpy", "pandas" + "torch", + "transformers", + "fastapi", + "pydantic", + "datasets", + "tokenizers", + "numpy", + "pandas", ] - + missing_packages = [] for package in required_packages: try: @@ -119,30 +123,30 @@ def validate_dependencies(self) -> bool: except ImportError: missing_packages.append(package) logger.error(f"โŒ {package} missing") - + if missing_packages: logger.error(f"โŒ Missing packages: {missing_packages}") return False - + logger.info("โœ… All dependencies validated") return True - + def run_ci_script(self, script_path: str) -> Tuple[bool, str]: """Run a single CI script and return success status and output.""" logger.info(f"๐Ÿš€ Running {script_path}...") - + try: # Use the correct Python interpreter python_executable = sys.executable - + # Run the script result = subprocess.run( [python_executable, script_path], capture_output=True, text=True, - timeout=300 # 5 minute timeout + timeout=300, # 5 minute timeout ) - + if result.returncode == 0: logger.info(f"โœ… {script_path} PASSED") return True, result.stdout @@ -150,26 +154,26 @@ def run_ci_script(self, script_path: str) -> Tuple[bool, str]: logger.error(f"โŒ {script_path} FAILED") logger.error(f"Error output: {result.stderr}") return False, result.stderr - + except subprocess.TimeoutExpired: logger.error(f"โฐ {script_path} TIMEOUT") return False, "Script timed out after 5 minutes" except Exception as e: logger.error(f"๐Ÿ’ฅ {script_path} ERROR: {e}") return False, str(e) - + def run_unit_tests(self) -> bool: """Run unit tests.""" logger.info("๐Ÿงช Running unit tests...") - + try: result = subprocess.run( [sys.executable, "-m", "pytest", "tests/unit/", "-v"], capture_output=True, text=True, - timeout=1200 # 20 minute timeout (increased from 10) + timeout=1200, # 20 minute timeout (increased from 10) ) - + if result.returncode == 0: logger.info("โœ… Unit tests PASSED") return True @@ -179,26 +183,26 @@ def run_unit_tests(self) -> bool: logger.error(f"Error output: {result.stderr}") logger.error(f"Standard output: {result.stdout}") return False - + except subprocess.TimeoutExpired: logger.error("โฐ Unit tests TIMEOUT") return False except Exception as e: logger.error(f"๐Ÿ’ฅ Unit tests ERROR: {e}") return False - + def run_e2e_tests(self) -> bool: """Run end-to-end tests.""" logger.info("๐ŸŽฏ Running E2E tests...") - + try: result = subprocess.run( [sys.executable, "-m", "pytest", "tests/e2e/", "-v"], capture_output=True, text=True, - timeout=900 # 15 minute timeout + timeout=900, # 15 minute timeout ) - + if result.returncode == 0: logger.info("โœ… E2E tests PASSED") return True @@ -206,142 +210,146 @@ def run_e2e_tests(self) -> bool: logger.error("โŒ E2E tests FAILED") logger.error(f"Error output: {result.stderr}") return False - + except Exception as e: logger.error(f"๐Ÿ’ฅ E2E tests ERROR: {e}") return False - + def test_gpu_compatibility(self) -> bool: """Test GPU compatibility if available.""" logger.info("๐Ÿ–ฅ๏ธ Testing GPU compatibility...") - + try: import torch - + if not torch.cuda.is_available(): logger.info("โ„น๏ธ No GPU available, skipping GPU tests") return True - + logger.info(f"๐ŸŽฎ GPU detected: {torch.cuda.get_device_name(0)}") - + # Test GPU model loading device = torch.device("cuda") - + # Add src to path for imports - import sys - from pathlib import Path sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) - + # Test BERT on GPU try: from models.emotion_detection.bert_classifier import BERTEmotionClassifier except ImportError: from src.models.emotion_detection.bert_classifier import BERTEmotionClassifier model = BERTEmotionClassifier().to(device) - + # Test forward pass import torch + dummy_input = torch.randint(0, 1000, (2, 512)).to(device) with torch.no_grad(): output = model(dummy_input, torch.ones_like(dummy_input)) - + logger.info(f"โœ… GPU forward pass successful, output shape: {output.shape}") return True - + except Exception as e: logger.error(f"โŒ GPU compatibility test failed: {e}") return False - + def run_performance_benchmarks(self) -> bool: """Run performance benchmarks.""" logger.info("โšก Running performance benchmarks...") - + try: # Simple performance test - model loading speed import time import torch - + # Test BERT model loading speed start_time = time.time() - + # Add src to path import sys from pathlib import Path + sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) - + try: from models.emotion_detection.bert_classifier import BERTEmotionClassifier except ImportError: from src.models.emotion_detection.bert_classifier import BERTEmotionClassifier - + model = BERTEmotionClassifier() loading_time = time.time() - start_time - + # Test inference speed start_time = time.time() dummy_input = torch.randint(0, 1000, (1, 512)) with torch.no_grad(): output = model(dummy_input, torch.ones_like(dummy_input)) inference_time = time.time() - start_time - + logger.info(f"โœ… Model loading time: {loading_time:.2f}s") logger.info(f"โœ… Inference time: {inference_time:.2f}s") - + # Check if times are reasonable - if loading_time < 10.0 and inference_time < 5.0: # Increased threshold for CPU environments + if ( + loading_time < 10.0 and inference_time < 5.0 + ): # Increased threshold for CPU environments logger.info("โœ… Performance benchmarks passed") return True else: - logger.error(f"โŒ Performance too slow - loading: {loading_time:.2f}s, inference: {inference_time:.2f}s") + logger.error( + f"โŒ Performance too slow - loading: {loading_time:.2f}s, inference: {inference_time:.2f}s" + ) return False - + except Exception as e: logger.error(f"โŒ Performance benchmark failed: {e}") return False - + def run_full_pipeline(self) -> Dict[str, bool]: """Run the complete CI pipeline.""" logger.info("๐Ÿš€ Starting Comprehensive CI Pipeline") logger.info("=" * 60) - + # Environment detection env_info = self.detect_environment() self.results["environment"] = env_info - + # Dependency validation self.results["dependencies"] = self.validate_dependencies() - + # Run individual CI scripts for script in self.ci_scripts: script_name = Path(script).stem success, output = self.run_ci_script(script) self.results[script_name] = success - + if not success: logger.error(f"โŒ {script_name} failed, but continuing...") - + # Run unit tests self.results["unit_tests"] = self.run_unit_tests() - + # Run E2E tests self.results["e2e_tests"] = self.run_e2e_tests() - + # Test GPU compatibility self.results["gpu_compatibility"] = self.test_gpu_compatibility() - + # Run performance benchmarks self.results["performance"] = self.run_performance_benchmarks() - + return self.results - + def generate_report(self) -> str: """Generate a comprehensive CI report.""" logger.info("๐Ÿ“Š Generating CI Report") logger.info("=" * 60) - + # Only count boolean results as actual tests test_results, total_tests, passed_tests = self._get_test_stats() - + # Guard against division by zero when no boolean tests were collected safe_total = total_tests if total_tests > 0 else 1 success_rate = (passed_tests / safe_total) * 100.0 @@ -358,28 +366,27 @@ def generate_report(self) -> str: ๐Ÿ” DETAILED RESULTS: """ - + for test_name, result in self.results.items(): if isinstance(result, bool): status = "โœ… PASSED" if result else "โŒ FAILED" report += f"- {test_name}: {status}\n" elif isinstance(result, dict): report += f"- {test_name}: {result}\n" - + report += f""" โฑ๏ธ EXECUTION TIME: {time.time() - self.start_time:.1f}s ๐ŸŽฏ RECOMMENDATIONS: """ - + if passed_tests == total_tests: report += "๐ŸŽ‰ All tests passed! Pipeline is ready for deployment.\n" else: - failed_test_names = [name for name, result in test_results.items() - if not result] + failed_test_names = [name for name, result in test_results.items() if not result] report += f"โš ๏ธ Failed tests: {', '.join(failed_test_names)}\n" report += "๐Ÿ”ง Please fix the failed tests before deployment.\n" - + return report @@ -393,25 +400,25 @@ def write_ci_report_if_needed(report: str) -> None: def main(): """Main function to run the CI pipeline.""" runner = CIPipelineRunner() - + try: _ = runner.run_full_pipeline() report = runner.generate_report() - + print(report) # Only write report to file in CI so it can be uploaded as an artifact write_ci_report_if_needed(report) - + # Exit with appropriate code _, total_tests, passed_tests = runner._get_test_stats() - + if passed_tests == total_tests: logger.info("๐ŸŽ‰ CI Pipeline completed successfully!") sys.exit(0) else: logger.error("โŒ CI Pipeline failed!") sys.exit(1) - + except KeyboardInterrupt: logger.info("โน๏ธ CI Pipeline interrupted by user") sys.exit(1) @@ -421,4 +428,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/ci/t5_summarization_test.py b/scripts/ci/t5_summarization_test.py index 239a5eb60..cdeacd09a 100755 --- a/scripts/ci/t5_summarization_test.py +++ b/scripts/ci/t5_summarization_test.py @@ -10,6 +10,8 @@ import sys from pathlib import Path +from .validation_utils import ensure, validate_hasattrs + # Add src to path sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) @@ -24,7 +26,6 @@ logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) -from .validation_utils import validate_hasattrs, ensure def test_t5_model_loading(): """Test T5 model initialization.""" @@ -72,11 +73,7 @@ def test_t5_summarization(): """ # Perform summarization - summary = model.generate_summary( - text=test_text.strip(), - max_length=50, - min_length=10 - ) + summary = model.generate_summary(text=test_text.strip(), max_length=50, min_length=10) logger.info(f"โœ… Summarization successful: {summary[:50]}...") diff --git a/scripts/ci/validation_utils.py b/scripts/ci/validation_utils.py index 9b9f3bcc2..df4d77aa9 100644 --- a/scripts/ci/validation_utils.py +++ b/scripts/ci/validation_utils.py @@ -57,4 +57,3 @@ def ensure(condition: bool, message: str) -> None: """ if not condition: raise AssertionError(message) - diff --git a/scripts/ci/whisper_transcription_test.py b/scripts/ci/whisper_transcription_test.py index 03ea37767..9fedc2312 100644 --- a/scripts/ci/whisper_transcription_test.py +++ b/scripts/ci/whisper_transcription_test.py @@ -8,12 +8,12 @@ import contextlib import logging -import numpy as np import os import sys import tempfile from pathlib import Path +import numpy as np from scipy.io import wavfile # Add src to path @@ -55,12 +55,10 @@ def test_whisper_imports(): # Test imports with fallback mechanism try: - from models.voice_processing.audio_preprocessor import AudioPreprocessor - from models.voice_processing.whisper_transcriber import WhisperTranscriber + pass except ImportError: # Fallback for different import paths - from src.models.voice_processing.audio_preprocessor import AudioPreprocessor - from src.models.voice_processing.whisper_transcriber import WhisperTranscriber + pass logger.info("โœ… Whisper imports successful") return True @@ -125,7 +123,7 @@ def test_audio_preprocessor(): try: preprocessor = AudioPreprocessor() - + # Test audio validation is_valid, error_msg = preprocessor.validate_audio_file(test_audio_path) if not is_valid: @@ -184,7 +182,7 @@ def test_minimal_transcription(): # Test transcription result = transcriber.transcribe(test_audio_path) - + if result and result.text: logger.info(f"โœ… Transcription successful: {result.text[:50]}...") return True diff --git a/scripts/cleanup_old_images.sh b/scripts/cleanup_old_images.sh new file mode 100755 index 000000000..4456cb5f0 --- /dev/null +++ b/scripts/cleanup_old_images.sh @@ -0,0 +1,115 @@ +#!/bin/bash +# Script to clean up old Docker images from Artifact Registry +# +# This script: +# - Uses fully-qualified image names (LOCATION-docker.pkg.dev/PROJECT/REPO/PACKAGE) +# - Handles both tagged versions and digest-only versions +# - Groups images by package and keeps N most recent per package +# - Uses --delete-tags flag to ensure complete removal of tagged images +# - Uses digest form (@sha256:...) for digest-only images + +# Enable strict bash options for fail-fast behavior +set -euo pipefail +IFS=$'\n\t' + +# Check for gcloud CLI presence +if ! command -v gcloud >/dev/null 2>&1; then + echo "โŒ Error: gcloud CLI not found" >&2 + echo " Please install gcloud CLI and authenticate:" >&2 + echo " https://cloud.google.com/sdk/docs/install" >&2 + exit 1 +fi + +# Verify gcloud authentication and configuration +if ! gcloud auth list --filter=status:ACTIVE --format="value(account)" | grep -q .; then + echo "โŒ Error: No active gcloud authentication found" >&2 + echo " Please run: gcloud auth login" >&2 + exit 1 +fi + +echo "๐Ÿงน Starting Docker image cleanup..." + +# Function to delete old images (keep only latest 2 per package) +cleanup_repo() { + local repo=$1 + local keep_count=${2:-2} # Default to keeping 2 most recent per package + + # Validate that repo is fully qualified + if [[ ! "$repo" =~ ^[a-z0-9-]+-docker\.pkg\.dev/[a-z0-9-]+/[a-z0-9-]+$ ]]; then + echo "โŒ Error: Repository '$repo' is not fully qualified" + echo " Expected format: LOCATION-docker.pkg.dev/PROJECT/REPO" + return 1 + fi + + echo "Cleaning up repository: $repo (keeping $keep_count most recent per package)" + + # Get all images in CSV format with stable delimiter, no header + local temp_file + temp_file=$(mktemp) + + # Use CSV format with comma delimiter and no header + gcloud artifacts docker images list "$repo" \ + --format="csv(package,version,createTime)" \ + --filter="createTime!=null" > "$temp_file" + + # Process each package separately to keep N most recent per package + local packages + packages=$(cut -d',' -f1 "$temp_file" | sort -u) + + for package in $packages; do + echo "Processing package: $package" + + # Get images for this package, sort by creation time (newest first) + local package_images + package_images=$(grep "^$package," "$temp_file" | sort -t',' -k3 -r) + + # Count total images for this package + local total_count + total_count=$(echo "$package_images" | wc -l) + + if [ "$total_count" -le "$keep_count" ]; then + echo " Package $package has $total_count images (โ‰ค $keep_count), skipping deletion" + continue + fi + + # Calculate how many to delete + local delete_count=$((total_count - keep_count)) + echo " Package $package has $total_count images, deleting $delete_count oldest" + + # Get images to delete (skip the first $keep_count, delete the rest) + echo "$package_images" | tail -n +$((keep_count + 1)) | while IFS=',' read -r pkg version _; do + # Construct fully-qualified image name + local full_image_name="$repo/$pkg" + + # Handle both tagged versions and digest-only versions + if [[ "$version" =~ ^sha256: ]]; then + # This is a digest-only version - use digest form + local digest_image="$full_image_name@$version" + echo " Deleting digest-only image: $digest_image" + gcloud artifacts docker images delete "$digest_image" --quiet || true + else + # This is a tagged version - use tag form with --delete-tags + local tagged_image="$full_image_name:$version" + echo " Deleting tagged image: $tagged_image" + gcloud artifacts docker images delete "$tagged_image" --delete-tags --quiet || true + fi + done + done + + # Clean up temp file + rm -f "$temp_file" +} + +# Clean up each repository (keeping 2 most recent per package by default) +echo "Cleaning emotion-detection-repo..." +cleanup_repo "us-central1-docker.pkg.dev/the-tendril-466607-n8/emotion-detection-repo" 2 + +echo "Cleaning samo-dl repo..." +cleanup_repo "us-central1-docker.pkg.dev/the-tendril-466607-n8/samo-dl" 2 + +echo "Cleaning cloud-run-source-deploy..." +cleanup_repo "us-central1-docker.pkg.dev/the-tendril-466607-n8/cloud-run-source-deploy" 2 + +echo "โœ… Cleanup complete!" +echo "๐Ÿ’ฐ This should significantly reduce your storage costs!" +echo "๐Ÿ“Š Each package now has at most 2 most recent images" diff --git a/scripts/database/check_pgvector.py b/scripts/database/check_pgvector.py index 413a7c24d..485ab07c7 100755 --- a/scripts/database/check_pgvector.py +++ b/scripts/database/check_pgvector.py @@ -12,6 +12,7 @@ # Load environment variables from .env file try: from dotenv import load_dotenv + load_dotenv() except ImportError: # dotenv not installed, skip loading @@ -59,10 +60,7 @@ def check_pgvector(): # Create a cursor with conn.cursor() as cur: # Check if vector extension is available - cur.execute( - "SELECT extname FROM pg_extension " - "WHERE extname = 'vector';" - ) + cur.execute("SELECT extname FROM pg_extension " "WHERE extname = 'vector';") extension_installed = cur.fetchone() is not None if extension_installed: @@ -77,9 +75,7 @@ def check_pgvector(): "# e.g., 14/15/16" ) logging.info(" - On macOS with Homebrew: brew install pgvector") - logging.info( - " - From source: https://github.com/pgvector/pgvector#installation" - ) + logging.info(" - From source: https://github.com/pgvector/pgvector#installation") logging.info("\n2. Enable the extension in your database:") logging.info(" - psql -U postgres") logging.info(" - \\c %s", DB_NAME) diff --git a/scripts/deployment/__pycache__/deploy_locally.cpython-38.pyc b/scripts/deployment/__pycache__/deploy_locally.cpython-38.pyc deleted file mode 100644 index 1a962cd14..000000000 Binary files a/scripts/deployment/__pycache__/deploy_locally.cpython-38.pyc and /dev/null differ diff --git a/scripts/deployment/bake_emotion_model.py b/scripts/deployment/bake_emotion_model.py index 84a8aa6cf..1aa10a3e4 100644 --- a/scripts/deployment/bake_emotion_model.py +++ b/scripts/deployment/bake_emotion_model.py @@ -1,8 +1,7 @@ #!/usr/bin/env python3 import os -import sys -from transformers import AutoTokenizer, AutoModelForSequenceClassification +from transformers import AutoModelForSequenceClassification, AutoTokenizer try: from huggingface_hub import login # type: ignore @@ -34,4 +33,4 @@ def main() -> int: if __name__ == "__main__": - raise SystemExit(main()) \ No newline at end of file + raise SystemExit(main()) diff --git a/scripts/deployment/complete_project_deployment.py b/scripts/deployment/complete_project_deployment.py index 1c9289553..db06d1adc 100644 --- a/scripts/deployment/complete_project_deployment.py +++ b/scripts/deployment/complete_project_deployment.py @@ -6,12 +6,13 @@ This script handles everything from model saving to final testing. """ -import os import json +import os import subprocess import sys from datetime import datetime + def print_banner(): """Print project completion banner""" print("๐ŸŽ‰" * 50) @@ -21,44 +22,48 @@ def print_banner(): print("โœ… STATUS: TARGET CRUSHED!") print("๐ŸŽ‰" * 50) + def check_project_status(): """Check the current project status""" print("๐Ÿ“Š CHECKING PROJECT STATUS") print("=" * 40) - + # Check for trained models model_paths = [ "./emotion_model_ensemble_final", - "./emotion_model_specialized_final", + "./emotion_model_specialized_final", "./emotion_model_fixed_bulletproof_final", - "./emotion_model" + "./emotion_model", ] - + found_models = [] for path in model_paths: if os.path.exists(path): found_models.append(path) print(f"โœ… Found model: {path}") - + if not found_models: print("โŒ No trained models found!") print("Please train a model first using the Colab notebooks.") return False - + print(f"๐Ÿ“Š Found {len(found_models)} trained model(s)") return True + def save_model_for_deployment(): """Save the trained model for deployment""" print("\n๐Ÿš€ SAVING MODEL FOR DEPLOYMENT") print("=" * 40) - + try: # Run the model saving script - result = subprocess.run([ - sys.executable, "scripts/save_trained_model_for_deployment.py" - ], capture_output=True, text=True) - + result = subprocess.run( + [sys.executable, "scripts/save_trained_model_for_deployment.py"], + capture_output=True, + text=True, + ) + if result.returncode == 0: print("โœ… Model saved successfully!") print(result.stdout) @@ -67,26 +72,27 @@ def save_model_for_deployment(): print("โŒ Failed to save model!") print(result.stderr) return False - + except Exception as e: print(f"โŒ Error saving model: {e}") return False + def test_deployment_package(): """Test the deployment package""" print("\n๐Ÿงช TESTING DEPLOYMENT PACKAGE") print("=" * 40) - + if not os.path.exists("deployment/model"): print("โŒ Model not found in deployment directory!") return False - + try: # Test the model - result = subprocess.run([ - sys.executable, "deployment/test_examples.py" - ], capture_output=True, text=True) - + result = subprocess.run( + [sys.executable, "deployment/test_examples.py"], capture_output=True, text=True + ) + if result.returncode == 0: print("โœ… Deployment package test passed!") print(result.stdout) @@ -95,16 +101,17 @@ def test_deployment_package(): print("โŒ Deployment package test failed!") print(result.stderr) return False - + except Exception as e: print(f"โŒ Error testing deployment: {e}") return False + def create_final_documentation(): """Create final project documentation""" print("\n๐Ÿ“š CREATING FINAL DOCUMENTATION") print("=" * 40) - + # Create project summary summary = { "project_name": "SAMO Emotion Detection", @@ -113,14 +120,14 @@ def create_final_documentation(): "target_f1": "75-85%", "achieved_f1": "99.48%", "improvement": "1,813%", - "target_achieved": True + "target_achieved": True, }, "technical_achievements": [ "Specialized emotion models (finiteautomata/bertweet-base-emotion-analysis)", "Data augmentation techniques (synonym replacement, word order changes)", "Model ensembling with automatic best model selection", "Hyperparameter optimization for small datasets", - "Production-ready deployment package" + "Production-ready deployment package", ], "files_created": [ "deployment/model/ (trained model)", @@ -128,31 +135,28 @@ def create_final_documentation(): "deployment/api_server.py (REST API)", "deployment/test_examples.py (testing script)", "deployment/deploy.sh (deployment script)", - "docs/reports/PROJECT_COMPLETION_SUMMARY.md (project summary)" + "docs/reports/PROJECT_COMPLETION_SUMMARY.md (project summary)", ], - "next_steps": [ - "cd deployment", - "./deploy.sh", - "Test API at http://localhost:5000" - ] + "next_steps": ["cd deployment", "./deploy.sh", "Test API at http://localhost:5000"], } - + # Save summary - with open("deployment/project_summary.json", 'w') as f: + with open("deployment/project_summary.json", "w") as f: json.dump(summary, f, indent=2) - + print("โœ… Final documentation created!") print("๐Ÿ“ Files created:") print(" - deployment/project_summary.json") print(" - docs/reports/PROJECT_COMPLETION_SUMMARY.md") - + return True + def create_deployment_instructions(): """Create deployment instructions""" print("\n๐Ÿ“‹ CREATING DEPLOYMENT INSTRUCTIONS") print("=" * 40) - + instructions = """# ๐Ÿš€ EMOTION DETECTION MODEL - DEPLOYMENT INSTRUCTIONS ## ๐ŸŽ‰ PROJECT COMPLETION STATUS @@ -232,27 +236,37 @@ def create_deployment_instructions(): **MISSION ACCOMPLISHED!** ๐Ÿš€ """ - - with open("deployment/DEPLOYMENT_INSTRUCTIONS.md", 'w') as f: + + with open("deployment/DEPLOYMENT_INSTRUCTIONS.md", "w") as f: f.write(instructions) - + print("โœ… Deployment instructions created!") return True + def run_final_tests(): """Run final comprehensive tests""" print("\n๐Ÿงช RUNNING FINAL TESTS") print("=" * 40) - + tests = [ - ("Model Loading", "python3.12 -c \"from deployment.inference import EmotionDetector; d = EmotionDetector(); print('โœ… Model loaded successfully!')\""), - ("API Health", "curl -s http://localhost:5000/health | grep -q 'healthy' && echo 'โœ… API health check passed' || echo 'โŒ API health check failed'"), - ("Single Prediction", "curl -s -X POST http://localhost:5000/predict -H 'Content-Type: application/json' -d '{\"text\": \"I am happy\"}' | grep -q 'emotion' && echo 'โœ… Single prediction passed' || echo 'โŒ Single prediction failed'"), + ( + "Model Loading", + "python3.12 -c \"from deployment.inference import EmotionDetector; d = EmotionDetector(); print('โœ… Model loaded successfully!')\"", + ), + ( + "API Health", + "curl -s http://localhost:5000/health | grep -q 'healthy' && echo 'โœ… API health check passed' || echo 'โŒ API health check failed'", + ), + ( + "Single Prediction", + "curl -s -X POST http://localhost:5000/predict -H 'Content-Type: application/json' -d '{\"text\": \"I am happy\"}' | grep -q 'emotion' && echo 'โœ… Single prediction passed' || echo 'โŒ Single prediction failed'", + ), ] - + passed = 0 total = len(tests) - + for test_name, command in tests: try: result = subprocess.run(command, shell=True, capture_output=True, text=True) @@ -263,33 +277,34 @@ def run_final_tests(): print(f"โŒ {test_name}: FAILED") except Exception as e: print(f"โŒ {test_name}: ERROR - {e}") - + print(f"\n๐Ÿ“Š Test Results: {passed}/{total} tests passed") return passed == total + def main(): """Main deployment process""" print_banner() - + # Check project status if not check_project_status(): print("\nโŒ Project not ready for deployment!") return False - + # Save model for deployment if not save_model_for_deployment(): print("\nโŒ Failed to save model!") return False - + # Test deployment package if not test_deployment_package(): print("\nโŒ Deployment package test failed!") return False - + # Create documentation create_final_documentation() create_deployment_instructions() - + # Final success message print("\n๐ŸŽ‰" * 50) print("๐Ÿ† PROJECT DEPLOYMENT COMPLETE!") @@ -297,7 +312,7 @@ def main(): print("๐Ÿ† ACHIEVED: 99.48% F1 Score") print("โœ… STATUS: TARGET CRUSHED!") print("๐ŸŽ‰" * 50) - + print("\n๐Ÿ“ DEPLOYMENT PACKAGE READY:") print(" - deployment/model/ (trained model)") print(" - deployment/inference.py (inference script)") @@ -305,18 +320,19 @@ def main(): print(" - deployment/test_examples.py (test script)") print(" - deployment/deploy.sh (deployment script)") print(" - deployment/DEPLOYMENT_INSTRUCTIONS.md (instructions)") - + print("\n๐Ÿš€ NEXT STEPS:") print(" 1. cd deployment") print(" 2. ./deploy.sh") print(" 3. Test API at: http://localhost:5000") - + print("\n๐ŸŽฏ MODEL PERFORMANCE: 99.48% F1 Score!") print("๐Ÿ† TARGET ACHIEVED: โœ… YES!") print("๐ŸŽ‰ MISSION ACCOMPLISHED!") - + return True + if __name__ == "__main__": success = main() - sys.exit(0 if success else 1) \ No newline at end of file + sys.exit(0 if success else 1) diff --git a/scripts/deployment/convert_model_to_onnx.py b/scripts/deployment/convert_model_to_onnx.py index d54a04dc7..3ea115f61 100644 --- a/scripts/deployment/convert_model_to_onnx.py +++ b/scripts/deployment/convert_model_to_onnx.py @@ -3,23 +3,27 @@ Convert PyTorch Model to ONNX for Deployment Quick conversion script to eliminate PyTorch dependencies """ -import sys -import torch -import logging import argparse +import logging +import sys from pathlib import Path -# Add src to path -sys.path.append(str(Path(__file__).parent.parent.parent / 'src')) +import torch +from transformers import AutoTokenizer from models.emotion_detection.bert_classifier import create_bert_emotion_classifier -from transformers import AutoTokenizer + +# Add src to path +sys.path.append(str(Path(__file__).parent.parent.parent / "src")) + logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) -def convert_model_to_onnx(model_path=None, onnx_output_path=None, tokenizer_name="bert-base-uncased"): +def convert_model_to_onnx( + model_path=None, onnx_output_path=None, tokenizer_name="bert-base-uncased" +): """Convert PyTorch model to ONNX format.""" try: # Default paths if not provided @@ -63,11 +67,7 @@ def convert_model_to_onnx(model_path=None, onnx_output_path=None, tokenizer_name logger.info("๐Ÿ”„ Creating dummy input for ONNX export...") dummy_text = "This is a test sentence for ONNX conversion." inputs = tokenizer( - dummy_text, - return_tensors="pt", - padding=True, - truncation=True, - max_length=128 + dummy_text, return_tensors="pt", padding=True, truncation=True, max_length=128 ) # Handle token_type_ids properly - use actual values if available, otherwise zeros @@ -81,11 +81,7 @@ def convert_model_to_onnx(model_path=None, onnx_output_path=None, tokenizer_name logger.info("๐Ÿ”„ Converting to ONNX format...") torch.onnx.export( model, - ( - inputs["input_ids"], - inputs["attention_mask"], - token_type_ids - ), + (inputs["input_ids"], inputs["attention_mask"], token_type_ids), onnx_output_path, export_params=True, opset_version=14, @@ -105,6 +101,7 @@ def convert_model_to_onnx(model_path=None, onnx_output_path=None, tokenizer_name # Verify ONNX model try: import onnx + onnx_model = onnx.load(onnx_output_path) onnx.checker.check_model(onnx_model) logger.info("โœ… ONNX model validation successful") @@ -117,10 +114,13 @@ def convert_model_to_onnx(model_path=None, onnx_output_path=None, tokenizer_name # Test ONNX model with ONNX Runtime try: import onnxruntime as ort + session = ort.InferenceSession(onnx_output_path) logger.info("โœ… ONNX Runtime test successful") except ImportError: - logger.error("โŒ ONNX Runtime is required for ONNX model validation. Please install it with 'pip install onnxruntime'.") + logger.error( + "โŒ ONNX Runtime is required for ONNX model validation. Please install it with 'pip install onnxruntime'." + ) return False except Exception as e: logger.error(f"โŒ ONNX Runtime test failed: {e}") @@ -140,19 +140,19 @@ def main(): "--model-path", type=str, default="models/best_simple_model.pth", - help="Path to PyTorch model file (default: models/best_simple_model.pth)" + help="Path to PyTorch model file (default: models/best_simple_model.pth)", ) parser.add_argument( "--onnx-output-path", type=str, default="deployment/cloud-run/model/bert_emotion_classifier.onnx", - help="Path for ONNX output file (default: deployment/cloud-run/model/bert_emotion_classifier.onnx)" + help="Path for ONNX output file (default: deployment/cloud-run/model/bert_emotion_classifier.onnx)", ) parser.add_argument( "--tokenizer-name", type=str, default="bert-base-uncased", - help="Tokenizer name to use (default: bert-base-uncased)" + help="Tokenizer name to use (default: bert-base-uncased)", ) args = parser.parse_args() @@ -160,7 +160,7 @@ def main(): if success := convert_model_to_onnx( model_path=args.model_path, onnx_output_path=args.onnx_output_path, - tokenizer_name=args.tokenizer_name + tokenizer_name=args.tokenizer_name, ): logger.info("๐ŸŽ‰ ONNX conversion completed successfully!") sys.exit(0) @@ -170,4 +170,4 @@ def main(): if __name__ == "__main__": - main() + main() diff --git a/scripts/deployment/convert_model_to_onnx_simple.py b/scripts/deployment/convert_model_to_onnx_simple.py index 74fe747d7..ca4543c50 100644 --- a/scripts/deployment/convert_model_to_onnx_simple.py +++ b/scripts/deployment/convert_model_to_onnx_simple.py @@ -3,23 +3,27 @@ Simple ONNX Conversion for Current Model Handles the actual model architecture we have """ -import sys -import torch -import logging import argparse +import logging +import sys from pathlib import Path -# Add src to path -sys.path.append(str(Path(__file__).parent.parent.parent / 'src')) +import torch +from transformers import AutoTokenizer from models.emotion_detection.bert_classifier import create_bert_emotion_classifier -from transformers import AutoTokenizer + +# Add src to path +sys.path.append(str(Path(__file__).parent.parent.parent / "src")) + logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) -def convert_model_to_onnx(model_path=None, onnx_output_path=None, tokenizer_name="bert-base-uncased"): +def convert_model_to_onnx( + model_path=None, onnx_output_path=None, tokenizer_name="bert-base-uncased" +): """Convert PyTorch model to ONNX format.""" try: # Default paths if not provided @@ -56,11 +60,7 @@ def convert_model_to_onnx(model_path=None, onnx_output_path=None, tokenizer_name logger.info("๐Ÿ”„ Creating dummy input for ONNX export...") dummy_text = "This is a test sentence for ONNX conversion." inputs = tokenizer( - dummy_text, - return_tensors="pt", - padding=True, - truncation=True, - max_length=128 + dummy_text, return_tensors="pt", padding=True, truncation=True, max_length=128 ) # Handle token_type_ids properly - use actual values from tokenizer @@ -76,19 +76,17 @@ def convert_model_to_onnx(model_path=None, onnx_output_path=None, tokenizer_name padding=True, truncation=True, max_length=128, - return_token_type_ids=True # Explicitly request token_type_ids + return_token_type_ids=True, # Explicitly request token_type_ids + ) + token_type_ids = tokenizer_output.get( + "token_type_ids", torch.zeros_like(inputs["input_ids"]) ) - token_type_ids = tokenizer_output.get("token_type_ids", torch.zeros_like(inputs["input_ids"])) # Export to ONNX logger.info("๐Ÿ”„ Converting to ONNX format...") torch.onnx.export( model, - ( - inputs["input_ids"], - inputs["attention_mask"], - token_type_ids - ), + (inputs["input_ids"], inputs["attention_mask"], token_type_ids), onnx_output_path, export_params=True, opset_version=14, @@ -108,10 +106,13 @@ def convert_model_to_onnx(model_path=None, onnx_output_path=None, tokenizer_name # Test ONNX model with ONNX Runtime try: import onnxruntime as ort + session = ort.InferenceSession(onnx_output_path) logger.info("โœ… ONNX model test successful") except ImportError: - logger.error("โŒ ONNX Runtime is required for ONNX model validation. Please install it with 'pip install onnxruntime'.") + logger.error( + "โŒ ONNX Runtime is required for ONNX model validation. Please install it with 'pip install onnxruntime'." + ) return False except Exception as e: logger.error(f"โŒ ONNX model validation failed: {e}") @@ -126,24 +127,26 @@ def convert_model_to_onnx(model_path=None, onnx_output_path=None, tokenizer_name def main(): """Main function with command-line argument parsing.""" - parser = argparse.ArgumentParser(description="Convert PyTorch model to ONNX format (simple version)") + parser = argparse.ArgumentParser( + description="Convert PyTorch model to ONNX format (simple version)" + ) parser.add_argument( "--model-path", type=str, default="models/best_simple_model.pth", - help="Path to PyTorch model file (default: models/best_simple_model.pth)" + help="Path to PyTorch model file (default: models/best_simple_model.pth)", ) parser.add_argument( "--onnx-output-path", type=str, default="deployment/cloud-run/model/bert_emotion_classifier.onnx", - help="Path for ONNX output file (default: deployment/cloud-run/model/bert_emotion_classifier.onnx)" + help="Path for ONNX output file (default: deployment/cloud-run/model/bert_emotion_classifier.onnx)", ) parser.add_argument( "--tokenizer-name", type=str, default="bert-base-uncased", - help="Tokenizer name to use (default: bert-base-uncased)" + help="Tokenizer name to use (default: bert-base-uncased)", ) args = parser.parse_args() @@ -151,7 +154,7 @@ def main(): if success := convert_model_to_onnx( model_path=args.model_path, onnx_output_path=args.onnx_output_path, - tokenizer_name=args.tokenizer_name + tokenizer_name=args.tokenizer_name, ): logger.info("๐ŸŽ‰ Simple ONNX conversion completed successfully!") sys.exit(0) @@ -161,4 +164,4 @@ def main(): if __name__ == "__main__": - main() + main() diff --git a/scripts/deployment/create_model_deployment_package.py b/scripts/deployment/create_model_deployment_package.py index 951fd0143..31085a6fc 100644 --- a/scripts/deployment/create_model_deployment_package.py +++ b/scripts/deployment/create_model_deployment_package.py @@ -7,9 +7,10 @@ """ import os + def create_model_deployment_package(): """Create the deployment package content""" - + # Create deployment directory structure deployment_files = { "README.md": """# ๐Ÿš€ EMOTION DETECTION MODEL - DEPLOYMENT PACKAGE @@ -56,7 +57,6 @@ def create_model_deployment_package(): - **Improvement**: 1,813% increase - **Target**: 75-85% F1 (CRUSHED!) """, - "requirements.txt": """transformers==4.35.0 torch==2.1.0 scikit-learn==1.3.0 @@ -65,7 +65,6 @@ def create_model_deployment_package(): flask==2.3.3 requests==2.32.4 """, - "inference.py": '''#!/usr/bin/env python3 """ ๐Ÿš€ EMOTION DETECTION INFERENCE SCRIPT @@ -83,23 +82,23 @@ class EmotionDetector: def __init__(self, model_path="./model"): """Initialize the emotion detector""" self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - + # Load model and tokenizer self.tokenizer = AutoTokenizer.from_pretrained(model_path) self.model = AutoModelForSequenceClassification.from_pretrained(model_path) self.model.to(self.device) self.model.eval() - + # Load label encoder with open(f"{model_path}/label_encoder.json", 'r') as f: label_data = json.load(f) self.label_encoder = LabelEncoder() self.label_encoder.classes_ = np.array(label_data['classes']) - + print(f"โœ… Model loaded successfully!") print(f"๐ŸŽฏ Device: {self.device}") print(f"๐Ÿ“Š Emotions: {list(self.label_encoder.classes_)}") - + def predict(self, text, return_confidence=True): """Predict emotion for given text""" # Tokenize input @@ -109,30 +108,30 @@ def predict(self, text, return_confidence=True): padding=True, return_tensors='pt' ).to(self.device) - + # Get predictions with torch.no_grad(): outputs = self.model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + # Decode prediction predicted_emotion = self.label_encoder.inverse_transform([predicted_class])[0] - + if return_confidence: return { 'text': text, 'emotion': predicted_emotion, 'confidence': confidence, 'probabilities': { - emotion: prob.item() + emotion: prob.item() for emotion, prob in zip(self.label_encoder.classes_, probabilities[0]) } } else: return predicted_emotion - + def predict_batch(self, texts): """Predict emotions for multiple texts""" results = [] @@ -149,7 +148,7 @@ def main(): except Exception: print("โŒ Failed to load model") return - + # Test examples test_texts = [ "I'm feeling really happy today!", @@ -158,10 +157,10 @@ def main(): "I'm grateful for all the support.", "I'm feeling overwhelmed with tasks." ] - + print("๐Ÿงช Testing Emotion Detection Model") print("=" * 50) - + for text in test_texts: result = detector.predict(text) print(f"Text: {text}") @@ -175,7 +174,6 @@ def main(): if __name__ == "__main__": main() ''', - "test_examples.py": '''#!/usr/bin/env python3 """ ๐Ÿงช TEST EMOTION DETECTION MODEL @@ -189,7 +187,7 @@ def test_model(): """Test the emotion detection model""" print("๐Ÿงช EMOTION DETECTION MODEL TESTING") print("=" * 50) - + # Initialize detector try: detector = EmotionDetector() @@ -197,7 +195,7 @@ def test_model(): except Exception: print("โŒ Failed to load model") return - + # Test cases test_cases = [ # Happy emotions @@ -205,44 +203,43 @@ def test_model(): "I'm excited about the new opportunities ahead.", "I'm grateful for all the support I've received.", "I'm proud of what I've accomplished so far.", - + # Negative emotions "I'm so frustrated with this project. Nothing is working.", "I feel anxious about the upcoming presentation.", "I'm feeling sad and lonely today.", "I'm feeling overwhelmed with all these tasks.", - + # Neutral emotions "I feel calm and peaceful right now.", "I'm content with how things are going.", "I'm hopeful that things will get better.", "I'm tired and need some rest." ] - + print("\\n๐Ÿ“Š Testing Results:") print("=" * 50) - + correct_predictions = 0 total_predictions = len(test_cases) - + for i, text in enumerate(test_cases, 1): result = detector.predict(text) - + print(f"{i:2d}. Text: {text}") print(f" Predicted: {result['emotion']} (confidence: {result['confidence']:.3f})") - + # Show top 3 predictions sorted_probs = sorted(result['probabilities'].items(), key=lambda x: x[1], reverse=True) print(f" Top 3: {', '.join([f'{emotion}({prob:.3f})' for emotion, prob in sorted_probs[:3]])}") print() - + print("๐ŸŽ‰ Testing completed!") print(f"๐Ÿ“Š Model confidence range: {min([detector.predict(text)['confidence'] for text in test_cases]):.3f} - {max([detector.predict(text)['confidence'] for text in test_cases]):.3f}") if __name__ == "__main__": test_model() ''', - "api_server.py": '''#!/usr/bin/env python3 """ ๐Ÿš€ EMOTION DETECTION API SERVER @@ -282,17 +279,17 @@ def predict_emotion(): """Predict emotion for given text""" if detector is None: return jsonify({'error': 'Model not loaded'}), 500 - + try: data = request.get_json() text = data.get('text', '') - + if not text: return jsonify({'error': 'No text provided'}), 400 - + result = detector.predict(text) return jsonify(result) - + except Exception: import uuid request_id = str(uuid.uuid4()) @@ -307,17 +304,17 @@ def predict_batch(): """Predict emotions for multiple texts""" if detector is None: return jsonify({'error': 'Model not loaded'}), 500 - + try: data = request.get_json() texts = data.get('texts', []) - + if not texts: return jsonify({'error': 'No texts provided'}), 400 - + results = detector.predict_batch(texts) return jsonify({'results': results}) - + except Exception: import uuid request_id = str(uuid.uuid4()) @@ -332,7 +329,7 @@ def get_emotions(): """Get list of supported emotions""" if detector is None: return jsonify({'error': 'Model not loaded'}), 500 - + return jsonify({ 'emotions': list(detector.label_encoder.classes_), 'count': len(detector.label_encoder.classes_) @@ -349,10 +346,9 @@ def get_emotions(): print(" - POST /predict_batch - Batch prediction") print(" - GET /emotions - List emotions") print("=" * 50) - + app.run(host='0.0.0.0', port=5000, debug=False) ''', - "deploy.sh": """#!/bin/bash # ๐Ÿš€ DEPLOYMENT SCRIPT # ==================== @@ -380,7 +376,6 @@ def get_emotions(): echo "Server will be available at: http://localhost:5000" python api_server.py """, - "dockerfile": """# ๐Ÿš€ EMOTION DETECTION MODEL DOCKERFILE # ===================================== @@ -409,7 +404,6 @@ def get_emotions(): # Run the application CMD ["python", "api_server.py"] """, - "docker-compose.yml": """version: '3.8' services: @@ -428,22 +422,22 @@ def get_emotions(): timeout: 10s retries: 3 start_period: 40s -""" +""", } - + # Create deployment directory deployment_dir = "deployment" os.makedirs(deployment_dir, exist_ok=True) - + # Write all files for filename, content in deployment_files.items(): filepath = os.path.join(deployment_dir, filename) - with open(filepath, 'w') as f: + with open(filepath, "w") as f: f.write(content) - + # Make shell script executable os.chmod(os.path.join(deployment_dir, "deploy.sh"), 0o755) - + print("โœ… Deployment package created: deployment/") print("๐Ÿ“ฆ Files included:") for filename in deployment_files.keys(): @@ -453,5 +447,6 @@ def get_emotions(): print(" 2. Run: cd deployment && ./deploy.sh") print(" 3. Test API at: http://localhost:5000") + if __name__ == "__main__": - create_model_deployment_package() \ No newline at end of file + create_model_deployment_package() diff --git a/scripts/deployment/deploy_locally.py b/scripts/deployment/deploy_locally.py index 9e7823169..8048f2201 100644 --- a/scripts/deployment/deploy_locally.py +++ b/scripts/deployment/deploy_locally.py @@ -7,39 +7,41 @@ for testing before cloud deployment. """ -import os import json import sys from datetime import datetime from pathlib import Path + def deploy_locally(): """Deploy the model locally for testing.""" print("๐Ÿš€ LOCAL DEPLOYMENT") print("=" * 50) print(f"โฐ Started at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print() - + # Check if model exists model_path = Path("deployment/models/default") if not model_path.exists(): print(f"โŒ Model not found at: {model_path}") return False - + print("โœ… Model found") - + # Create local deployment directory local_deployment_dir = Path("local_deployment") if local_deployment_dir.exists(): import shutil + shutil.rmtree(local_deployment_dir) local_deployment_dir.mkdir() - + # Copy model files import shutil + shutil.copytree(model_path, local_deployment_dir / "model") print("โœ… Model files copied") - + # Create local API server api_server_script = '''#!/usr/bin/env python3 """ @@ -62,38 +64,46 @@ def __init__(self): """Initialize the model.""" self.model_path = os.path.join(os.getcwd(), "model") print(f"Loading model from: {self.model_path}") - + self.tokenizer = AutoTokenizer.from_pretrained(self.model_path) - self.model = AutoModelForSequenceClassification.from_pretrained(self.model_path) - + self.model = AutoModelForSequenceClassification.from_pretrained( + self.model_path + ) + # Move to GPU if available if torch.cuda.is_available(): self.model = self.model.to('cuda') print("โœ… Model moved to GPU") else: print("โš ๏ธ CUDA not available, using CPU") - - self.emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] + + self.emotions = [ + 'anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', + 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired' + ] print("โœ… Model loaded successfully") - + def predict(self, text): """Make a prediction.""" # Tokenize input - inputs = self.tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=512) - + inputs = self.tokenizer( + text, return_tensors='pt', truncation=True, + padding=True, max_length=512 + ) + if torch.cuda.is_available(): inputs = {k: v.to('cuda') for k, v in inputs.items()} - + # Get prediction with torch.no_grad(): outputs = self.model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_label = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_label].item() - + # Get all probabilities all_probs = probabilities[0].cpu().numpy() - + # Get predicted emotion if predicted_label in self.model.config.id2label: predicted_emotion = self.model.config.id2label[predicted_label] @@ -101,7 +111,7 @@ def predict(self, text): predicted_emotion = self.model.config.id2label[str(predicted_label)] else: predicted_emotion = f"unknown_{predicted_label}" - + # Create response response = { 'text': text, @@ -118,7 +128,7 @@ def predict(self, text): 'average_confidence': '83.9%' } } - + return response # Initialize model @@ -140,19 +150,19 @@ def predict(): """Prediction endpoint.""" try: data = request.get_json() - + if not data or 'text' not in data: return jsonify({'error': 'No text provided'}), 400 - + text = data['text'] if not text.strip(): return jsonify({'error': 'Empty text provided'}), 400 - + # Make prediction result = model.predict(text) - + return jsonify(result) - + except Exception as e: return jsonify({'error': str(e)}), 500 @@ -161,25 +171,25 @@ def predict_batch(): """Batch prediction endpoint.""" try: data = request.get_json() - + if not data or 'texts' not in data: return jsonify({'error': 'No texts provided'}), 400 - + texts = data['texts'] if not isinstance(texts, list): return jsonify({'error': 'Texts must be a list'}), 400 - + results = [] for text in texts: if text.strip(): result = model.predict(text) results.append(result) - + return jsonify({ 'predictions': results, 'count': len(results) }) - + except Exception as e: return jsonify({'error': str(e)}), 500 @@ -229,27 +239,27 @@ def home(): print(" -H 'Content-Type: application/json' \\") print(" -d '{\\"text\\": \\"I am feeling happy today!\\"}'") print() - + app.run(host='0.0.0.0', port=5000, debug=False) ''' - + api_server_path = local_deployment_dir / "api_server.py" - with api_server_path.open('w', encoding='utf-8') as f: + with api_server_path.open("w", encoding="utf-8") as f: f.write(api_server_script) print("โœ… API server script created") - + # Create requirements.txt - requirements = '''flask>=2.0.0 + requirements = """flask>=2.0.0 torch>=2.0.0 transformers>=4.30.0 numpy>=1.21.0 -''' - +""" + requirements_path = local_deployment_dir / "requirements.txt" - with requirements_path.open('w', encoding='utf-8') as f: + with requirements_path.open("w", encoding="utf-8") as f: f.write(requirements) print("โœ… Requirements file created") - + # Create test script test_script = '''#!/usr/bin/env python3 """ @@ -266,10 +276,10 @@ def home(): def test_api(): """Test the local API server.""" base_url = "http://localhost:5000" - + print("๐Ÿงช TESTING LOCAL API SERVER") print("=" * 50) - + # Test health check print("1. Testing health check...") try: @@ -283,7 +293,7 @@ def test_api(): except Exception as e: print(f"โŒ Health check error: {e}") return False - + # Test single prediction print("\\n2. Testing single prediction...") test_cases = [ @@ -293,7 +303,7 @@ def test_api(): "I feel anxious about the test", "I am calm and relaxed" ] - + for i, text in enumerate(test_cases, 1): try: response = requests.post( @@ -301,16 +311,16 @@ def test_api(): json={"text": text}, headers={"Content-Type": "application/json"} ) - + if response.status_code == 200: result = response.json() print(f"โœ… Test {i}: '{text}' โ†’ {result['predicted_emotion']} (conf: {result['confidence']:.3f})") else: print(f"โŒ Test {i} failed: {response.status_code}") - + except Exception as e: print(f"โŒ Test {i} error: {e}") - + # Test batch prediction print("\\n3. Testing batch prediction...") try: @@ -319,7 +329,7 @@ def test_api(): json={"texts": test_cases}, headers={"Content-Type": "application/json"} ) - + if response.status_code == 200: result = response.json() print(f"โœ… Batch prediction successful: {result['count']} predictions") @@ -327,10 +337,10 @@ def test_api(): print(f" {i+1}. '{pred['text']}' โ†’ {pred['predicted_emotion']} (conf: {pred['confidence']:.3f})") else: print(f"โŒ Batch prediction failed: {response.status_code}") - + except Exception as e: print(f"โŒ Batch prediction error: {e}") - + print("\\n๐ŸŽ‰ API testing completed!") return True @@ -338,17 +348,17 @@ def test_api(): # Wait a bit for server to start print("โณ Waiting for server to start...") time.sleep(3) - + test_api() ''' - + test_script_path = local_deployment_dir / "test_api.py" - with test_script_path.open('w', encoding='utf-8') as f: + with test_script_path.open("w", encoding="utf-8") as f: f.write(test_script) print("โœ… Test script created") - + # Create start script - start_script = '''#!/bin/bash + start_script = """#!/bin/bash # Start local deployment set -euo pipefail @@ -383,37 +393,37 @@ def test_api(): echo "" exec python3 -u "$SCRIPT_DIR/api_server.py" -''' - +""" + start_script_path = local_deployment_dir / "start.sh" start_script_path.write_text(start_script) start_script_path.chmod(0o755) print("โœ… Start script created") - + # Create deployment summary deployment_summary = { - 'status': 'ready', - 'timestamp': datetime.now().isoformat(), - 'model_path': str(model_path), - 'deployment_dir': str(local_deployment_dir), - 'endpoints': { - 'health': 'GET http://localhost:5000/health', - 'predict': 'POST http://localhost:5000/predict', - 'predict_batch': 'POST http://localhost:5000/predict_batch', - 'docs': 'GET http://localhost:5000/' + "status": "ready", + "timestamp": datetime.now().isoformat(), + "model_path": str(model_path), + "deployment_dir": str(local_deployment_dir), + "endpoints": { + "health": "GET http://localhost:5000/health", + "predict": "POST http://localhost:5000/predict", + "predict_batch": "POST http://localhost:5000/predict_batch", + "docs": "GET http://localhost:5000/", + }, + "usage": { + "start_server": "./start.sh", + "test_api": "python test_api.py", + "manual_test": 'curl -X POST http://localhost:5000/predict -H "Content-Type: application/json" -d \'{"text": "I am happy"}\'', }, - 'usage': { - 'start_server': './start.sh', - 'test_api': 'python test_api.py', - 'manual_test': 'curl -X POST http://localhost:5000/predict -H "Content-Type: application/json" -d \'{"text": "I am happy"}\'' - } } - + deployment_info_path = local_deployment_dir / "deployment_info.json" deployment_info_path.write_text(json.dumps(deployment_summary, indent=2)) print("โœ… Deployment info created") - - print(f"\nโœ… LOCAL DEPLOYMENT READY!") + + print("\nโœ… LOCAL DEPLOYMENT READY!") print("=" * 50) print(f"๐Ÿ“ Deployment directory: {local_deployment_dir}") print() @@ -432,12 +442,13 @@ def test_api(): print(" POST http://localhost:5000/predict_batch - Batch prediction") print() print("๐Ÿ“ Example usage:") - print(' curl -X POST http://localhost:5000/predict \\') + print(" curl -X POST http://localhost:5000/predict \\") print(' -H "Content-Type: application/json" \\') print(' -d \'{"text": "I am feeling happy today!"}\'') - + return True + if __name__ == "__main__": success = deploy_locally() - sys.exit(0 if success else 1) \ No newline at end of file + sys.exit(0 if success else 1) diff --git a/scripts/deployment/deploy_to_gcp_vertex_ai.py b/scripts/deployment/deploy_to_gcp_vertex_ai.py index 34798f4d1..197967442 100644 --- a/scripts/deployment/deploy_to_gcp_vertex_ai.py +++ b/scripts/deployment/deploy_to_gcp_vertex_ai.py @@ -7,20 +7,21 @@ for production use. """ -import os import json +import os import subprocess import sys from datetime import datetime + def check_prerequisites(): """Check if all prerequisites are met for GCP deployment.""" print("๐Ÿ” CHECKING DEPLOYMENT PREREQUISITES") print("=" * 50) - + # Check if gcloud is installed try: - result = subprocess.run(['gcloud', '--version'], capture_output=True, text=True) + result = subprocess.run(["gcloud", "--version"], capture_output=True, text=True) if result.returncode == 0: print("โœ… gcloud CLI is installed") else: @@ -30,11 +31,13 @@ def check_prerequisites(): print("โŒ gcloud CLI is not installed") print(" Install from: https://cloud.google.com/sdk/docs/install") return False - + # Check if user is authenticated try: - result = subprocess.run(['gcloud', 'auth', 'list', '--filter=status:ACTIVE'], capture_output=True, text=True) - if result.returncode == 0 and 'ACTIVE' in result.stdout: + result = subprocess.run( + ["gcloud", "auth", "list", "--filter=status:ACTIVE"], capture_output=True, text=True + ) + if result.returncode == 0 and "ACTIVE" in result.stdout: print("โœ… User is authenticated with gcloud") else: print("โŒ User is not authenticated with gcloud") @@ -43,10 +46,12 @@ def check_prerequisites(): except Exception as e: print(f"โŒ Error checking authentication: {e}") return False - + # Check if project is set try: - result = subprocess.run(['gcloud', 'config', 'get-value', 'project'], capture_output=True, text=True) + result = subprocess.run( + ["gcloud", "config", "get-value", "project"], capture_output=True, text=True + ) if result.returncode == 0 and result.stdout.strip(): project_id = result.stdout.strip() print(f"โœ… Project is set: {project_id}") @@ -57,11 +62,15 @@ def check_prerequisites(): except Exception as e: print(f"โŒ Error checking project: {e}") return False - + # Check if Vertex AI API is enabled try: - result = subprocess.run(['gcloud', 'services', 'list', '--enabled', '--filter=name:aiplatform.googleapis.com'], capture_output=True, text=True) - if result.returncode == 0 and 'aiplatform.googleapis.com' in result.stdout: + result = subprocess.run( + ["gcloud", "services", "list", "--enabled", "--filter=name:aiplatform.googleapis.com"], + capture_output=True, + text=True, + ) + if result.returncode == 0 and "aiplatform.googleapis.com" in result.stdout: print("โœ… Vertex AI API is enabled") else: print("โŒ Vertex AI API is not enabled") @@ -70,67 +79,71 @@ def check_prerequisites(): except Exception as e: print(f"โŒ Error checking Vertex AI API: {e}") return False - + print("โœ… All prerequisites are met!") return True + def prepare_model_for_deployment(): """Prepare the model for deployment.""" print("\n๐Ÿ“ฆ PREPARING MODEL FOR DEPLOYMENT") print("=" * 50) - + # Check if default model exists default_model_path = "deployment/models/default" if not os.path.exists(default_model_path): print(f"โŒ Default model not found at: {default_model_path}") return False - + # Check model files - required_files = ['config.json', 'model.safetensors', 'tokenizer.json', 'vocab.json'] + required_files = ["config.json", "model.safetensors", "tokenizer.json", "vocab.json"] missing_files = [] - + for file in required_files: if not os.path.exists(os.path.join(default_model_path, file)): missing_files.append(file) - + if missing_files: print(f"โŒ Missing model files: {missing_files}") return False - + print("โœ… Model files are complete") - + # Read model metadata metadata_path = os.path.join(default_model_path, "model_metadata.json") if os.path.exists(metadata_path): - with open(metadata_path, 'r') as f: + with open(metadata_path, "r") as f: metadata = json.load(f) print(f"โœ… Model metadata: {metadata.get('version', 'Unknown')}") print(f" Performance: {metadata.get('performance', {}).get('test_accuracy', 'Unknown')}") else: print("โš ๏ธ No model metadata found") - + return True + def create_deployment_package(): """Create a deployment package for Vertex AI.""" print("\n๐Ÿ“ฆ CREATING DEPLOYMENT PACKAGE") print("=" * 50) - + # Create deployment directory deployment_dir = "gcp_deployment" if os.path.exists(deployment_dir): import shutil + shutil.rmtree(deployment_dir) os.makedirs(deployment_dir) - + # Copy model files model_source = "deployment/models/default" model_dest = os.path.join(deployment_dir, "model") - + import shutil + shutil.copytree(model_source, model_dest) print(f"โœ… Model copied to: {model_dest}") - + # Create prediction script prediction_script = '''#!/usr/bin/env python3 """ @@ -152,31 +165,31 @@ def __init__(self): self.model_path = os.path.join(os.getcwd(), "model") self.tokenizer = AutoTokenizer.from_pretrained(self.model_path) self.model = AutoModelForSequenceClassification.from_pretrained(self.model_path) - + # Move to GPU if available if torch.cuda.is_available(): self.model = self.model.to('cuda') - + self.emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] - + def predict(self, text): """Make a prediction.""" # Tokenize input inputs = self.tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=512) - + if torch.cuda.is_available(): inputs = {k: v.to('cuda') for k, v in inputs.items()} - + # Get prediction with torch.no_grad(): outputs = self.model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_label = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_label].item() - + # Get all probabilities all_probs = probabilities[0].cpu().numpy() - + # Get predicted emotion if predicted_label in self.model.config.id2label: predicted_emotion = self.model.config.id2label[predicted_label] @@ -184,7 +197,7 @@ def predict(self, text): predicted_emotion = self.model.config.id2label[str(predicted_label)] else: predicted_emotion = f"unknown_{predicted_label}" - + # Create response response = { 'text': text, @@ -196,7 +209,7 @@ def predict(self, text): 'model_version': '2.0', 'model_type': 'comprehensive_emotion_detection' } - + return response # Initialize model @@ -210,37 +223,37 @@ def predict(request): request_json = json.loads(request) else: request_json = request - + # Get text from request text = request_json.get('text', '') if not text: return json.dumps({'error': 'No text provided'}) - + # Make prediction result = model.predict(text) - + return json.dumps(result) - + except Exception as e: return json.dumps({'error': str(e)}) ''' - - with open(os.path.join(deployment_dir, "predict.py"), 'w') as f: + + with open(os.path.join(deployment_dir, "predict.py"), "w") as f: f.write(prediction_script) print("โœ… Prediction script created") - + # Create requirements.txt - requirements = '''torch>=2.0.0 + requirements = """torch>=2.0.0 transformers>=4.30.0 numpy>=1.21.0 -''' - - with open(os.path.join(deployment_dir, "requirements.txt"), 'w') as f: +""" + + with open(os.path.join(deployment_dir, "requirements.txt"), "w") as f: f.write(requirements) print("โœ… Requirements file created") - + # Create Dockerfile - dockerfile = '''FROM python:3.9-slim + dockerfile = """FROM python:3.9-slim WORKDIR /app @@ -267,221 +280,278 @@ def predict(request): # Run the prediction service CMD ["python", "predict.py"] -''' - - with open(os.path.join(deployment_dir, "Dockerfile"), 'w') as f: +""" + + with open(os.path.join(deployment_dir, "Dockerfile"), "w") as f: f.write(dockerfile) print("โœ… Dockerfile created") - + # Create deployment configuration deployment_config = { - 'model_info': { - 'name': 'comprehensive_emotion_detection', - 'version': '2.0', - 'description': 'Comprehensive emotion detection model with focal loss, class weighting, and advanced data augmentation', - 'performance': { - 'basic_accuracy': '100.00%', - 'real_world_accuracy': '93.75%', - 'average_confidence': '83.9%' - } + "model_info": { + "name": "comprehensive_emotion_detection", + "version": "2.0", + "description": "Comprehensive emotion detection model with focal loss, class weighting, and advanced data augmentation", + "performance": { + "basic_accuracy": "100.00%", + "real_world_accuracy": "93.75%", + "average_confidence": "83.9%", + }, + }, + "deployment_info": { + "created_at": datetime.now().isoformat(), + "model_path": model_source, + "deployment_package": deployment_dir, }, - 'deployment_info': { - 'created_at': datetime.now().isoformat(), - 'model_path': model_source, - 'deployment_package': deployment_dir - } } - - with open(os.path.join(deployment_dir, "deployment_config.json"), 'w') as f: + + with open(os.path.join(deployment_dir, "deployment_config.json"), "w") as f: json.dump(deployment_config, f, indent=2) print("โœ… Deployment configuration created") - + print(f"โœ… Deployment package created at: {deployment_dir}") return deployment_dir + def deploy_to_vertex_ai(deployment_dir): """Deploy the model to Vertex AI.""" print("\n๐Ÿš€ DEPLOYING TO VERTEX AI") print("=" * 50) - + # Get project ID - result = subprocess.run(['gcloud', 'config', 'get-value', 'project'], capture_output=True, text=True) + result = subprocess.run( + ["gcloud", "config", "get-value", "project"], capture_output=True, text=True + ) project_id = result.stdout.strip() - + # Set region region = "us-central1" # You can change this - + # Create model name model_name = "comprehensive-emotion-detection" endpoint_name = "emotion-detection-endpoint" - + print(f"๐Ÿ“‹ Deployment Configuration:") print(f" Project ID: {project_id}") print(f" Region: {region}") print(f" Model Name: {model_name}") print(f" Endpoint Name: {endpoint_name}") print() - + # Build and push Docker image print("๐Ÿณ Building and pushing Docker image...") - + # Create repository name repository_name = "emotion-detection" - + # Configure Docker for gcloud - subprocess.run(['gcloud', 'auth', 'configure-docker'], check=True) - + subprocess.run(["gcloud", "auth", "configure-docker"], check=True) + # Build and push image image_uri = f"gcr.io/{project_id}/{repository_name}:latest" - + try: # Build image - subprocess.run([ - 'docker', 'build', '-t', image_uri, deployment_dir - ], check=True) + subprocess.run(["docker", "build", "-t", image_uri, deployment_dir], check=True) print("โœ… Docker image built") - + # Push image - subprocess.run(['docker', 'push', image_uri], check=True) + subprocess.run(["docker", "push", image_uri], check=True) print("โœ… Docker image pushed to Container Registry") - + except subprocess.CalledProcessError as e: print(f"โŒ Error building/pushing Docker image: {e}") return False - + # Create Vertex AI model print("\n๐Ÿค– Creating Vertex AI model...") - + try: # Create model - subprocess.run([ - 'gcloud', 'ai', 'models', 'upload', - '--region', region, - '--display-name', model_name, - '--container-image-uri', image_uri, - '--container-predict-route', '/predict', - '--container-health-route', '/health' - ], check=True) + subprocess.run( + [ + "gcloud", + "ai", + "models", + "upload", + "--region", + region, + "--display-name", + model_name, + "--container-image-uri", + image_uri, + "--container-predict-route", + "/predict", + "--container-health-route", + "/health", + ], + check=True, + ) print("โœ… Vertex AI model created") - + except subprocess.CalledProcessError as e: print(f"โŒ Error creating Vertex AI model: {e}") return False - + # Create endpoint print("\n๐ŸŒ Creating endpoint...") - + try: - subprocess.run([ - 'gcloud', 'ai', 'endpoints', 'create', - '--region', region, - '--display-name', endpoint_name - ], check=True) + subprocess.run( + [ + "gcloud", + "ai", + "endpoints", + "create", + "--region", + region, + "--display-name", + endpoint_name, + ], + check=True, + ) print("โœ… Endpoint created") - + except subprocess.CalledProcessError as e: print(f"โŒ Error creating endpoint: {e}") return False - + # Deploy model to endpoint print("\n๐Ÿš€ Deploying model to endpoint...") - + try: # Get model ID - result = subprocess.run([ - 'gcloud', 'ai', 'models', 'list', - '--region', region, - '--filter', f'displayName={model_name}', - '--format', 'value(name)' - ], capture_output=True, text=True, check=True) - + result = subprocess.run( + [ + "gcloud", + "ai", + "models", + "list", + "--region", + region, + "--filter", + f"displayName={model_name}", + "--format", + "value(name)", + ], + capture_output=True, + text=True, + check=True, + ) + model_id = result.stdout.strip() - + # Get endpoint ID - result = subprocess.run([ - 'gcloud', 'ai', 'endpoints', 'list', - '--region', region, - '--filter', f'displayName={endpoint_name}', - '--format', 'value(name)' - ], capture_output=True, text=True, check=True) - + result = subprocess.run( + [ + "gcloud", + "ai", + "endpoints", + "list", + "--region", + region, + "--filter", + f"displayName={endpoint_name}", + "--format", + "value(name)", + ], + capture_output=True, + text=True, + check=True, + ) + endpoint_id = result.stdout.strip() - + # Deploy model - subprocess.run([ - 'gcloud', 'ai', 'endpoints', 'deploy-model', endpoint_id, - '--region', region, - '--model', model_id, - '--display-name', f'{model_name}-deployment', - '--machine-type', 'n1-standard-2', - '--min-replica-count', '1', - '--max-replica-count', '10' - ], check=True) + subprocess.run( + [ + "gcloud", + "ai", + "endpoints", + "deploy-model", + endpoint_id, + "--region", + region, + "--model", + model_id, + "--display-name", + f"{model_name}-deployment", + "--machine-type", + "n1-standard-2", + "--min-replica-count", + "1", + "--max-replica-count", + "10", + ], + check=True, + ) print("โœ… Model deployed to endpoint") - + except subprocess.CalledProcessError as e: print(f"โŒ Error deploying model: {e}") return False - + print(f"\n๐ŸŽ‰ DEPLOYMENT COMPLETE!") print(f"๐Ÿ“‹ Endpoint ID: {endpoint_id}") print(f"๐ŸŒ Region: {region}") print(f"๐Ÿค– Model: {model_name}") - + # Create deployment summary deployment_summary = { - 'status': 'success', - 'timestamp': datetime.now().isoformat(), - 'project_id': project_id, - 'region': region, - 'model_name': model_name, - 'endpoint_id': endpoint_id, - 'image_uri': image_uri, - 'deployment_dir': deployment_dir + "status": "success", + "timestamp": datetime.now().isoformat(), + "project_id": project_id, + "region": region, + "model_name": model_name, + "endpoint_id": endpoint_id, + "image_uri": image_uri, + "deployment_dir": deployment_dir, } - - with open(os.path.join(deployment_dir, "deployment_summary.json"), 'w') as f: + + with open(os.path.join(deployment_dir, "deployment_summary.json"), "w") as f: json.dump(deployment_summary, f, indent=2) - + print(f"\n๐Ÿ“ Deployment summary saved to: {deployment_dir}/deployment_summary.json") - + return True + def main(): """Main deployment function.""" print("๐Ÿš€ GCP/VERTEX AI DEPLOYMENT") print("=" * 60) print(f"โฐ Started at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print() - + # Check prerequisites if not check_prerequisites(): print("\nโŒ Prerequisites not met. Please fix the issues above.") return False - + # Prepare model if not prepare_model_for_deployment(): print("\nโŒ Model preparation failed.") return False - + # Create deployment package deployment_dir = create_deployment_package() if not deployment_dir: print("\nโŒ Failed to create deployment package.") return False - + # Deploy to Vertex AI if not deploy_to_vertex_ai(deployment_dir): print("\nโŒ Deployment to Vertex AI failed.") return False - + print("\n๐ŸŽ‰ DEPLOYMENT SUCCESSFUL!") print("=" * 60) print("Your comprehensive emotion detection model is now deployed on GCP/Vertex AI!") print("You can now make predictions using the Vertex AI endpoint.") - + return True + if __name__ == "__main__": success = main() - sys.exit(0 if success else 1) \ No newline at end of file + sys.exit(0 if success else 1) diff --git a/scripts/deployment/fix_model_loading_issues.py b/scripts/deployment/fix_model_loading_issues.py index 17ccf9ec3..0016244bb 100644 --- a/scripts/deployment/fix_model_loading_issues.py +++ b/scripts/deployment/fix_model_loading_issues.py @@ -8,6 +8,7 @@ import sys from pathlib import Path + def check_current_status(): """Check current deployment status""" print("๐Ÿ” Checking Current Deployment Status") @@ -38,6 +39,7 @@ def check_current_status(): return True + def fix_race_condition(): """Fix race condition in model loading""" print("\n๐Ÿ”ง Fixing Race Condition Issues") @@ -49,6 +51,7 @@ def fix_race_condition(): print(" - model_loaded flag set inside lock") print(" - All state changes protected by locks") + def improve_error_handling(): """Improve error handling and logging""" print("\n๐Ÿ”ง Improving Error Handling") @@ -60,6 +63,7 @@ def improve_error_handling(): print(" - Better exception handling with context") print(" - Enhanced logging for debugging") + def optimize_model_loading(): """Optimize model loading for Cloud Run""" print("\n๐Ÿ”ง Optimizing Model Loading") @@ -71,6 +75,7 @@ def optimize_model_loading(): print(" - low_cpu_mem_usage=True for memory efficiency") print(" - Better logging during loading process") + def check_cloud_run_config(): """Check Cloud Run configuration for better model loading""" print("\n๐Ÿ”ง Updating Cloud Run Configuration") @@ -94,7 +99,7 @@ def check_cloud_run_config(): if not model_file_path.exists(): print("โŒ Error: model.safetensors file not found") return False - model_size_mb = (model_file_path.stat().st_size / (1024 * 1024)) + model_size_mb = model_file_path.stat().st_size / (1024 * 1024) print(f" - Model size: {model_size_mb:.1f}MB") if model_size_mb > 300: @@ -103,6 +108,7 @@ def check_cloud_run_config(): return True + def create_health_check_script(): """Create a health check script for model loading""" print("\n๐Ÿ”ง Creating Health Check Script") @@ -180,7 +186,7 @@ def check_model_health(base_url): script_path = Path("scripts/testing/check_model_health.py") script_path.parent.mkdir(parents=True, exist_ok=True) - with open(script_path, 'w') as f: + with open(script_path, "w") as f: f.write(health_check_script) # Make executable @@ -189,6 +195,7 @@ def check_model_health(base_url): print(f"โœ… Created health check script: {script_path}") return True + def create_deployment_guide(): """Create a deployment guide with troubleshooting steps""" print("\n๐Ÿ”ง Creating Deployment Guide") @@ -265,12 +272,13 @@ def create_deployment_guide(): guide_path = Path("docs/cloud-run-model-loading-fix-guide.md") guide_path.parent.mkdir(parents=True, exist_ok=True) - with open(guide_path, 'w') as f: + with open(guide_path, "w") as f: f.write(guide_content) print(f"โœ… Created deployment guide: {guide_path}") return True + def main(): """Main function to run all fixes""" print("๐Ÿš€ Cloud Run Model Loading Fix Script") @@ -303,5 +311,6 @@ def main(): print("3. Test with: python scripts/testing/check_model_health.py") print("4. Monitor logs for any remaining issues") + if __name__ == "__main__": - main() + main() diff --git a/scripts/deployment/hf_upload/__init__.py b/scripts/deployment/hf_upload/__init__.py index 6b062e2f3..319dc0ae0 100644 --- a/scripts/deployment/hf_upload/__init__.py +++ b/scripts/deployment/hf_upload/__init__.py @@ -7,4 +7,4 @@ - config_update: update deployment configs and env templates """ -from . import discovery, prepare, upload, config_update # noqa: F401 +from . import config_update, discovery, prepare, upload # noqa: F401 diff --git a/scripts/deployment/hf_upload/cli.py b/scripts/deployment/hf_upload/cli.py index 73d271a0a..1435999d1 100644 --- a/scripts/deployment/hf_upload/cli.py +++ b/scripts/deployment/hf_upload/cli.py @@ -1,13 +1,19 @@ +import argparse +import logging import os import shutil -import logging -import argparse from typing import Optional from . import discovery -from .prepare import prepare_model_for_upload -from .upload import setup_huggingface_auth, choose_repository_privacy, setup_git_lfs, resolve_repo_id, upload_to_huggingface from .config_update import update_deployment_config +from .prepare import prepare_model_for_upload +from .upload import ( + choose_repository_privacy, + resolve_repo_id, + setup_git_lfs, + setup_huggingface_auth, + upload_to_huggingface, +) def configure_logging(verbosity: int) -> None: @@ -16,25 +22,33 @@ def configure_logging(verbosity: int) -> None: level = logging.INFO elif verbosity >= 2: level = logging.DEBUG - logging.basicConfig(level=level, format='%(asctime)s %(levelname)s %(message)s') + logging.basicConfig(level=level, format="%(asctime)s %(levelname)s %(message)s") def parse_args(argv: Optional[list] = None) -> argparse.Namespace: p = argparse.ArgumentParser(description="Upload a custom model to HuggingFace Hub") - p.add_argument('--model-path', help='Path to trained model (.pth or HF dir)') - p.add_argument('--base-model', help='Base model to reconstruct HF weights (if checkpoint)') - p.add_argument('--repo-id', help='Target repo id (username/model)') - p.add_argument('--repo-name', help='Target repo name (defaults to samo-dl-emotion-model)') - p.add_argument('--private', dest='private', action='store_true', help='Force private repository') - p.add_argument('--public', dest='private', action='store_false', help='Force public repository') + p.add_argument("--model-path", help="Path to trained model (.pth or HF dir)") + p.add_argument("--base-model", help="Base model to reconstruct HF weights (if checkpoint)") + p.add_argument("--repo-id", help="Target repo id (username/model)") + p.add_argument("--repo-name", help="Target repo name (defaults to samo-dl-emotion-model)") + p.add_argument( + "--private", dest="private", action="store_true", help="Force private repository" + ) + p.add_argument("--public", dest="private", action="store_false", help="Force public repository") p.set_defaults(private=None) - p.add_argument('--allow-missing-files', action='store_true', help='Allow upload when critical files are missing') - p.add_argument('--temp-dir', default='./temp_model_upload', help='Temporary working directory') - p.add_argument('--no-lfs', action='store_true', help='Skip Git LFS setup') - p.add_argument('--retries', type=int, default=5, help='Max upload retries') - p.add_argument('--backoff', type=int, default=2, help='Exponential backoff factor') - p.add_argument('--initial-delay', type=int, default=2, help='Initial backoff delay (seconds)') - p.add_argument('-v', '--verbose', action='count', default=1, help='Increase verbosity (-v, -vv)') + p.add_argument( + "--allow-missing-files", + action="store_true", + help="Allow upload when critical files are missing", + ) + p.add_argument("--temp-dir", default="./temp_model_upload", help="Temporary working directory") + p.add_argument("--no-lfs", action="store_true", help="Skip Git LFS setup") + p.add_argument("--retries", type=int, default=5, help="Max upload retries") + p.add_argument("--backoff", type=int, default=2, help="Exponential backoff factor") + p.add_argument("--initial-delay", type=int, default=2, help="Initial backoff delay (seconds)") + p.add_argument( + "-v", "--verbose", action="count", default=1, help="Increase verbosity (-v, -vv)" + ) return p.parse_args(argv) @@ -45,7 +59,9 @@ def main(argv: Optional[list] = None) -> int: # Step 1: Find or use provided model path model_path = args.model_path or discovery.find_best_trained_model() if not model_path: - logging.error("No model found. Provide --model-path or place model in the expected directory.") + logging.error( + "No model found. Provide --model-path or place model in the expected directory." + ) return 1 # Step 2: HuggingFace auth @@ -53,7 +69,7 @@ def main(argv: Optional[list] = None) -> int: return 1 # Step 3: Prepare model - templates_dir = os.path.join(os.path.dirname(__file__), 'templates') + templates_dir = os.path.join(os.path.dirname(__file__), "templates") temp_dir = args.temp_dir repo_id_resolved = resolve_repo_id(args.repo_id, args.repo_name) try: @@ -61,7 +77,8 @@ def main(argv: Optional[list] = None) -> int: model_path=model_path, temp_dir=temp_dir, templates_dir=templates_dir, - allow_missing=args.allow_missing_files or os.getenv('ALLOW_UPLOAD_WITH_MISSING_FILES', '').lower() in ('1', 'true', 'yes'), + allow_missing=args.allow_missing_files + or os.getenv("ALLOW_UPLOAD_WITH_MISSING_FILES", "").lower() in ("1", "true", "yes"), base_model_override=args.base_model, repo_id=repo_id_resolved, ) diff --git a/scripts/deployment/hf_upload/config_update.py b/scripts/deployment/hf_upload/config_update.py index 3458484bf..a32fe2742 100644 --- a/scripts/deployment/hf_upload/config_update.py +++ b/scripts/deployment/hf_upload/config_update.py @@ -1,18 +1,18 @@ -import os import json import logging -from typing import Any, Dict +import os from string import Template +from typing import Any, Dict def _read(path: str) -> str: - with open(path, 'r') as f: + with open(path, "r") as f: return f.read() def _write(path: str, content: str) -> None: os.makedirs(os.path.dirname(path), exist_ok=True) - with open(path, 'w') as f: + with open(path, "w") as f: f.write(content) @@ -23,10 +23,10 @@ def update_deployment_config(repo_id: str, model_info: Dict[str, Any], templates cfg = { "model_name": repo_id, "model_type": "custom_trained", - "emotion_labels": model_info['emotion_labels'], - "num_labels": model_info['num_labels'], - "id2label": model_info['id2label'], - "label2id": model_info['label2id'], + "emotion_labels": model_info["emotion_labels"], + "num_labels": model_info["num_labels"], + "id2label": model_info["id2label"], + "label2id": model_info["label2id"], "deployment_ready": True, "deployment_options": { "serverless_api": { diff --git a/scripts/deployment/hf_upload/discovery.py b/scripts/deployment/hf_upload/discovery.py index 59983e361..5ee3ef56c 100644 --- a/scripts/deployment/hf_upload/discovery.py +++ b/scripts/deployment/hf_upload/discovery.py @@ -1,14 +1,14 @@ +import logging import os import sys -import logging -from typing import Optional, List, Tuple +from typing import List, Optional, Tuple def get_base_model_name(override: Optional[str] = None) -> str: if override: logging.info("Using base model from CLI: %s", override) return override - base_model = os.getenv('BASE_MODEL_NAME') + base_model = os.getenv("BASE_MODEL_NAME") if base_model: logging.info("Using BASE_MODEL_NAME from environment: %s", base_model) return base_model @@ -18,7 +18,7 @@ def get_base_model_name(override: Optional[str] = None) -> str: def get_model_base_directory() -> str: - env_base_dir = os.getenv('SAMO_DL_BASE_DIR') or os.getenv('MODEL_BASE_DIR') + env_base_dir = os.getenv("SAMO_DL_BASE_DIR") or os.getenv("MODEL_BASE_DIR") if env_base_dir: base_dir = os.path.expanduser(env_base_dir) if os.path.exists(base_dir): @@ -30,7 +30,7 @@ def get_model_base_directory() -> str: max_levels = 5 for _ in range(max_levels): - indicators = ['deployment', 'src'] + indicators = ["deployment", "src"] if all(os.path.exists(os.path.join(search_dir, indicator)) for indicator in indicators): return os.path.join(search_dir, "deployment", "models") parent_dir = os.path.dirname(search_dir) @@ -43,10 +43,10 @@ def get_model_base_directory() -> str: def is_interactive_environment() -> bool: non_interactive_indicators = [ - os.getenv('CI'), - os.getenv('DOCKER_CONTAINER'), - os.getenv('KUBERNETES_SERVICE_HOST'), - os.getenv('JENKINS_URL'), + os.getenv("CI"), + os.getenv("DOCKER_CONTAINER"), + os.getenv("KUBERNETES_SERVICE_HOST"), + os.getenv("JENKINS_URL"), not sys.stdin.isatty(), ] return not any(non_interactive_indicators) @@ -67,7 +67,7 @@ def _calculate_directory_size(directory: str) -> int: def find_best_trained_model() -> Optional[str]: logging.info("Searching for trained models") primary_model_dir = get_model_base_directory() - env_override = os.getenv('SAMO_DL_BASE_DIR') or os.getenv('MODEL_BASE_DIR') + env_override = os.getenv("SAMO_DL_BASE_DIR") or os.getenv("MODEL_BASE_DIR") if env_override: logging.info("Using environment override: %s", env_override) logging.info("Primary search location: %s", primary_model_dir) @@ -137,10 +137,16 @@ def find_best_trained_model() -> Optional[str]: has_config = os.path.exists(config_file) has_tokenizer = any(os.path.exists(p) for p in tokenizer_candidates) weight_files = [ - os.path.join(path, f) for f in [ - "pytorch_model.bin", "model.safetensors", "pytorch_model.safetensors", - "model.bin", "tf_model.h5", "flax_model.msgpack" - ] if os.path.exists(os.path.join(path, f)) + os.path.join(path, f) + for f in [ + "pytorch_model.bin", + "model.safetensors", + "pytorch_model.safetensors", + "model.bin", + "tf_model.h5", + "flax_model.msgpack", + ] + if os.path.exists(os.path.join(path, f)) ] has_weights = len(weight_files) > 0 if has_config and has_tokenizer and has_weights: @@ -158,7 +164,12 @@ def find_best_trained_model() -> Optional[str]: missing_components.append("tokenizer") if not has_weights: missing_components.append("model weights") - logging.warning("Incomplete HF model: %s (%s bytes) missing: %s", path, f"{size:,}", ', '.join(missing_components)) + logging.warning( + "Incomplete HF model: %s (%s bytes) missing: %s", + path, + f"{size:,}", + ", ".join(missing_components), + ) else: size = os.path.getsize(path) found_models.append((path, size, "model_file")) diff --git a/scripts/deployment/hf_upload/prepare.py b/scripts/deployment/hf_upload/prepare.py index 0140559fa..c8e5a1c84 100644 --- a/scripts/deployment/hf_upload/prepare.py +++ b/scripts/deployment/hf_upload/prepare.py @@ -1,18 +1,18 @@ -import os import json import logging +import os import shutil -from typing import Any, Dict, List, Optional from string import Template +from typing import Any, Dict, List, Optional import torch -from transformers import AutoTokenizer, AutoModelForSequenceClassification +from transformers import AutoModelForSequenceClassification, AutoTokenizer from .discovery import get_base_model_name def _render_template(path: str, context: Dict[str, Any]) -> str: - with open(path, 'r') as f: + with open(path, "r") as f: raw = f.read() # Simple $var substitution return Template(raw).safe_substitute(**context) @@ -24,37 +24,43 @@ def load_emotion_labels_from_model(model_path: str) -> List[str]: config_path = os.path.join(model_path, "config.json") if os.path.exists(config_path): try: - with open(config_path, 'r') as f: + with open(config_path, "r") as f: config = json.load(f) - if 'id2label' in config: - id2label = config['id2label'] + if "id2label" in config: + id2label = config["id2label"] sorted_labels = [id2label[str(i)] for i in range(len(id2label))] logging.info("Loaded %d labels from HF config.json", len(sorted_labels)) return sorted_labels except Exception as e: logging.warning("Could not load labels from config.json: %s", e) # Method 2: checkpoint - elif model_path.endswith('.pth') and os.path.exists(model_path): + elif model_path.endswith(".pth") and os.path.exists(model_path): try: try: - checkpoint = torch.load(model_path, map_location='cpu', weights_only=False) + checkpoint = torch.load(model_path, map_location="cpu", weights_only=False) except TypeError: - checkpoint = torch.load(model_path, map_location='cpu') + checkpoint = torch.load(model_path, map_location="cpu") logging.info("Using legacy torch.load; consider upgrading PyTorch") - for key in ['id2label', 'label2id', 'labels', 'emotion_labels', 'class_names']: + for key in ["id2label", "label2id", "labels", "emotion_labels", "class_names"]: if key in checkpoint: labels_data = checkpoint[key] - if key == 'id2label' and isinstance(labels_data, dict): + if key == "id2label" and isinstance(labels_data, dict): sorted_labels = [labels_data[str(i)] for i in range(len(labels_data))] - logging.info("Loaded %d labels from checkpoint['%s']", len(sorted_labels), key) + logging.info( + "Loaded %d labels from checkpoint['%s']", len(sorted_labels), key + ) return sorted_labels - if key == 'label2id' and isinstance(labels_data, dict): + if key == "label2id" and isinstance(labels_data, dict): id2label = {v: k for k, v in labels_data.items()} sorted_labels = [id2label[i] for i in range(len(id2label))] - logging.info("Loaded %d labels from checkpoint['%s']", len(sorted_labels), key) + logging.info( + "Loaded %d labels from checkpoint['%s']", len(sorted_labels), key + ) return sorted_labels if isinstance(labels_data, (list, tuple)): - logging.info("Loaded %d labels from checkpoint['%s']", len(labels_data), key) + logging.info( + "Loaded %d labels from checkpoint['%s']", len(labels_data), key + ) return list(labels_data) except Exception as e: logging.warning("Could not load labels from checkpoint: %s", e) @@ -64,19 +70,19 @@ def load_emotion_labels_from_model(model_path: str) -> List[str]: labels_path = os.path.join(model_dir, name) if os.path.exists(labels_path): try: - with open(labels_path, 'r') as f: + with open(labels_path, "r") as f: data = json.load(f) if isinstance(data, list): logging.info("Loaded %d labels from %s", len(data), labels_path) return data - if isinstance(data, dict) and 'labels' in data: - labels = data['labels'] + if isinstance(data, dict) and "labels" in data: + labels = data["labels"] logging.info("Loaded %d labels from %s", len(labels), labels_path) return labels except Exception as e: logging.warning("Could not load labels from %s: %s", labels_path, e) # Method 4: env - env_labels = os.getenv('EMOTION_LABELS') + env_labels = os.getenv("EMOTION_LABELS") if env_labels: try: labels = json.loads(env_labels) @@ -84,13 +90,25 @@ def load_emotion_labels_from_model(model_path: str) -> List[str]: logging.info("Loaded %d labels from EMOTION_LABELS", len(labels)) return labels except json.JSONDecodeError: - labels = [s.strip() for s in env_labels.split(',') if s.strip()] + labels = [s.strip() for s in env_labels.split(",") if s.strip()] if labels: logging.info("Loaded %d labels from EMOTION_LABELS (csv)", len(labels)) return labels # Default - default_labels = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', - 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] + default_labels = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", + ] logging.warning("Using default emotion labels (%d)", len(default_labels)) return default_labels @@ -121,9 +139,9 @@ def prepare_model_for_upload( logging.info("Converting .pth checkpoint to HuggingFace format...") try: try: - checkpoint = torch.load(model_path, map_location='cpu', weights_only=False) + checkpoint = torch.load(model_path, map_location="cpu", weights_only=False) except TypeError: - checkpoint = torch.load(model_path, map_location='cpu') + checkpoint = torch.load(model_path, map_location="cpu") logging.info("Using legacy torch.load; consider upgrading PyTorch") except Exception as e: raise ValueError(f"Cannot load checkpoint from {model_path}: {e}") @@ -138,8 +156,8 @@ def prepare_model_for_upload( label2id=label2id, ) try: - if 'model_state_dict' in checkpoint: - model.load_state_dict(checkpoint['model_state_dict']) + if "model_state_dict" in checkpoint: + model.load_state_dict(checkpoint["model_state_dict"]) else: model.load_state_dict(checkpoint) except Exception as e: @@ -148,52 +166,52 @@ def prepare_model_for_upload( tokenizer.save_pretrained(temp_dir) # Render model card from template - model_card_path = os.path.join(templates_dir, 'model_card.md.tmpl') + model_card_path = os.path.join(templates_dir, "model_card.md.tmpl") model_card = _render_template( model_card_path, { - 'labels_json': json.dumps(emotion_labels, indent=2), - 'labels_joined': ', '.join(emotion_labels), - 'num_labels': str(len(emotion_labels)), - 'repo_id': repo_id or 'your-username/samo-dl-emotion-model', + "labels_json": json.dumps(emotion_labels, indent=2), + "labels_joined": ", ".join(emotion_labels), + "num_labels": str(len(emotion_labels)), + "repo_id": repo_id or "your-username/samo-dl-emotion-model", }, ) - with open(os.path.join(temp_dir, 'README.md'), 'w') as f: + with open(os.path.join(temp_dir, "README.md"), "w") as f: f.write(model_card) # requirements.txt from template - req_path = os.path.join(templates_dir, 'requirements_model.txt.tmpl') - with open(req_path, 'r') as f: + req_path = os.path.join(templates_dir, "requirements_model.txt.tmpl") + with open(req_path, "r") as f: requirements = f.read() - with open(os.path.join(temp_dir, 'requirements.txt'), 'w') as f: + with open(os.path.join(temp_dir, "requirements.txt"), "w") as f: f.write(requirements) # Validate critical files - critical_files = ['config.json', 'tokenizer.json', 'tokenizer_config.json'] + critical_files = ["config.json", "tokenizer.json", "tokenizer_config.json"] missing_files: List[str] = [] for name in critical_files: if not os.path.exists(os.path.join(temp_dir, name)): missing_files.append(name) if missing_files and not allow_missing: raise RuntimeError( - "Missing required files for HuggingFace model upload: " + ', '.join(missing_files) + "Missing required files for HuggingFace model upload: " + ", ".join(missing_files) ) # Validate label mappings present in config if exists - config_json = os.path.join(temp_dir, 'config.json') + config_json = os.path.join(temp_dir, "config.json") if os.path.exists(config_json): try: - with open(config_json, 'r') as f: + with open(config_json, "r") as f: cfg = json.load(f) - if 'id2label' not in cfg or 'label2id' not in cfg: + if "id2label" not in cfg or "label2id" not in cfg: logging.warning("config.json missing id2label/label2id mappings") except Exception as e: logging.warning("Could not read config.json: %s", e) return { - 'emotion_labels': emotion_labels, - 'id2label': id2label, - 'label2id': label2id, - 'num_labels': len(emotion_labels), - 'validation_warnings': missing_files, + "emotion_labels": emotion_labels, + "id2label": id2label, + "label2id": label2id, + "num_labels": len(emotion_labels), + "validation_warnings": missing_files, } diff --git a/scripts/deployment/hf_upload/upload.py b/scripts/deployment/hf_upload/upload.py index 857c7d582..b62d6d124 100644 --- a/scripts/deployment/hf_upload/upload.py +++ b/scripts/deployment/hf_upload/upload.py @@ -1,18 +1,18 @@ +import logging import os import shutil -import time -import logging import subprocess +import time from typing import Optional -from huggingface_hub import HfApi, login, create_repo +from huggingface_hub import HfApi, create_repo, login from .discovery import is_interactive_environment def setup_huggingface_auth() -> bool: logging.info("Authenticating with HuggingFace") - hf_token = os.getenv('HUGGINGFACE_TOKEN') or os.getenv('HF_TOKEN') + hf_token = os.getenv("HUGGINGFACE_TOKEN") or os.getenv("HF_TOKEN") if not hf_token: if is_interactive_environment(): logging.error("HuggingFace token not found in env (HUGGINGFACE_TOKEN/HF_TOKEN)") @@ -30,7 +30,7 @@ def setup_huggingface_auth() -> bool: def choose_repository_privacy(cli_private: Optional[bool] = None) -> bool: if cli_private is not None: - logging.info("Repository privacy from CLI: %s", 'private' if cli_private else 'public') + logging.info("Repository privacy from CLI: %s", "private" if cli_private else "public") return cli_private hf_repo_private = os.environ.get("HF_REPO_PRIVATE") if hf_repo_private: @@ -50,27 +50,31 @@ def choose_repository_privacy(cli_private: Optional[bool] = None) -> bool: def setup_git_lfs() -> bool: logging.info("Setting up Git LFS for large files") - if shutil.which('git') is None: + if shutil.which("git") is None: logging.warning("Git not installed. Skipping Git LFS setup") return False try: - result = subprocess.run(['git', 'lfs', 'version'], capture_output=True, text=True, check=True) + result = subprocess.run( + ["git", "lfs", "version"], capture_output=True, text=True, check=True + ) if result.returncode != 0: logging.warning("Git LFS not available. Install with: git lfs install") return False lfs_patterns = ["*.bin", "*.safetensors", "*.onnx", "*.pkl", "*.pth", "*.pt", "*.h5"] for pattern in lfs_patterns: - subprocess.run(['git', 'lfs', 'track', pattern], capture_output=True, text=True, check=True) + subprocess.run( + ["git", "lfs", "track", pattern], capture_output=True, text=True, check=True + ) # Update .gitattributes if exists gitattributes_path = ".gitattributes" if os.path.exists(gitattributes_path): - with open(gitattributes_path, 'r') as f: + with open(gitattributes_path, "r") as f: content = f.read() for pattern in lfs_patterns: lfs_line = f"{pattern} filter=lfs diff=lfs merge=lfs -text" if lfs_line not in content: content += f"\n{lfs_line}" - with open(gitattributes_path, 'w') as f: + with open(gitattributes_path, "w") as f: f.write(content) return True except Exception as e: @@ -83,8 +87,8 @@ def resolve_repo_id(repo_id: Optional[str], repo_name: Optional[str]) -> str: return repo_id api = HfApi() user_info = api.whoami() - username = user_info['name'] - name = repo_name or 'samo-dl-emotion-model' + username = user_info["name"] + name = repo_name or "samo-dl-emotion-model" return f"{username}/{name}" @@ -103,7 +107,9 @@ def upload_to_huggingface( try: create_repo(repo_id, exist_ok=True, private=is_private, repo_type="model") if attempt == 1: - logging.info("Repository created/confirmed (%s)", "private" if is_private else "public") + logging.info( + "Repository created/confirmed (%s)", "private" if is_private else "public" + ) api.upload_folder( folder_path=temp_dir, repo_id=repo_id, diff --git a/scripts/deployment/integrate_security_fixes.py b/scripts/deployment/integrate_security_fixes.py index e579d9b9b..70c142b34 100644 --- a/scripts/deployment/integrate_security_fixes.py +++ b/scripts/deployment/integrate_security_fixes.py @@ -12,12 +12,14 @@ """ import os -import subprocess import shlex +import subprocess import time -import requests from pathlib import Path -from typing import Dict, List, Optional +from typing import List + +import requests + class IntegratedSecurityOptimization: def __init__(self): @@ -31,11 +33,15 @@ def __init__(self): def get_project_id(): """Get current GCP project ID dynamically""" try: - result = subprocess.run(['gcloud', 'config', 'get-value', 'project'], - capture_output=True, text=True, check=True) + result = subprocess.run( + ["gcloud", "config", "get-value", "project"], + capture_output=True, + text=True, + check=True, + ) return result.stdout.strip() except subprocess.CalledProcessError: - return os.environ.get('GOOGLE_CLOUD_PROJECT', 'the-tendril-466607-n8') + return os.environ.get("GOOGLE_CLOUD_PROJECT", "the-tendril-466607-n8") @staticmethod def log(message: str, level: str = "INFO"): @@ -100,7 +106,7 @@ def update_requirements_with_security(self): """ requirements_file = self.deployment_dir / "requirements_secure.txt" - with open(requirements_file, 'w') as f: + with open(requirements_file, "w") as f: f.write(secure_requirements) self.log("โœ… Requirements updated with security fixes") @@ -117,7 +123,7 @@ def enhance_cloudbuild_with_security(self): timeout: '1800s' env: - 'PROJECT_ID={self.project_id}' - + - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk' args: - 'gcloud' @@ -150,7 +156,7 @@ def enhance_cloudbuild_with_security(self): """ cloudbuild_file = self.deployment_dir / "cloudbuild.yaml" - with open(cloudbuild_file, 'w') as f: + with open(cloudbuild_file, "w") as f: f.write(enhanced_cloudbuild) self.log("โœ… Cloud Build configuration enhanced with security") @@ -161,10 +167,14 @@ def deploy_integrated_service(self): # Build and deploy using Cloud Build build_command = [ - 'gcloud', 'builds', 'submit', - '--config', str(self.deployment_dir / 'cloudbuild.yaml'), - '--substitutions', f'_ADMIN_API_KEY=samo-admin-key-2024-secure-{int(time.time())}', - str(self.deployment_dir) + "gcloud", + "builds", + "submit", + "--config", + str(self.deployment_dir / "cloudbuild.yaml"), + "--substitutions", + f"_ADMIN_API_KEY=samo-admin-key-2024-secure-{int(time.time())}", + str(self.deployment_dir), ] self.run_command(build_command) @@ -175,10 +185,19 @@ def test_integrated_deployment(self): self.log("Testing integrated deployment...") # Get service URL - result = self.run_command([ - 'gcloud', 'run', 'services', 'describe', self.service_name, - '--region', self.region, '--format', 'value(status.url)' - ]) + result = self.run_command( + [ + "gcloud", + "run", + "services", + "describe", + self.service_name, + "--region", + self.region, + "--format", + "value(status.url)", + ] + ) service_url = result.stdout.strip() if not service_url: @@ -196,10 +215,10 @@ def test_integrated_deployment(self): # Test security headers headers_response = requests.get(f"{service_url}/health", timeout=10) security_headers = [ - 'Content-Security-Policy', - 'X-Content-Type-Options', - 'X-Frame-Options', - 'X-XSS-Protection' + "Content-Security-Policy", + "X-Content-Type-Options", + "X-Frame-Options", + "X-XSS-Protection", ] missing_headers = [] @@ -220,7 +239,7 @@ def test_integrated_deployment(self): f"{service_url}/predict", json={"text": "test"}, headers={"Content-Type": "application/json"}, - timeout=5 + timeout=5, ) responses.append(response.status_code) except requests.exceptions.RequestException: @@ -236,12 +255,12 @@ def test_integrated_deployment(self): f"{service_url}/predict", json={"text": "I am feeling happy today!"}, headers={"Content-Type": "application/json"}, - timeout=10 + timeout=10, ) if prediction_response.status_code == 200: result = prediction_response.json() - if 'emotion' in result and 'confidence' in result: + if "emotion" in result and "confidence" in result: self.log(f"โœ… Prediction working: {result['emotion']} ({result['confidence']:.2f})") else: self.log("โš ๏ธ Prediction response format unexpected") @@ -285,12 +304,15 @@ def run(self): self.log("โœ… Graceful shutdown enabled") self.log("") self.log("๐Ÿ”— Service URL: Check Cloud Run console or run:") - self.log(f" gcloud run services describe {self.service_name} --region={self.region} --format='value(status.url)'") + self.log( + f" gcloud run services describe {self.service_name} --region={self.region} --format='value(status.url)'" + ) except Exception as e: self.log(f"โŒ Integration failed: {str(e)}", "ERROR") raise + if __name__ == "__main__": integrator = IntegratedSecurityOptimization() - integrator.run() + integrator.run() diff --git a/scripts/deployment/patch_config_and_upload.py b/scripts/deployment/patch_config_and_upload.py index 429b3afcb..9c6320e69 100644 --- a/scripts/deployment/patch_config_and_upload.py +++ b/scripts/deployment/patch_config_and_upload.py @@ -2,8 +2,9 @@ # pip install -U transformers huggingface_hub import os import tempfile -from transformers import AutoConfig + from huggingface_hub import HfApi, HfFolder +from transformers import AutoConfig MODEL_ID = os.getenv("MODEL_ID", "0xmnrv/samo") @@ -20,12 +21,34 @@ # Define the new labels we want to use new_labels = [ - "admiration", "amusement", "anger", "annoyance", "approval", - "caring", "confusion", "curiosity", "desire", "disappointment", - "disapproval", "disgust", "embarrassment", "excitement", "fear", - "gratitude", "grief", "joy", "love", "nervousness", - "optimism", "pride", "realization", "relief", "remorse", - "sadness", "surprise", "neutral", + "admiration", + "amusement", + "anger", + "annoyance", + "approval", + "caring", + "confusion", + "curiosity", + "desire", + "disappointment", + "disapproval", + "disgust", + "embarrassment", + "excitement", + "fear", + "gratitude", + "grief", + "joy", + "love", + "nervousness", + "optimism", + "pride", + "realization", + "relief", + "remorse", + "sadness", + "surprise", + "neutral", ] print("Token configured successfully") @@ -36,7 +59,7 @@ print(f"Current model has {getattr(cfg, 'num_labels', 'unknown')} labels") # Check if labels need updating -if hasattr(cfg, 'id2label') and cfg.id2label: +if hasattr(cfg, "id2label") and cfg.id2label: print("Current labels:") items = sorted( cfg.id2label.items(), @@ -52,12 +75,9 @@ # Sanity check: ensure new label count aligns with existing config (and model head) orig_num_labels = getattr(cfg, "num_labels", None) if orig_num_labels not in (None, len(new_labels)): + print(f"โš ๏ธ Existing cfg.num_labels={orig_num_labels}, new_labels={len(new_labels)}.") print( - f"โš ๏ธ Existing cfg.num_labels={orig_num_labels}, new_labels={len(new_labels)}." - ) - print( - f"Ensure the classifier head out_features matches {len(new_labels)} " - "before publishing." + f"Ensure the classifier head out_features matches {len(new_labels)} " "before publishing." ) # Update config with new labels @@ -82,7 +102,7 @@ repo_type="model", commit_message="fix: set id2label/label2id + multi_label_classification", ) - commit_id = getattr(info, 'oid', getattr(info, 'commit_sha', 'unknown')) + commit_id = getattr(info, "oid", getattr(info, "commit_sha", "unknown")) print(f"โœ… Uploaded config.json with proper labels (commit: {commit_id})") except Exception as e: print(f"โŒ Failed to upload config.json: {e}") diff --git a/scripts/deployment/prefetch_models.py b/scripts/deployment/prefetch_models.py new file mode 100644 index 000000000..a9c712ce4 --- /dev/null +++ b/scripts/deployment/prefetch_models.py @@ -0,0 +1,84 @@ +#!/usr/bin/env python3 +"""Prefetch models for Docker builds to improve startup time.""" + +import logging +import sys +from pathlib import Path + +# Configure logging +logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") +logger = logging.getLogger(__name__) + +def prefetch_emotion_model(cache_dir: str = "/app/models"): + """Prefetch emotion detection model.""" + try: + from transformers import AutoTokenizer, AutoModelForSequenceClassification + + model_name = 'duelker/samo-goemotions-deberta-v3-large' + logger.info(f"Downloading emotion model {model_name}...") + + AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir) + AutoModelForSequenceClassification.from_pretrained(model_name, cache_dir=cache_dir) + + logger.info("โœ“ Emotion model downloaded successfully") + return True + except Exception as e: + logger.error(f"Failed to download emotion model: {e}") + return False + +def prefetch_t5_model(cache_dir: str = "/app/models"): + """Prefetch T5 summarization model.""" + try: + from transformers import T5Tokenizer, T5ForConditionalGeneration + + model_name = 't5-small' + logger.info(f"Downloading T5 model {model_name}...") + + T5Tokenizer.from_pretrained(model_name, cache_dir=cache_dir) + T5ForConditionalGeneration.from_pretrained(model_name, cache_dir=cache_dir) + + logger.info("โœ“ T5 model downloaded successfully") + return True + except Exception as e: + logger.error(f"Failed to download T5 model: {e}") + return False + +def prefetch_whisper_model(cache_dir: str = "/app/models"): + """Prefetch Whisper transcription model.""" + try: + import whisper + + model_size = 'base' + logger.info(f"Downloading Whisper model {model_size}...") + + whisper.load_model(model_size, download_root=cache_dir) + + logger.info("โœ“ Whisper model downloaded successfully") + return True + except Exception as e: + logger.error(f"Failed to download Whisper model: {e}") + return False + +def main(): + """Main prefetch function.""" + cache_dir = sys.argv[1] if len(sys.argv) > 1 else "/app/models" + + logger.info("Starting model prefetch...") + + # Create cache directory + Path(cache_dir).mkdir(parents=True, exist_ok=True) + + success = True + success &= prefetch_emotion_model(cache_dir) + success &= prefetch_t5_model(cache_dir) + success &= prefetch_whisper_model(cache_dir) + + if success: + logger.info("All models downloaded successfully!") + sys.exit(0) + else: + logger.error("Some models failed to download") + sys.exit(1) + +if __name__ == "__main__": + main() diff --git a/scripts/deployment/save_trained_model_for_deployment.py b/scripts/deployment/save_trained_model_for_deployment.py index 8ef6a37d4..828678232 100644 --- a/scripts/deployment/save_trained_model_for_deployment.py +++ b/scripts/deployment/save_trained_model_for_deployment.py @@ -6,17 +6,19 @@ This includes model files, tokenizer, and label encoder. """ -import os import json -from transformers import AutoTokenizer, AutoModelForSequenceClassification +import os + from sklearn.preprocessing import LabelEncoder +from transformers import AutoModelForSequenceClassification, AutoTokenizer + def save_model_for_deployment(): """Save the trained model for deployment""" - + print("๐Ÿš€ SAVING TRAINED MODEL FOR DEPLOYMENT") print("=" * 50) - + # Define model paths model_paths = [ "./emotion_model_ensemble_final", # Latest ensemble model @@ -24,7 +26,7 @@ def save_model_for_deployment(): "./emotion_model_fixed_bulletproof_final", # Bulletproof model "./emotion_model", # Generic model path ] - + # Find the best model best_model_path = None for path in model_paths: @@ -32,125 +34,137 @@ def save_model_for_deployment(): print(f"โœ… Found model at: {path}") best_model_path = path break - + if not best_model_path: print("โŒ No trained model found!") print("๐Ÿ“‹ Available paths checked:") for path in model_paths: print(f" - {path}: {'โœ… EXISTS' if os.path.exists(path) else 'โŒ NOT FOUND'}") return False - + print(f"๐ŸŽฏ Using model: {best_model_path}") - + # Create deployment model directory deployment_model_dir = "deployment/model" os.makedirs(deployment_model_dir, exist_ok=True) - + try: # Load the model and tokenizer print("๐Ÿ”ง Loading model and tokenizer...") tokenizer = AutoTokenizer.from_pretrained(best_model_path) model = AutoModelForSequenceClassification.from_pretrained(best_model_path) - + # Save model and tokenizer print("๐Ÿ’พ Saving model and tokenizer...") model.save_pretrained(deployment_model_dir) tokenizer.save_pretrained(deployment_model_dir) - + # Create label encoder (12 emotions) print("๐Ÿท๏ธ Creating label encoder...") emotions = [ - 'anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', - 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired' + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", ] - + label_encoder = LabelEncoder() label_encoder.fit(emotions) - + # Save label encoder label_encoder_data = { - 'classes': label_encoder.classes_.tolist(), - 'n_classes': len(label_encoder.classes_) + "classes": label_encoder.classes_.tolist(), + "n_classes": len(label_encoder.classes_), } - - with open(f"{deployment_model_dir}/label_encoder.json", 'w') as f: + + with open(f"{deployment_model_dir}/label_encoder.json", "w") as f: json.dump(label_encoder_data, f, indent=2) - + # Create model info file model_info = { - 'model_name': best_model_path, - 'emotions': emotions, - 'n_emotions': len(emotions), - 'performance': { - 'f1_score': 0.9948, # 99.48% - 'accuracy': 0.9948, # 99.48% - 'target_achieved': True, - 'improvement': 1813 # 1,813% improvement + "model_name": best_model_path, + "emotions": emotions, + "n_emotions": len(emotions), + "performance": { + "f1_score": 0.9948, # 99.48% + "accuracy": 0.9948, # 99.48% + "target_achieved": True, + "improvement": 1813, # 1,813% improvement }, - 'training_info': { - 'specialized_model': 'finiteautomata/bertweet-base-emotion-analysis', - 'data_augmentation': True, - 'model_ensembling': True, - 'hyperparameter_optimization': True + "training_info": { + "specialized_model": "finiteautomata/bertweet-base-emotion-analysis", + "data_augmentation": True, + "model_ensembling": True, + "hyperparameter_optimization": True, }, - 'deployment_ready': True, - 'created_at': '2025-08-03' + "deployment_ready": True, + "created_at": "2025-08-03", } - - with open(f"{deployment_model_dir}/model_info.json", 'w') as f: + + with open(f"{deployment_model_dir}/model_info.json", "w") as f: json.dump(model_info, f, indent=2) - + print("โœ… Model saved successfully!") print(f"๐Ÿ“ Deployment directory: {deployment_model_dir}") print(f"๐Ÿ“Š Model info:") print(f" - Emotions: {len(emotions)} classes") print(f" - F1 Score: 99.48%") print(f" - Target Achieved: โœ… YES!") - + # Test the saved model print("๐Ÿงช Testing saved model...") test_saved_model(deployment_model_dir) - + return True - + except Exception as e: print(f"โŒ Error saving model: {e}") return False + def test_saved_model(model_dir): """Test the saved model""" try: from inference import EmotionDetector - + # Initialize detector with saved model detector = EmotionDetector(model_dir) - + # Test cases test_texts = [ "I'm feeling really happy today!", "I'm so frustrated with this project.", "I feel anxious about the presentation.", "I'm grateful for all the support.", - "I'm feeling overwhelmed with tasks." + "I'm feeling overwhelmed with tasks.", ] - + print("๐Ÿ“Š Testing saved model:") print("-" * 30) - + for text in test_texts: result = detector.predict(text) print(f"Text: {text}") print(f"Emotion: {result['emotion']} (confidence: {result['confidence']:.3f})") print() - + print("โœ… Saved model test completed!") - + except Exception as e: print(f"โš ๏ธ Could not test saved model: {e}") + def create_deployment_script(): """Create a deployment script""" - + deployment_script = """#!/bin/bash # ๐Ÿš€ EMOTION DETECTION MODEL DEPLOYMENT # ===================================== @@ -186,17 +200,18 @@ def create_deployment_script(): echo "Press Ctrl+C to stop the server" python api_server.py """ - - with open("deployment/deploy.sh", 'w') as f: + + with open("deployment/deploy.sh", "w") as f: f.write(deployment_script) - + # Make executable os.chmod("deployment/deploy.sh", 0o755) print("โœ… Deployment script updated!") + if __name__ == "__main__": success = save_model_for_deployment() - + if success: create_deployment_script() print("\n๐ŸŽ‰ DEPLOYMENT PACKAGE READY!") @@ -215,4 +230,4 @@ def create_deployment_script(): print("๐Ÿ† Target Achieved: โœ… YES!") else: print("\nโŒ Failed to create deployment package!") - print("Please ensure you have a trained model available.") \ No newline at end of file + print("Please ensure you have a trained model available.") diff --git a/scripts/deployment/security_deployment_fix.py b/scripts/deployment/security_deployment_fix.py index f133d76f7..6a4d614cf 100644 --- a/scripts/deployment/security_deployment_fix.py +++ b/scripts/deployment/security_deployment_fix.py @@ -12,24 +12,28 @@ """ import os -import sys -import subprocess import shlex +import subprocess +import sys import time -import requests from pathlib import Path -from typing import Dict, List, Optional +from typing import List + +import requests + # Configuration def get_project_id(): """Get current GCP project ID dynamically""" try: - result = subprocess.run(['gcloud', 'config', 'get-value', 'project'], - capture_output=True, text=True, check=True) + result = subprocess.run( + ["gcloud", "config", "get-value", "project"], capture_output=True, text=True, check=True + ) return result.stdout.strip() except subprocess.CalledProcessError: # Fallback to environment variable or default - return os.environ.get('GOOGLE_CLOUD_PROJECT', 'the-tendril-466607-n8') + return os.environ.get("GOOGLE_CLOUD_PROJECT", "the-tendril-466607-n8") + PROJECT_ID = get_project_id() REGION = "us-central1" @@ -46,6 +50,7 @@ def get_project_id(): RATE_LIMIT_PER_MINUTE = 100 MAX_INPUT_LENGTH = 512 + class SecurityDeploymentFix: def __init__(self): self.base_dir = Path(__file__).parent.parent.parent @@ -90,7 +95,7 @@ def verify_static_files_exist(self): self.secure_dockerfile, self.secure_api, self.deployment_dir / "security_headers.py", - self.deployment_dir / "rate_limiter.py" + self.deployment_dir / "rate_limiter.py", ] missing_files = [] @@ -135,7 +140,7 @@ def create_secure_requirements(self): cryptography>=41.0.0,<42.0.0 """ - with open(self.secure_requirements, 'w') as f: + with open(self.secure_requirements, "w") as f: f.write(secure_requirements) self.log("โœ… Secure requirements.txt created") @@ -149,44 +154,65 @@ def build_and_deploy(self): # Create a temporary cloudbuild.yaml file cloudbuild_path = self.deployment_dir / "cloudbuild.yaml" - cloudbuild_content = f'''steps: + cloudbuild_content = f"""steps: - name: 'gcr.io/cloud-builders/docker' args: ['build', '-t', '{ARTIFACT_REGISTRY}/{SERVICE_NAME}', '-f', 'Dockerfile.secure', '.'] images: - '{ARTIFACT_REGISTRY}/{SERVICE_NAME}' -''' +""" - with open(cloudbuild_path, 'w') as f: + with open(cloudbuild_path, "w") as f: f.write(cloudbuild_content) # Build container self.log("Building secure container...") - build_result = self.run_command([ - 'gcloud', 'builds', 'submit', - str(self.deployment_dir), - '--config', str(cloudbuild_path) - ]) + build_result = self.run_command( + [ + "gcloud", + "builds", + "submit", + str(self.deployment_dir), + "--config", + str(cloudbuild_path), + ] + ) if build_result.returncode != 0: raise RuntimeError("Container build failed") # Deploy to Cloud Run self.log("Deploying to Cloud Run...") - deploy_result = self.run_command([ - 'gcloud', 'run', 'deploy', SERVICE_NAME, - '--image', f'{ARTIFACT_REGISTRY}/{SERVICE_NAME}', - '--region', REGION, - '--platform', 'managed', - '--allow-unauthenticated', - '--port', str(PORT), - '--memory', '2Gi', - '--cpu', '2', - '--max-instances', '10', - '--min-instances', '0', - '--concurrency', '80', - '--timeout', '300', - '--set-env-vars', f'ADMIN_API_KEY={ADMIN_API_KEY},MAX_INPUT_LENGTH={MAX_INPUT_LENGTH},RATE_LIMIT_PER_MINUTE={RATE_LIMIT_PER_MINUTE},MODEL_PATH={MODEL_PATH}' - ]) + deploy_result = self.run_command( + [ + "gcloud", + "run", + "deploy", + SERVICE_NAME, + "--image", + f"{ARTIFACT_REGISTRY}/{SERVICE_NAME}", + "--region", + REGION, + "--platform", + "managed", + "--allow-unauthenticated", + "--port", + str(PORT), + "--memory", + "2Gi", + "--cpu", + "2", + "--max-instances", + "10", + "--min-instances", + "0", + "--concurrency", + "80", + "--timeout", + "300", + "--set-env-vars", + f"ADMIN_API_KEY={ADMIN_API_KEY},MAX_INPUT_LENGTH={MAX_INPUT_LENGTH},RATE_LIMIT_PER_MINUTE={RATE_LIMIT_PER_MINUTE},MODEL_PATH={MODEL_PATH}", + ] + ) if deploy_result.returncode != 0: raise RuntimeError("Cloud Run deployment failed") @@ -199,11 +225,19 @@ def test_deployment(self): # Get service URL try: - result = self.run_command([ - 'gcloud', 'run', 'services', 'describe', SERVICE_NAME, - '--region', REGION, - '--format', 'value(status.url)' - ]) + result = self.run_command( + [ + "gcloud", + "run", + "services", + "describe", + SERVICE_NAME, + "--region", + REGION, + "--format", + "value(status.url)", + ] + ) service_url = result.stdout.strip() except Exception as e: self.log(f"Failed to get service URL: {e}", "ERROR") @@ -232,11 +266,11 @@ def test_deployment(self): headers = response.headers security_headers = [ - 'Content-Security-Policy', - 'X-Content-Type-Options', - 'X-Frame-Options', - 'X-XSS-Protection', - 'Strict-Transport-Security' + "Content-Security-Policy", + "X-Content-Type-Options", + "X-Frame-Options", + "X-XSS-Protection", + "Strict-Transport-Security", ] missing_headers = [] @@ -269,9 +303,7 @@ def test_deployment(self): responses = [] for i in range(105): # Exceed rate limit response = requests.post( - f"{service_url}/predict", - json={"text": f"Test text {i}"}, - timeout=30 + f"{service_url}/predict", json={"text": f"Test text {i}"}, timeout=30 ) responses.append(response.status_code) @@ -322,7 +354,8 @@ def run(self): self.log(f"โŒ Security deployment fix failed: {e}", "ERROR") return False + if __name__ == "__main__": fixer = SecurityDeploymentFix() success = fixer.run() - sys.exit(0 if success else 1) + sys.exit(0 if success else 1) diff --git a/scripts/deployment/upload_model_to_huggingface.py b/scripts/deployment/upload_model_to_huggingface.py index 0f4886e51..c05c90cb5 100755 --- a/scripts/deployment/upload_model_to_huggingface.py +++ b/scripts/deployment/upload_model_to_huggingface.py @@ -6,6 +6,7 @@ """ import sys + from hf_upload.cli import main if __name__ == "__main__": diff --git a/scripts/deployment/vertex_ai_phase4_automation.py b/scripts/deployment/vertex_ai_phase4_automation.py deleted file mode 100644 index 84302b9e4..000000000 --- a/scripts/deployment/vertex_ai_phase4_automation.py +++ /dev/null @@ -1,793 +0,0 @@ -#!/usr/bin/env python3 -""" -Phase 4: Vertex AI Deployment Automation -======================================== - -Enhanced Vertex AI deployment with automated model versioning, rollback capabilities, -A/B testing support, performance monitoring, and cost optimization. - -Features: -- Automated model versioning and deployment -- Rollback capabilities and A/B testing support -- Model performance monitoring and alerting -- Cost optimization and resource management -- Comprehensive testing and validation -""" - -import os -import json -import subprocess -import sys -import logging -from datetime import datetime -from typing import Dict, List, Optional, Tuple -from dataclasses import dataclass - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' -) -logger = logging.getLogger(__name__) - -@dataclass -class DeploymentConfig: - """Configuration for Vertex AI deployment.""" - project_id: str - region: str = "us-central1" - model_name: str = "comprehensive-emotion-detection" - endpoint_name: str = "emotion-detection-endpoint" - repository_name: str = "emotion-detection" - machine_type: str = "n1-standard-2" - min_replicas: int = 1 - max_replicas: int = 10 - traffic_split: Dict[str, float] = None - monitoring_interval: int = 300 # 5 minutes - cost_budget: float = 100.0 # USD per day - rollback_threshold: float = 0.8 # 80% performance threshold - -class VertexAIPhase4Automation: - """Enhanced Vertex AI deployment automation with Phase 4 features.""" - - def __init__(self, config: DeploymentConfig): - self.config = config - self.current_version = None - self.deployment_history = [] - - def check_prerequisites(self) -> bool: - """Enhanced prerequisites checking for Phase 4 features.""" - logger.info("๐Ÿ” CHECKING PHASE 4 DEPLOYMENT PREREQUISITES") - print("=" * 60) - - checks = [ - ("gcloud CLI", self._check_gcloud), - ("Authentication", self._check_authentication), - ("Project Configuration", self._check_project), - ("Vertex AI API", self._check_vertex_ai_api), - ("Cloud Monitoring API", self._check_monitoring_api), - ("Cloud Logging API", self._check_logging_api), - ("Artifact Registry", self._check_artifact_registry), - ("IAM Permissions", self._check_iam_permissions), - ] - - all_passed = True - for check_name, check_func in checks: - try: - if check_func(): - print(f"โœ… {check_name}") - else: - print(f"โŒ {check_name}") - all_passed = False - except Exception as e: - print(f"โŒ {check_name}: {e}") - all_passed = False - - return all_passed - - @staticmethod - def _check_gcloud() -> bool: - """Check if gcloud CLI is installed and working.""" - try: - result = subprocess.run(['gcloud', '--version'], capture_output=True, text=True, check=True) - return result.returncode == 0 - except FileNotFoundError: - return False - - @staticmethod - def _check_authentication() -> bool: - """Check if user is authenticated.""" - try: - result = subprocess.run(['gcloud', 'auth', 'list', '--filter=status:ACTIVE'], - capture_output=True, text=True, check=True) - return result.returncode == 0 and 'ACTIVE' in result.stdout - except Exception: - return False - - def _check_project(self) -> bool: - """Check if project is properly configured.""" - try: - result = subprocess.run(['gcloud', 'config', 'get-value', 'project'], - capture_output=True, text=True, check=True) - return result.returncode == 0 and result.stdout.strip() == self.config.project_id - except Exception: - return False - - @staticmethod - def _check_vertex_ai_api() -> bool: - """Check if Vertex AI API is enabled.""" - try: - result = subprocess.run(['gcloud', 'services', 'list', '--enabled', - '--filter=name:aiplatform.googleapis.com'], - capture_output=True, text=True, check=True) - return result.returncode == 0 and 'aiplatform.googleapis.com' in result.stdout - except Exception: - return False - - @staticmethod - def _check_monitoring_api() -> bool: - """Check if Cloud Monitoring API is enabled.""" - try: - result = subprocess.run(['gcloud', 'services', 'list', '--enabled', - '--filter=name:monitoring.googleapis.com'], - capture_output=True, text=True, check=True) - return result.returncode == 0 and 'monitoring.googleapis.com' in result.stdout - except Exception: - return False - - @staticmethod - def _check_logging_api() -> bool: - """Check if Cloud Logging API is enabled.""" - try: - result = subprocess.run(['gcloud', 'services', 'list', '--enabled', - '--filter=name:logging.googleapis.com'], - capture_output=True, text=True, check=True) - return result.returncode == 0 and 'logging.googleapis.com' in result.stdout - except Exception: - return False - - @staticmethod - def _check_artifact_registry() -> bool: - """Check if Artifact Registry is enabled.""" - try: - result = subprocess.run(['gcloud', 'services', 'list', '--enabled', - '--filter=name:artifactregistry.googleapis.com'], - capture_output=True, text=True, check=True) - return result.returncode == 0 and 'artifactregistry.googleapis.com' in result.stdout - except Exception: - return False - - def _check_iam_permissions(self) -> bool: - """Check if user has required IAM permissions.""" - required_roles = [ - 'roles/aiplatform.admin', - 'roles/monitoring.admin', - 'roles/logging.admin', - 'roles/artifactregistry.admin' - ] - - try: - result = subprocess.run(['gcloud', 'projects', 'get-iam-policy', self.config.project_id, - '--flatten=bindings[].members', - '--format=value(bindings.role)'], - capture_output=True, text=True, check=True) - user_email = subprocess.run(['gcloud', 'config', 'get-value', 'account'], - capture_output=True, text=True, check=True).stdout.strip(check=True) - - user_roles = result.stdout.split('\n') - return any(role in user_roles for role in required_roles) - except Exception: - return False - - def generate_model_version(self) -> str: - """Generate a unique model version based on timestamp and git commit.""" - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - - # Get git commit hash if available - try: - result = subprocess.run(['git', 'rev-parse', '--short', 'HEAD'], - capture_output=True, text=True, check=True) - git_hash = result.stdout.strip() if result.returncode == 0 else "unknown" - except Exception: - git_hash = "unknown" - - version = f"v{timestamp}_{git_hash}" - self.current_version = version - return version - - def create_deployment_package(self, version: str) -> str: - """Create deployment package with versioning.""" - logger.info(f"๐Ÿ“ฆ CREATING DEPLOYMENT PACKAGE FOR VERSION {version}") - print("=" * 60) - - # Create versioned deployment directory - deployment_dir = f"deployment/vertex_ai/{version}" - os.makedirs(deployment_dir, exist_ok=True) - - # Copy model files - source_model_path = "deployment/models/default" - if not os.path.exists(source_model_path): - raise FileNotFoundError(f"Source model not found: {source_model_path}") - - # Create Dockerfile with versioning - dockerfile_content = f""" -FROM python:3.9-slim - -WORKDIR /app - -# Copy requirements -COPY requirements.txt . -RUN pip install --no-cache-dir -r requirements.txt - -# Copy model files -COPY model/ ./model/ - -# Copy prediction code -COPY predict.py . - -# Set environment variables -ENV MODEL_VERSION={version} -ENV MODEL_PATH=/app/model - -# Expose port -EXPOSE 8080 - -# Health check -HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \\ - CMD curl -f http://localhost:8080/health || exit 1 - -# Start the server -CMD ["python", "predict.py"] -""" - - with open(f"{deployment_dir}/Dockerfile", "w") as f: - f.write(dockerfile_content) - - # Copy model files - subprocess.run(['cp', '-r', source_model_path, f"{deployment_dir}/model"], check=True) - - # Copy requirements - subprocess.run(['cp', 'deployment/gcp/requirements.txt', f"{deployment_dir}/"], check=True) - - # Copy prediction code - subprocess.run(['cp', 'deployment/gcp/predict.py', f"{deployment_dir}/"], check=True) - - # Create version metadata - metadata = { - "version": version, - "created_at": datetime.now().isoformat(), - "model_info": { - "name": self.config.model_name, - "description": "Comprehensive emotion detection model with Phase 4 enhancements" - }, - "deployment_config": { - "machine_type": self.config.machine_type, - "min_replicas": self.config.min_replicas, - "max_replicas": self.config.max_replicas, - "traffic_split": self.config.traffic_split or {"100": 1.0} - } - } - - with open(f"{deployment_dir}/version_metadata.json", "w") as f: - json.dump(metadata, f, indent=2) - - print(f"โœ… Deployment package created: {deployment_dir}") - return deployment_dir - - def build_and_push_image(self, deployment_dir: str, version: str) -> str: - """Build and push Docker image with versioning.""" - logger.info(f"๐Ÿณ BUILDING AND PUSHING DOCKER IMAGE FOR VERSION {version}") - print("=" * 60) - - # Configure Docker for gcloud - subprocess.run(['gcloud', 'auth', 'configure-docker'], check=True) - - # Create image URI with version - image_uri = f"gcr.io/{self.config.project_id}/{self.config.repository_name}:{version}" - - try: - # Build image - subprocess.run(['docker', 'build', '-t', image_uri, deployment_dir], check=True) - print("โœ… Docker image built") - - # Push image - subprocess.run(['docker', 'push', image_uri], check=True) - print("โœ… Docker image pushed to Container Registry") - - return image_uri - - except subprocess.CalledProcessError as e: - logger.error(f"Error building/pushing Docker image: {e}") - raise - - def create_vertex_ai_model(self, image_uri: str, version: str) -> str: - """Create Vertex AI model with versioning.""" - logger.info(f"๐Ÿค– CREATING VERTEX AI MODEL FOR VERSION {version}") - print("=" * 60) - - model_display_name = f"{self.config.model_name}-{version}" - - try: - # Create model - subprocess.run([ - 'gcloud', 'ai', 'models', 'upload', - '--region', self.config.region, - '--display-name', model_display_name, - '--container-image-uri', image_uri, - '--container-predict-route', '/predict', - '--container-health-route', '/health', - '--container-env-vars', f'MODEL_VERSION={version}' - ], check=True) - print("โœ… Vertex AI model created") - - # Get model ID - result = subprocess.run([ - 'gcloud', 'ai', 'models', 'list', - '--region', self.config.region, - '--filter', f'displayName={model_display_name}', - '--format', 'value(name)' - ], capture_output=True, text=True, check=True) - - model_id = result.stdout.strip() - return model_id - - except subprocess.CalledProcessError as e: - logger.error(f"Error creating Vertex AI model: {e}") - raise - - def deploy_model_to_endpoint(self, model_id: str, version: str) -> str: - """Deploy model to endpoint with traffic management.""" - logger.info(f"๐Ÿš€ DEPLOYING MODEL TO ENDPOINT FOR VERSION {version}") - print("=" * 60) - - try: - # Get or create endpoint - endpoint_id = self._get_or_create_endpoint() - - # Deploy model with traffic split - traffic_split = self.config.traffic_split or {"100": 1.0} - - subprocess.run([ - 'gcloud', 'ai', 'endpoints', 'deploy-model', - '--region', self.config.region, - '--endpoint', endpoint_id, - '--model', model_id, - '--traffic-split', ','.join([f"{k}={v}" for k, v in traffic_split.items()]), - '--machine-type', self.config.machine_type, - '--min-replica-count', str(self.config.min_replicas), - '--max-replica-count', str(self.config.max_replicas) - ], check=True) - - print("โœ… Model deployed to endpoint") - - # Record deployment - deployment_record = { - "version": version, - "model_id": model_id, - "endpoint_id": endpoint_id, - "deployed_at": datetime.now().isoformat(), - "traffic_split": traffic_split - } - self.deployment_history.append(deployment_record) - - return endpoint_id - - except subprocess.CalledProcessError as e: - logger.error(f"Error deploying model to endpoint: {e}") - raise - - def _get_or_create_endpoint(self) -> str: - """Get existing endpoint or create new one.""" - try: - # Try to get existing endpoint - result = subprocess.run([ - 'gcloud', 'ai', 'endpoints', 'list', - '--region', self.config.region, - '--filter', f'displayName={self.config.endpoint_name}', - '--format', 'value(name)' - ], capture_output=True, text=True, check=True) - - if result.returncode == 0 and result.stdout.strip(): - return result.stdout.strip() - - # Create new endpoint - result = subprocess.run([ - 'gcloud', 'ai', 'endpoints', 'create', - '--region', self.config.region, - '--display-name', self.config.endpoint_name - ], capture_output=True, text=True, check=True) - - # Get the created endpoint ID - result = subprocess.run([ - 'gcloud', 'ai', 'endpoints', 'list', - '--region', self.config.region, - '--filter', f'displayName={self.config.endpoint_name}', - '--format', 'value(name)' - ], capture_output=True, text=True, check=True) - - return result.stdout.strip() - - except subprocess.CalledProcessError as e: - logger.error(f"Error getting/creating endpoint: {e}") - raise - - def setup_monitoring_and_alerting(self, endpoint_id: str) -> None: - """Setup monitoring and alerting for the deployment.""" - logger.info("๐Ÿ“Š SETTING UP MONITORING AND ALERTING") - print("=" * 60) - - # Create monitoring policy - policy_name = f"emotion-detection-monitoring-{self.current_version}" - - policy_config = { - "displayName": policy_name, - "conditions": [ - { - "displayName": "High Error Rate", - "conditionThreshold": { - "filter": f'resource.type="aiplatform.googleapis.com/Endpoint" AND resource.labels.endpoint_id="{endpoint_id}"', - "comparison": "COMPARISON_GREATER_THAN", - "thresholdValue": 0.05, # 5% error rate - "duration": "300s" - } - }, - { - "displayName": "High Latency", - "conditionThreshold": { - "filter": f'resource.type="aiplatform.googleapis.com/Endpoint" AND resource.labels.endpoint_id="{endpoint_id}"', - "comparison": "COMPARISON_GREATER_THAN", - "thresholdValue": 5000, # 5 seconds - "duration": "300s" - } - } - ], - "alertStrategy": { - "autoClose": "604800s" # 7 days - } - } - - # Write policy to file - policy_file = f"deployment/vertex_ai/{self.current_version}/monitoring_policy.json" - with open(policy_file, "w") as f: - json.dump(policy_config, f, indent=2) - - try: - # Create monitoring policy - subprocess.run([ - 'gcloud', 'alpha', 'monitoring', 'policies', 'create', - '--policy-from-file', policy_file - ], check=True) - print("โœ… Monitoring policy created") - - except subprocess.CalledProcessError as e: - logger.warning(f"Could not create monitoring policy: {e}") - print("โš ๏ธ Monitoring policy creation failed (may need additional permissions)") - - def setup_cost_monitoring(self) -> None: - """Setup cost monitoring and budget alerts.""" - logger.info("๐Ÿ’ฐ SETTING UP COST MONITORING") - print("=" * 60) - - budget_name = f"emotion-detection-budget-{self.current_version}" - - budget_config = { - "displayName": budget_name, - "budgetFilter": { - "projects": [f"projects/{self.config.project_id}"] - }, - "amount": { - "specifiedAmount": { - "currencyCode": "USD", - "units": str(int(self.config.cost_budget)) - } - }, - "thresholdRules": [ - { - "thresholdPercent": 0.5, # 50% of budget - "spendBasis": "CURRENT_SPEND" - }, - { - "thresholdPercent": 0.8, # 80% of budget - "spendBasis": "CURRENT_SPEND" - }, - { - "thresholdPercent": 1.0, # 100% of budget - "spendBasis": "CURRENT_SPEND" - } - ] - } - - # Write budget to file - budget_file = f"deployment/vertex_ai/{self.current_version}/budget_config.json" - with open(budget_file, "w") as f: - json.dump(budget_config, f, indent=2) - - try: - # Create budget - subprocess.run([ - 'gcloud', 'billing', 'budgets', 'create', - '--billing-account', self._get_billing_account(), - '--budget-file', budget_file - ], check=True) - print("โœ… Cost budget created") - - except subprocess.CalledProcessError as e: - logger.warning(f"Could not create budget: {e}") - print("โš ๏ธ Budget creation failed (may need billing permissions)") - - def _get_billing_account(self) -> str: - """Get the billing account for the project.""" - try: - result = subprocess.run([ - 'gcloud', 'billing', 'projects', 'describe', self.config.project_id, - '--format', 'value(billingAccountName)' - ], capture_output=True, text=True, check=True) - return result.stdout.strip() - except subprocess.CalledProcessError: - return "" - - def rollback_deployment(self, target_version: str) -> bool: - """Rollback to a previous version.""" - logger.info(f"๐Ÿ”„ ROLLING BACK TO VERSION {target_version}") - print("=" * 60) - - # Find the target deployment - target_deployment = None - for deployment in self.deployment_history: - if deployment["version"] == target_version: - target_deployment = deployment - break - - if not target_deployment: - logger.error(f"Target version {target_version} not found in deployment history") - return False - - try: - # Update traffic to 100% for target version - subprocess.run([ - 'gcloud', 'ai', 'endpoints', 'deploy-model', - '--region', self.config.region, - '--endpoint', target_deployment["endpoint_id"], - '--model', target_deployment["model_id"], - '--traffic-split', '100=1.0', - '--machine-type', self.config.machine_type, - '--min-replica-count', str(self.config.min_replicas), - '--max-replica-count', str(self.config.max_replicas) - ], check=True) - - print(f"โœ… Successfully rolled back to version {target_version}") - return True - - except subprocess.CalledProcessError as e: - logger.error(f"Error during rollback: {e}") - return False - - def setup_ab_testing(self, version_a: str, version_b: str, traffic_split: Dict[str, float]) -> bool: - """Setup A/B testing between two versions.""" - logger.info(f"๐Ÿงช SETTING UP A/B TESTING: {version_a} vs {version_b}") - print("=" * 60) - - # Find both versions in deployment history - version_a_deployment = None - version_b_deployment = None - - for deployment in self.deployment_history: - if deployment["version"] == version_a: - version_a_deployment = deployment - elif deployment["version"] == version_b: - version_b_deployment = deployment - - if not version_a_deployment or not version_b_deployment: - logger.error("Both versions must be deployed before A/B testing") - return False - - try: - # Deploy both versions with traffic split - traffic_config = ','.join([f"{k}={v}" for k, v in traffic_split.items()]) - - subprocess.run([ - 'gcloud', 'ai', 'endpoints', 'deploy-model', - '--region', self.config.region, - '--endpoint', version_a_deployment["endpoint_id"], - '--model', version_a_deployment["model_id"], - '--traffic-split', traffic_config, - '--machine-type', self.config.machine_type, - '--min-replica-count', str(self.config.min_replicas), - '--max-replica-count', str(self.config.max_replicas) - ], check=True) - - print("โœ… A/B testing setup completed") - return True - - except subprocess.CalledProcessError as e: - logger.error(f"Error setting up A/B testing: {e}") - return False - - def get_performance_metrics(self, endpoint_id: str) -> Dict: - """Get performance metrics for the deployment.""" - logger.info("๐Ÿ“ˆ GETTING PERFORMANCE METRICS") - print("=" * 60) - - try: - # Get prediction latency - result = subprocess.run([ - 'gcloud', 'ai', 'endpoints', 'describe', - '--region', self.config.region, - '--endpoint', endpoint_id, - '--format', 'value(predictRequestResponseLoggingConfig.enabled)' - ], capture_output=True, text=True, check=True) - - # Get model performance metrics - result = subprocess.run([ - 'gcloud', 'ai', 'models', 'list', - '--region', self.config.region, - '--filter', f'endpointId={endpoint_id}', - '--format', 'value(displayName,createTime)' - ], capture_output=True, text=True, check=True) - - metrics = { - "endpoint_id": endpoint_id, - "timestamp": datetime.now().isoformat(), - "logging_enabled": result.stdout.strip() == "True", - "models": result.stdout.strip().split('\n') if result.stdout.strip() else [] - } - - print("โœ… Performance metrics retrieved") - return metrics - - except subprocess.CalledProcessError as e: - logger.error(f"Error getting performance metrics: {e}") - return {} - - def cleanup_old_versions(self, keep_versions: int = 3) -> None: - """Clean up old model versions to save costs.""" - logger.info(f"๐Ÿงน CLEANING UP OLD VERSIONS (keeping {keep_versions})") - print("=" * 60) - - if len(self.deployment_history) <= keep_versions: - print("โœ… No cleanup needed") - return - - # Sort by deployment time and keep only the latest versions - sorted_deployments = sorted( - self.deployment_history, - key=lambda x: x["deployed_at"], - reverse=True - ) - - versions_to_cleanup = sorted_deployments[keep_versions:] - - for deployment in versions_to_cleanup: - try: - # Delete model - subprocess.run([ - 'gcloud', 'ai', 'models', 'delete', - '--region', self.config.region, - '--model', deployment["model_id"] - ], check=True) - - print(f"โœ… Deleted model version: {deployment['version']}") - - except subprocess.CalledProcessError as e: - logger.warning(f"Could not delete model {deployment['version']}: {e}") - - def run_full_deployment(self) -> bool: - """Run the complete Phase 4 deployment process.""" - logger.info("๐Ÿš€ STARTING PHASE 4 VERTEX AI DEPLOYMENT") - print("=" * 60) - - try: - # 1. Check prerequisites - if not self.check_prerequisites(): - logger.error("Prerequisites check failed") - return False - - # 2. Generate version - version = self.generate_model_version() - print(f"๐Ÿ“‹ Generated version: {version}") - - # 3. Create deployment package - deployment_dir = self.create_deployment_package(version) - - # 4. Build and push image - image_uri = self.build_and_push_image(deployment_dir, version) - - # 5. Create Vertex AI model - model_id = self.create_vertex_ai_model(image_uri, version) - - # 6. Deploy to endpoint - endpoint_id = self.deploy_model_to_endpoint(model_id, version) - - # 7. Setup monitoring and alerting - self.setup_monitoring_and_alerting(endpoint_id) - - # 8. Setup cost monitoring - self.setup_cost_monitoring() - - # 9. Get performance metrics - metrics = self.get_performance_metrics(endpoint_id) - - # 10. Cleanup old versions - self.cleanup_old_versions() - - # 11. Save deployment summary - self._save_deployment_summary(version, endpoint_id, metrics) - - logger.info("โœ… Phase 4 deployment completed successfully!") - return True - - except Exception as e: - logger.error(f"Deployment failed: {e}") - return False - - def _save_deployment_summary(self, version: str, endpoint_id: str, metrics: Dict) -> None: - """Save deployment summary for future reference.""" - summary = { - "version": version, - "endpoint_id": endpoint_id, - "deployed_at": datetime.now().isoformat(), - "config": { - "project_id": self.config.project_id, - "region": self.config.region, - "machine_type": self.config.machine_type, - "min_replicas": self.config.min_replicas, - "max_replicas": self.config.max_replicas - }, - "metrics": metrics, - "deployment_history": self.deployment_history - } - - summary_file = f"deployment/vertex_ai/{version}/deployment_summary.json" - with open(summary_file, "w") as f: - json.dump(summary, f, indent=2) - - print(f"๐Ÿ“„ Deployment summary saved: {summary_file}") - -def main(): - """Main function for Phase 4 Vertex AI deployment.""" - print("๐ŸŽฏ PHASE 4: VERTEX AI DEPLOYMENT AUTOMATION") - print("=" * 60) - - # Get project ID - try: - result = subprocess.run(['gcloud', 'config', 'get-value', 'project'], - capture_output=True, text=True, check=True) - project_id = result.stdout.strip() - except Exception: - print("โŒ Could not get project ID. Please run: gcloud config set project YOUR_PROJECT_ID") - sys.exit(1) - - # Create configuration - config = DeploymentConfig( - project_id=project_id, - region="us-central1", - model_name="comprehensive-emotion-detection", - endpoint_name="emotion-detection-endpoint", - machine_type="n1-standard-2", - min_replicas=1, - max_replicas=10, - cost_budget=100.0 - ) - - # Create automation instance - automation = VertexAIPhase4Automation(config) - - # Run deployment - if automation.run_full_deployment(): - print("\n๐ŸŽ‰ PHASE 4 DEPLOYMENT COMPLETED SUCCESSFULLY!") - print("=" * 60) - print("โœ… Automated model versioning and deployment") - print("โœ… Rollback capabilities and A/B testing support") - print("โœ… Model performance monitoring and alerting") - print("โœ… Cost optimization and resource management") - print("โœ… Comprehensive testing and validation") - print("\n๐Ÿ“Š Next steps:") - print(" - Monitor performance metrics") - print(" - Set up additional alerting if needed") - print(" - Configure A/B testing for new versions") - print(" - Review cost optimization opportunities") - else: - print("\nโŒ PHASE 4 DEPLOYMENT FAILED!") - sys.exit(1) - -if __name__ == "__main__": - main() diff --git a/scripts/ensure_local_emotion_model.py b/scripts/ensure_local_emotion_model.py index a76b36e6d..ba3d412c9 100644 --- a/scripts/ensure_local_emotion_model.py +++ b/scripts/ensure_local_emotion_model.py @@ -16,19 +16,18 @@ from __future__ import annotations -import sys -from pathlib import Path - -# Add src to path to import constants -sys.path.insert(0, str(Path(__file__).parent.parent / 'src')) - import argparse import logging import os -from typing import List +import sys +from pathlib import Path from constants import EMOTION_MODEL_DIR +# Add src to path to import constants +sys.path.insert(0, str(Path(__file__).parent.parent / "src")) + + DEFAULT_REPO_ID = "j-hartmann/emotion-english-distilroberta-base" DEFAULT_TARGET_DIR = EMOTION_MODEL_DIR LOG_DIR = Path(".logs") @@ -38,20 +37,15 @@ LOG_DIR.mkdir(parents=True, exist_ok=True) logging.basicConfig( level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', - handlers=[ - logging.FileHandler(str(LOG_FILE)), - logging.StreamHandler(sys.stdout) - ] + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + handlers=[logging.FileHandler(str(LOG_FILE)), logging.StreamHandler(sys.stdout)], ) logger = logging.getLogger("ensure_local_emotion_model") def parse_args() -> argparse.Namespace: """Parse CLI arguments and environment overrides.""" - parser = argparse.ArgumentParser( - description="Ensure local HF emotion model is available" - ) + parser = argparse.ArgumentParser(description="Ensure local HF emotion model is available") parser.add_argument( "--repo-id", default=os.environ.get("EMOTION_MODEL_REPO", DEFAULT_REPO_ID), @@ -88,15 +82,11 @@ def required_files_present(target_dir: Path) -> bool: "tokenizer_config.json", ] have_all_required = all((target_dir / f).exists() for f in required) - have_any_tokenizer = any( - all((target_dir / f).exists() for f in group) for group in files_any - ) + have_any_tokenizer = any(all((target_dir / f).exists() for f in group) for group in files_any) return have_all_required and have_any_tokenizer -def ensure_with_hf_hub( - repo_id: str, target_dir: Path, token: str | None -) -> bool: +def ensure_with_hf_hub(repo_id: str, target_dir: Path, token: str | None) -> bool: """Download model snapshot via huggingface_hub if available.""" try: from huggingface_hub import snapshot_download # type: ignore @@ -104,9 +94,7 @@ def ensure_with_hf_hub( logger.warning("huggingface_hub not available: %s", e) return False - logger.info( - "Using huggingface_hub.snapshot_download for repo %s", repo_id - ) + logger.info("Using huggingface_hub.snapshot_download for repo %s", repo_id) try: snapshot_download( repo_id=repo_id, @@ -122,9 +110,7 @@ def ensure_with_hf_hub( return False -def ensure_with_transformers( - repo_id: str, target_dir: Path, token: str | None -) -> bool: +def ensure_with_transformers(repo_id: str, target_dir: Path, token: str | None) -> bool: """Download and save model/tokenizer via transformers.*_pretrained APIs.""" try: from transformers import ( @@ -137,12 +123,8 @@ def ensure_with_transformers( logger.info("Using transformers save_pretrained for repo %s", repo_id) try: - tokenizer = AutoTokenizer.from_pretrained( - repo_id, token=token or None - ) - model = AutoModelForSequenceClassification.from_pretrained( - repo_id, token=token or None - ) + tokenizer = AutoTokenizer.from_pretrained(repo_id, token=token or None) + model = AutoModelForSequenceClassification.from_pretrained(repo_id, token=token or None) target_dir.mkdir(parents=True, exist_ok=True) tokenizer.save_pretrained(str(target_dir)) model.save_pretrained(str(target_dir)) @@ -170,15 +152,11 @@ def main() -> int: ok = ensure_with_hf_hub(repo_id, target_dir, token) if not ok: - logger.info( - "Falling back to transformers save_pretrained approach" - ) + logger.info("Falling back to transformers save_pretrained approach") ok = ensure_with_transformers(repo_id, target_dir, token) if not ok: - logger.error( - "Failed to materialize model to %s", str(target_dir) - ) + logger.error("Failed to materialize model to %s", str(target_dir)) return 2 if required_files_present(target_dir): diff --git a/scripts/fix_linting_issues.py b/scripts/fix_linting_issues.py index f6fa68df8..c5138c447 100644 --- a/scripts/fix_linting_issues.py +++ b/scripts/fix_linting_issues.py @@ -7,15 +7,14 @@ Use with care. """ -import os import argparse +import contextlib +import os import shutil import tempfile -import contextlib from pathlib import Path from typing import Optional - PROJECT_ROOT = Path(__file__).resolve().parent.parent @@ -32,9 +31,7 @@ def _resolve_safe_path(path: Path) -> Path: except ValueError: is_under = False if not is_under: - raise ValueError( - f"Refusing to operate outside project root: {resolved}" - ) + raise ValueError(f"Refusing to operate outside project root: {resolved}") if not resolved.exists() or not resolved.is_file(): raise FileNotFoundError(f"File not found: {resolved}") return resolved @@ -47,9 +44,22 @@ def find_python_files( """Find all Python files in the project, skipping excluded directories.""" if excluded_dirs is None: excluded_dirs = { - '.git', '__pycache__', '.venv', 'venv', 'node_modules', 'build', 'dist', - '.mypy_cache', '.pytest_cache', '.cache', '.coverage', '.eggs', '.tox', - '.idea', '.vscode', '.DS_Store' + ".git", + "__pycache__", + ".venv", + "venv", + "node_modules", + "build", + "dist", + ".mypy_cache", + ".pytest_cache", + ".cache", + ".coverage", + ".eggs", + ".tox", + ".idea", + ".vscode", + ".DS_Store", } python_files = [] @@ -57,9 +67,7 @@ def find_python_files( # Skip certain directories dirs[:] = [d for d in dirs if d not in excluded_dirs] - python_files.extend( - Path(root) / file for file in files if file.endswith('.py') - ) + python_files.extend(Path(root) / file for file in files if file.endswith(".py")) return python_files @@ -73,17 +81,17 @@ def fix_trailing_whitespace( issues_fixed: list[str] = [] try: safe_path = _resolve_safe_path(file_path) - with open(safe_path, encoding='utf-8') as src, tempfile.NamedTemporaryFile( - 'w', delete=False, encoding='utf-8' + with open(safe_path, encoding="utf-8") as src, tempfile.NamedTemporaryFile( + "w", delete=False, encoding="utf-8" ) as tmp: for i, line in enumerate(src, 1): # Remove trailing whitespace and normalize newline - stripped_line_no_nl = line.rstrip('\r\n') + stripped_line_no_nl = line.rstrip("\r\n") stripped_line = stripped_line_no_nl.rstrip() if stripped_line != stripped_line_no_nl: changed = True issues_fixed.append(f"Line {i}: Removed trailing whitespace") - tmp.write(stripped_line + '\n') + tmp.write(stripped_line + "\n") # If content changed, optionally back up and replace if changed: if backup: @@ -96,7 +104,7 @@ def fix_trailing_whitespace( return changed, issues_fixed except Exception as e: # Best-effort cleanup of temp file if it still exists - if 'tmp' in locals(): + if "tmp" in locals(): with contextlib.suppress(FileNotFoundError): Path(tmp.name).unlink() return False, [f"Error processing {file_path}: {e}"] @@ -106,11 +114,12 @@ def fix_indentation_issues(file_path: Path) -> tuple[bool, list[str]]: """Detect indentation issues using AST; do not attempt automatic fixes.""" try: safe_path = _resolve_safe_path(file_path) - with open(safe_path, encoding='utf-8') as f: + with open(safe_path, encoding="utf-8") as f: original_content = f.read() # Use ast to check for indentation/syntax issues without modifying the file import ast + try: ast.parse(original_content) return False, [] # Parsed successfully; assume no indentation issues @@ -129,7 +138,7 @@ def fix_blank_lines_with_whitespace( """Fix blank lines that contain whitespace.""" try: safe_path = _resolve_safe_path(file_path) - with open(safe_path, encoding='utf-8') as f: + with open(safe_path, encoding="utf-8") as f: content = f.read() original_content = content @@ -139,26 +148,24 @@ def fix_blank_lines_with_whitespace( for i, line in enumerate(lines, 1): # Check if line is blank but contains whitespace - if not line.strip() and line != '': - issues_fixed.append( - f"Line {i}: Removed whitespace from blank line" - ) - fixed_lines.append('') + if not line.strip() and line != "": + issues_fixed.append(f"Line {i}: Removed whitespace from blank line") + fixed_lines.append("") continue fixed_lines.append(line) # Reconstruct content - fixed_content = '\n'.join(fixed_lines) - if fixed_content and not fixed_content.endswith('\n'): - fixed_content += '\n' + fixed_content = "\n".join(fixed_lines) + if fixed_content and not fixed_content.endswith("\n"): + fixed_content += "\n" if fixed_content != original_content: if backup: bak = Path(f"{safe_path}.bak") if not bak.exists(): shutil.copyfile(safe_path, bak) - with open(safe_path, 'w', encoding='utf-8') as f_out: + with open(safe_path, "w", encoding="utf-8") as f_out: f_out.write(fixed_content) return True, issues_fixed @@ -170,9 +177,7 @@ def fix_blank_lines_with_whitespace( def main(): """Main function to fix all linting issues.""" - parser = argparse.ArgumentParser( - description="Fix linting issues in files." - ) + parser = argparse.ArgumentParser(description="Fix linting issues in files.") parser.add_argument( "--backup", action="store_true", @@ -186,9 +191,7 @@ def main(): "โš ๏ธ WARNING: No backups will be created before modifying files. " "This may result in accidental data loss." ) - print( - " Use the --backup option to create .bak files before changes are made.\n" - ) + print(" Use the --backup option to create .bak files before changes are made.\n") print("๐Ÿ”ง SAMO Linting Issues Fix Script") print("=" * 50) @@ -243,8 +246,7 @@ def main(): if detected_issues: print( - f" โš ๏ธ Detected {len(detected_issues)} issues that may require " - f"manual attention:" + f" โš ๏ธ Detected {len(detected_issues)} issues that may require " f"manual attention:" ) for issue in detected_issues: print(f" - {issue}") diff --git a/scripts/fix_linting_issues_comprehensive.py.backup b/scripts/fix_linting_issues_comprehensive.py.backup index 0af640dab..9deba7897 100644 --- a/scripts/fix_linting_issues_comprehensive.py.backup +++ b/scripts/fix_linting_issues_comprehensive.py.backup @@ -1,12 +1,12 @@ #!/usr/bin/env python3 -import os +import ast +import logging import re -import sys import shutil -import logging from pathlib import Path -from typing import List, Set, Tuple, Dict -import ast +from typing import List, Tuple + + # Find all import lines # Reconstruct with imports at top # Fix unused exception variables @@ -273,4 +273,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/fix_syntax_errors.py b/scripts/fix_syntax_errors.py new file mode 100644 index 000000000..96c0f09fc --- /dev/null +++ b/scripts/fix_syntax_errors.py @@ -0,0 +1,157 @@ +#!/usr/bin/env python3 +"""Script to fix common syntax errors in Python files""" + +import os +import re + +def fix_syntax_errors(file_path): + """Fix common syntax errors in a Python file.""" + try: + with open(file_path, 'r', encoding='utf-8') as f: + content = f.read() + + original_content = content + + # Fix 1: Move shebang to the top + if '#!/usr/bin/env python3' in content: + # Remove shebang from anywhere in the file + content = re.sub(r'#!/usr/bin/env python3\n?', '', content) + # Add shebang at the top + content = '#!/usr/bin/env python3\n' + content + + # Fix 2: Fix indentation issues - remove leading spaces from lines that should be at module level + lines = content.split('\n') + fixed_lines = [] + in_function = False + indent_level = 0 + + for line in lines: + stripped = line.strip() + + # Skip empty lines + if not stripped: + fixed_lines.append('') + continue + + # Check if this is a function definition + if stripped.startswith('def ') or stripped.startswith('class '): + in_function = True + indent_level = 0 + fixed_lines.append(line) + continue + + # Check if this is a comment or import at module level + if (stripped.startswith('import ') or + stripped.startswith('from ') or + stripped.startswith('#') or + stripped.startswith('"""') or + stripped.startswith("'''")): + + # If we're in a function but this looks like module-level code, fix indentation + if in_function and not line.startswith(' '): + # This should be at module level + in_function = False + indent_level = 0 + fixed_lines.append(line) + else: + fixed_lines.append(line) + continue + + # Check if this is a return statement or other function content + if (stripped.startswith('return ') or + stripped.startswith('if ') or + stripped.startswith('for ') or + stripped.startswith('while ') or + stripped.startswith('try:') or + stripped.startswith('except ') or + stripped.startswith('finally:') or + stripped.startswith('else:') or + stripped.startswith('elif ')): + + if not in_function and not line.startswith(' '): + # This should be in a function, add indentation + fixed_lines.append(' ' + line) + else: + fixed_lines.append(line) + continue + + # Default: keep the line as is + fixed_lines.append(line) + + content = '\n'.join(fixed_lines) + + # Fix 3: Remove duplicate imports + lines = content.split('\n') + seen_imports = set() + fixed_lines = [] + + for line in lines: + stripped = line.strip() + if stripped.startswith('import ') or stripped.startswith('from '): + if stripped not in seen_imports: + seen_imports.add(stripped) + fixed_lines.append(line) + # Skip duplicate imports + else: + fixed_lines.append(line) + + content = '\n'.join(fixed_lines) + + # Only write if content changed + if content != original_content: + with open(file_path, 'w', encoding='utf-8') as f: + f.write(content) + print(f"Fixed: {file_path}") + return True + print(f"No changes needed: {file_path}") + return False + + except Exception as e: + print(f"Error fixing {file_path}: {e}") + return False + +def main(): + """Fix syntax errors in all Python files.""" + # Get list of files with syntax errors + files_to_fix = [ + "scripts/training/pre_training_validation.py", + "scripts/training/minimal_working_training.py", + "scripts/training/focal_loss_training.py", + "scripts/training/fixed_training_with_optimized_config.py", + "scripts/training/final_bulletproof_training_cell.py", + "scripts/training/bulletproof_training_cell_fixed.py", + "scripts/training/bulletproof_training_cell.py", + "scripts/testing/test_domain_adaptation.py", + "scripts/testing/standalone_focal_test.py", + "scripts/testing/simple_test.py", + "scripts/testing/simple_temperature_test_local.py", + "scripts/testing/quick_focal_test.py", + "scripts/testing/quick_f1_test.py", + "scripts/testing/local_validation_debug.py", + "scripts/maintenance/vertex_ai_setup_fixed.py", + "scripts/legacy/vertex_ai_setup.py", + "scripts/legacy/validate_and_train.py", + "scripts/legacy/threshold_optimization.py", + "scripts/legacy/temperature_scaling.py", + "scripts/legacy/start_monitoring_dashboard.py", + "scripts/legacy/simple_vertex_ai_validation.py", + "scripts/legacy/simple_validation.py", + "scripts/legacy/simple_finalize_model.py", + "scripts/legacy/model_optimization.py", + "scripts/legacy/model_monitoring.py", + "scripts/legacy/minimal_validation.py", + "scripts/legacy/fine_tune_emotion_model.py" + ] + + fixed_count = 0 + for file_path in files_to_fix: + if os.path.exists(file_path): + if fix_syntax_errors(file_path): + fixed_count += 1 + else: + print(f"File not found: {file_path}") + + print(f"\nFixed {fixed_count} files") + +if __name__ == "__main__": + main() diff --git a/scripts/fix_whitespace.py b/scripts/fix_whitespace.py new file mode 100644 index 000000000..f04be0405 --- /dev/null +++ b/scripts/fix_whitespace.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python3 +"""Script to fix whitespace issues in Python files""" + +import os + +def fix_whitespace_issues(file_path): + """Fix whitespace issues in a Python file.""" + try: + with open(file_path, 'r', encoding='utf-8') as f: + content = f.read() + + original_content = content + + # Fix 1: Remove trailing whitespace from lines + lines = content.split('\n') + fixed_lines = [] + + for line in lines: + # Remove trailing whitespace + fixed_line = line.rstrip() + fixed_lines.append(fixed_line) + + content = '\n'.join(fixed_lines) + + # Fix 2: Ensure file ends with newline + if content and not content.endswith('\n'): + content += '\n' + + # Fix 3: Remove blank lines that contain only whitespace + lines = content.split('\n') + fixed_lines = [] + + for line in lines: + if line.strip() == '': + # Empty line - keep it + fixed_lines.append('') + else: + # Non-empty line - keep it + fixed_lines.append(line) + + content = '\n'.join(fixed_lines) + + # Only write if content changed + if content != original_content: + with open(file_path, 'w', encoding='utf-8') as f: + f.write(content) + print(f"Fixed whitespace: {file_path}") + return True + return False + + except Exception as e: + print(f"Error fixing {file_path}: {e}") + return False + +def main(): + """Fix whitespace issues in all Python files.""" + # Find all Python files + python_files = [] + for root, dirs, files in os.walk('.'): + # Skip certain directories + if any(skip in root for skip in ['.git', '__pycache__', '.pytest_cache', 'node_modules']): + continue + + for file in files: + if file.endswith('.py'): + python_files.append(os.path.join(root, file)) + + fixed_count = 0 + for file_path in python_files: + if fix_whitespace_issues(file_path): + fixed_count += 1 + + print(f"\nFixed whitespace in {fixed_count} files") + +if __name__ == "__main__": + main() diff --git a/scripts/legacy/add_comprehensive_features.py b/scripts/legacy/add_comprehensive_features.py index a4fc9c308..89374c307 100644 --- a/scripts/legacy/add_comprehensive_features.py +++ b/scripts/legacy/add_comprehensive_features.py @@ -9,21 +9,20 @@ import json + def add_comprehensive_features(): """Add all advanced features to the comprehensive notebook.""" - + # Read the existing notebook - with open('notebooks/COMPREHENSIVE_ULTIMATE_TRAINING_COLAB.ipynb', 'r') as f: + with open("notebooks/COMPREHENSIVE_ULTIMATE_TRAINING_COLAB.ipynb", "r") as f: notebook = json.load(f) - + # Add all the advanced features as new cells advanced_cells = [ { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ”ง MODEL SETUP WITH ARCHITECTURE FIXES" - ] + "source": ["## ๐Ÿ”ง MODEL SETUP WITH ARCHITECTURE FIXES"], }, { "cell_type": "code", @@ -77,15 +76,13 @@ def add_comprehensive_features(): " model = model.to('cuda')\n", " print('โœ… Model moved to GPU')\n", "else:\n", - " print('โš ๏ธ CUDA not available, model will run on CPU')" - ] + " print('โš ๏ธ CUDA not available, model will run on CPU')", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ“Š DATA PREPROCESSING AND SPLITTING" - ] + "source": ["## ๐Ÿ“Š DATA PREPROCESSING AND SPLITTING"], }, { "cell_type": "code", @@ -108,15 +105,13 @@ def add_comprehensive_features(): "train_dataset = {'text': train_texts, 'label': train_labels}\n", "val_dataset = {'text': val_texts, 'label': val_labels}\n", "\n", - "print('โœ… Data split and prepared')" - ] + "print('โœ… Data split and prepared')", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## โš–๏ธ FOCAL LOSS AND CLASS WEIGHTING" - ] + "source": ["## โš–๏ธ FOCAL LOSS AND CLASS WEIGHTING"], }, { "cell_type": "code", @@ -154,16 +149,10 @@ def add_comprehensive_features(): " focal_loss = self.alpha * (1-pt)**self.gamma * ce_loss\n", " return focal_loss.mean()\n", "\n", - "print('โœ… Focal Loss class defined')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐ŸŽฏ WEIGHTED LOSS TRAINER" - ] + "print('โœ… Focal Loss class defined')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐ŸŽฏ WEIGHTED LOSS TRAINER"]}, { "cell_type": "code", "execution_count": None, @@ -200,16 +189,10 @@ def add_comprehensive_features(): " \n", " return (loss, outputs) if return_outputs else loss\n", "\n", - "print('โœ… WeightedLossTrainer created with focal loss and class weighting')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ”ง DATA PREPROCESSING FUNCTION" - ] + "print('โœ… WeightedLossTrainer created with focal loss and class weighting')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ”ง DATA PREPROCESSING FUNCTION"]}, { "cell_type": "code", "execution_count": None, @@ -244,16 +227,10 @@ def add_comprehensive_features(): ")\n", "\n", "print('โœ… Data preprocessing completed')\n", - "print('โœ… Data collator created')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## โš™๏ธ TRAINING ARGUMENTS" - ] + "print('โœ… Data collator created')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## โš™๏ธ TRAINING ARGUMENTS"]}, { "cell_type": "code", "execution_count": None, @@ -282,16 +259,10 @@ def add_comprehensive_features(): " report_to=None if 'WANDB_API_KEY' not in os.environ else ['wandb']\n", ")\n", "\n", - "print('โœ… Training arguments configured')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ“Š COMPUTE METRICS FUNCTION" - ] + "print('โœ… Training arguments configured')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ“Š COMPUTE METRICS FUNCTION"]}, { "cell_type": "code", "execution_count": None, @@ -319,16 +290,10 @@ def add_comprehensive_features(): " 'recall': recall\n", " }\n", "\n", - "print('โœ… Compute metrics function defined')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿš€ TRAINING EXECUTION" - ] + "print('โœ… Compute metrics function defined')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿš€ TRAINING EXECUTION"]}, { "cell_type": "code", "execution_count": None, @@ -364,16 +329,10 @@ def add_comprehensive_features(): "# Train the model\n", "trainer.train()\n", "\n", - "print('โœ… Training completed successfully!')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ“Š EVALUATION AND VALIDATION" - ] + "print('โœ… Training completed successfully!')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ“Š EVALUATION AND VALIDATION"]}, { "cell_type": "code", "execution_count": None, @@ -397,16 +356,10 @@ def add_comprehensive_features(): "pred_labels = np.argmax(predictions.predictions, axis=1)\n", "true_labels = val_labels\n", "\n", - "print(classification_report(true_labels, pred_labels, target_names=emotions))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ” ADVANCED VALIDATION" - ] + "print(classification_report(true_labels, pred_labels, target_names=emotions))", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ” ADVANCED VALIDATION"]}, { "cell_type": "code", "execution_count": None, @@ -463,15 +416,13 @@ def add_comprehensive_features(): " percentage = (count / len(pred_labels)) * 100\n", " print(f'{emotion:12s}: {count:3d} ({percentage:5.1f}%)')\n", "\n", - "print('\\nโœ… Advanced validation completed')" - ] + "print('\\nโœ… Advanced validation completed')", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ’พ MODEL SAVING WITH VERIFICATION" - ] + "source": ["## ๐Ÿ’พ MODEL SAVING WITH VERIFICATION"], }, { "cell_type": "code", @@ -530,33 +481,34 @@ def add_comprehensive_features(): "\n", "print(f'\\n๐ŸŽ‰ COMPREHENSIVE TRAINING COMPLETED!')\n", "print(f'๐Ÿ“ Model saved to: {model_save_path}')\n", - "print(f'๐Ÿ“Š Final F1 Score: {eval_results.get(\"eval_f1\", \"N/A\"):.4f}')\n", - "print(f'๐Ÿ“Š Final Accuracy: {eval_results.get(\"eval_accuracy\", \"N/A\"):.4f}')" - ] - } + 'print(f\'๐Ÿ“Š Final F1 Score: {eval_results.get("eval_f1", "N/A"):.4f}\')\n', + 'print(f\'๐Ÿ“Š Final Accuracy: {eval_results.get("eval_accuracy", "N/A"):.4f}\')', + ], + }, ] - + # Add all the advanced cells to the notebook - notebook['cells'].extend(advanced_cells) - + notebook["cells"].extend(advanced_cells) + # Save the updated notebook - with open('notebooks/COMPREHENSIVE_ULTIMATE_TRAINING_COLAB.ipynb', 'w') as f: + with open("notebooks/COMPREHENSIVE_ULTIMATE_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook, f, indent=2) - - print('โœ… Added all comprehensive features!') - print('๐Ÿ“‹ Advanced features added:') - print(' โœ… Model setup with architecture fixes') - print(' โœ… Data preprocessing and splitting') - print(' โœ… Focal loss and class weighting') - print(' โœ… WeightedLossTrainer with advanced loss') - print(' โœ… Data preprocessing function') - print(' โœ… Training arguments configuration') - print(' โœ… Compute metrics function') - print(' โœ… Training execution') - print(' โœ… Evaluation and validation') - print(' โœ… Advanced validation with bias analysis') - print(' โœ… Model saving with verification') - print('\\n๐Ÿš€ COMPREHENSIVE NOTEBOOK IS NOW COMPLETE!') + + print("โœ… Added all comprehensive features!") + print("๐Ÿ“‹ Advanced features added:") + print(" โœ… Model setup with architecture fixes") + print(" โœ… Data preprocessing and splitting") + print(" โœ… Focal loss and class weighting") + print(" โœ… WeightedLossTrainer with advanced loss") + print(" โœ… Data preprocessing function") + print(" โœ… Training arguments configuration") + print(" โœ… Compute metrics function") + print(" โœ… Training execution") + print(" โœ… Evaluation and validation") + print(" โœ… Advanced validation with bias analysis") + print(" โœ… Model saving with verification") + print("\\n๐Ÿš€ COMPREHENSIVE NOTEBOOK IS NOW COMPLETE!") + if __name__ == "__main__": - add_comprehensive_features() \ No newline at end of file + add_comprehensive_features() diff --git a/scripts/legacy/add_wandb_setup.py b/scripts/legacy/add_wandb_setup.py index 35c8bb753..74b4fc596 100644 --- a/scripts/legacy/add_wandb_setup.py +++ b/scripts/legacy/add_wandb_setup.py @@ -9,22 +9,21 @@ import json + def add_wandb_setup(): """Add wandb setup to the minimal notebook.""" - + # Read the existing notebook - with open('notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb', 'r') as f: + with open("notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb", "r") as f: notebook = json.load(f) - + # Add wandb setup cell after the imports wandb_setup_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ”‘ WANDB API KEY SETUP" - ] + "source": ["## ๐Ÿ”‘ WANDB API KEY SETUP"], } - + wandb_setup_code = { "cell_type": "code", "execution_count": None, @@ -93,23 +92,23 @@ def add_wandb_setup(): " print('3. Enter your API key when prompted')\n", " print('\\nโš ๏ธ Continuing without WandB logging...')\n", "\n", - "print('\\nโœ… WandB setup completed')" - ] + "print('\\nโœ… WandB setup completed')", + ], } - + # Find the imports cell and add wandb setup after it - for i, cell in enumerate(notebook['cells']): - if cell['cell_type'] == 'code' and 'import torch' in ''.join(cell['source']): + for i, cell in enumerate(notebook["cells"]): + if cell["cell_type"] == "code" and "import torch" in "".join(cell["source"]): # Insert wandb setup after imports - notebook['cells'].insert(i + 2, wandb_setup_cell) - notebook['cells'].insert(i + 3, wandb_setup_code) + notebook["cells"].insert(i + 2, wandb_setup_cell) + notebook["cells"].insert(i + 3, wandb_setup_code) break - + # Also update the training arguments to disable wandb if no API key - for cell in notebook['cells']: - if cell['cell_type'] == 'code' and 'TrainingArguments(' in ''.join(cell['source']): + for cell in notebook["cells"]: + if cell["cell_type"] == "code" and "TrainingArguments(" in "".join(cell["source"]): # Update training arguments to handle wandb properly - cell['source'] = [ + cell["source"] = [ "# Minimal training arguments - only essential parameters\n", "training_args = TrainingArguments(\n", " output_dir='./minimal_emotion_model',\n", @@ -127,26 +126,27 @@ def add_wandb_setup(): "if 'WANDB_API_KEY' in os.environ:\n", " print('โœ… WandB logging enabled')\n", "else:\n", - " print('โš ๏ธ WandB logging disabled (no API key)')" + " print('โš ๏ธ WandB logging disabled (no API key)')", ] break - + # Save the updated notebook - with open('notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb', 'w') as f: + with open("notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook, f, indent=2) - - print('โœ… Added WandB setup to minimal notebook!') - print('๐Ÿ“‹ Changes made:') - print(' โœ… Added WandB API key setup from Colab secrets') - print(' โœ… Tries multiple possible secret names') - print(' โœ… Graceful fallback if no API key found') - print(' โœ… Updated training arguments to handle WandB properly') - print('\\n๐Ÿ“‹ TO SET UP THE SECRET:') - print('1. Go to Colab โ†’ Settings โ†’ Secrets') - print('2. Add new secret:') - print(' Name: WANDB_API_KEY') - print(' Value: Your API key from https://wandb.ai/authorize') - print('3. Restart runtime and run the notebook') + + print("โœ… Added WandB setup to minimal notebook!") + print("๐Ÿ“‹ Changes made:") + print(" โœ… Added WandB API key setup from Colab secrets") + print(" โœ… Tries multiple possible secret names") + print(" โœ… Graceful fallback if no API key found") + print(" โœ… Updated training arguments to handle WandB properly") + print("\\n๐Ÿ“‹ TO SET UP THE SECRET:") + print("1. Go to Colab โ†’ Settings โ†’ Secrets") + print("2. Add new secret:") + print(" Name: WANDB_API_KEY") + print(" Value: Your API key from https://wandb.ai/authorize") + print("3. Restart runtime and run the notebook") + if __name__ == "__main__": - add_wandb_setup() \ No newline at end of file + add_wandb_setup() diff --git a/scripts/legacy/calibrate_model.py b/scripts/legacy/calibrate_model.py index 964dbb372..b50306d43 100644 --- a/scripts/legacy/calibrate_model.py +++ b/scripts/legacy/calibrate_model.py @@ -1,25 +1,24 @@ - # --- Calibration Search --- - # --- Load Data --- - # --- Load Model --- - # --- Report Results --- -#!/usr/bin/env python3 -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier, EmotionDataset -from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader +# --- Calibration Search --- +# --- Load Data --- +# --- Load Model --- +# --- Report Results --- +import logging +import sys from pathlib import Path + +import numpy as np +import torch from sklearn.metrics import f1_score from torch.utils.data import DataLoader from tqdm import tqdm from transformers import AutoTokenizer -import logging -import numpy as np -import sys -import torch - - - - - +#!/usr/bin/env python3 +from src.models.emotion_detection.bert_classifier import ( + EmotionDataset, + create_bert_emotion_classifier, +) +from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader """ Model Calibration Script @@ -30,6 +29,7 @@ sys.path.append(str(Path.cwd() / "src")) + def calibrate_model(): """Find the best temperature and threshold for the model.""" logging.info("๐Ÿš€ Starting Model Calibration Script") diff --git a/scripts/legacy/comprehensive_model_validation.py b/scripts/legacy/comprehensive_model_validation.py index 61aecd9a7..277625f5e 100644 --- a/scripts/legacy/comprehensive_model_validation.py +++ b/scripts/legacy/comprehensive_model_validation.py @@ -5,28 +5,30 @@ Thoroughly validates the emotion detection model to ensure 100% reliability """ -import torch import json -import numpy as np -from transformers import AutoTokenizer, AutoModelForSequenceClassification -from pathlib import Path import time +from pathlib import Path + +import numpy as np +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + def comprehensive_validation(): """Comprehensive validation of the emotion detection model""" - + print("๐Ÿ”ฌ COMPREHENSIVE MODEL VALIDATION") print("=" * 60) print("๐ŸŽฏ Goal: Verify 99.54% F1 score reliability") print("=" * 60) - + # Check model files - model_dir = Path(__file__).parent.parent / 'deployment' / 'model' - required_files = ['config.json', 'model.safetensors', 'training_args.bin'] - + model_dir = Path(__file__).parent.parent / "deployment" / "model" + required_files = ["config.json", "model.safetensors", "training_args.bin"] + print(f"\n๐Ÿ“ MODEL FILE VALIDATION") print("-" * 40) - + missing_files = [] for file in required_files: file_path = model_dir / file @@ -36,58 +38,71 @@ def comprehensive_validation(): else: print(f"โŒ {file}: MISSING") missing_files.append(file) - + if missing_files: print(f"\nโŒ CRITICAL: Missing files: {missing_files}") return False - + print(f"โœ… All model files present and valid") - + # Load model configuration print(f"\n๐Ÿ”ง MODEL CONFIGURATION VALIDATION") print("-" * 40) - - with open(model_dir / 'config.json', 'r') as f: + + with open(model_dir / "config.json", "r") as f: config = json.load(f) - + print(f"Model Type: {config.get('model_type', 'unknown')}") print(f"Architecture: {config.get('architectures', ['unknown'])[0]}") print(f"Hidden Size: {config.get('hidden_size', 'unknown')}") print(f"Number of Labels: {len(config.get('id2label', {}))}") print(f"Vocab Size: {config.get('vocab_size', 'unknown')}") - + # Define emotion mapping - emotion_mapping = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] + emotion_mapping = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", + ] print(f"Emotion Classes: {len(emotion_mapping)}") - + # Load model and tokenizer print(f"\n๐Ÿ”ง MODEL LOADING VALIDATION") print("-" * 40) - + try: start_time = time.time() tokenizer = AutoTokenizer.from_pretrained("roberta-base") load_time = time.time() - start_time print(f"โœ… Tokenizer loaded: {load_time:.2f}s") - + start_time = time.time() model = AutoModelForSequenceClassification.from_pretrained(str(model_dir)) load_time = time.time() - start_time print(f"โœ… Model loaded: {load_time:.2f}s") - - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) model.eval() print(f"โœ… Model moved to {device}") - + except Exception as e: print(f"โŒ Model loading failed: {str(e)}") return False - + # Test 1: Basic Functionality print(f"\n๐Ÿงช TEST 1: BASIC FUNCTIONALITY") print("-" * 40) - + test_cases = [ ("I'm feeling really happy today!", "happy"), ("I'm so frustrated with this project.", "frustrated"), @@ -100,79 +115,83 @@ def comprehensive_validation(): ("I feel calm and peaceful.", "calm"), ("I'm excited about the new opportunity.", "excited"), ("I feel content with my life.", "content"), - ("I'm hopeful for the future.", "hopeful") + ("I'm hopeful for the future.", "hopeful"), ] - + correct_predictions = 0 total_predictions = len(test_cases) - + for text, expected_emotion in test_cases: try: # Tokenize - inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True) + inputs = tokenizer( + text, return_tensors="pt", truncation=True, max_length=512, padding=True + ) inputs = {k: v.to(device) for k, v in inputs.items()} - + # Predict with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + predicted_emotion = emotion_mapping[predicted_class] is_correct = predicted_emotion == expected_emotion - + if is_correct: correct_predictions += 1 status = "โœ…" else: status = "โŒ" - - print(f"{status} '{text}' โ†’ {predicted_emotion} (expected: {expected_emotion}, confidence: {confidence:.3f})") - + + print( + f"{status} '{text}' โ†’ {predicted_emotion} (expected: {expected_emotion}, confidence: {confidence:.3f})" + ) + except Exception as e: print(f"โŒ Error predicting '{text}': {str(e)}") return False - + accuracy = correct_predictions / total_predictions print(f"\n๐Ÿ“Š Basic Functionality Results:") print(f" Correct: {correct_predictions}/{total_predictions}") print(f" Accuracy: {accuracy:.1%}") - + if accuracy < 0.8: print(f"โŒ CRITICAL: Basic accuracy too low ({accuracy:.1%})") return False - + # Test 2: Confidence Distribution print(f"\n๐Ÿงช TEST 2: CONFIDENCE DISTRIBUTION") print("-" * 40) - + confidence_scores = [] for text, _ in test_cases: inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True) inputs = {k: v.to(device) for k, v in inputs.items()} - + with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) confidence = torch.max(probabilities, dim=1)[0].item() confidence_scores.append(confidence) - + avg_confidence = np.mean(confidence_scores) min_confidence = np.min(confidence_scores) max_confidence = np.max(confidence_scores) - + print(f"Average Confidence: {avg_confidence:.3f}") print(f"Min Confidence: {min_confidence:.3f}") print(f"Max Confidence: {max_confidence:.3f}") - + if avg_confidence < 0.5: print(f"โš ๏ธ WARNING: Low average confidence ({avg_confidence:.3f})") - + # Test 3: Edge Cases print(f"\n๐Ÿงช TEST 3: EDGE CASES") print("-" * 40) - + edge_cases = [ "", # Empty string "a", # Single character @@ -183,114 +202,123 @@ def comprehensive_validation(): "I'M FEELING HAPPY TODAY!", # All caps "i am feeling happy today", # All lowercase ] - + edge_case_success = 0 for text in edge_cases: try: - inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True) + inputs = tokenizer( + text, return_tensors="pt", truncation=True, max_length=512, padding=True + ) inputs = {k: v.to(device) for k, v in inputs.items()} - + with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + predicted_emotion = emotion_mapping[predicted_class] edge_case_success += 1 - print(f"โœ… Edge case handled: '{text[:30]}...' โ†’ {predicted_emotion} ({confidence:.3f})") - + print( + f"โœ… Edge case handled: '{text[:30]}...' โ†’ {predicted_emotion} ({confidence:.3f})" + ) + except Exception as e: print(f"โŒ Edge case failed: '{text[:30]}...' - {str(e)}") - + print(f"\n๐Ÿ“Š Edge Case Results: {edge_case_success}/{len(edge_cases)} successful") - + # Test 4: Performance Benchmark print(f"\n๐Ÿงช TEST 4: PERFORMANCE BENCHMARK") print("-" * 40) - + benchmark_text = "I'm feeling really happy today!" num_iterations = 100 - + start_time = time.time() for _ in range(num_iterations): - inputs = tokenizer(benchmark_text, return_tensors="pt", truncation=True, max_length=512, padding=True) + inputs = tokenizer( + benchmark_text, return_tensors="pt", truncation=True, max_length=512, padding=True + ) inputs = {k: v.to(device) for k, v in inputs.items()} - + with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) - + total_time = time.time() - start_time avg_time = total_time / num_iterations throughput = num_iterations / total_time - + print(f"Total Time: {total_time:.2f}s") print(f"Average Time per Prediction: {avg_time:.4f}s") print(f"Throughput: {throughput:.1f} predictions/second") - + if avg_time > 1.0: print(f"โš ๏ธ WARNING: Slow inference time ({avg_time:.4f}s)") - + # Test 5: Consistency Check print(f"\n๐Ÿงช TEST 5: CONSISTENCY CHECK") print("-" * 40) - + consistency_text = "I'm feeling happy today!" predictions = [] - + for _ in range(10): - inputs = tokenizer(consistency_text, return_tensors="pt", truncation=True, max_length=512, padding=True) + inputs = tokenizer( + consistency_text, return_tensors="pt", truncation=True, max_length=512, padding=True + ) inputs = {k: v.to(device) for k, v in inputs.items()} - + with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + predictions.append((emotion_mapping[predicted_class], confidence)) - + # Check if all predictions are the same unique_predictions = set(pred[0] for pred in predictions) is_consistent = len(unique_predictions) == 1 - + if is_consistent: emotion, avg_conf = unique_predictions.pop(), np.mean([p[1] for p in predictions]) print(f"โœ… Consistent predictions: {emotion} (avg confidence: {avg_conf:.3f})") else: print(f"โŒ Inconsistent predictions: {unique_predictions}") return False - + # Final Validation Summary print(f"\n๐ŸŽฏ FINAL VALIDATION SUMMARY") print("=" * 60) - + validation_results = { "model_files": True, "model_loading": True, "basic_functionality": accuracy >= 0.8, "edge_cases": edge_case_success >= len(edge_cases) * 0.8, "performance": avg_time < 1.0, - "consistency": is_consistent + "consistency": is_consistent, } - + all_passed = all(validation_results.values()) - + for test, passed in validation_results.items(): status = "โœ… PASS" if passed else "โŒ FAIL" print(f"{status} {test.replace('_', ' ').title()}") - + print(f"\n{'๐ŸŽ‰ ALL TESTS PASSED!' if all_passed else 'โŒ SOME TESTS FAILED'}") - + if all_passed: print(f"โœ… Your 99.54% F1 score model is 100% RELIABLE!") print(f"๐Ÿš€ Ready for production deployment!") else: print(f"โš ๏ธ Model needs further validation before deployment") - + return all_passed + if __name__ == "__main__": success = comprehensive_validation() - exit(0 if success else 1) \ No newline at end of file + exit(0 if success else 1) diff --git a/scripts/legacy/compress_model.py b/scripts/legacy/compress_model.py index 8cd254b7d..109efd3d9 100755 --- a/scripts/legacy/compress_model.py +++ b/scripts/legacy/compress_model.py @@ -1,39 +1,41 @@ - # Benchmark original model - # Benchmark quantized model - # Calculate speedup - # Check if input model exists - # Create model - # Create output directory if it doesn't exist - # Define quantization configuration - # Load checkpoint - # Load state dict - # Measure original model size - # Measure quantized model size - # Prepare model for quantization - # Quantize - # Quantize model - # Save compression metrics - # Save quantized model - # Set model to evaluation mode - # Set optimal temperature and threshold - # Benchmark - # Create dummy input (batch_size=1, seq_len=128) - # Warm up -# Add src to path -# Configure logging -# Constants -#!/usr/bin/env python3 -from pathlib import Path -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier +# Benchmark original model +# Benchmark quantized model +# Calculate speedup +# Check if input model exists +# Create model +# Create output directory if it doesn't exist +# Define quantization configuration +# Load checkpoint +# Load state dict +# Measure original model size +# Measure quantized model size +# Prepare model for quantization +# Quantize +# Quantize model +# Save compression metrics +# Save quantized model +# Set model to evaluation mode +# Set optimal temperature and threshold +# Benchmark +# Create dummy input (batch_size=1, seq_len=128) +# Warm up + + import argparse import logging import sys import time -import torch -import torch.quantization +# Constants +#!/usr/bin/env python3 +from pathlib import Path +import torch +import torch.quantization +# Add src to path +# Configure logging +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier """ Compress Model diff --git a/scripts/legacy/convert_to_onnx.py b/scripts/legacy/convert_to_onnx.py index 7ac653e8b..d1b3a86f8 100755 --- a/scripts/legacy/convert_to_onnx.py +++ b/scripts/legacy/convert_to_onnx.py @@ -139,7 +139,7 @@ def wrapper_function(input_ids, attention_mask, token_type_ids): def benchmark_pytorch_inference(model, input_ids, attention_mask, num_runs=50): """Benchmark PyTorch model inference time.""" model.eval() - + # Warm up with torch.no_grad(): for _ in range(10): @@ -161,7 +161,7 @@ def benchmark_onnx_inference(model_path, input_ids, attention_mask, token_type_i # Create ONNX session session = ort.InferenceSession(model_path) - + # Prepare inputs input_feed = { "input_ids": input_ids.numpy(), diff --git a/scripts/legacy/create_bulletproof_cell.py b/scripts/legacy/create_bulletproof_cell.py index 4fa79be07..26461887d 100644 --- a/scripts/legacy/create_bulletproof_cell.py +++ b/scripts/legacy/create_bulletproof_cell.py @@ -3,10 +3,11 @@ Create a bulletproof notebook cell that can be run in a fresh kernel. """ + def create_bulletproof_cell(): """Create a bulletproof training cell.""" - - cell_code = '''# ๐Ÿš€ BULLETPROOF TRAINING CELL - RUN IN FRESH KERNEL + + cell_code = """# ๐Ÿš€ BULLETPROOF TRAINING CELL - RUN IN FRESH KERNEL # Runtime โ†’ Change runtime type โ†’ GPU (T4 or V100) # Kernel โ†’ Restart and run all @@ -138,30 +139,30 @@ def __init__(self, texts, labels, tokenizer, max_length=128): self.labels = labels self.tokenizer = tokenizer self.max_length = max_length - + # Validate data if len(texts) != len(labels): raise ValueError(f"Texts and labels have different lengths: {len(texts)} vs {len(labels)}") - + # Validate labels for i, label in enumerate(labels): if not isinstance(label, int) or label < 0: raise ValueError(f"Invalid label at index {i}: {label}") - + def __len__(self): return len(self.texts) - + def __getitem__(self, idx): text = self.texts[idx] label = self.labels[idx] - + # Validate inputs if not isinstance(text, str) or not text.strip(): raise ValueError(f"Invalid text at index {idx}") - + if not isinstance(label, int) or label < 0: raise ValueError(f"Invalid label at index {idx}: {label}") - + encoding = self.tokenizer( text, truncation=True, @@ -169,7 +170,7 @@ def __getitem__(self, idx): max_length=self.max_length, return_tensors='pt' ) - + return { 'input_ids': encoding['input_ids'].flatten(), 'attention_mask': encoding['attention_mask'].flatten(), @@ -180,33 +181,33 @@ def __getitem__(self, idx): class SimpleEmotionClassifier(nn.Module): def __init__(self, model_name="bert-base-uncased", num_labels=None): super().__init__() - + if num_labels is None or num_labels <= 0: raise ValueError(f"Invalid num_labels: {num_labels}") - + self.num_labels = num_labels self.bert = AutoModel.from_pretrained(model_name) self.dropout = nn.Dropout(0.3) self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels) - + print(f"โœ… Model initialized with {num_labels} labels") - + def forward(self, input_ids, attention_mask): # Validate inputs if input_ids.dim() != 2: raise ValueError(f"Expected input_ids to be 2D, got {input_ids.dim()}D") - + if attention_mask.dim() != 2: raise ValueError(f"Expected attention_mask to be 2D, got {attention_mask.dim()}D") - + outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) pooled_output = outputs.pooler_output logits = self.classifier(self.dropout(pooled_output)) - + # Validate outputs if logits.shape[-1] != self.num_labels: raise ValueError(f"Expected {self.num_labels} output classes, got {logits.shape[-1]}") - + return logits # Step 7: Setup training @@ -252,12 +253,12 @@ def forward(self, input_ids, attention_mask): for epoch in range(num_epochs): print(f"\\n๐Ÿ”„ Epoch {epoch + 1}/{num_epochs}") - + # Training model.train() total_loss = 0 num_batches = 0 - + # Train on GoEmotions print(" ๐Ÿ“š Training on GoEmotions...") for i, batch in enumerate(go_loader): @@ -266,34 +267,34 @@ def forward(self, input_ids, attention_mask): if 'input_ids' not in batch or 'attention_mask' not in batch or 'labels' not in batch: print(f"โš ๏ธ Invalid batch structure at batch {i}") continue - + # Move to device with validation input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + # Validate labels if torch.any(labels >= num_labels) or torch.any(labels < 0): print(f"โš ๏ธ Invalid labels in batch {i}: {labels}") continue - + # Forward pass optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() num_batches += 1 - + if i % 50 == 0: print(f" Batch {i}/{len(go_loader)}, Loss: {loss.item():.4f}") - + except Exception as e: print(f"โŒ Error in batch {i}: {e}") continue - + # Train on journal data print(" ๐Ÿ“ Training on journal data...") for i, batch in enumerate(journal_train_loader): @@ -301,67 +302,67 @@ def forward(self, input_ids, attention_mask): input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + if torch.any(labels >= num_labels) or torch.any(labels < 0): continue - + optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() num_batches += 1 - + if i % 10 == 0: print(f" Batch {i}/{len(journal_train_loader)}, Loss: {loss.item():.4f}") - + except Exception as e: print(f"โŒ Error in journal batch {i}: {e}") continue - + # Validation print(" ๐ŸŽฏ Validating...") model.eval() all_preds = [] all_labels = [] - + with torch.no_grad(): for batch in journal_val_loader: try: input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + outputs = model(input_ids=input_ids, attention_mask=attention_mask) preds = torch.argmax(outputs, dim=1) - + all_preds.extend(preds.cpu().numpy()) all_labels.extend(labels.cpu().numpy()) - + except Exception as e: print(f"โŒ Error in validation batch: {e}") continue - + # Calculate metrics if all_preds and all_labels: f1_macro = f1_score(all_labels, all_preds, average='macro') accuracy = accuracy_score(all_labels, all_preds) - + avg_loss = total_loss / num_batches if num_batches > 0 else 0 - + print(f" ๐Ÿ“Š Epoch {epoch + 1} Results:") print(f" Average Loss: {avg_loss:.4f}") print(f" Validation F1 (Macro): {f1_macro:.4f}") print(f" Validation Accuracy: {accuracy:.4f}") - + # Save best model if f1_macro > best_f1: best_f1 = f1_macro torch.save(model.state_dict(), 'best_simple_model.pth') print(f" ๐Ÿ’พ New best model saved! F1: {best_f1:.4f}") - + # Clear GPU cache if torch.cuda.is_available(): torch.cuda.empty_cache() @@ -390,12 +391,12 @@ def forward(self, input_ids, attention_mask): files.download('simple_training_results.json') print("\\n๐ŸŽ‰ BULLETPROOF TRAINING COMPLETED!") -print("๐Ÿ“ Files downloaded: best_simple_model.pth, simple_training_results.json")''' - +print("๐Ÿ“ Files downloaded: best_simple_model.pth, simple_training_results.json")""" + # Write to file - with open('bulletproof_training_cell.py', 'w') as f: + with open("bulletproof_training_cell.py", "w") as f: f.write(cell_code) - + print("โœ… Created bulletproof training cell: bulletproof_training_cell.py") print("๐Ÿ“‹ Instructions:") print("1. Copy the code from bulletproof_training_cell.py") @@ -405,5 +406,6 @@ def forward(self, input_ids, attention_mask): print("5. Run the cell") print("6. This will work in a fresh kernel without any state corruption!") + if __name__ == "__main__": - create_bulletproof_cell() \ No newline at end of file + create_bulletproof_cell() diff --git a/scripts/legacy/create_final_bulletproof_cell.py b/scripts/legacy/create_final_bulletproof_cell.py index 499fb7be0..0f01299ed 100644 --- a/scripts/legacy/create_final_bulletproof_cell.py +++ b/scripts/legacy/create_final_bulletproof_cell.py @@ -3,10 +3,11 @@ Create the FINAL bulletproof training cell with proper integer-to-emotion mapping. """ + def create_final_bulletproof_cell(): """Create the final bulletproof cell with proper label mapping.""" - - cell_code = '''# ๐Ÿš€ FINAL BULLETPROOF TRAINING CELL - PROPER LABEL MAPPING + + cell_code = """# ๐Ÿš€ FINAL BULLETPROOF TRAINING CELL - PROPER LABEL MAPPING # Runtime โ†’ Change runtime type โ†’ GPU (T4 or V100) # Kernel โ†’ Restart and run all @@ -174,30 +175,30 @@ def __init__(self, texts, labels, tokenizer, max_length=128): self.labels = labels self.tokenizer = tokenizer self.max_length = max_length - + # Validate data if len(texts) != len(labels): raise ValueError(f"Texts and labels have different lengths: {len(texts)} vs {len(labels)}") - + # Validate labels for i, label in enumerate(labels): if not isinstance(label, int) or label < 0: raise ValueError(f"Invalid label at index {i}: {label}") - + def __len__(self): return len(self.texts) - + def __getitem__(self, idx): text = self.texts[idx] label = self.labels[idx] - + # Validate inputs if not isinstance(text, str) or not text.strip(): raise ValueError(f"Invalid text at index {idx}") - + if not isinstance(label, int) or label < 0: raise ValueError(f"Invalid label at index {idx}: {label}") - + encoding = self.tokenizer( text, truncation=True, @@ -205,7 +206,7 @@ def __getitem__(self, idx): max_length=self.max_length, return_tensors='pt' ) - + return { 'input_ids': encoding['input_ids'].flatten(), 'attention_mask': encoding['attention_mask'].flatten(), @@ -216,33 +217,33 @@ def __getitem__(self, idx): class SimpleEmotionClassifier(nn.Module): def __init__(self, model_name="bert-base-uncased", num_labels=None): super().__init__() - + if num_labels is None or num_labels <= 0: raise ValueError(f"Invalid num_labels: {num_labels}") - + self.num_labels = num_labels self.bert = AutoModel.from_pretrained(model_name) self.dropout = nn.Dropout(0.3) self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels) - + print(f"โœ… Model initialized with {num_labels} labels") - + def forward(self, input_ids, attention_mask): # Validate inputs if input_ids.dim() != 2: raise ValueError(f"Expected input_ids to be 2D, got {input_ids.dim()}D") - + if attention_mask.dim() != 2: raise ValueError(f"Expected attention_mask to be 2D, got {attention_mask.dim()}D") - + outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) pooled_output = outputs.pooler_output logits = self.classifier(self.dropout(pooled_output)) - + # Validate outputs if logits.shape[-1] != self.num_labels: raise ValueError(f"Expected {self.num_labels} output classes, got {logits.shape[-1]}") - + return logits # Step 9: Setup training @@ -288,12 +289,12 @@ def forward(self, input_ids, attention_mask): for epoch in range(num_epochs): print(f"\\n๐Ÿ”„ Epoch {epoch + 1}/{num_epochs}") - + # Training model.train() total_loss = 0 num_batches = 0 - + # Train on GoEmotions print(" ๐Ÿ“š Training on GoEmotions...") for i, batch in enumerate(go_loader): @@ -302,34 +303,34 @@ def forward(self, input_ids, attention_mask): if 'input_ids' not in batch or 'attention_mask' not in batch or 'labels' not in batch: print(f"โš ๏ธ Invalid batch structure at batch {i}") continue - + # Move to device with validation input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + # Validate labels if torch.any(labels >= num_labels) or torch.any(labels < 0): print(f"โš ๏ธ Invalid labels in batch {i}: {labels}") continue - + # Forward pass optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() num_batches += 1 - + if i % 50 == 0: print(f" Batch {i}/{len(go_loader)}, Loss: {loss.item():.4f}") - + except Exception as e: print(f"โŒ Error in batch {i}: {e}") continue - + # Train on journal data print(" ๐Ÿ“ Training on journal data...") for i, batch in enumerate(journal_train_loader): @@ -337,67 +338,67 @@ def forward(self, input_ids, attention_mask): input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + if torch.any(labels >= num_labels) or torch.any(labels < 0): continue - + optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() num_batches += 1 - + if i % 10 == 0: print(f" Batch {i}/{len(journal_train_loader)}, Loss: {loss.item():.4f}") - + except Exception as e: print(f"โŒ Error in journal batch {i}: {e}") continue - + # Validation print(" ๐ŸŽฏ Validating...") model.eval() all_preds = [] all_labels = [] - + with torch.no_grad(): for batch in journal_val_loader: try: input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + outputs = model(input_ids=input_ids, attention_mask=attention_mask) preds = torch.argmax(outputs, dim=1) - + all_preds.extend(preds.cpu().numpy()) all_labels.extend(labels.cpu().numpy()) - + except Exception as e: print(f"โŒ Error in validation batch: {e}") continue - + # Calculate metrics if all_preds and all_labels: f1_macro = f1_score(all_labels, all_preds, average='macro') accuracy = accuracy_score(all_labels, all_preds) - + avg_loss = total_loss / num_batches if num_batches > 0 else 0 - + print(f" ๐Ÿ“Š Epoch {epoch + 1} Results:") print(f" Average Loss: {avg_loss:.4f}") print(f" Validation F1 (Macro): {f1_macro:.4f}") print(f" Validation Accuracy: {accuracy:.4f}") - + # Save best model if f1_macro > best_f1: best_f1 = f1_macro torch.save(model.state_dict(), 'best_simple_model.pth') print(f" ๐Ÿ’พ New best model saved! F1: {best_f1:.4f}") - + # Clear GPU cache if torch.cuda.is_available(): torch.cuda.empty_cache() @@ -431,15 +432,16 @@ def forward(self, input_ids, attention_mask): print("๐Ÿ“ Files downloaded: best_simple_model.pth, simple_training_results.json") print("\\n๐Ÿ”ฅ THIS VERSION HAS PROPER INTEGER-TO-EMOTION MAPPING!") print("๐Ÿ”ฅ NO MORE ZERO SAMPLES ISSUE!") -print("๐Ÿ”ฅ READY TO ACHIEVE 70% F1 SCORE!")''' - +print("๐Ÿ”ฅ READY TO ACHIEVE 70% F1 SCORE!")""" + # Write to file - with open('final_bulletproof_training_cell.py', 'w') as f: + with open("final_bulletproof_training_cell.py", "w") as f: f.write(cell_code) - + print("โœ… Created FINAL bulletproof training cell: final_bulletproof_training_cell.py") print("๐Ÿ“‹ This version has PROPER INTEGER-TO-EMOTION MAPPING!") print("๐ŸŽฏ This will solve the zero samples issue!") + if __name__ == "__main__": - create_final_bulletproof_cell() \ No newline at end of file + create_final_bulletproof_cell() diff --git a/scripts/legacy/create_unique_fallback_dataset.py b/scripts/legacy/create_unique_fallback_dataset.py index 8386cc31d..d035d856f 100644 --- a/scripts/legacy/create_unique_fallback_dataset.py +++ b/scripts/legacy/create_unique_fallback_dataset.py @@ -9,12 +9,13 @@ import json import random + def create_unique_fallback_dataset(): """Create a unique fallback dataset with no duplicates""" - + # Define unique templates for each emotion with variations emotion_templates = { - 'happy': [ + "happy": [ "I'm feeling really happy today! Everything is going well.", "I'm so happy with how things turned out.", "I feel joyful and content right now.", @@ -26,9 +27,9 @@ def create_unique_fallback_dataset(): "I'm feeling cheerful and upbeat.", "I'm happy about the opportunities ahead.", "I feel blessed and grateful for today.", - "I'm excited and happy about the future." + "I'm excited and happy about the future.", ], - 'frustrated': [ + "frustrated": [ "I'm so frustrated with this project. Nothing is working.", "I'm getting really annoyed with these constant issues.", "I feel irritated by the lack of progress.", @@ -40,9 +41,9 @@ def create_unique_fallback_dataset(): "I feel irritated by the lack of support.", "I'm annoyed with the constant delays.", "I'm frustrated with the unclear instructions.", - "I feel exasperated with these obstacles." + "I feel exasperated with these obstacles.", ], - 'anxious': [ + "anxious": [ "I feel anxious about the upcoming presentation.", "I'm worried about the meeting tomorrow.", "I feel nervous about the interview.", @@ -54,9 +55,9 @@ def create_unique_fallback_dataset(): "I feel worried about the future.", "I'm nervous about the performance review.", "I feel uneasy about the changes.", - "I'm anxious about the responsibilities." + "I'm anxious about the responsibilities.", ], - 'grateful': [ + "grateful": [ "I'm grateful for all the support I've received.", "I feel thankful for the opportunities given to me.", "I'm grateful for the help from my friends.", @@ -68,9 +69,9 @@ def create_unique_fallback_dataset(): "I'm grateful for the kindness shown to me.", "I feel thankful for the understanding.", "I'm grateful for the patience of others.", - "I feel blessed for the love and support." + "I feel blessed for the love and support.", ], - 'overwhelmed': [ + "overwhelmed": [ "I'm feeling overwhelmed with all these tasks.", "I feel swamped with the amount of work.", "I'm overwhelmed by the responsibilities.", @@ -82,9 +83,9 @@ def create_unique_fallback_dataset(): "I'm overwhelmed by the changes.", "I feel swamped with the demands.", "I'm overwhelmed by the uncertainty.", - "I feel buried under the workload." + "I feel buried under the workload.", ], - 'proud': [ + "proud": [ "I'm proud of what I've accomplished so far.", "I feel proud of my achievements.", "I'm proud of how far I've come.", @@ -96,9 +97,9 @@ def create_unique_fallback_dataset(): "I'm proud of the skills I've developed.", "I feel proud of my resilience.", "I'm proud of the challenges I've overcome.", - "I feel proud of my determination." + "I feel proud of my determination.", ], - 'sad': [ + "sad": [ "I'm feeling sad and lonely today.", "I feel down about the recent events.", "I'm sad about the loss I experienced.", @@ -110,9 +111,9 @@ def create_unique_fallback_dataset(): "I'm saddened by the lack of progress.", "I feel melancholy about the changes.", "I'm sad about the broken promises.", - "I feel down about the setbacks." + "I feel down about the setbacks.", ], - 'excited': [ + "excited": [ "I'm excited about the new opportunities ahead.", "I feel thrilled about the upcoming adventure.", "I'm excited about the possibilities.", @@ -124,9 +125,9 @@ def create_unique_fallback_dataset(): "I'm excited about the potential outcomes.", "I feel thrilled about the new experiences.", "I'm excited about the growth opportunities.", - "I feel enthusiastic about the journey ahead." + "I feel enthusiastic about the journey ahead.", ], - 'calm': [ + "calm": [ "I feel calm and peaceful right now.", "I'm feeling serene and relaxed.", "I feel tranquil about the situation.", @@ -138,9 +139,9 @@ def create_unique_fallback_dataset(): "I feel tranquil and centered.", "I'm feeling peaceful and balanced.", "I feel calm about the future.", - "I'm serene about the present moment." + "I'm serene about the present moment.", ], - 'hopeful': [ + "hopeful": [ "I'm hopeful that things will get better.", "I feel optimistic about the future.", "I'm hopeful about the possibilities ahead.", @@ -152,9 +153,9 @@ def create_unique_fallback_dataset(): "I'm hopeful that we'll find solutions.", "I feel optimistic about the results.", "I'm hopeful about the positive changes.", - "I feel optimistic about the journey ahead." + "I feel optimistic about the journey ahead.", ], - 'tired': [ + "tired": [ "I'm tired and need some rest.", "I feel exhausted from the long day.", "I'm tired of dealing with these issues.", @@ -166,9 +167,9 @@ def create_unique_fallback_dataset(): "I'm tired of the ongoing problems.", "I feel exhausted from the demands.", "I'm tired of the uncertainty.", - "I feel worn out from the responsibilities." + "I feel worn out from the responsibilities.", ], - 'content': [ + "content": [ "I'm content with how things are going.", "I feel satisfied with the current situation.", "I'm content with my progress.", @@ -180,58 +181,57 @@ def create_unique_fallback_dataset(): "I'm content with the direction.", "I feel satisfied with the achievements.", "I'm content with the balance in my life.", - "I feel satisfied with the growth experienced." - ] + "I feel satisfied with the growth experienced.", + ], } - + # Create unique samples unique_samples = [] - + for emotion, templates in emotion_templates.items(): for i, template in enumerate(templates): - unique_samples.append({ - 'text': template, - 'emotion': emotion, - 'sample_id': f"{emotion}_{i+1}" - }) - + unique_samples.append( + {"text": template, "emotion": emotion, "sample_id": f"{emotion}_{i+1}"} + ) + # Shuffle the samples for better training random.shuffle(unique_samples) - + print(f"โœ… Created {len(unique_samples)} UNIQUE samples") print(f"๐Ÿ“Š Samples per emotion: {len(unique_samples) // 12}") - + # Verify no duplicates - texts = [sample['text'] for sample in unique_samples] + texts = [sample["text"] for sample in unique_samples] unique_texts = set(texts) print(f"๐Ÿ” Duplicate check: {len(texts)} total, {len(unique_texts)} unique") - + if len(texts) != len(unique_texts): print("โŒ WARNING: DUPLICATES FOUND!") return None - + print("โœ… All samples are unique!") - + # Save the dataset - with open('data/unique_fallback_dataset.json', 'w') as f: + with open("data/unique_fallback_dataset.json", "w") as f: json.dump(unique_samples, f, indent=2) - + print("๐Ÿ’พ Saved unique fallback dataset to data/unique_fallback_dataset.json") - + # Show emotion distribution emotion_counts = {} for sample in unique_samples: - emotion = sample['emotion'] + emotion = sample["emotion"] emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1 - + print("\n๐Ÿ“Š Emotion Distribution:") for emotion, count in sorted(emotion_counts.items()): print(f" {emotion}: {count} samples") - + return unique_samples + if __name__ == "__main__": print("๐Ÿš€ CREATE UNIQUE FALLBACK DATASET") print("=" * 40) create_unique_fallback_dataset() - print("\n๐ŸŽ‰ Unique fallback dataset created successfully!") \ No newline at end of file + print("\n๐ŸŽ‰ Unique fallback dataset created successfully!") diff --git a/scripts/legacy/deep_model_analysis.py b/scripts/legacy/deep_model_analysis.py index c1683680a..dc59653e5 100644 --- a/scripts/legacy/deep_model_analysis.py +++ b/scripts/legacy/deep_model_analysis.py @@ -5,39 +5,54 @@ Analyzes the model's behavior to understand performance discrepancies """ -import torch -from transformers import AutoTokenizer, AutoModelForSequenceClassification from pathlib import Path +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + + def deep_model_analysis(): """Deep analysis of the model's behavior""" - + print("๐Ÿ” DEEP MODEL ANALYSIS") print("=" * 50) print("๐ŸŽฏ Goal: Understand 99.54% F1 vs 58.3% basic accuracy") print("=" * 50) - + # Load model - model_dir = Path(__file__).parent.parent / 'deployment' / 'model' + model_dir = Path(__file__).parent.parent / "deployment" / "model" tokenizer = AutoTokenizer.from_pretrained("roberta-base") model = AutoModelForSequenceClassification.from_pretrained(str(model_dir)) - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) model.eval() - + # Define emotion mapping - emotion_mapping = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] - + emotion_mapping = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", + ] + print(f"\n๐Ÿ“Š EMOTION MAPPING ANALYSIS") print("-" * 40) print("Current mapping (LABEL_0 to LABEL_11):") for i, emotion in enumerate(emotion_mapping): print(f" LABEL_{i} โ†’ {emotion}") - + # Test with different variations print(f"\n๐Ÿงช DETAILED PREDICTION ANALYSIS") print("-" * 40) - + test_cases = [ ("I'm grateful for all the support.", "grateful"), ("I'm feeling overwhelmed with tasks.", "overwhelmed"), @@ -45,82 +60,93 @@ def deep_model_analysis(): ("I'm excited about the new opportunity.", "excited"), ("I'm hopeful for the future.", "hopeful"), ] - + for text, expected_emotion in test_cases: print(f"\n๐Ÿ“ Text: '{text}'") print(f"๐ŸŽฏ Expected: {expected_emotion}") - + # Tokenize inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True) inputs = {k: v.to(device) for k, v in inputs.items()} - + # Get all probabilities with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) - + # Get top 3 predictions top_probs, top_indices = torch.topk(probabilities[0], 3) - + print(f"๐Ÿ” Top 3 predictions:") for i, (prob, idx) in enumerate(zip(top_probs, top_indices)): emotion = emotion_mapping[idx.item()] print(f" {i+1}. {emotion}: {prob.item():.3f}") - + # Check if expected emotion is in top 3 expected_idx = emotion_mapping.index(expected_emotion) expected_prob = probabilities[0][expected_idx].item() print(f"๐Ÿ“Š Expected emotion '{expected_emotion}' probability: {expected_prob:.3f}") - + # Analyze model confidence patterns print(f"\n๐Ÿ“ˆ CONFIDENCE PATTERN ANALYSIS") print("-" * 40) - + confidence_by_emotion = {emotion: [] for emotion in emotion_mapping} - + # Test with simple emotion words simple_tests = [ - "happy", "sad", "angry", "excited", "calm", "anxious", "proud", "grateful", "hopeful", "tired", "content", "overwhelmed" + "happy", + "sad", + "angry", + "excited", + "calm", + "anxious", + "proud", + "grateful", + "hopeful", + "tired", + "content", + "overwhelmed", ] - + for word in simple_tests: inputs = tokenizer(word, return_tensors="pt", truncation=True, max_length=512, padding=True) inputs = {k: v.to(device) for k, v in inputs.items()} - + with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + predicted_emotion = emotion_mapping[predicted_class] confidence_by_emotion[predicted_emotion].append(confidence) - + print(f"'{word}' โ†’ {predicted_emotion} (confidence: {confidence:.3f})") - + # Check for bias towards certain emotions print(f"\n๐ŸŽฏ EMOTION BIAS ANALYSIS") print("-" * 40) - + emotion_counts = {} for emotion in emotion_mapping: emotion_counts[emotion] = len(confidence_by_emotion[emotion]) - + print("Prediction frequency by emotion:") for emotion, count in sorted(emotion_counts.items(), key=lambda x: x[1], reverse=True): print(f" {emotion}: {count} predictions") - + # Check if model is biased towards certain emotions most_common = max(emotion_counts.items(), key=lambda x: x[1]) print(f"\nโš ๏ธ Most predicted emotion: {most_common[0]} ({most_common[1]} times)") - + if most_common[1] > len(simple_tests) * 0.3: print(f"โŒ WARNING: Model shows bias towards '{most_common[0]}'") - + # Test with training-like data print(f"\n๐ŸŽ“ TRAINING-LIKE DATA TEST") print("-" * 40) - + # These should be more similar to what the model was trained on training_like_tests = [ "I am feeling really happy today!", @@ -134,29 +160,29 @@ def deep_model_analysis(): "I feel calm and peaceful.", "I am excited about the new opportunity.", "I feel content with my life.", - "I am hopeful for the future." + "I am hopeful for the future.", ] - + correct_training_like = 0 for text in training_like_tests: inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True) inputs = {k: v.to(device) for k, v in inputs.items()} - + with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + predicted_emotion = emotion_mapping[predicted_class] - + # Extract expected emotion from text expected_emotion = None for emotion in emotion_mapping: if emotion in text.lower(): expected_emotion = emotion break - + if expected_emotion: is_correct = predicted_emotion == expected_emotion if is_correct: @@ -164,16 +190,18 @@ def deep_model_analysis(): status = "โœ…" else: status = "โŒ" - - print(f"{status} '{text}' โ†’ {predicted_emotion} (expected: {expected_emotion}, confidence: {confidence:.3f})") - + + print( + f"{status} '{text}' โ†’ {predicted_emotion} (expected: {expected_emotion}, confidence: {confidence:.3f})" + ) + training_like_accuracy = correct_training_like / len(training_like_tests) print(f"\n๐Ÿ“Š Training-like accuracy: {training_like_accuracy:.1%}") - + # Final analysis print(f"\n๐Ÿ” ANALYSIS SUMMARY") print("=" * 50) - + if training_like_accuracy > 0.8: print(f"โœ… Model performs well on training-like data ({training_like_accuracy:.1%})") print(f"โš ๏ธ Issue: Model may be overfitting to specific training patterns") @@ -182,9 +210,10 @@ def deep_model_analysis(): print(f"โŒ Model performs poorly even on training-like data ({training_like_accuracy:.1%})") print(f"โš ๏ธ Issue: Fundamental problem with model training or label mapping") print(f"๐Ÿ’ก Solution: Retrain model with better data or check label mapping") - + return training_like_accuracy > 0.8 + if __name__ == "__main__": success = deep_model_analysis() - exit(0 if success else 1) \ No newline at end of file + exit(0 if success else 1) diff --git a/scripts/legacy/diagnose_f1_issue.py b/scripts/legacy/diagnose_f1_issue.py deleted file mode 100644 index 0fd24008f..000000000 --- a/scripts/legacy/diagnose_f1_issue.py +++ /dev/null @@ -1,247 +0,0 @@ -#!/usr/bin/env python3 -""" -Diagnose F1 Score Issue - -This script investigates why F1 scores are 0% despite good training loss. -It checks label formats, prediction outputs, and evaluation logic. - -Usage: - python3 diagnose_f1_issue.py -""" - -import logging -import numpy as np -import torch -from pathlib import Path -from sklearn.metrics import f1_score, precision_score, recall_score -from torch import nn -from transformers import AutoModel, AutoTokenizer -import sys - -# Configure logging -logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") -logger = logging.getLogger(__name__) - - -class SimpleBERTClassifier(nn.Module): - """Simple BERT classifier for emotion detection.""" - - def __init__(self, model_name="bert-base-uncased", num_classes=28): - super().__init__() - self.bert = AutoModel.from_pretrained(model_name) - self.classifier = nn.Linear(self.bert.config.hidden_size, num_classes) - self.tokenizer = AutoTokenizer.from_pretrained(model_name) - - def forward(self, input_ids, attention_mask): - outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) - logits = self.classifier(outputs.last_hidden_state[:, 0, :]) # Use [CLS] token - return logits - - -def load_trained_model(model_path): - """Load the trained model.""" - logger.info(f"๐Ÿ“‚ Loading trained model from {model_path}") - - model = SimpleBERTClassifier(model_name="bert-base-uncased", num_classes=28) - checkpoint = torch.load(model_path, map_location="cpu") - model.load_state_dict(checkpoint["model_state_dict"]) - - logger.info("โœ… Model loaded successfully") - return model - - -def create_test_data(): - """Create test data with proper emotion labels.""" - logger.info("๐Ÿ“Š Creating test data with proper emotion labels...") - - # Create test examples with proper emotion labels - test_data = [ - { - "text": "I am so happy today!", - "labels": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # joy - }, - { - "text": "This makes me very angry!", - "labels": [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # anger - }, - { - "text": "I feel sad and disappointed.", - "labels": [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0] # disappointment, sadness - }, - { - "text": "This is amazing and exciting!", - "labels": [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # admiration, excitement - }, - { - "text": "I'm neutral about this.", - "labels": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] # neutral - } - ] - - logger.info(f"โœ… Created {len(test_data)} test examples") - return test_data - - -def diagnose_predictions(model, test_data, device): - """Diagnose predictions and evaluation logic.""" - logger.info("๐Ÿ” Diagnosing predictions...") - - model.eval() - results = [] - - for i, example in enumerate(test_data): - text = example["text"] - true_labels = example["labels"] - - # Tokenize - inputs = model.tokenizer( - text, - return_tensors="pt", - truncation=True, - max_length=512, - padding=True - ) - - input_ids = inputs["input_ids"].to(device) - attention_mask = inputs["attention_mask"].to(device) - - # Get predictions - with torch.no_grad(): - logits = model(input_ids=input_ids, attention_mask=attention_mask) - predictions = torch.sigmoid(logits) - - # Convert to numpy - pred_np = predictions.cpu().numpy()[0] - true_np = np.array(true_labels) - - # Calculate metrics - f1_macro = f1_score(true_np, pred_np > 0.5, average='macro', zero_division=0) - f1_micro = f1_score(true_np, pred_np > 0.5, average='micro', zero_division=0) - precision = precision_score(true_np, pred_np > 0.5, average='macro', zero_division=0) - recall = recall_score(true_np, pred_np > 0.5, average='macro', zero_division=0) - - results.append({ - "text": text, - "true_labels": true_labels, - "predictions": pred_np.tolist(), - "f1_macro": f1_macro, - "f1_micro": f1_micro, - "precision": precision, - "recall": recall - }) - - logger.info(f"๐Ÿ“Š Example {i+1}:") - logger.info(f" Text: {text}") - logger.info(f" True labels: {true_labels}") - logger.info(f" Predictions: {pred_np.tolist()}") - logger.info(f" F1 Macro: {f1_macro:.4f}") - logger.info(f" F1 Micro: {f1_micro:.4f}") - logger.info(f" Precision: {precision:.4f}") - logger.info(f" Recall: {recall:.4f}") - - return results - - -def test_evaluation_logic(): - """Test evaluation logic with synthetic data.""" - logger.info("๐Ÿงช Testing evaluation logic with synthetic data...") - - # Create synthetic data - num_samples = 100 - num_classes = 28 - rng = np.random.default_rng() - - # Perfect predictions - perfect_true = rng.integers(0, 2, (num_samples, num_classes)) - perfect_pred = perfect_true.copy() - perfect_f1 = f1_score(perfect_true, perfect_pred, average='macro', zero_division=0) - logger.info(f"โœ… Perfect predictions F1: {perfect_f1:.4f}") - - # Random predictions - random_pred = rng.integers(0, 2, (num_samples, num_classes)) - random_f1 = f1_score(perfect_true, random_pred, average='macro', zero_division=0) - logger.info(f"๐Ÿ“Š Random predictions F1: {random_f1:.4f}") - - # All ones predictions - all_ones_pred = np.ones((num_samples, num_classes)) - all_ones_f1 = f1_score(perfect_true, all_ones_pred, average='macro', zero_division=0) - logger.info(f"๐Ÿ“Š All ones predictions F1: {all_ones_f1:.4f}") - - # All zeros predictions - all_zeros_pred = np.zeros((num_samples, num_classes)) - all_zeros_f1 = f1_score(perfect_true, all_zeros_pred, average='macro', zero_division=0) - logger.info(f"๐Ÿ“Š All zeros predictions F1: {all_zeros_f1:.4f}") - - # Test different thresholds - thresholds = [0.1, 0.3, 0.5, 0.7, 0.9] - for threshold in thresholds: - threshold_pred = (perfect_pred > threshold).astype(int) - threshold_f1 = f1_score(perfect_true, threshold_pred, average='macro', zero_division=0) - logger.info(f"๐Ÿ“Š Threshold {threshold} F1: {threshold_f1:.4f}") - - return True - - -def main(): - """Main function.""" - logger.info("๐Ÿš€ Starting F1 Score Diagnosis...") - - # Setup device - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - logger.info(f"๐Ÿ”ง Using device: {device}") - - # Test evaluation logic first - if not test_evaluation_logic(): - logger.error("โŒ Evaluation logic test failed") - return False - - # Check if model file exists - model_path = Path("test_checkpoints/best_model.pt") - if not model_path.exists(): - logger.warning(f"โš ๏ธ Model file not found: {model_path}") - logger.info("๐Ÿ“Š Running diagnosis with synthetic data only") - return True - - try: - # Load trained model - model = load_trained_model(model_path) - model.to(device) - - # Create test data - test_data = create_test_data() - - # Diagnose predictions - results = diagnose_predictions(model, test_data, device) - - # Summary - avg_f1_macro = np.mean([r["f1_macro"] for r in results]) - avg_f1_micro = np.mean([r["f1_micro"] for r in results]) - avg_precision = np.mean([r["precision"] for r in results]) - avg_recall = np.mean([r["recall"] for r in results]) - - logger.info("๐Ÿ“‹ Summary:") - logger.info(f" Average F1 Macro: {avg_f1_macro:.4f}") - logger.info(f" Average F1 Micro: {avg_f1_micro:.4f}") - logger.info(f" Average Precision: {avg_precision:.4f}") - logger.info(f" Average Recall: {avg_recall:.4f}") - - if avg_f1_macro < 0.1: - logger.warning("โš ๏ธ Very low F1 scores detected!") - logger.info(" Possible issues:") - logger.info(" - Label format mismatch") - logger.info(" - Threshold too high/low") - logger.info(" - Model not trained properly") - logger.info(" - Evaluation logic error") - - logger.info("๐ŸŽ‰ F1 Score Diagnosis Complete!") - return True - - except Exception as e: - logger.error(f"โŒ Diagnosis failed: {e}") - return False - - -if __name__ == "__main__": - success = main() - if not success: - sys.exit(1) diff --git a/scripts/legacy/diagnose_model_issue.py b/scripts/legacy/diagnose_model_issue.py index d7a927f96..4ac40f435 100644 --- a/scripts/legacy/diagnose_model_issue.py +++ b/scripts/legacy/diagnose_model_issue.py @@ -1,28 +1,29 @@ - # Check if all probabilities are high - # Forward pass - # Sample analysis - # Check gradients - # Create fake logits and labels - # Create simple test case - # Create trainer - # Get a few samples from validation set - # Get one batch for detailed analysis - # Load trained model - # Prepare data - # Set some emotions as positive - # Test BCE loss - # Test with class weights -# Add src to path -# Configure logging -#!/usr/bin/env python3 -from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer -from pathlib import Path +# Check if all probabilities are high +# Forward pass +# Sample analysis +# Check gradients +# Create fake logits and labels +# Create simple test case +# Create trainer +# Get a few samples from validation set +# Get one batch for detailed analysis +# Load trained model +# Prepare data +# Set some emotions as positive +# Test BCE loss +# Test with class weights + import logging import sys -import torch +# Add src to path +from pathlib import Path +import torch +# Configure logging +#!/usr/bin/env python3 +from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer """Diagnose Model Issue - Why is the model predicting all emotions? diff --git a/scripts/legacy/evaluate_focal_model.py b/scripts/legacy/evaluate_focal_model.py deleted file mode 100644 index 3c5cfe063..000000000 --- a/scripts/legacy/evaluate_focal_model.py +++ /dev/null @@ -1,305 +0,0 @@ -#!/usr/bin/env python3 -""" -Evaluate Focal Loss Trained Model - -This script evaluates the trained focal loss model and calculates F1 scores. -It also implements threshold optimization to improve performance. - -Usage: - python3 evaluate_focal_model.py -""" - -import json -import logging -import numpy as np -import sys -import torch -from pathlib import Path -from sklearn.metrics import f1_score, precision_score, recall_score -from torch import nn -from tqdm import tqdm -from transformers import AutoModel, AutoTokenizer - -# Configure logging -logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") -logger = logging.getLogger(__name__) - - -class SimpleBERTClassifier(nn.Module): - """Simple BERT classifier for emotion detection.""" - - def __init__(self, model_name="bert-base-uncased", num_classes=28): - super().__init__() - self.bert = AutoModel.from_pretrained(model_name) - self.classifier = nn.Linear(self.bert.config.hidden_size, num_classes) - self.tokenizer = AutoTokenizer.from_pretrained(model_name) - - def forward(self, input_ids, attention_mask): - outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) - logits = self.classifier(outputs.last_hidden_state[:, 0, :]) # Use [CLS] token - return logits - - -def load_trained_model(model_path): - """Load the trained focal loss model.""" - logger.info(f"๐Ÿ“‚ Loading trained model from {model_path}") - - model = SimpleBERTClassifier(model_name="bert-base-uncased", num_classes=28) - - checkpoint = torch.load(model_path, map_location="cpu") - model.load_state_dict(checkpoint["model_state_dict"]) - - logger.info("โœ… Model loaded successfully") - logger.info(f" โ€ข Final loss: {checkpoint['final_loss']:.4f}") - logger.info(f" โ€ข Focal loss alpha: {checkpoint['focal_loss_alpha']}") - logger.info(f" โ€ข Focal loss gamma: {checkpoint['focal_loss_gamma']}") - logger.info(f" โ€ข Learning rate: {checkpoint['learning_rate']}") - logger.info(f" โ€ข Epochs trained: {checkpoint['epochs']}") - - return model - - -def create_test_data(): - """Create test data for evaluation.""" - logger.info("๐Ÿ“Š Creating test data for evaluation...") - - # Test examples with known emotions - test_data = [ - { - "text": "I am extremely happy today!", - "labels": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # joy - }, - { - "text": "This makes me so angry!", - "labels": [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # anger - }, - { - "text": "I feel sad and disappointed.", - "labels": [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0] # disappointment, sadness - }, - { - "text": "This is amazing and exciting!", - "labels": [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # admiration, excitement - }, - { - "text": "I'm neutral about this.", - "labels": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] # neutral - } - ] - - logger.info(f"โœ… Created {len(test_data)} test examples") - return test_data - - -def evaluate_model(model, test_data, threshold=0.5): - """Evaluate model with given threshold.""" - logger.info(f"๐Ÿ” Evaluating model with threshold {threshold}...") - - model.eval() - device = next(model.parameters()).device - - all_true_labels = [] - all_predictions = [] - all_probabilities = [] - - for example in tqdm(test_data, desc="Evaluating"): - text = example["text"] - true_labels = example["labels"] - - # Tokenize - inputs = model.tokenizer( - text, - return_tensors="pt", - truncation=True, - max_length=512, - padding=True - ) - - # Move to device - input_ids = inputs["input_ids"].to(device) - attention_mask = inputs["attention_mask"].to(device) - - # Get raw predictions - with torch.no_grad(): - logits = model(input_ids=input_ids, attention_mask=attention_mask) - probabilities = torch.sigmoid(logits) - - # Get predictions - predictions = (probabilities > threshold).float() - - # Convert to numpy arrays - pred_np = predictions.cpu().numpy()[0] - true_np = np.array(true_labels) - prob_np = probabilities.cpu().numpy()[0] - - all_true_labels.append(true_np) - all_predictions.append(pred_np) - all_probabilities.append(prob_np) - - # Calculate metrics - all_true = np.array(all_true_labels) - all_pred = np.array(all_predictions) - all_probs = np.array(all_probabilities) - - f1_macro = f1_score(all_true, all_pred, average='macro', zero_division=0) - f1_micro = f1_score(all_true, all_pred, average='micro', zero_division=0) - precision = precision_score(all_true, all_pred, average='macro', zero_division=0) - recall = recall_score(all_true, all_pred, average='macro', zero_division=0) - - logger.info(f"๐Ÿ“Š Results with threshold {threshold}:") - logger.info(f" F1 Macro: {f1_macro:.4f}") - logger.info(f" F1 Micro: {f1_micro:.4f}") - logger.info(f" Precision: {precision:.4f}") - logger.info(f" Recall: {recall:.4f}") - - return { - 'f1_macro': f1_macro, - 'f1_micro': f1_micro, - 'precision': precision, - 'recall': recall, - 'probabilities': all_probs, - 'predictions': all_pred, - 'true_labels': all_true - } - - -def optimize_threshold(model, test_data): - """Optimize threshold for best F1 score.""" - logger.info("๐ŸŽฏ Optimizing threshold for best F1 score...") - - # Get raw probabilities first - model.eval() - device = next(model.parameters()).device - - all_true_labels = [] - all_probabilities = [] - - for example in tqdm(test_data, desc="Getting probabilities"): - text = example["text"] - true_labels = example["labels"] - - # Tokenize - inputs = model.tokenizer( - text, - return_tensors="pt", - truncation=True, - max_length=512, - padding=True - ) - - # Move to device - input_ids = inputs["input_ids"].to(device) - attention_mask = inputs["attention_mask"].to(device) - - # Get raw probabilities - with torch.no_grad(): - logits = model(input_ids=input_ids, attention_mask=attention_mask) - probabilities = torch.sigmoid(logits) - - all_true_labels.append(np.array(true_labels)) - all_probabilities.append(probabilities.cpu().numpy()[0]) - - all_true = np.array(all_true_labels) - all_probs = np.array(all_probabilities) - - # Try different thresholds - thresholds = np.arange(0.1, 0.9, 0.05) - best_f1 = 0 - best_threshold = 0.5 - results = [] - - for threshold in thresholds: - predictions = (all_probs > threshold).astype(float) - f1 = f1_score(all_true, predictions, average='macro', zero_division=0) - results.append({'threshold': threshold, 'f1': f1}) - - if f1 > best_f1: - best_f1 = f1 - best_threshold = threshold - - # Show top 5 thresholds - results.sort(key=lambda x: x['f1'], reverse=True) - logger.info("๐Ÿ“Š Top 5 thresholds:") - for i, result in enumerate(results[:5]): - logger.info(f" {i+1}. Threshold {result['threshold']:.2f}: F1 = {result['f1']:.4f}") - - logger.info(f"๐ŸŽฏ Best threshold: {best_threshold:.2f} (F1 = {best_f1:.4f})") - - return best_threshold, best_f1 - - -def main(): - """Main evaluation function.""" - logger.info("๐Ÿš€ Starting Focal Loss Model Evaluation...") - - # Setup device - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - logger.info(f"๐Ÿ”ง Using device: {device}") - - # Check if model file exists - model_path = Path("test_checkpoints/best_model.pt") - if not model_path.exists(): - logger.error(f"โŒ Model file not found: {model_path}") - return False - - try: - # Load trained model - model = load_trained_model(model_path) - model.to(device) - - # Create test data - test_data = create_test_data() - - # Evaluate with default threshold - logger.info("=" * 50) - default_results = evaluate_model(model, test_data, threshold=0.5) - - # Optimize threshold - logger.info("=" * 50) - best_threshold, best_f1 = optimize_threshold(model, test_data) - - # Evaluate with optimized threshold - logger.info("=" * 50) - optimized_results = evaluate_model(model, test_data, threshold=best_threshold) - - # Compare results - logger.info("=" * 50) - logger.info("๐Ÿ“‹ Comparison:") - logger.info(f" Default threshold (0.5): F1 = {default_results['f1_macro']:.4f}") - logger.info(f" Optimized threshold ({best_threshold:.2f}): F1 = {optimized_results['f1_macro']:.4f}") - logger.info(f" Improvement: {optimized_results['f1_macro'] - default_results['f1_macro']:.4f}") - - # Save results - results = { - 'default_threshold': { - 'threshold': 0.5, - 'f1_macro': default_results['f1_macro'], - 'f1_micro': default_results['f1_micro'], - 'precision': default_results['precision'], - 'recall': default_results['recall'] - }, - 'optimized_threshold': { - 'threshold': best_threshold, - 'f1_macro': optimized_results['f1_macro'], - 'f1_micro': optimized_results['f1_micro'], - 'precision': optimized_results['precision'], - 'recall': optimized_results['recall'] - } - } - - with open('evaluation_results.json', 'w') as f: - json.dump(results, f, indent=2) - - logger.info("๐Ÿ’พ Results saved to evaluation_results.json") - logger.info("๐ŸŽ‰ Evaluation Complete!") - return True - - except Exception as e: - logger.error(f"โŒ Evaluation failed: {e}") - return False - - -if __name__ == "__main__": - success = main() - if not success: - sys.exit(1) diff --git a/scripts/legacy/evaluate_whisper_wer.py b/scripts/legacy/evaluate_whisper_wer.py index 453cc96ce..320c20817 100644 --- a/scripts/legacy/evaluate_whisper_wer.py +++ b/scripts/legacy/evaluate_whisper_wer.py @@ -19,12 +19,13 @@ import pandas as pd import soundfile as sf import tqdm + from datasets import load_dataset +from src.models.voice_processing.transcription_api import TranscriptionAPI, create_transcription_api # Add src directory to path sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "src")) -from src.models.voice_processing.transcription_api import TranscriptionAPI, create_transcription_api # Configure logging logging.basicConfig( @@ -70,11 +71,7 @@ def download_librispeech_sample( sf.write(audio_path, audio["array"], audio["sampling_rate"]) # Store result - samples.append({ - "audio_path": str(audio_path), - "reference_text": text, - "sample_id": i - }) + samples.append({"audio_path": str(audio_path), "reference_text": text, "sample_id": i}) logger.info(f"Downloaded {len(samples)} samples to {output_dir}") return samples @@ -117,39 +114,43 @@ def evaluate_wer(api: TranscriptionAPI, samples: list[dict], model_size: str) -> wer_score = jiwer.wer(reference, hypothesis) # Store result - results.append({ - "sample_id": sample["sample_id"], - "reference": reference_text, - "hypothesis": transcription_result.text, - "wer": wer_score, - "processing_time": processing_time, - "language": transcription_result.language - }) + results.append( + { + "sample_id": sample["sample_id"], + "reference": reference_text, + "hypothesis": transcription_result.text, + "wer": wer_score, + "processing_time": processing_time, + "language": transcription_result.language, + } + ) except Exception as e: logger.warning(f"Failed to transcribe {audio_path}: {e}") - results.append({ - "sample_id": sample["sample_id"], - "reference": reference_text, - "hypothesis": "", - "wer": 1.0, - "processing_time": 0.0, - "language": "unknown", - "error": str(e) - }) + results.append( + { + "sample_id": sample["sample_id"], + "reference": reference_text, + "hypothesis": "", + "wer": 1.0, + "processing_time": 0.0, + "language": "unknown", + "error": str(e), + } + ) # Calculate metrics if results: avg_wer = sum(r["wer"] for r in results) / len(results) avg_time = total_time / len(results) - + return { "model_size": model_size, "num_samples": len(results), "average_wer": avg_wer, "average_processing_time": avg_time, "total_processing_time": total_time, - "detailed_results": results + "detailed_results": results, } else: return { @@ -158,34 +159,25 @@ def evaluate_wer(api: TranscriptionAPI, samples: list[dict], model_size: str) -> "average_wer": 1.0, "average_processing_time": 0.0, "total_processing_time": 0.0, - "detailed_results": [] + "detailed_results": [], } def main(): """Main evaluation function.""" parser = argparse.ArgumentParser(description="Evaluate Whisper WER on LibriSpeech") + parser.add_argument("--output-dir", type=str, help="Directory to save results and audio files") parser.add_argument( - "--output-dir", - type=str, - help="Directory to save results and audio files" + "--max-samples", type=int, default=50, help="Maximum number of samples to evaluate" ) parser.add_argument( - "--max-samples", - type=int, - default=50, - help="Maximum number of samples to evaluate" + "--model-size", + type=str, + default="base", + help="Whisper model size (tiny, base, small, medium, large)", ) parser.add_argument( - "--model-size", - type=str, - default="base", - help="Whisper model size (tiny, base, small, medium, large)" - ) - parser.add_argument( - "--save-results", - action="store_true", - help="Save detailed results to JSON file" + "--save-results", action="store_true", help="Save detailed results to JSON file" ) args = parser.parse_args() @@ -198,10 +190,7 @@ def main(): output_dir = None # Download or load LibriSpeech samples - samples = download_librispeech_sample( - output_dir=args.output_dir, - max_samples=args.max_samples - ) + samples = download_librispeech_sample(output_dir=args.output_dir, max_samples=args.max_samples) if not samples: logger.error("No samples available for evaluation") diff --git a/scripts/legacy/expand_journal_dataset.py b/scripts/legacy/expand_journal_dataset.py index 0786d8f7d..5656937ec 100644 --- a/scripts/legacy/expand_journal_dataset.py +++ b/scripts/legacy/expand_journal_dataset.py @@ -5,7 +5,8 @@ import json import random -from typing import List, Dict +from typing import Dict, List + def load_current_dataset(): """Load the current journal dataset.""" @@ -21,60 +22,60 @@ def save_expanded_dataset(data, filename='data/expanded_journal_dataset.json'): def create_balanced_dataset(target_size=1000): """Create a balanced expanded dataset.""" print("๐Ÿ”ง Creating balanced expanded dataset...") - + # Load current data current_data = load_current_dataset() - + # Analyze current distribution emotion_counts = {} for entry in current_data: emotion = entry['emotion'] emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1 - + print(f"๐Ÿ“Š Current emotion distribution:") for emotion, count in sorted(emotion_counts.items()): print(f" {emotion}: {count} samples") - + # Calculate target per emotion target_per_emotion = target_size // len(emotion_counts) print(f"\n๐ŸŽฏ Target: {target_per_emotion} samples per emotion") - + # Create expanded dataset expanded_data = [] - + for emotion in emotion_counts.keys(): # Get existing samples for this emotion existing_samples = [entry for entry in current_data if entry['emotion'] == emotion] current_count = len(existing_samples) - + print(f"\n๐Ÿ“ Expanding '{emotion}' from {current_count} to {target_per_emotion} samples...") - + # Add existing samples expanded_data.extend(existing_samples) - + # Generate additional samples needed_samples = target_per_emotion - current_count - + if needed_samples > 0: # Create variations of existing samples for i in range(needed_samples): # Pick a random existing sample to base variation on base_sample = random.choice(existing_samples) - + # Create variation variation = create_variation(base_sample, emotion) expanded_data.append(variation) - + print(f"\nโœ… Expanded dataset created:") print(f" Original samples: {len(current_data)}") print(f" Expanded samples: {len(expanded_data)}") print(f" Target size: {target_size}") - + return expanded_data def create_variation(base_sample: Dict, emotion: str) -> Dict: """Create a variation of a base sample.""" - + # Templates for different emotions emotion_templates = { 'happy': [ @@ -222,22 +223,22 @@ def create_variation(base_sample: Dict, emotion: str) -> Dict: "I'm really tired of dealing with this." ] } - + # Get templates for this emotion templates = emotion_templates.get(emotion, [f"I'm feeling {emotion}."]) - + # Create variation template = random.choice(templates) - + # Add some variety to the content variations = [ f"{template} {random.choice(['It\'s been a long day.', 'Things are going well.', 'I need to process this.', 'This is important to me.'])}", f"{template} {random.choice(['I hope this continues.', 'I wonder what\'s next.', 'This feels right.', 'I\'m processing this.'])}", f"{template} {random.choice(['I should reflect on this.', 'This is meaningful.', 'I appreciate this moment.', 'I\'m learning from this.'])}" ] - + content = random.choice(variations) - + return { 'content': content, 'emotion': emotion, @@ -248,16 +249,16 @@ def analyze_expanded_dataset(data): """Analyze the expanded dataset.""" print("\n๐Ÿ“Š Expanded Dataset Analysis:") print("=" * 40) - + emotion_counts = {} for entry in data: emotion = entry['emotion'] emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1 - + print("Emotion distribution:") for emotion, count in sorted(emotion_counts.items()): print(f" {emotion}: {count} samples") - + print(f"\nTotal samples: {len(data)}") print(f"Unique emotions: {len(emotion_counts)}") @@ -265,16 +266,16 @@ def main(): """Main function to expand the dataset.""" print("๐Ÿš€ JOURNAL DATASET EXPANSION") print("=" * 50) - + # Create expanded dataset expanded_data = create_balanced_dataset(target_size=1000) - + # Analyze expanded dataset analyze_expanded_dataset(expanded_data) - + # Save expanded dataset save_expanded_dataset(expanded_data) - + print("\n๐ŸŽ‰ Dataset expansion completed!") print("๐Ÿ“‹ Next steps:") print(" 1. Review expanded dataset") @@ -282,4 +283,4 @@ def main(): print(" 3. Expect 75-85% F1 score!") if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/legacy/finalize_emotion_model.py b/scripts/legacy/finalize_emotion_model.py index 014101800..0affcef52 100755 --- a/scripts/legacy/finalize_emotion_model.py +++ b/scripts/legacy/finalize_emotion_model.py @@ -22,7 +22,7 @@ import logging import sys from pathlib import Path -from typing import Optional, Any +from typing import Any, Optional import torch import torch.nn.functional as F @@ -30,13 +30,12 @@ from torch import nn from transformers import AutoTokenizer +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier +from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader + # Add src to path sys.path.append(str(Path(__file__).parent.parent.resolve())) -from src.models.emotion_detection.bert_classifier import ( - create_bert_emotion_classifier, - ) -from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") logger = logging.getLogger(__name__) @@ -134,10 +133,10 @@ def forward(self, **kwargs) -> torch.Tensor: # Weighted average of predictions weighted_pred = sum(w * p for w, p in zip(self.weights, predictions)) - + # Apply temperature scaling scaled_pred = weighted_pred / self.temperature - + return scaled_pred def set_temperature(self, temperature: float) -> None: @@ -160,7 +159,7 @@ def create_augmented_dataset(data_loader: GoEmotionsDataLoader, tokenizer: AutoT Augmented dataset """ logger.info("Creating augmented dataset with back-translation...") - + # For now, return the original dataset # TODO: Implement back-translation augmentation return data_loader.get_train_data() @@ -180,7 +179,7 @@ def train_final_model( Training metrics """ logger.info(f"Training final model for {epochs} epochs with batch size {batch_size}") - + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") logger.info(f"Using device: {device}") @@ -205,61 +204,60 @@ def train_final_model( best_f1 = 0.0 for epoch in range(epochs): logger.info(f"Epoch {epoch + 1}/{epochs}") - + # Training model.train() total_loss = 0.0 - + for batch in train_data: optimizer.zero_grad() - + # Forward pass outputs = model(batch["input_ids"], batch["attention_mask"]) loss = focal_loss(outputs, batch["labels"]) - + # Backward pass loss.backward() optimizer.step() - + total_loss += loss.item() - + # Validation model.eval() val_predictions = [] val_labels = [] - + with torch.no_grad(): for batch in val_data: outputs = model(batch["input_ids"], batch["attention_mask"]) predictions = (torch.sigmoid(outputs) > OPTIMAL_THRESHOLD).float() - + val_predictions.append(predictions.cpu()) val_labels.append(batch["labels"].cpu()) - + # Calculate F1 score val_predictions = torch.cat(val_predictions, dim=0) val_labels = torch.cat(val_labels, dim=0) - - f1 = f1_score(val_labels, val_predictions, average='micro', zero_division=0) - + + f1 = f1_score(val_labels, val_predictions, average="micro", zero_division=0) + logger.info(f"Epoch {epoch + 1}: Loss = {total_loss:.4f}, F1 = {f1:.4f}") - + # Save best model if f1 > best_f1: best_f1 = f1 - torch.save({ - 'model_state_dict': model.state_dict(), - 'optimizer_state_dict': optimizer.state_dict(), - 'epoch': epoch, - 'f1_score': f1, - }, output_model) + torch.save( + { + "model_state_dict": model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "epoch": epoch, + "f1_score": f1, + }, + output_model, + ) logger.info(f"New best model saved with F1 = {f1:.4f}") - return { - 'best_f1': best_f1, - 'final_model_path': output_model, - 'epochs_trained': epochs - } + return {"best_f1": best_f1, "final_model_path": output_model, "epochs_trained": epochs} def create_ensemble_model(model_path: str, device: torch.device) -> EnsembleModel: @@ -273,19 +271,19 @@ def create_ensemble_model(model_path: str, device: torch.device) -> EnsembleMode Ensemble model """ logger.info("Creating ensemble model...") - + # For now, create a single model ensemble # TODO: Implement multiple model ensemble model, _ = create_bert_emotion_classifier() - + if Path(model_path).exists(): checkpoint = torch.load(model_path, map_location=device) - model.load_state_dict(checkpoint['model_state_dict']) + model.load_state_dict(checkpoint["model_state_dict"]) logger.info(f"Loaded model from {model_path}") - + model.to(device) model.eval() - + return EnsembleModel([model]) @@ -304,39 +302,34 @@ def evaluate_ensemble( Evaluation metrics """ logger.info("Evaluating ensemble model...") - + ensemble.eval() predictions = [] labels = [] - + with torch.no_grad(): for batch in test_data: outputs = ensemble( input_ids=batch["input_ids"].to(device), - attention_mask=batch["attention_mask"].to(device) + attention_mask=batch["attention_mask"].to(device), ) batch_predictions = (torch.sigmoid(outputs) > OPTIMAL_THRESHOLD).float() - + predictions.append(batch_predictions.cpu()) labels.append(batch["labels"].cpu()) - + # Concatenate results predictions = torch.cat(predictions, dim=0) labels = torch.cat(labels, dim=0) - + # Calculate metrics - micro_f1 = f1_score(labels, predictions, average='micro', zero_division=0) - macro_f1 = f1_score(labels, predictions, average='macro', zero_division=0) + micro_f1 = f1_score(labels, predictions, average="micro", zero_division=0) + macro_f1 = f1_score(labels, predictions, average="macro", zero_division=0) precision, recall, _, _ = precision_recall_fscore_support( - labels, predictions, average='micro', zero_division=0 + labels, predictions, average="micro", zero_division=0 ) - - return { - 'micro_f1': micro_f1, - 'macro_f1': macro_f1, - 'precision': precision, - 'recall': recall - } + + return {"micro_f1": micro_f1, "macro_f1": macro_f1, "precision": precision, "recall": recall} def save_ensemble_model( @@ -350,18 +343,21 @@ def save_ensemble_model( output_path: Path to save the model """ logger.info(f"Saving ensemble model to {output_path}") - + # Create output directory Path(output_path).parent.mkdir(parents=True, exist_ok=True) - + # Save model - torch.save({ - 'ensemble_state_dict': ensemble.state_dict(), - 'metrics': metrics, - 'temperature': ensemble.temperature, - 'threshold': ensemble.threshold, - }, output_path) - + torch.save( + { + "ensemble_state_dict": ensemble.state_dict(), + "metrics": metrics, + "temperature": ensemble.temperature, + "threshold": ensemble.threshold, + }, + output_path, + ) + logger.info(f"Model saved successfully!") logger.info(f"Final metrics: {metrics}") @@ -373,54 +369,44 @@ def main(): "--output_model", type=str, default=DEFAULT_OUTPUT_MODEL, - help="Path to save the final model" - ) - parser.add_argument( - "--epochs", - type=int, - default=5, - help="Number of training epochs" + help="Path to save the final model", ) - parser.add_argument( - "--batch_size", - type=int, - default=16, - help="Training batch size" - ) - + parser.add_argument("--epochs", type=int, default=5, help="Number of training epochs") + parser.add_argument("--batch_size", type=int, default=16, help="Training batch size") + args = parser.parse_args() - + logger.info("๐Ÿš€ Starting emotion detection model finalization...") - + # Train final model training_results = train_final_model( - output_model=args.output_model, - epochs=args.epochs, - batch_size=args.batch_size + output_model=args.output_model, epochs=args.epochs, batch_size=args.batch_size ) - + logger.info(f"Training completed! Best F1: {training_results['best_f1']:.4f}") - + # Check if target F1 score is achieved - if training_results['best_f1'] >= TARGET_F1_SCORE: + if training_results["best_f1"] >= TARGET_F1_SCORE: logger.info(f"๐ŸŽ‰ Target F1 score of {TARGET_F1_SCORE} achieved!") - + # Create and evaluate ensemble device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ensemble = create_ensemble_model(args.output_model, device) - + data_loader = GoEmotionsDataLoader() test_data = data_loader.get_test_data() _, tokenizer = create_bert_emotion_classifier() - + metrics = evaluate_ensemble(ensemble, test_data, tokenizer, device) - + # Save ensemble model - ensemble_path = args.output_model.replace('.pt', '_ensemble.pt') + ensemble_path = args.output_model.replace(".pt", "_ensemble.pt") save_ensemble_model(ensemble, metrics, ensemble_path) - + else: - logger.warning(f"โš ๏ธ Target F1 score of {TARGET_F1_SCORE} not achieved. Best: {training_results['best_f1']:.4f}") + logger.warning( + f"โš ๏ธ Target F1 score of {TARGET_F1_SCORE} not achieved. Best: {training_results['best_f1']:.4f}" + ) if __name__ == "__main__": diff --git a/scripts/legacy/fine_tune_emotion_model.py b/scripts/legacy/fine_tune_emotion_model.py index 140187404..608e2c672 100644 --- a/scripts/legacy/fine_tune_emotion_model.py +++ b/scripts/legacy/fine_tune_emotion_model.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Backward pass # Forward pass # Log progress every 100 batches @@ -14,22 +15,19 @@ # Training loop import traceback # Setup device -# Add project root to path -# Configure logging -#!/usr/bin/env python3 -from pathlib import Path -from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader -from src.models.emotion_detection.training_pipeline import create_bert_emotion_classifier -from torch import nn import logging import os import sys -import torch -import traceback - - +# Add project root to path +# Configure logging +from pathlib import Path +import torch +from torch import nn +from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader +from src.models.emotion_detection.training_pipeline import \ + create_bert_emotion_classifier """ Fine-tune Emotion Detection Model on GoEmotions Dataset diff --git a/scripts/legacy/improve_model_f1.py b/scripts/legacy/improve_model_f1.py index 5e05e5972..e0e5e32d9 100755 --- a/scripts/legacy/improve_model_f1.py +++ b/scripts/legacy/improve_model_f1.py @@ -13,10 +13,11 @@ import torch from torch import nn +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier + # Add src to path sys.path.append(str(Path.cwd() / "src")) -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier # Configure logging logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") @@ -34,9 +35,7 @@ def __init__(self, alpha=1, gamma=2, reduction="mean"): def forward(self, inputs, targets): """Forward pass of focal loss.""" - bce_loss = nn.functional.binary_cross_entropy_with_logits( - inputs, targets, reduction="none" - ) + bce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none") pt = torch.exp(-bce_loss) focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss @@ -139,7 +138,7 @@ def improve_model_f1(): for epoch in range(5): logger.info(f"๐Ÿ“š Epoch {epoch + 1}/5") epoch_loss = 0.0 - + for batch_idx, batch in enumerate(train_dataloader): input_ids, attention_mask, batch_labels = batch input_ids = input_ids.to(device) diff --git a/scripts/legacy/integrate_cmu_mosei.py b/scripts/legacy/integrate_cmu_mosei.py index 686b0c743..c5918aa65 100644 --- a/scripts/legacy/integrate_cmu_mosei.py +++ b/scripts/legacy/integrate_cmu_mosei.py @@ -7,66 +7,69 @@ Target: Use 23,500+ high-quality samples to achieve 75-85% F1 score. """ -import sys import json -import numpy as np +import sys from collections import defaultdict +import numpy as np + # Add CMU-MultimodalDataSDK to path -sys.path.append('/Users/minervae/Projects/SAMO--GENERAL/SAMO--DL/CMU-MultimodalDataSDK') +sys.path.append("/Users/minervae/Projects/SAMO--GENERAL/SAMO--DL/CMU-MultimodalDataSDK") try: import mmdata - from mmdata import Dataset + print("โœ… CMU Multimodal Data SDK imported successfully!") except ImportError as e: print(f"โŒ Error importing CMU SDK: {e}") print("Make sure you've cloned the repository and set PYTHONPATH") sys.exit(1) + def download_cmu_mosei(): """Download CMU-MOSEI dataset""" print("๐Ÿ“ฅ Downloading CMU-MOSEI dataset...") - + try: # Initialize MOSEI loader mosei = mmdata.MOSEI() - + # Download text embeddings (transcribed sentences) print("๐Ÿ“ Downloading text embeddings...") mosei_emb = mosei.embeddings() - + # Download words (transcribed text) print("๐Ÿ“ Downloading transcribed words...") mosei_words = mosei.words() - + # Get sentiment labels print("๐Ÿท๏ธ Downloading sentiment labels...") sentiments = mosei.sentiments() - + # Get train/validation/test splits print("๐Ÿ“Š Getting dataset splits...") train_ids = mosei.train() valid_ids = mosei.valid() test_ids = mosei.test() - + print(f"โœ… CMU-MOSEI downloaded successfully!") print(f"๐Ÿ“Š Train videos: {len(train_ids)}") print(f"๐Ÿ“Š Validation videos: {len(valid_ids)}") print(f"๐Ÿ“Š Test videos: {len(test_ids)}") - + return mosei_emb, mosei_words, sentiments, train_ids, valid_ids, test_ids - + except Exception as e: print(f"โŒ Error downloading CMU-MOSEI: {e}") return None, None, None, None, None, None + def extract_text_and_emotions(mosei_words, sentiments, train_ids, valid_ids, test_ids): """Extract text sentences and emotion labels from CMU-MOSEI""" print("๐Ÿ” Extracting text and emotion data...") - + dataset_samples = [] - + # Process each video for video_id in list(train_ids) + list(valid_ids) + list(test_ids): if video_id in mosei_words and video_id in sentiments: @@ -77,156 +80,162 @@ def extract_text_and_emotions(mosei_words, sentiments, train_ids, valid_ids, tes if segment_words: # Convert word timestamps to text text = " ".join([word[2] for word in segment_words if word[2]]) - + # Get sentiment label sentiment = sentiments[video_id][segment_id] - + if text.strip() and sentiment is not None: - dataset_samples.append({ - 'text': text.strip(), - 'sentiment': sentiment, - 'video_id': video_id, - 'segment_id': segment_id - }) - + dataset_samples.append( + { + "text": text.strip(), + "sentiment": sentiment, + "video_id": video_id, + "segment_id": segment_id, + } + ) + print(f"โœ… Extracted {len(dataset_samples)} samples") return dataset_samples + def map_sentiment_to_emotions(samples): """Map CMU-MOSEI sentiment scores to our 12 target emotions""" print("๐Ÿ—บ๏ธ Mapping sentiments to emotions...") - + # CMU-MOSEI sentiment range: [-3, 3] # Our target emotions: anxious, calm, content, excited, frustrated, grateful, happy, hopeful, overwhelmed, proud, sad, tired - + emotion_mapping = { # Very negative sentiments - (-3, -2.5): 'sad', - (-2.5, -2): 'frustrated', - (-2, -1.5): 'anxious', - (-1.5, -1): 'tired', - (-1, -0.5): 'overwhelmed', - + (-3, -2.5): "sad", + (-2.5, -2): "frustrated", + (-2, -1.5): "anxious", + (-1.5, -1): "tired", + (-1, -0.5): "overwhelmed", # Neutral sentiments - (-0.5, 0.5): 'calm', - + (-0.5, 0.5): "calm", # Positive sentiments - (0.5, 1): 'content', - (1, 1.5): 'hopeful', - (1.5, 2): 'grateful', - (2, 2.5): 'happy', - (2.5, 3): 'excited', + (0.5, 1): "content", + (1, 1.5): "hopeful", + (1.5, 2): "grateful", + (2, 2.5): "happy", + (2.5, 3): "excited", } - + mapped_samples = [] - + for sample in samples: - sentiment = sample['sentiment'] - + sentiment = sample["sentiment"] + # Find appropriate emotion mapping mapped_emotion = None for (min_sent, max_sent), emotion in emotion_mapping.items(): if min_sent <= sentiment < max_sent: mapped_emotion = emotion break - + # Default mapping for edge cases if mapped_emotion is None: if sentiment < -2.5: - mapped_emotion = 'sad' + mapped_emotion = "sad" elif sentiment > 2.5: - mapped_emotion = 'excited' + mapped_emotion = "excited" else: - mapped_emotion = 'calm' - - mapped_samples.append({ - 'text': sample['text'], - 'emotion': mapped_emotion, - 'original_sentiment': sentiment, - 'video_id': sample['video_id'], - 'segment_id': sample['segment_id'] - }) - + mapped_emotion = "calm" + + mapped_samples.append( + { + "text": sample["text"], + "emotion": mapped_emotion, + "original_sentiment": sentiment, + "video_id": sample["video_id"], + "segment_id": sample["segment_id"], + } + ) + print(f"โœ… Mapped {len(mapped_samples)} samples to emotions") - + # Show emotion distribution emotion_counts = defaultdict(int) for sample in mapped_samples: - emotion_counts[sample['emotion']] += 1 - + emotion_counts[sample["emotion"]] += 1 + print("๐Ÿ“Š Emotion distribution:") for emotion, count in sorted(emotion_counts.items()): print(f" {emotion}: {count} samples") - + return mapped_samples + def save_cmu_mosei_dataset(samples): """Save processed CMU-MOSEI dataset""" print("๐Ÿ’พ Saving CMU-MOSEI dataset...") - + # Save full dataset - output_file = 'data/cmu_mosei_emotion_dataset.json' - with open(output_file, 'w') as f: + output_file = "data/cmu_mosei_emotion_dataset.json" + with open(output_file, "w") as f: json.dump(samples, f, indent=2) - + print(f"โœ… Saved {len(samples)} samples to {output_file}") - + # Create balanced subset for training (similar to your 12 emotions) print("โš–๏ธ Creating balanced training subset...") - + emotion_samples = defaultdict(list) for sample in samples: - emotion_samples[sample['emotion']].append(sample) - + emotion_samples[sample["emotion"]].append(sample) + # Find minimum samples per emotion min_samples = min(len(samples) for samples in emotion_samples.values()) print(f"๐Ÿ“Š Minimum samples per emotion: {min_samples}") - + # Create balanced dataset balanced_samples = [] for emotion, samples_list in emotion_samples.items(): # Randomly sample min_samples from each emotion selected_samples = np.random.choice(samples_list, size=min_samples, replace=False) balanced_samples.extend(selected_samples) - - balanced_file = 'data/cmu_mosei_balanced_dataset.json' - with open(balanced_file, 'w') as f: + + balanced_file = "data/cmu_mosei_balanced_dataset.json" + with open(balanced_file, "w") as f: json.dump(balanced_samples, f, indent=2) - + print(f"โœ… Saved {len(balanced_samples)} balanced samples to {balanced_file}") - + return output_file, balanced_file + def main(): """Main integration process""" print("๐Ÿš€ CMU-MOSEI DATASET INTEGRATION") print("=" * 50) - + # Step 1: Download dataset mosei_emb, mosei_words, sentiments, train_ids, valid_ids, test_ids = download_cmu_mosei() - + if mosei_words is None: print("โŒ Failed to download CMU-MOSEI dataset") return - + # Step 2: Extract text and emotions samples = extract_text_and_emotions(mosei_words, sentiments, train_ids, valid_ids, test_ids) - + if not samples: print("โŒ No samples extracted") return - + # Step 3: Map to target emotions mapped_samples = map_sentiment_to_emotions(samples) - + # Step 4: Save datasets full_file, balanced_file = save_cmu_mosei_dataset(mapped_samples) - + print("\n๐ŸŽ‰ CMU-MOSEI Integration Complete!") print("๐Ÿ“‹ Next steps:") print(" 1. Review the datasets in data/") print(" 2. Use cmu_mosei_balanced_dataset.json for training") print(" 3. Upload to Colab and achieve 75-85% F1 score!") + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/legacy/minimal_validation.py b/scripts/legacy/minimal_validation.py index 6d696c1db..2b980b1c7 100644 --- a/scripts/legacy/minimal_validation.py +++ b/scripts/legacy/minimal_validation.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Add src to path # Create model # Test with dummy data @@ -5,23 +6,15 @@ from torch import nn import sklearn import torch - import torch import torch.nn.functional as F import transformers # Summary -# Configure logging -#!/usr/bin/env python3 -from pathlib import Path import logging -import numpy as np import sys +# Configure logging +from pathlib import Path - - - - - - +import numpy as np """ Minimal Validation for Core Components diff --git a/scripts/legacy/model_monitoring.py b/scripts/legacy/model_monitoring.py index d145c911d..3f2087ae3 100755 --- a/scripts/legacy/model_monitoring.py +++ b/scripts/legacy/model_monitoring.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Calculate drift score using KL divergence or statistical distance # Check for data drift (if detector is initialized) # Check for degradation @@ -39,32 +40,30 @@ # Create monitor # Save configuration # Start monitoring -# Add src to path + +import argparse +import json +import logging +import sys +import threading +import time # Configure logging # Constants -#!/usr/bin/env python3 from collections import deque -from dataclasses import dataclass, asdict +# Add src to path +from dataclasses import asdict, dataclass from datetime import datetime, timedelta from pathlib import Path -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier -from transformers import AutoTokenizer from typing import Any, Optional -import argparse -import json -import logging + import numpy as np import pandas as pd -import sys -import threading -import time import torch import yaml +from transformers import AutoTokenizer - - - - +from src.models.emotion_detection.bert_classifier import \ + create_bert_emotion_classifier """ Model Monitoring Script for REQ-DL-010 diff --git a/scripts/legacy/model_optimization.py b/scripts/legacy/model_optimization.py index 5a85e814d..20aeafc39 100755 --- a/scripts/legacy/model_optimization.py +++ b/scripts/legacy/model_optimization.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Check if target speedup is achieved with ONNX # Prepare ONNX inputs # Benchmark ONNX model @@ -55,28 +56,25 @@ # Set models to evaluation mode # Verify GPU compatibility # Verify ONNX model -# Add src to path -# Configure logging -# Constants -#!/usr/bin/env python3 -from pathlib import Path -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier -from tqdm import tqdm -from transformers import AutoTokenizer -from typing import Any, Union, Optional + import argparse import json import logging -import numpy as np import sys import time -import torch - - - - +# Configure logging +# Constants +from pathlib import Path +from typing import Any, Optional, Union +import numpy as np +import torch +from tqdm import tqdm +from transformers import AutoTokenizer +# Add src to path +from src.models.emotion_detection.bert_classifier import \ + create_bert_emotion_classifier """ Model Optimization Script for REQ-DL-008 diff --git a/scripts/legacy/optimize_model_performance.py b/scripts/legacy/optimize_model_performance.py index 36d4c1120..59aff946f 100644 --- a/scripts/legacy/optimize_model_performance.py +++ b/scripts/legacy/optimize_model_performance.py @@ -1,57 +1,57 @@ - # Prune 20% of weights with lowest magnitude - # Benchmark - # Get predictions - # Measure inference time - # Tokenize - # Tokenize batch - # Warmup - # 1. Batch processing - # 1. Pruning - Remove less important weights - # 2. Input preprocessing optimization - # 2. Quantization - Reduce precision - # 3. Knowledge distillation (if teacher model available) - # 3. Memory optimization - # Apply optimizations - # Benchmark metrics - # Benchmark performance - # Cache tokenizer vocabulary - # Calculate statistics - # Convert to ONNX - # Create dummy input - # Enable gradient checkpointing for memory efficiency - # For now, skip this step - # Initialize model - # Initialize optimizer - # Load checkpoint - # Load model - # Load state dict - # Load tokenizer - # ONNX export - # Overall assessment - # Prune attention heads and layers - # Quantize the model - # Save optimized model - # Success criteria check - # This would require a larger teacher model - # Use mixed precision if available - # Check if model exists -# Add src to path -# Configure logging -#!/usr/bin/env python3 -from src.models.emotion_detection.bert_classifier import BERTEmotionClassifier -from pathlib import Path -from torch import nn -from transformers import AutoTokenizer -from typing import Any +# Prune 20% of weights with lowest magnitude +# Benchmark +# Get predictions +# Measure inference time +# Tokenize +# Tokenize batch +# Warmup +# 1. Batch processing +# 1. Pruning - Remove less important weights +# 2. Input preprocessing optimization +# 2. Quantization - Reduce precision +# 3. Knowledge distillation (if teacher model available) +# 3. Memory optimization +# Apply optimizations +# Benchmark metrics +# Benchmark performance +# Cache tokenizer vocabulary +# Calculate statistics +# Convert to ONNX +# Create dummy input +# Enable gradient checkpointing for memory efficiency +# For now, skip this step +# Initialize model +# Initialize optimizer +# Load checkpoint +# Load model +# Load state dict +# Load tokenizer +# ONNX export +# Overall assessment +# Prune attention heads and layers +# Quantize the model +# Save optimized model +# Success criteria check +# This would require a larger teacher model +# Use mixed precision if available +# Check if model exists + import logging -import numpy as np import sys import time -import torch - +# Add src to path +from pathlib import Path +from typing import Any +import numpy as np +import torch +from torch import nn +from transformers import AutoTokenizer +# Configure logging +#!/usr/bin/env python3 +from src.models.emotion_detection.bert_classifier import BERTEmotionClassifier """SAMO Model Performance Optimization Script. diff --git a/scripts/legacy/optimize_performance.py b/scripts/legacy/optimize_performance.py index dfade22a6..78873fb38 100644 --- a/scripts/legacy/optimize_performance.py +++ b/scripts/legacy/optimize_performance.py @@ -19,11 +19,11 @@ from pathlib import Path import numpy as np -import onnx -import onnxruntime as ort import torch from transformers import AutoTokenizer +import onnx +import onnxruntime as ort from src.models.emotion_detection.bert_classifier import BERTEmotionClassifier # Add project root to Python path - more robust for CI environments @@ -37,7 +37,7 @@ logging.info(f"Project root added to path: {project_root}") # Set up logging -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) diff --git a/scripts/legacy/prepare_vertex_data.py b/scripts/legacy/prepare_vertex_data.py index a43f4bb96..6cd3b4159 100644 --- a/scripts/legacy/prepare_vertex_data.py +++ b/scripts/legacy/prepare_vertex_data.py @@ -36,6 +36,7 @@ def prepare_vertex_data(): # Save training data import json + with open(data_dir / "training_data.json", "w") as f: json.dump(sample_data, f, indent=2) diff --git a/scripts/legacy/reorganize_model_directory.py b/scripts/legacy/reorganize_model_directory.py index eaf859d7a..807c3e0c6 100644 --- a/scripts/legacy/reorganize_model_directory.py +++ b/scripts/legacy/reorganize_model_directory.py @@ -9,40 +9,41 @@ 3. Create clear versioning and documentation """ +import json import os import shutil -import json from datetime import datetime + def reorganize_model_directory(): """Reorganize the model directory with versioning.""" - + print("๐Ÿ“ REORGANIZING MODEL DIRECTORY") print("=" * 50) - + # Define paths current_model_path = "deployment/model" models_dir = "deployment/models" model_1_path = os.path.join(models_dir, "model_1_fallback") default_model_path = os.path.join(models_dir, "default") - + # Create models directory if it doesn't exist if not os.path.exists(models_dir): os.makedirs(models_dir) print(f"โœ… Created models directory: {models_dir}") - + # 1. Save current model as model_1 (fallback) print(f"\n๐Ÿ’พ SAVING CURRENT MODEL AS FALLBACK") print("-" * 40) - + if os.path.exists(current_model_path): # Copy current model to model_1_fallback if os.path.exists(model_1_path): shutil.rmtree(model_1_path) - + shutil.copytree(current_model_path, model_1_path) print(f"โœ… Saved current model as: {model_1_path}") - + # Create model metadata model_1_metadata = { "version": "1.0", @@ -54,38 +55,38 @@ def reorganize_model_directory(): "average_confidence": "0.298", "architecture": "DistilRoBERTa", "num_labels": 12, - "problem_type": "single_label_classification" + "problem_type": "single_label_classification", }, "training_details": { "dataset_size": "60 samples (48 train, 12 validation)", "training_epochs": 3, "final_f1_score": "0.8889", - "final_accuracy": "0.9167" + "final_accuracy": "0.9167", }, "status": "fallback_model", - "notes": "Successfully resolved configuration persistence issue. Ready for deployment." + "notes": "Successfully resolved configuration persistence issue. Ready for deployment.", } - + # Save metadata metadata_path = os.path.join(model_1_path, "model_metadata.json") - with open(metadata_path, 'w') as f: + with open(metadata_path, "w") as f: json.dump(model_1_metadata, f, indent=2) print(f"โœ… Created model metadata: {metadata_path}") - + else: print(f"โŒ Current model not found at: {current_model_path}") return - + # 2. Create default model directory structure print(f"\n๐Ÿ“‚ CREATING DEFAULT MODEL STRUCTURE") print("-" * 40) - + if os.path.exists(default_model_path): shutil.rmtree(default_model_path) - + os.makedirs(default_model_path) print(f"โœ… Created default model directory: {default_model_path}") - + # Create placeholder metadata for default model default_metadata = { "version": "2.0", @@ -97,7 +98,7 @@ def reorganize_model_directory(): "average_confidence": "pending", "architecture": "DistilRoBERTa", "num_labels": 12, - "problem_type": "single_label_classification" + "problem_type": "single_label_classification", }, "training_details": { "dataset_size": "240+ samples with augmentation", @@ -107,23 +108,23 @@ def reorganize_model_directory(): "Class weighting", "Advanced data augmentation", "Comprehensive validation", - "Configuration persistence" - ] + "Configuration persistence", + ], }, "status": "pending_training", - "notes": "Will be trained using COMPREHENSIVE_ULTIMATE_TRAINING_COLAB.ipynb" + "notes": "Will be trained using COMPREHENSIVE_ULTIMATE_TRAINING_COLAB.ipynb", } - + # Save default metadata default_metadata_path = os.path.join(default_model_path, "model_metadata.json") - with open(default_metadata_path, 'w') as f: + with open(default_metadata_path, "w") as f: json.dump(default_metadata, f, indent=2) print(f"โœ… Created default model metadata: {default_metadata_path}") - + # 3. Create models index file print(f"\n๐Ÿ“‹ CREATING MODELS INDEX") print("-" * 40) - + models_index = { "models_directory": models_dir, "current_default": "default", @@ -133,28 +134,28 @@ def reorganize_model_directory(): "path": "model_1_fallback", "version": "1.0", "status": "ready", - "description": "Working model with configuration persistence fix" + "description": "Working model with configuration persistence fix", }, "default": { "path": "default", "version": "2.0", "status": "pending", - "description": "Comprehensive model with all advanced features" - } + "description": "Comprehensive model with all advanced features", + }, }, "last_updated": datetime.now().isoformat(), - "notes": "Use default model for production, model_1_fallback as backup" + "notes": "Use default model for production, model_1_fallback as backup", } - + index_path = os.path.join(models_dir, "models_index.json") - with open(index_path, 'w') as f: + with open(index_path, "w") as f: json.dump(models_index, f, indent=2) print(f"โœ… Created models index: {index_path}") - + # 4. Create README for models directory print(f"\n๐Ÿ“– CREATING MODELS README") print("-" * 40) - + readme_content = """# Model Versions This directory contains different versions of the emotion detection model. @@ -174,7 +175,7 @@ def reorganize_model_directory(): - **Version**: 1.0 - **Status**: Ready for deployment - **Performance**: 91.67% test accuracy -- **Features**: +- **Features**: - Configuration persistence fix - DistilRoBERTa architecture - 12 emotion classes @@ -222,16 +223,16 @@ def reorganize_model_directory(): - Always test models before deployment - Keep fallback models for safety """ - + readme_path = os.path.join(models_dir, "README.md") - with open(readme_path, 'w') as f: + with open(readme_path, "w") as f: f.write(readme_content) print(f"โœ… Created models README: {readme_path}") - + # 5. Create symlink for easy access print(f"\n๐Ÿ”— CREATING SYMLINKS") print("-" * 40) - + # Create symlink from deployment/model to default model symlink_path = "deployment/model" if os.path.exists(symlink_path): @@ -244,7 +245,7 @@ def reorganize_model_directory(): shutil.rmtree(backup_path) shutil.move(symlink_path, backup_path) print(f"โœ… Backed up original model to: {backup_path}") - + # Create symlink to default model try: os.symlink(default_model_path, symlink_path) @@ -252,11 +253,11 @@ def reorganize_model_directory(): except Exception as e: print(f"โš ๏ธ Could not create symlink: {e}") print(f" You can manually link {symlink_path} to {default_model_path}") - + # 6. Summary print(f"\n๐Ÿ“‹ REORGANIZATION SUMMARY") print("=" * 50) - + print("โœ… Model directory reorganized successfully!") print() print("๐Ÿ“ New Structure:") @@ -277,5 +278,6 @@ def reorganize_model_directory(): print(" - Original model backed up to deployment/model_backup/") print(" - Clear versioning and documentation") + if __name__ == "__main__": - reorganize_model_directory() \ No newline at end of file + reorganize_model_directory() diff --git a/scripts/legacy/retrain_with_expanded_dataset.py b/scripts/legacy/retrain_with_expanded_dataset.py index a2845206f..e1f053322 100644 --- a/scripts/legacy/retrain_with_expanded_dataset.py +++ b/scripts/legacy/retrain_with_expanded_dataset.py @@ -4,63 +4,67 @@ """ import json + import torch import torch.nn as nn -from torch.utils.data import Dataset, DataLoader -from transformers import AutoModel, AutoTokenizer -from sklearn.preprocessing import LabelEncoder +from sklearn.metrics import accuracy_score, f1_score from sklearn.model_selection import train_test_split -from sklearn.metrics import f1_score, accuracy_score +from sklearn.preprocessing import LabelEncoder +from torch.utils.data import DataLoader, Dataset +from transformers import AutoModel, AutoTokenizer + def load_expanded_dataset(): """Load the expanded journal dataset.""" print("๐Ÿ“Š Loading expanded dataset...") - - with open('data/expanded_journal_dataset.json', 'r') as f: + + with open("data/expanded_journal_dataset.json", "r") as f: data = json.load(f) - + print(f"โœ… Loaded {len(data)} samples") - + # Analyze distribution emotion_counts = {} for entry in data: - emotion = entry['emotion'] + emotion = entry["emotion"] emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1 - + print("๐Ÿ“ˆ Emotion distribution:") for emotion, count in sorted(emotion_counts.items()): print(f" {emotion}: {count} samples") - + return data + class ExpandedEmotionDataset(Dataset): def __init__(self, texts, labels, tokenizer, max_length=128): self.texts = texts self.labels = labels self.tokenizer = tokenizer self.max_length = max_length - + def __len__(self): return len(self.texts) - + def __getitem__(self, idx): text = self.texts[idx] label = self.labels[idx] - + encoding = self.tokenizer( text, truncation=True, - padding='max_length', + padding="max_length", max_length=self.max_length, - return_tensors='pt' + return_tensors="pt", ) - + return { - 'input_ids': encoding['input_ids'].flatten(), - 'attention_mask': encoding['attention_mask'].flatten(), - 'labels': torch.tensor(label, dtype=torch.long) + "input_ids": encoding["input_ids"].flatten(), + "attention_mask": encoding["attention_mask"].flatten(), + "labels": torch.tensor(label, dtype=torch.long), } + class ExpandedEmotionClassifier(nn.Module): def __init__(self, model_name="bert-base-uncased", num_labels=12): super().__init__() @@ -68,228 +72,246 @@ def __init__(self, model_name="bert-base-uncased", num_labels=12): self.bert = AutoModel.from_pretrained(model_name) self.dropout = nn.Dropout(0.3) self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels) - + def forward(self, input_ids, attention_mask): - outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) - pooled_output = outputs.pooler_output + outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, return_dict=True) + pooled_output = getattr(outputs, "pooler_output", None) + if pooled_output is None: + last_hidden = outputs.last_hidden_state + mask = attention_mask.unsqueeze(-1) + pooled_output = (last_hidden * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-9) logits = self.classifier(self.dropout(pooled_output)) return logits + def prepare_expanded_data(data, test_size=0.2, val_size=0.1): """Prepare data for training with expanded dataset.""" print("๐Ÿ”ง Preparing expanded data...") - + # Extract texts and emotions - texts = [entry['content'] for entry in data] - emotions = [entry['emotion'] for entry in data] - + texts = [entry["content"] for entry in data] + emotions = [entry["emotion"] for entry in data] + # Create label encoder label_encoder = LabelEncoder() labels = label_encoder.fit_transform(emotions) - + print(f"โœ… Label encoder created with {len(label_encoder.classes_)} classes") print(f"๐Ÿ“Š Classes: {list(label_encoder.classes_)}") - + # Split data X_temp, X_test, y_temp, y_test = train_test_split( texts, labels, test_size=test_size, random_state=42, stratify=labels ) - + X_train, X_val, y_train, y_val = train_test_split( - X_temp, y_temp, test_size=val_size/(1-test_size), random_state=42, stratify=y_temp + X_temp, y_temp, test_size=val_size / (1 - test_size), random_state=42, stratify=y_temp ) - + print(f"๐Ÿ“Š Data split:") print(f" Training: {len(X_train)} samples") print(f" Validation: {len(X_val)} samples") print(f" Test: {len(X_test)} samples") - + return (X_train, y_train), (X_val, y_val), (X_test, y_test), label_encoder + def train_expanded_model(train_data, val_data, label_encoder, epochs=5, batch_size=16): """Train the model with expanded dataset.""" print("๐Ÿš€ Training with expanded dataset...") - + # Setup - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"โœ… Using device: {device}") - + # Load tokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") - + # Create datasets X_train, y_train = train_data X_val, y_val = val_data - + train_dataset = ExpandedEmotionDataset(X_train, y_train, tokenizer) val_dataset = ExpandedEmotionDataset(X_val, y_val, tokenizer) - + train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False) - + # Initialize model model = ExpandedEmotionClassifier(num_labels=len(label_encoder.classes_)) model.to(device) - + # Setup training optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5) criterion = nn.CrossEntropyLoss() - + # Training loop best_f1 = 0 training_history = [] - + for epoch in range(epochs): print(f"\n๐Ÿ”„ Epoch {epoch + 1}/{epochs}") - + # Training model.train() total_loss = 0 - + for i, batch in enumerate(train_loader): - input_ids = batch['input_ids'].to(device) - attention_mask = batch['attention_mask'].to(device) - labels = batch['labels'].to(device) - + input_ids = batch["input_ids"].to(device) + attention_mask = batch["attention_mask"].to(device) + labels = batch["labels"].to(device) + optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() - + if i % 50 == 0: print(f" Batch {i}/{len(train_loader)}, Loss: {loss.item():.4f}") - + # Validation model.eval() val_loss = 0 all_preds = [] all_labels = [] - + with torch.no_grad(): for batch in val_loader: - input_ids = batch['input_ids'].to(device) - attention_mask = batch['attention_mask'].to(device) - labels = batch['labels'].to(device) - + input_ids = batch["input_ids"].to(device) + attention_mask = batch["attention_mask"].to(device) + labels = batch["labels"].to(device) + outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) val_loss += loss.item() - + preds = torch.argmax(outputs, dim=1) all_preds.extend(preds.cpu().numpy()) all_labels.extend(labels.cpu().numpy()) - + # Calculate metrics avg_train_loss = total_loss / len(train_loader) avg_val_loss = val_loss / len(val_loader) - f1_macro = f1_score(all_labels, all_preds, average='macro') + f1_macro = f1_score(all_labels, all_preds, average="macro") accuracy = accuracy_score(all_labels, all_preds) - + print(f"๐Ÿ“Š Epoch {epoch + 1} Results:") print(f" Train Loss: {avg_train_loss:.4f}") print(f" Val Loss: {avg_val_loss:.4f}") print(f" Val F1 (Macro): {f1_macro:.4f}") print(f" Val Accuracy: {accuracy:.4f}") - + # Save best model if f1_macro > best_f1: best_f1 = f1_macro - torch.save(model.state_dict(), 'best_expanded_model.pth') + torch.save(model.state_dict(), "best_expanded_model.pth") print(f"๐Ÿ’พ New best model saved! F1: {best_f1:.4f}") - - training_history.append({ - 'epoch': epoch, - 'train_loss': avg_train_loss, - 'val_loss': avg_val_loss, - 'val_f1_macro': f1_macro, - 'val_accuracy': accuracy - }) - + + training_history.append( + { + "epoch": epoch, + "train_loss": avg_train_loss, + "val_loss": avg_val_loss, + "val_f1_macro": f1_macro, + "val_accuracy": accuracy, + } + ) + return model, training_history, best_f1 -def save_expanded_results(training_history, best_f1, label_encoder, test_data): + +def save_expanded_results( + training_history, best_f1, label_encoder, train_data, val_data, test_data +): """Save training results.""" print("๐Ÿ’พ Saving results...") - + # Test final model + X_train, y_train = train_data + X_val, y_val = val_data X_test, y_test = test_data - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + # Load best model model = ExpandedEmotionClassifier(num_labels=len(label_encoder.classes_)) - model.load_state_dict(torch.load('best_expanded_model.pth')) + model.load_state_dict(torch.load("best_expanded_model.pth")) model.to(device) model.eval() - + # Test predictions tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") test_dataset = ExpandedEmotionDataset(X_test, y_test, tokenizer) test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False) - + all_preds = [] all_labels = [] - + with torch.no_grad(): for batch in test_loader: - input_ids = batch['input_ids'].to(device) - attention_mask = batch['attention_mask'].to(device) - labels = batch['labels'].to(device) - + input_ids = batch["input_ids"].to(device) + attention_mask = batch["attention_mask"].to(device) + labels = batch["labels"].to(device) + outputs = model(input_ids=input_ids, attention_mask=attention_mask) preds = torch.argmax(outputs, dim=1) all_preds.extend(preds.cpu().numpy()) all_labels.extend(labels.cpu().numpy()) - + # Calculate final metrics - final_f1 = f1_score(all_labels, all_preds, average='macro') + final_f1 = f1_score(all_labels, all_preds, average="macro") final_accuracy = accuracy_score(all_labels, all_preds) - + + # Calculate total expanded dataset size + total_expanded_samples = len(X_train) + len(X_val) + len(X_test) + # Save results results = { - 'best_f1': best_f1, - 'final_f1': final_f1, - 'final_accuracy': final_accuracy, - 'target_achieved': final_f1 >= 0.70, - 'num_labels': len(label_encoder.classes_), - 'all_emotions': list(label_encoder.classes_), - 'training_history': training_history, - 'expanded_samples': len(X_test) + len([x for x in train_data[0]]) + len([x for x in val_data[0]]), - 'test_samples': len(X_test) + "best_f1": best_f1, + "final_f1": final_f1, + "final_accuracy": final_accuracy, + "target_achieved": final_f1 >= 0.70, + "num_labels": len(label_encoder.classes_), + "all_emotions": list(label_encoder.classes_), + "training_history": training_history, + "expanded_samples": total_expanded_samples, + "test_samples": len(X_test), } - - with open('expanded_training_results.json', 'w') as f: + + with open("expanded_training_results.json", "w") as f: json.dump(results, f, indent=2) - + print(f"โœ… Results saved!") print(f"๐Ÿ“Š Final F1 Score: {final_f1:.4f}") print(f"๐Ÿ“Š Final Accuracy: {final_accuracy:.4f}") print(f"๐ŸŽฏ Target Achieved: {final_f1 >= 0.70}") + def main(): """Main training function.""" print("๐Ÿš€ RETRAINING WITH EXPANDED DATASET") print("=" * 60) - + # Load expanded dataset data = load_expanded_dataset() - + # Prepare data train_data, val_data, test_data, label_encoder = prepare_expanded_data(data) - + # Train model model, training_history, best_f1 = train_expanded_model(train_data, val_data, label_encoder) - + # Save results - save_expanded_results(training_history, best_f1, label_encoder, test_data) - + save_expanded_results(training_history, best_f1, label_encoder, train_data, val_data, test_data) + print("\n๐ŸŽ‰ Retraining completed!") print("๐Ÿ“‹ Next steps:") print(" 1. Test the new model") print(" 2. Compare performance") print(" 3. Deploy if target achieved!") + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/legacy/retrain_with_validation.py b/scripts/legacy/retrain_with_validation.py index 8710f134a..d71debb13 100644 --- a/scripts/legacy/retrain_with_validation.py +++ b/scripts/legacy/retrain_with_validation.py @@ -6,21 +6,22 @@ """ from pathlib import Path + def create_improved_training_plan(): """Create an improved training plan with proper validation""" - + print("๐Ÿ”„ IMPROVED TRAINING PLAN") print("=" * 50) print("๐ŸŽฏ Goal: Retrain model to achieve reliable 75-85% F1 score") print("=" * 50) - + print(f"\nโŒ CURRENT ISSUES IDENTIFIED:") print("-" * 40) print("1. Model bias towards 'grateful' and 'happy' emotions") print("2. Poor generalization (58.3% accuracy on basic tests)") print("3. Overfitting to specific training patterns") print("4. Label mapping inconsistencies") - + print(f"\nโœ… IMPROVED TRAINING STRATEGY:") print("-" * 40) print("1. Use balanced dataset with equal emotion distribution") @@ -28,7 +29,7 @@ def create_improved_training_plan(): print("3. Add regularization to prevent overfitting") print("4. Use early stopping based on validation performance") print("5. Test on diverse, realistic examples") - + print(f"\n๐Ÿ“Š VALIDATION REQUIREMENTS:") print("-" * 40) print("โœ… Basic functionality test: >80% accuracy") @@ -36,7 +37,7 @@ def create_improved_training_plan(): print("โœ… Edge case handling: >70% success rate") print("โœ… No emotion bias: <30% predictions for any single emotion") print("โœ… Consistent predictions: 100% consistency for same input") - + print(f"\n๐Ÿš€ RECOMMENDED ACTIONS:") print("-" * 40) print("1. Create balanced training dataset") @@ -44,16 +45,17 @@ def create_improved_training_plan(): print("3. Use regularization techniques") print("4. Test extensively before deployment") print("5. Monitor for bias and overfitting") - + # Create improved training notebook create_improved_notebook() - + return True + def create_improved_notebook(): """Create an improved training notebook""" - - notebook_content = '''{ + + notebook_content = """{ "cells": [ { "cell_type": "markdown", @@ -381,13 +383,15 @@ def create_improved_notebook(): }, "nbformat": 4, "nbformat_minor": 4 -}''' - +}""" + # Save the notebook - notebook_path = Path(__file__).parent.parent / 'notebooks' / 'IMPROVED_TRAINING_WITH_VALIDATION.ipynb' - with open(notebook_path, 'w') as f: + notebook_path = ( + Path(__file__).parent.parent / "notebooks" / "IMPROVED_TRAINING_WITH_VALIDATION.ipynb" + ) + with open(notebook_path, "w") as f: f.write(notebook_content) - + print(f"โœ… Created improved training notebook: {notebook_path}") print(f"๐Ÿ“‹ Instructions:") print(f" 1. Download the notebook file") @@ -396,6 +400,7 @@ def create_improved_notebook(): print(f" 4. Run all cells") print(f" 5. Verify reliability before deployment") + if __name__ == "__main__": success = create_improved_training_plan() - exit(0 if success else 1) \ No newline at end of file + exit(0 if success else 1) diff --git a/scripts/legacy/simple_cmu_mosei_download.py b/scripts/legacy/simple_cmu_mosei_download.py index 1723581c8..4cc47f03f 100644 --- a/scripts/legacy/simple_cmu_mosei_download.py +++ b/scripts/legacy/simple_cmu_mosei_download.py @@ -7,24 +7,27 @@ """ import json -import numpy as np from collections import defaultdict +import numpy as np + + def download_cmu_mosei_sample(): """Download a sample of CMU-MOSEI data from Hugging Face""" print("๐Ÿ“ฅ Attempting to download CMU-MOSEI sample...") - + # Try to get CMU-MOSEI from Hugging Face datasets try: from datasets import load_dataset + print("โœ… Hugging Face datasets available") - + # Try to load CMU-MOSEI dataset = load_dataset("cmu-mosei") print("โœ… CMU-MOSEI dataset loaded successfully!") - + return dataset - + except ImportError: print("โŒ Hugging Face datasets not available") return None @@ -32,13 +35,14 @@ def download_cmu_mosei_sample(): print(f"โŒ Error loading CMU-MOSEI: {e}") return None + def create_synthetic_cmu_mosei(): """Create synthetic CMU-MOSEI-like data for testing""" print("๐Ÿ”ง Creating synthetic CMU-MOSEI-like dataset...") - + # Generate realistic text samples with sentiment scores synthetic_data = [] - + # Negative sentiment samples (sad, frustrated, anxious) negative_samples = [ ("I'm really disappointed with how this turned out", -2.5), @@ -52,7 +56,7 @@ def create_synthetic_cmu_mosei(): ("I'm tired of dealing with this", -1.6), ("This situation is really stressful", -2.1), ] - + # Neutral sentiment samples (calm, content) neutral_samples = [ ("I'm feeling okay about this", 0.2), @@ -66,7 +70,7 @@ def create_synthetic_cmu_mosei(): ("I'm feeling calm", 0.4), ("It's manageable", 0.2), ] - + # Positive sentiment samples (happy, excited, grateful, hopeful, proud) positive_samples = [ ("I'm really happy with the results", 2.5), @@ -80,104 +84,109 @@ def create_synthetic_cmu_mosei(): ("I'm optimistic about this", 1.9), ("This is fantastic", 2.9), ] - + # Combine all samples all_samples = negative_samples + neutral_samples + positive_samples - + # Create dataset entries for i, (text, sentiment) in enumerate(all_samples): - synthetic_data.append({ - 'text': text, - 'sentiment': sentiment, - 'video_id': f'video_{i//10:03d}', - 'segment_id': f'{i%10}' - }) - + synthetic_data.append( + { + "text": text, + "sentiment": sentiment, + "video_id": f"video_{i//10:03d}", + "segment_id": f"{i%10}", + } + ) + print(f"โœ… Created {len(synthetic_data)} synthetic samples") return synthetic_data + def map_sentiment_to_emotions(samples): """Map sentiment scores to our 12 target emotions""" print("๐Ÿ—บ๏ธ Mapping sentiments to emotions...") - + emotion_mapping = { # Very negative sentiments - (-3, -2.5): 'sad', - (-2.5, -2): 'frustrated', - (-2, -1.5): 'anxious', - (-1.5, -1): 'tired', - (-1, -0.5): 'overwhelmed', - + (-3, -2.5): "sad", + (-2.5, -2): "frustrated", + (-2, -1.5): "anxious", + (-1.5, -1): "tired", + (-1, -0.5): "overwhelmed", # Neutral sentiments - (-0.5, 0.5): 'calm', - + (-0.5, 0.5): "calm", # Positive sentiments - (0.5, 1): 'content', - (1, 1.5): 'hopeful', - (1.5, 2): 'grateful', - (2, 2.5): 'happy', - (2.5, 3): 'excited', + (0.5, 1): "content", + (1, 1.5): "hopeful", + (1.5, 2): "grateful", + (2, 2.5): "happy", + (2.5, 3): "excited", } - + mapped_samples = [] - + for sample in samples: - sentiment = sample['sentiment'] - + sentiment = sample["sentiment"] + # Find appropriate emotion mapping mapped_emotion = None for (min_sent, max_sent), emotion in emotion_mapping.items(): if min_sent <= sentiment < max_sent: mapped_emotion = emotion break - + # Default mapping for edge cases if mapped_emotion is None: if sentiment < -2.5: - mapped_emotion = 'sad' + mapped_emotion = "sad" elif sentiment > 2.5: - mapped_emotion = 'excited' + mapped_emotion = "excited" else: - mapped_emotion = 'calm' - - mapped_samples.append({ - 'text': sample['text'], - 'emotion': mapped_emotion, - 'original_sentiment': sentiment, - 'video_id': sample['video_id'], - 'segment_id': sample['segment_id'] - }) - + mapped_emotion = "calm" + + mapped_samples.append( + { + "text": sample["text"], + "emotion": mapped_emotion, + "original_sentiment": sentiment, + "video_id": sample["video_id"], + "segment_id": sample["segment_id"], + } + ) + print(f"โœ… Mapped {len(mapped_samples)} samples to emotions") - + # Show emotion distribution emotion_counts = defaultdict(int) for sample in mapped_samples: - emotion_counts[sample['emotion']] += 1 - + emotion_counts[sample["emotion"]] += 1 + print("๐Ÿ“Š Emotion distribution:") for emotion, count in sorted(emotion_counts.items()): print(f" {emotion}: {count} samples") - + return mapped_samples + def save_dataset(samples, filename): """Save dataset to JSON file""" print(f"๐Ÿ’พ Saving dataset to {filename}...") - - with open(filename, 'w') as f: + + with open(filename, "w") as f: json.dump(samples, f, indent=2) - + print(f"โœ… Saved {len(samples)} samples to {filename}") + def main(): """Main function""" print("๐Ÿš€ SIMPLE CMU-MOSEI DOWNLOAD") print("=" * 40) - + # Try to download real CMU-MOSEI dataset = download_cmu_mosei_sample() - + if dataset is None: print("๐Ÿ“ Using synthetic CMU-MOSEI-like data for testing...") samples = create_synthetic_cmu_mosei() @@ -185,44 +194,47 @@ def main(): print("๐Ÿ“ Processing real CMU-MOSEI data...") # Extract samples from dataset samples = [] - for split in ['train', 'validation', 'test']: + for split in ["train", "validation", "test"]: if split in dataset: for item in dataset[split]: - if 'text' in item and 'sentiment' in item: - samples.append({ - 'text': item['text'], - 'sentiment': item['sentiment'], - 'video_id': item.get('video_id', 'unknown'), - 'segment_id': item.get('segment_id', '0') - }) - + if "text" in item and "sentiment" in item: + samples.append( + { + "text": item["text"], + "sentiment": item["sentiment"], + "video_id": item.get("video_id", "unknown"), + "segment_id": item.get("segment_id", "0"), + } + ) + # Map to emotions mapped_samples = map_sentiment_to_emotions(samples) - + # Save datasets - save_dataset(mapped_samples, 'data/cmu_mosei_emotion_dataset.json') - + save_dataset(mapped_samples, "data/cmu_mosei_emotion_dataset.json") + # Create balanced subset print("โš–๏ธ Creating balanced training subset...") emotion_samples = defaultdict(list) for sample in mapped_samples: - emotion_samples[sample['emotion']].append(sample) - + emotion_samples[sample["emotion"]].append(sample) + min_samples = min(len(samples) for samples in emotion_samples.values()) print(f"๐Ÿ“Š Minimum samples per emotion: {min_samples}") - + balanced_samples = [] for emotion, samples_list in emotion_samples.items(): selected_samples = np.random.choice(samples_list, size=min_samples, replace=False) balanced_samples.extend(selected_samples) - - save_dataset(balanced_samples, 'data/cmu_mosei_balanced_dataset.json') - + + save_dataset(balanced_samples, "data/cmu_mosei_balanced_dataset.json") + print("\n๐ŸŽ‰ CMU-MOSEI Integration Complete!") print("๐Ÿ“‹ Next steps:") print(" 1. Review the datasets in data/") print(" 2. Use cmu_mosei_balanced_dataset.json for training") print(" 3. Upload to Colab and achieve 75-85% F1 score!") + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/legacy/simple_f1_evaluation.py b/scripts/legacy/simple_f1_evaluation.py index 66e99ccc4..500619e81 100644 --- a/scripts/legacy/simple_f1_evaluation.py +++ b/scripts/legacy/simple_f1_evaluation.py @@ -11,13 +11,14 @@ import torch from sklearn.metrics import f1_score, precision_score, recall_score +from transformers import AutoTokenizer + +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier +from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader # Add src to path sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier -from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader -from transformers import AutoTokenizer # Configure logging logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") @@ -39,14 +40,14 @@ def evaluate_current_f1(): # Load model logger.info("๐Ÿค– Loading emotion detection model...") model, loss_fn = create_bert_emotion_classifier() - + # Check for existing checkpoint checkpoint_paths = [ "models/checkpoints/bert_emotion_classifier_final.pt", "test_checkpoints/best_model.pt", "test_checkpoints_dev/best_model.pt", ] - + checkpoint_loaded = False for checkpoint_path in checkpoint_paths: if Path(checkpoint_path).exists(): @@ -61,7 +62,7 @@ def evaluate_current_f1(): except Exception as e: logger.warning(f"โš ๏ธ Failed to load checkpoint {checkpoint_path}: {e}") continue - + if not checkpoint_loaded: logger.warning("โš ๏ธ No valid checkpoint found, using untrained model") @@ -75,22 +76,22 @@ def evaluate_current_f1(): # Evaluate on test set logger.info("๐Ÿงช Evaluating on test set...") - + test_data = datasets["test_data"] all_predictions = [] all_labels = [] - + batch_size = 16 num_classes = 28 # GoEmotions has 28 emotion classes - + with torch.no_grad(): for i in range(0, len(test_data), batch_size): end_idx = min(i + batch_size, len(test_data)) batch_data = test_data.select(range(i, end_idx)) - + texts = batch_data["text"] labels = batch_data["labels"] - + # Convert labels to one-hot format batch_labels = [] for label_list in labels: @@ -99,45 +100,43 @@ def evaluate_current_f1(): if 0 <= label_idx < num_classes: label_vector[label_idx] = 1 batch_labels.append(label_vector) - + # Tokenize inputs = tokenizer( - texts, - padding=True, - truncation=True, - max_length=512, - return_tensors="pt" + texts, padding=True, truncation=True, max_length=512, return_tensors="pt" ) - + input_ids = inputs["input_ids"].to(device) attention_mask = inputs["attention_mask"].to(device) - + # Get predictions outputs = model(input_ids, attention_mask) predictions = torch.sigmoid(outputs) > 0.5 - + all_predictions.extend(predictions.cpu().numpy()) all_labels.extend(batch_labels) - + if (i // batch_size + 1) % 10 == 0: logger.info(f" Processed {end_idx}/{len(test_data)} samples") # Calculate metrics logger.info("๐Ÿ“ˆ Calculating metrics...") - + # Convert to numpy arrays all_predictions = np.array(all_predictions) all_labels = np.array(all_labels) - + # Calculate F1 scores - micro_f1 = f1_score(all_labels, all_predictions, average='micro', zero_division=0) - macro_f1 = f1_score(all_labels, all_predictions, average='macro', zero_division=0) - weighted_f1 = f1_score(all_labels, all_predictions, average='weighted', zero_division=0) - + micro_f1 = f1_score(all_labels, all_predictions, average="micro", zero_division=0) + macro_f1 = f1_score(all_labels, all_predictions, average="macro", zero_division=0) + weighted_f1 = f1_score(all_labels, all_predictions, average="weighted", zero_division=0) + # Calculate precision and recall - micro_precision = precision_score(all_labels, all_predictions, average='micro', zero_division=0) - micro_recall = recall_score(all_labels, all_predictions, average='micro', zero_division=0) - + micro_precision = precision_score( + all_labels, all_predictions, average="micro", zero_division=0 + ) + micro_recall = recall_score(all_labels, all_predictions, average="micro", zero_division=0) + # Display results logger.info("๐Ÿ“Š EVALUATION RESULTS:") logger.info("=" * 50) @@ -147,21 +146,21 @@ def evaluate_current_f1(): logger.info(f"Micro Precision: {micro_precision:.4f} ({micro_precision*100:.2f}%)") logger.info(f"Micro Recall: {micro_recall:.4f} ({micro_recall*100:.2f}%)") logger.info("=" * 50) - + # Assessment target_f1 = 0.80 # 80% target progress = (micro_f1 / target_f1) * 100 - + logger.info(f"๐ŸŽฏ TARGET F1: {target_f1*100:.0f}%") logger.info(f"๐Ÿ“Š CURRENT F1: {micro_f1*100:.2f}%") logger.info(f"๐Ÿ“ˆ PROGRESS: {progress:.1f}% of target") - + if micro_f1 >= target_f1: logger.info("๐ŸŽ‰ TARGET ACHIEVED!") else: gap = target_f1 - micro_f1 logger.info(f"๐Ÿ“‰ GAP: {gap*100:.2f} percentage points needed") - + return { "micro_f1": micro_f1, "macro_f1": macro_f1, @@ -169,21 +168,23 @@ def evaluate_current_f1(): "micro_precision": micro_precision, "micro_recall": micro_recall, "target_f1": target_f1, - "progress_percent": progress + "progress_percent": progress, } except Exception as e: logger.error(f"โŒ Evaluation failed: {e}") import traceback + traceback.print_exc() return None if __name__ == "__main__": import numpy as np + results = evaluate_current_f1() if results: logger.info("โœ… Evaluation completed successfully") else: logger.error("โŒ Evaluation failed") - sys.exit(1) \ No newline at end of file + sys.exit(1) diff --git a/scripts/legacy/simple_finalize_model.py b/scripts/legacy/simple_finalize_model.py index c1e6e6cb8..dd41e023e 100644 --- a/scripts/legacy/simple_finalize_model.py +++ b/scripts/legacy/simple_finalize_model.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Check if checkpoint exists # Copy checkpoint to final location # Create final model @@ -6,17 +7,14 @@ # Save metadata # Verify requirements import shutil + # Add src to path -# Configure logging -# Constants -#!/usr/bin/env python3 -from pathlib import Path import json import logging import sys - - - +# Configure logging +# Constants +from pathlib import Path """ Simple Model Finalization Script diff --git a/scripts/legacy/simple_validation.py b/scripts/legacy/simple_validation.py index e281358fc..8a7ca43f3 100644 --- a/scripts/legacy/simple_validation.py +++ b/scripts/legacy/simple_validation.py @@ -1,27 +1,20 @@ +#!/usr/bin/env python3 # Test with dummy data from torch import nn import sklearn import torch - import torch import torch.nn.functional as F import transformers # Check if gcloud is available # Check if we have the deployment guide # Summary import subprocess -# Configure logging -#!/usr/bin/env python3 -from pathlib import Path import logging -import numpy as np import sys +# Configure logging +from pathlib import Path - - - - - - +import numpy as np """ Simple Validation for GCP Deployment diff --git a/scripts/legacy/simple_vertex_ai_validation.py b/scripts/legacy/simple_vertex_ai_validation.py index b04bc6734..fa748b9ed 100644 --- a/scripts/legacy/simple_vertex_ai_validation.py +++ b/scripts/legacy/simple_vertex_ai_validation.py @@ -1,17 +1,14 @@ +#!/usr/bin/env python3 # Create a simple custom training job # Get project ID # Import Vertex AI # Initialize Vertex AI from google.cloud import aiplatform -# Configure logging -#!/usr/bin/env python3 -from pathlib import Path import logging import os import sys - - - +# Configure logging +from pathlib import Path """ Simple Vertex AI Validation for SAMO Deep Learning. diff --git a/scripts/legacy/start_monitoring_dashboard.py b/scripts/legacy/start_monitoring_dashboard.py index dc2bbcb2b..62c6fb9d5 100644 --- a/scripts/legacy/start_monitoring_dashboard.py +++ b/scripts/legacy/start_monitoring_dashboard.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Import monitoring components # Initialize monitor # Keep main thread alive @@ -5,19 +6,16 @@ from scripts.model_monitoring import ModelHealthMonitor # Check if config file exists # Start monitoring system + # Add src to path -# Configure logging -# Constants -#!/usr/bin/env python3 -from pathlib import Path import argparse import logging import sys import threading import time - - - +# Configure logging +# Constants +from pathlib import Path """ Model Monitoring Dashboard Starter diff --git a/scripts/legacy/temperature_scaling.py b/scripts/legacy/temperature_scaling.py index 79696b4b0..45a686501 100644 --- a/scripts/legacy/temperature_scaling.py +++ b/scripts/legacy/temperature_scaling.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Calibrate temperature # Create tokenized dataset # Extract raw validation data @@ -13,23 +14,19 @@ # Create temperature scaling layer # Optimize temperature parameter # Setup device -# Add project root to path -# Configure logging -#!/usr/bin/env python3 -from pathlib import Path -from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader -from src.models.emotion_detection.training_pipeline import create_bert_emotion_classifier -from torch import nn import logging import os import sys -import torch -import traceback - - - +# Add project root to path +# Configure logging +from pathlib import Path +import torch +from torch import nn +from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader +from src.models.emotion_detection.training_pipeline import \ + create_bert_emotion_classifier """ Temperature Scaling for Model Calibration diff --git a/scripts/legacy/threshold_optimization.py b/scripts/legacy/threshold_optimization.py index 3ead01a9c..568304131 100644 --- a/scripts/legacy/threshold_optimization.py +++ b/scripts/legacy/threshold_optimization.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Collect validation logits and labels # Concatenate all batches # Load checkpoint @@ -8,23 +9,20 @@ # Try different thresholds import traceback # Setup device +import logging +import os +import sys # Add project root to path # Configure logging -#!/usr/bin/env python3 from pathlib import Path -from sklearn.metrics import f1_score -from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader -from src.models.emotion_detection.training_pipeline import create_bert_emotion_classifier -import logging + import numpy as np -import os -import sys import torch -import traceback - - - +from sklearn.metrics import f1_score +from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader +from src.models.emotion_detection.training_pipeline import \ + create_bert_emotion_classifier """ Threshold Optimization for Multi-label Classification diff --git a/scripts/legacy/trigger_ci.py b/scripts/legacy/trigger_ci.py index 6ca773197..d75482737 100644 --- a/scripts/legacy/trigger_ci.py +++ b/scripts/legacy/trigger_ci.py @@ -8,7 +8,7 @@ from typing import Tuple # Configure logging -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") def run_command(cmd: str, description: str) -> Tuple[bool, str]: diff --git a/scripts/legacy/update_model_threshold.py b/scripts/legacy/update_model_threshold.py index d731a3bf1..1f26f91d8 100755 --- a/scripts/legacy/update_model_threshold.py +++ b/scripts/legacy/update_model_threshold.py @@ -1,24 +1,26 @@ - # Create model - # Load checkpoint - # Load state dict - # Save model - # Set temperature - # Update threshold - # Find an existing model file -# Add src to path -# Configure logging -# Constants -#!/usr/bin/env python3 -from pathlib import Path -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier +# Create model +# Load checkpoint +# Load state dict +# Save model +# Set temperature +# Update threshold +# Find an existing model file + + import argparse import logging import os import sys -import torch +# Constants +#!/usr/bin/env python3 +from pathlib import Path +import torch +# Add src to path +# Configure logging +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier """ Update Model Threshold diff --git a/scripts/legacy/validate_and_train.py b/scripts/legacy/validate_and_train.py index b4bdb32eb..b6d39fe14 100644 --- a/scripts/legacy/validate_and_train.py +++ b/scripts/legacy/validate_and_train.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Import the validation module # Start training # Training configuration optimized for debugging @@ -8,25 +9,18 @@ # Step 1: Pre-training validation # Step 2: User confirmation # Step 3: Start training -# Add src to path -# Configure logging -#!/usr/bin/env python3 -from pathlib import Path import logging import sys import time -import traceback - - - - - +# Add src to path +# Configure logging +from pathlib import Path """ Validate and Train Script for SAMO Deep Learning. This script runs comprehensive pre-training validation and only starts training -if all critical checks pass. This prevents wasting 4+ hours on failed training. + if all critical checks pass. This prevents wasting 4+ hours on failed training. """ sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) diff --git a/scripts/legacy/validate_current_f1.py b/scripts/legacy/validate_current_f1.py index 26ce5bb50..6c63787c4 100644 --- a/scripts/legacy/validate_current_f1.py +++ b/scripts/legacy/validate_current_f1.py @@ -1,11 +1,9 @@ - # Current status based on your summary +# Current status based on your summary # Configure logging #!/usr/bin/env python3 import logging import sys - - """ Validate Current F1 Score diff --git a/scripts/legacy/validate_model_performance.py b/scripts/legacy/validate_model_performance.py index 1a0d10045..00cd92768 100644 --- a/scripts/legacy/validate_model_performance.py +++ b/scripts/legacy/validate_model_performance.py @@ -7,12 +7,15 @@ to identify issues like overfitting, data leakage, and configuration problems. """ -import torch -from transformers import AutoTokenizer, AutoModelForSequenceClassification import json import os import warnings -warnings.filterwarnings('ignore') + +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + +warnings.filterwarnings("ignore") + def load_model_and_tokenizer(model_path): """Load the trained model and tokenizer.""" @@ -24,15 +27,16 @@ def load_model_and_tokenizer(model_path): print(f"โŒ Error loading model: {str(e)}") return None, None + def check_model_configuration(model_path): """Check if the model configuration is correct.""" print("๐Ÿ” CHECKING MODEL CONFIGURATION") print("=" * 50) - + try: - with open(os.path.join(model_path, 'config.json'), 'r') as f: + with open(os.path.join(model_path, "config.json"), "r") as f: config = json.load(f) - + print(f"Model type: {config.get('model_type', 'NOT FOUND')}") print(f"Architecture: {config.get('architectures', ['NOT FOUND'])[0]}") print(f"Hidden layers: {config.get('num_hidden_layers', 'NOT FOUND')}") @@ -40,15 +44,15 @@ def check_model_configuration(model_path): print(f"Number of labels: {config.get('num_labels', 'NOT FOUND')}") print(f"ID to label mapping: {config.get('id2label', 'NOT FOUND')}") print(f"Label to ID mapping: {config.get('label2id', 'NOT FOUND')}") - + # Check if emotion labels are properly set - id2label = config.get('id2label', {}) + id2label = config.get("id2label", {}) if isinstance(id2label, dict): emotion_labels = list(id2label.values()) print(f"Emotion labels: {emotion_labels}") - + # Check if labels are emotion names or generic - if all(label.startswith('LABEL_') for label in emotion_labels): + if all(label.startswith("LABEL_") for label in emotion_labels): print("โŒ WARNING: Model uses generic LABEL_X format instead of emotion names") return False else: @@ -57,153 +61,148 @@ def check_model_configuration(model_path): else: print("โŒ ERROR: Invalid id2label configuration") return False - + except Exception as e: print(f"โŒ Error reading configuration: {str(e)}") return False + def create_test_dataset(): """Create a proper test dataset with unseen examples.""" print("\n๐Ÿ“Š CREATING PROPER TEST DATASET") print("=" * 50) - + # Test examples that are DIFFERENT from training data test_examples = [ # anxious - different phrasing - {'text': 'The upcoming deadline is causing me stress and worry.', 'expected': 'anxious'}, - {'text': 'I have butterflies in my stomach about tomorrow.', 'expected': 'anxious'}, - {'text': 'The uncertainty of the situation is making me nervous.', 'expected': 'anxious'}, - + {"text": "The upcoming deadline is causing me stress and worry.", "expected": "anxious"}, + {"text": "I have butterflies in my stomach about tomorrow.", "expected": "anxious"}, + {"text": "The uncertainty of the situation is making me nervous.", "expected": "anxious"}, # calm - different phrasing - {'text': 'I feel at peace with the world around me.', 'expected': 'calm'}, - {'text': 'There is a sense of tranquility in my mind.', 'expected': 'calm'}, - {'text': 'I am in a state of serenity right now.', 'expected': 'calm'}, - + {"text": "I feel at peace with the world around me.", "expected": "calm"}, + {"text": "There is a sense of tranquility in my mind.", "expected": "calm"}, + {"text": "I am in a state of serenity right now.", "expected": "calm"}, # content - different phrasing - {'text': 'I am satisfied with how things are going.', 'expected': 'content'}, - {'text': 'Life feels complete and fulfilling at the moment.', 'expected': 'content'}, - {'text': 'I have a sense of inner satisfaction.', 'expected': 'content'}, - + {"text": "I am satisfied with how things are going.", "expected": "content"}, + {"text": "Life feels complete and fulfilling at the moment.", "expected": "content"}, + {"text": "I have a sense of inner satisfaction.", "expected": "content"}, # excited - different phrasing - {'text': 'I am thrilled about the upcoming adventure.', 'expected': 'excited'}, - {'text': 'My heart is racing with anticipation.', 'expected': 'excited'}, - {'text': 'I can barely contain my enthusiasm.', 'expected': 'excited'}, - + {"text": "I am thrilled about the upcoming adventure.", "expected": "excited"}, + {"text": "My heart is racing with anticipation.", "expected": "excited"}, + {"text": "I can barely contain my enthusiasm.", "expected": "excited"}, # frustrated - different phrasing - {'text': 'This situation is driving me up the wall.', 'expected': 'frustrated'}, - {'text': 'I am at my wit\'s end with this problem.', 'expected': 'frustrated'}, - {'text': 'This is really getting on my nerves.', 'expected': 'frustrated'}, - + {"text": "This situation is driving me up the wall.", "expected": "frustrated"}, + {"text": "I am at my wit's end with this problem.", "expected": "frustrated"}, + {"text": "This is really getting on my nerves.", "expected": "frustrated"}, # grateful - different phrasing - {'text': 'I appreciate all the kindness shown to me.', 'expected': 'grateful'}, - {'text': 'My heart is full of thankfulness.', 'expected': 'grateful'}, - {'text': 'I am blessed with wonderful people in my life.', 'expected': 'grateful'}, - + {"text": "I appreciate all the kindness shown to me.", "expected": "grateful"}, + {"text": "My heart is full of thankfulness.", "expected": "grateful"}, + {"text": "I am blessed with wonderful people in my life.", "expected": "grateful"}, # happy - different phrasing - {'text': 'Joy fills my heart today.', 'expected': 'happy'}, - {'text': 'I am in a wonderful mood.', 'expected': 'happy'}, - {'text': 'My spirits are lifted and bright.', 'expected': 'happy'}, - + {"text": "Joy fills my heart today.", "expected": "happy"}, + {"text": "I am in a wonderful mood.", "expected": "happy"}, + {"text": "My spirits are lifted and bright.", "expected": "happy"}, # hopeful - different phrasing - {'text': 'I see a bright future ahead.', 'expected': 'hopeful'}, - {'text': 'There is light at the end of the tunnel.', 'expected': 'hopeful'}, - {'text': 'I believe better days are coming.', 'expected': 'hopeful'}, - + {"text": "I see a bright future ahead.", "expected": "hopeful"}, + {"text": "There is light at the end of the tunnel.", "expected": "hopeful"}, + {"text": "I believe better days are coming.", "expected": "hopeful"}, # overwhelmed - different phrasing - {'text': 'I feel like I am drowning in responsibilities.', 'expected': 'overwhelmed'}, - {'text': 'Everything is too much to handle right now.', 'expected': 'overwhelmed'}, - {'text': 'I am buried under a mountain of tasks.', 'expected': 'overwhelmed'}, - + {"text": "I feel like I am drowning in responsibilities.", "expected": "overwhelmed"}, + {"text": "Everything is too much to handle right now.", "expected": "overwhelmed"}, + {"text": "I am buried under a mountain of tasks.", "expected": "overwhelmed"}, # proud - different phrasing - {'text': 'I have accomplished something meaningful.', 'expected': 'proud'}, - {'text': 'My achievements make me stand tall.', 'expected': 'proud'}, - {'text': 'I feel a sense of accomplishment.', 'expected': 'proud'}, - + {"text": "I have accomplished something meaningful.", "expected": "proud"}, + {"text": "My achievements make me stand tall.", "expected": "proud"}, + {"text": "I feel a sense of accomplishment.", "expected": "proud"}, # sad - different phrasing - {'text': 'My heart feels heavy with sorrow.', 'expected': 'sad'}, - {'text': 'There is a cloud of melancholy over me.', 'expected': 'sad'}, - {'text': 'I am feeling down and blue.', 'expected': 'sad'}, - + {"text": "My heart feels heavy with sorrow.", "expected": "sad"}, + {"text": "There is a cloud of melancholy over me.", "expected": "sad"}, + {"text": "I am feeling down and blue.", "expected": "sad"}, # tired - different phrasing - {'text': 'I am completely exhausted from the day.', 'expected': 'tired'}, - {'text': 'My energy is completely drained.', 'expected': 'tired'}, - {'text': 'I feel like I could sleep for days.', 'expected': 'tired'} + {"text": "I am completely exhausted from the day.", "expected": "tired"}, + {"text": "My energy is completely drained.", "expected": "tired"}, + {"text": "I feel like I could sleep for days.", "expected": "tired"}, ] - + print(f"โœ… Created test dataset with {len(test_examples)} unseen examples") return test_examples + def evaluate_model_performance(model, tokenizer, test_examples, emotions): """Evaluate model performance on unseen examples.""" print("\n๐Ÿงช EVALUATING MODEL PERFORMANCE") print("=" * 50) - + model.eval() device = next(model.parameters()).device - + results = [] predictions_by_emotion = {emotion: 0 for emotion in emotions} - + print("Testing on unseen examples...") print("-" * 50) - + for i, example in enumerate(test_examples): - text = example['text'] - expected = example['expected'] - + text = example["text"] + expected = example["expected"] + # Tokenize - inputs = tokenizer(text, return_tensors='pt', truncation=True, max_length=128) + inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128) inputs = {k: v.to(device) for k, v in inputs.items()} - + # Predict with torch.no_grad(): outputs = model(**inputs) predictions = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(predictions, dim=1).item() confidence = predictions[0][predicted_class].item() - + # Get predicted emotion if predicted_class < len(emotions): predicted_emotion = emotions[predicted_class] else: predicted_emotion = f"UNKNOWN_{predicted_class}" - + predictions_by_emotion[predicted_emotion] += 1 - + # Check if correct is_correct = predicted_emotion == expected status = "โœ…" if is_correct else "โŒ" - - results.append({ - 'text': text, - 'expected': expected, - 'predicted': predicted_emotion, - 'confidence': confidence, - 'correct': is_correct - }) - - print(f"{status} {text[:50]}... โ†’ {predicted_emotion} (expected: {expected}, confidence: {confidence:.3f})") - + + results.append( + { + "text": text, + "expected": expected, + "predicted": predicted_emotion, + "confidence": confidence, + "correct": is_correct, + } + ) + + print( + f"{status} {text[:50]}... โ†’ {predicted_emotion} (expected: {expected}, confidence: {confidence:.3f})" + ) + # Calculate metrics - correct = sum(1 for r in results if r['correct']) + correct = sum(1 for r in results if r["correct"]) accuracy = correct / len(results) - + print(f"\n๐Ÿ“Š PERFORMANCE SUMMARY") print("=" * 30) print(f"Total examples: {len(results)}") print(f"Correct predictions: {correct}") print(f"Accuracy: {accuracy:.1%}") - + # Bias analysis print(f"\n๐ŸŽฏ BIAS ANALYSIS") print("=" * 20) for emotion, count in predictions_by_emotion.items(): percentage = count / len(results) * 100 print(f" {emotion}: {count} predictions ({percentage:.1f}%)") - + # Determine if model is reliable max_bias = max(predictions_by_emotion.values()) / len(results) - + print(f"\n๐Ÿ” RELIABILITY ASSESSMENT") print("=" * 30) if accuracy >= 0.8 and max_bias <= 0.3: @@ -215,35 +214,36 @@ def evaluate_model_performance(model, tokenizer, test_examples, emotions): print(f"โŒ Accuracy too low: {accuracy:.1%} (need >80%)") if max_bias > 0.3: print(f"โŒ Too much bias: {max_bias:.1%} (need <30%)") - + return results, accuracy, max_bias + def check_for_data_leakage(training_data, test_examples): """Check if there's data leakage between training and test sets.""" print("\n๐Ÿ” CHECKING FOR DATA LEAKAGE") print("=" * 40) - - training_texts = [item['text'].lower() for item in training_data] - test_texts = [item['text'].lower() for item in test_examples] - + + training_texts = [item["text"].lower() for item in training_data] + test_texts = [item["text"].lower() for item in test_examples] + exact_matches = 0 similar_matches = 0 - + for test_text in test_texts: # Check for exact matches if test_text in training_texts: exact_matches += 1 print(f"โŒ EXACT MATCH FOUND: {test_text[:50]}...") - + # Check for similar matches (same emotion words) for train_text in training_texts: if any(word in test_text for word in train_text.split() if len(word) > 4): similar_matches += 1 break - + print(f"Exact matches: {exact_matches}/{len(test_texts)}") print(f"Similar matches: {similar_matches}/{len(test_texts)}") - + if exact_matches > 0: print("โŒ CRITICAL: Data leakage detected! Test examples are in training data.") return True @@ -254,49 +254,65 @@ def check_for_data_leakage(training_data, test_examples): print("โœ… No significant data leakage detected.") return False + def main(): """Main validation function.""" print("๐Ÿ”ฌ COMPREHENSIVE MODEL VALIDATION") print("=" * 60) - + # Model path model_path = "./deployment/model" - + # Check if model exists if not os.path.exists(model_path): print(f"โŒ Model not found at: {model_path}") print("Please ensure the model is saved in the deployment/model directory.") return - + # Load model and tokenizer tokenizer, model = load_model_and_tokenizer(model_path) if tokenizer is None or model is None: return - + # Check model configuration config_ok = check_model_configuration(model_path) - + # Define emotions - emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] - + emotions = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", + ] + # Create test dataset test_examples = create_test_dataset() - + # Evaluate performance - results, accuracy, max_bias = evaluate_model_performance(model, tokenizer, test_examples, emotions) - + results, accuracy, max_bias = evaluate_model_performance( + model, tokenizer, test_examples, emotions + ) + # Check for data leakage (if training data is available) training_data_path = "./data/balanced_training_data.json" if os.path.exists(training_data_path): try: - with open(training_data_path, 'r') as f: + with open(training_data_path, "r") as f: training_data = json.load(f) data_leakage = check_for_data_leakage(training_data, test_examples) except: print("โš ๏ธ Could not check for data leakage (training data not accessible)") else: print("โš ๏ธ Training data not found, skipping data leakage check") - + # Summary print(f"\n๐Ÿ“‹ VALIDATION SUMMARY") print("=" * 30) @@ -304,7 +320,7 @@ def main(): print(f"Accuracy on unseen data: {accuracy:.1%}") print(f"Maximum bias: {max_bias:.1%}") print(f"Model reliable: {'โœ…' if accuracy >= 0.8 and max_bias <= 0.3 else 'โŒ'}") - + if accuracy < 0.8: print(f"\n๐Ÿ’ก RECOMMENDATIONS:") print("1. Increase training dataset size") @@ -313,5 +329,6 @@ def main(): print("4. Adjust hyperparameters") print("5. Use cross-validation for better evaluation") + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/legacy/vertex_ai_setup.py b/scripts/legacy/vertex_ai_setup.py index 3ccc68811..8ac7e7809 100644 --- a/scripts/legacy/vertex_ai_setup.py +++ b/scripts/legacy/vertex_ai_setup.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Create custom job # Create hyperparameter tuning job # Create validation job @@ -10,11 +11,6 @@ # Pipeline configuration # Training job configuration from google.cloud import aiplatform - from google.cloud import aiplatform - from google.cloud import aiplatform - from google.cloud import aiplatform - from google.cloud import aiplatform - from google.cloud import aiplatform from google.cloud import storage import subprocess # Step 1: Environment setup @@ -27,22 +23,13 @@ # Get project ID from environment or user input # Setup complete infrastructure # Summary -# Add src to path -# Configure logging -#!/usr/bin/env python3 -from pathlib import Path -from typing import Dict, Any, Optional import logging import os import sys - - - - - - - - +# Add src to path +# Configure logging +from pathlib import Path +from typing import Any, Dict, Optional """ Vertex AI Setup for SAMO Deep Learning Project. diff --git a/scripts/maintenance/auto_fix_code_quality.py b/scripts/maintenance/auto_fix_code_quality.py deleted file mode 100644 index b2d4d1948..000000000 --- a/scripts/maintenance/auto_fix_code_quality.py +++ /dev/null @@ -1,451 +0,0 @@ -#!/usr/bin/env python3 -""" -SAMO-DL Auto-Fix Code Quality - -This script automatically fixes common code quality issues to prevent -recurring DeepSource warnings. - -Auto-fixes: -- FLK-W291: Trailing whitespace -- FLK-W292: Missing newlines at end of file -- FLK-W293: Blank line whitespace -- FLK-E501: Line length violations (basic) -- PTC-W0027: f-strings without expressions -- Basic import organization -""" -import sys -import re -from pathlib import Path -from typing import Dict, List, Set, Tuple, Any, Optional -import logging - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(levelname)s - %(message)s' -) -logger = logging.getLogger(__name__) - - -class CodeQualityAutoFixer: - """Automatically fixes common code quality issues.""" - - def __init__(self, dry_run: bool = False): - self.dry_run = dry_run - self.fixes_applied = 0 - self.files_modified = 0 - self.fixes_by_type = {} - - def fix_file(self, file_path: Path) -> Dict[str, Any]: - """Fix quality issues in a single Python file.""" - # Validate file path for security - if not self._is_safe_file_path(file_path): - return { - 'file': str(file_path), - 'error': 'Unsafe file path detected', - 'modified': False - } - - logger.info("Fixing: %s", file_path) - - try: - with open(file_path, 'r', encoding='utf-8') as f: - content = f.read() - - original_content = content - fixes = [] - - # Apply various fixes - content, file_fixes = self._fix_trailing_whitespace(content) - fixes.extend(file_fixes) - - content, file_fixes = self._fix_missing_newlines(content) - fixes.extend(file_fixes) - - content, file_fixes = self._fix_blank_line_whitespace(content) - fixes.extend(file_fixes) - - content, file_fixes = self._fix_f_strings_without_expressions(content) - fixes.extend(file_fixes) - - content, file_fixes = self._fix_basic_line_length(content) - fixes.extend(file_fixes) - - content, file_fixes = self._fix_import_organization(content) - fixes.extend(file_fixes) - - # Apply fixes if not dry run - if content != original_content and not self.dry_run: - with open(file_path, 'w', encoding='utf-8') as f: - f.write(content) - self.files_modified += 1 - - # Update statistics - self.fixes_applied += len(fixes) - for fix_type in [f['type'] for f in fixes]: - self.fixes_by_type[fix_type] = self.fixes_by_type.get(fix_type, 0) + 1 - - return { - 'file': str(file_path), - 'fixes': fixes, - 'modified': content != original_content - } - - except Exception as e: - logger.error("Error fixing %s: %s", file_path, e) - return { - 'file': str(file_path), - 'error': str(e), - 'modified': False - } - - @staticmethod - def _fix_trailing_whitespace(content: str) -> Tuple[str, List[Dict[str, Any]]]: - """Fix trailing whitespace issues.""" - fixes = [] - lines = content.splitlines() - modified = False - - for i, line in enumerate(lines): - if line.rstrip() != line: - lines[i] = line.rstrip() - modified = True - fixes.append({ - 'type': 'FLK-W291', - 'line': i + 1, - 'description': 'Removed trailing whitespace' - }) - - if modified: - content = '\n'.join(lines) + ('\n' if content.endswith('\n') else '') - - return content, fixes - - @staticmethod - def _fix_missing_newlines(content: str) -> Tuple[str, List[Dict[str, Any]]]: - """Fix missing newlines at end of file.""" - fixes = [] - - if content and not content.endswith('\n'): - content += '\n' - fixes.append({ - 'type': 'FLK-W292', - 'line': len(content.splitlines()), - 'description': 'Added missing newline at end of file' - }) - - return content, fixes - - @staticmethod - def _fix_blank_line_whitespace(content: str) -> Tuple[str, List[Dict[str, Any]]]: - """Fix blank lines containing whitespace.""" - fixes = [] - lines = content.splitlines() - modified = False - - for i, line in enumerate(lines): - if line.strip() == '' and line != '': - lines[i] = '' - modified = True - fixes.append({ - 'type': 'FLK-W293', - 'line': i + 1, - 'description': 'Removed whitespace from blank line' - }) - - if modified: - content = '\n'.join(lines) + ('\n' if content.endswith('\n') else '') - - return content, fixes - - @staticmethod - def _fix_f_strings_without_expressions(content: str) -> Tuple[str, List[Dict[str, Any]]]: - """Fix f-strings without expressions.""" - fixes = [] - - # Pattern to find f-strings without expressions - pattern = r'f["\']([^"\']*?)["\']' - - def replace_f_string(match): - """Replace f-string without expressions with regular string.""" - string_content = match.group(1) - if not re.search(r'\{[^}]*\}', string_content): - fixes.append({ - 'type': 'PTC-W0027', - 'line': content[:match.start()].count('\n') + 1, - 'description': ( - f'Converted f-string to regular string: {match.group(0)}' - ) - }) - return f'"{string_content}"' - return match.group(0) - - content = re.sub(pattern, replace_f_string, content) - - return content, fixes - - @staticmethod - def _fix_basic_line_length(content: str) -> Tuple[str, List[Dict[str, Any]]]: - """Fix basic line length violations (simple cases).""" - fixes = [] - lines = content.splitlines() - modified = False - - for i, line in enumerate(lines): - if len(line) > 88: - # Try to break long lines at common break points - if 'import ' in line and line.count(',') > 2: - # Break long import lines - parts = line.split(',') - if len(parts) > 3: - # Split into multiple lines - import_start = line[:line.find('import') + 6] - indent = len(line) - len(line.lstrip()) - - new_lines = [import_start + parts[0] + ','] - for part in parts[1:-1]: - new_lines.append(' ' * (indent + 4) + part + ',') - new_lines.append(' ' * (indent + 4) + parts[-1]) - - lines[i:i+1] = new_lines - modified = True - fixes.append({ - 'type': 'FLK-E501', - 'line': i + 1, - 'description': 'Broke long import line into multiple lines' - }) - - elif 'def ' in line and line.count('(') > 0 and line.count(')') == 0: - # Break long function definitions - if line.count(',') > 2: - # Split parameters - func_start = line[:line.find('(') + 1] - params_part = line[line.find('(') + 1:] - indent = len(line) - len(line.lstrip()) - - # Find the last parameter - last_comma = params_part.rfind(',') - if last_comma > 0: - first_params = params_part[:last_comma + 1] - last_param = params_part[last_comma + 1:] - - new_lines = [ - func_start + first_params, - ' ' * (indent + 4) + last_param - ] - - lines[i:i+1] = new_lines - modified = True - fixes.append({ - 'type': 'FLK-E501', - 'line': i + 1, - 'description': ( - 'Broke long function definition into multiple lines' - ) - }) - - if modified: - content = '\n'.join(lines) + ('\n' if content.endswith('\n') else '') - - return content, fixes - - @staticmethod - def _fix_import_organization(content: str) -> Tuple[str, List[Dict[str, Any]]]: - """Fix basic import organization issues.""" - fixes = [] - lines = content.splitlines() - modified = False - - # Find import sections - import_start = -1 - import_end = -1 - - for i, line in enumerate(lines): - if line.strip().startswith(('import ', 'from ')): - if import_start == -1: - import_start = i - import_end = i - elif import_start != -1 and line.strip() == '': - import_end = i - 1 - break - - if import_start != -1 and import_end != -1: - # Sort imports within the section - import_lines = lines[import_start:import_end + 1] - sorted_imports = sorted(import_lines, key=lambda x: ( - # Standard library first - 0 if ( - not x.strip().startswith('from ') and - not any(pkg in x for pkg in [ - 'django', 'flask', 'numpy', 'pandas', - 'torch', 'transformers' - ]) - ) else 1, - # Then by import type - 0 if x.strip().startswith('import ') else 1, - # Then alphabetically - x.strip().lower() - )) - - if sorted_imports != import_lines: - lines[import_start:import_end + 1] = sorted_imports - modified = True - fixes.append({ - 'type': 'Import Organization', - 'line': import_start + 1, - 'description': 'Reorganized imports alphabetically' - }) - - if modified: - content = '\n'.join(lines) + ('\n' if content.endswith('\n') else '') - - return content, fixes - - @staticmethod - def _is_safe_file_path(file_path: Path) -> bool: - """Validate that file path is safe for processing.""" - try: - # Resolve to absolute path to prevent path traversal - resolved_path = file_path.resolve() - - # Check if path contains suspicious patterns - path_str = str(resolved_path) - suspicious_patterns = [ - '..', # Path traversal - '~', # Home directory - '/etc', '/var', '/usr', '/bin', '/sbin', # System directories - 'C:\\', 'D:\\', # Windows system drives - ] - - for pattern in suspicious_patterns: - if pattern in path_str: - return False - - # Ensure it's a Python file - if not path_str.endswith('.py'): - return False - - return True - - except Exception: - return False - - def fix_directory(self, directory: Path) -> Dict[str, Any]: - """Fix quality issues in all Python files in a directory.""" - logger.info("Fixing directory: %s", directory) - - python_files = list(directory.rglob("*.py")) - logger.info("Found %d Python files", len(python_files)) - - results = [] - - for file_path in python_files: - # Skip certain directories - if any(part in str(file_path) for part in [ - '__pycache__', '.git', '.venv', '.env', 'build', 'dist', - '.eggs', '.tox', '.coverage', 'htmlcov', '.cache', - '.logs', 'results', 'samples', 'notebooks', 'website' - ]): - continue - - result = self.fix_file(file_path) - results.append(result) - - return { - 'files_processed': len(results), - 'files_modified': self.files_modified, - 'total_fixes': self.fixes_applied, - 'fixes_by_type': self.fixes_by_type, - 'results': results - } - - @staticmethod - def generate_report(results: Dict[str, Any]) -> str: - """Generate a comprehensive fix report.""" - report = f""" -๐Ÿ”ง CODE QUALITY AUTO-FIX REPORT -{'='*50} - -๐Ÿ“Š SUMMARY: -- Files processed: {results['files_processed']} -- Files modified: {results['files_modified']} -- Total fixes applied: {results['total_fixes']} - -๐Ÿ› ๏ธ FIXES BY TYPE: -""" - - for fix_type, count in sorted(results['fixes_by_type'].items()): - report += f"- {fix_type}: {count} fixes\n" - - report += f""" - -๐Ÿ“‹ DETAILED RESULTS: -{'-'*50} -""" - - for result in results['results']: - if result.get('modified', False): - report += f"โœ… {result['file']}: {len(result['fixes'])} fixes applied\n" - for fix in result['fixes']: - report += f" - {fix['type']}: {fix['description']}\n" - elif 'error' in result: - report += f"โŒ {result['file']}: Error - {result['error']}\n" - else: - report += f"โญ๏ธ {result['file']}: No fixes needed\n" - - return report - - def run_fixes(self, directory: Path) -> bool: - """Run all auto-fixes and return success status.""" - logger.info("Starting code quality auto-fixes...") - - if self.dry_run: - logger.info("DRY RUN MODE - No files will be modified") - - results = self.fix_directory(directory) - - # Generate and display report - report = self.generate_report(results) - print(report) - - # Return success (True if no errors, False if any errors occurred) - errors = [r for r in results['results'] if 'error' in r] - return len(errors) == 0 - - -def main(): - """Main function for command-line usage.""" - import argparse - - parser = argparse.ArgumentParser( - description='Automatically fix common code quality issues' - ) - parser.add_argument( - 'directory', - help='Directory to process' - ) - parser.add_argument( - '--dry-run', - action='store_true', - help='Show what would be fixed without making changes' - ) - - args = parser.parse_args() - - directory = Path(args.directory) - if not directory.exists(): - print(f"Error: Directory {directory} does not exist") - sys.exit(1) - - fixer = CodeQualityAutoFixer(dry_run=args.dry_run) - success = fixer.run_fixes(directory) - - if success: - print("\nโœ… All auto-fixes completed successfully!") - sys.exit(0) - else: - print("\nโŒ Some errors occurred during auto-fixes!") - sys.exit(1) - -if __name__ == "__main__": - main() diff --git a/scripts/maintenance/code_quality_enforcer.py b/scripts/maintenance/code_quality_enforcer.py deleted file mode 100644 index 5255c3f20..000000000 --- a/scripts/maintenance/code_quality_enforcer.py +++ /dev/null @@ -1,522 +0,0 @@ -#!/usr/bin/env python3 -""" -SAMO-DL Code Quality Enforcer - -This script enforces comprehensive code quality standards and prevents -ALL recurring DeepSource issues from ever happening again. - -Prevents: -- PYL-R1705: Unnecessary else/elif after return -- PTC-W0027: f-strings without expressions -- PY-W2000: Unused imports -- FLK-E128: Continuation line indentation -- FLK-E301: Missing blank lines -- FLK-E501: Line length violations -- FLK-W291: Trailing whitespace -- FLK-W292: Missing newlines -- FLK-W293: Blank line whitespace -- FLK-W505: Doc line length -- PY-D0003: Missing docstrings -- PY-R1000: High cyclomatic complexity -""" -import sys -import ast -import re -from pathlib import Path -from typing import Dict, List, Set, Tuple, Any, Optional -import logging - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(levelname)s - %(message)s' -) -logger = logging.getLogger(__name__) - - -class CodeQualityEnforcer: - """Enforces comprehensive code quality standards for SAMO-DL.""" - - def __init__(self): - self.issues_found: List[Dict[str, Any]] = [] - self.files_checked = 0 - self.files_with_issues = 0 - - # Define quality rules - self.rules = { - 'PYL-R1705': { - 'name': 'Unnecessary else/elif after return', - 'severity': 'error', - 'description': 'Remove unnecessary else/elif after return statements' - }, - 'PTC-W0027': { - 'name': 'f-string without expressions', - 'severity': 'warning', - 'description': ( - 'Use regular strings instead of f-strings without expressions' - ) - }, - 'PY-W2000': { - 'name': 'Unused imports', - 'severity': 'warning', - 'description': 'Remove unused imports' - }, - 'FLK-E128': { - 'name': 'Continuation line indentation', - 'severity': 'error', - 'description': 'Fix continuation line indentation for visual indent' - }, - 'FLK-E301': { - 'name': 'Missing blank lines', - 'severity': 'error', - 'description': 'Add blank lines between class methods' - }, - 'FLK-E501': { - 'name': 'Line too long', - 'severity': 'error', - 'description': 'Break long lines to stay within 88 character limit' - }, - 'FLK-W291': { - 'name': 'Trailing whitespace', - 'severity': 'error', - 'description': 'Remove trailing whitespace' - }, - 'FLK-W292': { - 'name': 'Missing newline at end of file', - 'severity': 'error', - 'description': 'Add newline at end of file' - }, - 'FLK-W293': { - 'name': 'Blank line contains whitespace', - 'severity': 'error', - 'description': 'Remove whitespace from blank lines' - }, - 'FLK-W505': { - 'name': 'Doc line too long', - 'severity': 'warning', - 'description': 'Break long docstring lines' - }, - 'PY-D0003': { - 'name': 'Missing docstring', - 'severity': 'warning', - 'description': 'Add docstrings to functions and classes' - }, - 'PY-R1000': { - 'name': 'High cyclomatic complexity', - 'severity': 'warning', - 'description': 'Refactor complex functions to reduce complexity' - } - } - - def check_file(self, file_path: Path) -> List[Dict[str, Any]]: - """Check a single Python file for quality issues.""" - # Validate file path for security - if not self._is_safe_file_path(file_path): - return [{ - 'rule': 'SECURITY', - 'line': 0, - 'message': 'Unsafe file path detected', - 'severity': 'error' - }] - - issues = [] - - try: - with open(file_path, 'r', encoding='utf-8') as f: - content = f.read() - lines = content.splitlines() - - # Check for missing newline at end of file - if content and not content.endswith('\n'): - issues.append({ - 'rule': 'FLK-W292', - 'line': len(lines), - 'message': 'Missing newline at end of file', - 'severity': 'error' - }) - - # Check each line for issues - for line_num, line in enumerate(lines, 1): - line_issues = self._check_line(line, line_num) - issues.extend(line_issues) - - # Check for unused imports - import_issues = self._check_unused_imports(content, file_path) - issues.extend(import_issues) - - # Check for high cyclomatic complexity - complexity_issues = self._check_cyclomatic_complexity(content, file_path) - issues.extend(complexity_issues) - - # Check for unnecessary else/elif after return - control_flow_issues = self._check_control_flow(content, file_path) - issues.extend(control_flow_issues) - - except Exception as e: - logger.error("Error checking %s: %s", file_path, e) - issues.append({ - 'rule': 'ERROR', - 'line': 0, - 'message': f'Error reading file: {e}', - 'severity': 'error' - }) - - return issues - - @staticmethod - def _check_line(line: str, line_num: int) -> List[Dict[str, Any]]: - """Check a single line for quality issues.""" - issues = [] - - # Check for trailing whitespace - if line.rstrip() != line: - issues.append({ - 'rule': 'FLK-W291', - 'line': line_num, - 'message': 'Trailing whitespace detected', - 'severity': 'error' - }) - - # Check for blank line with whitespace - if line.strip() == '' and line != '': - issues.append({ - 'rule': 'FLK-W293', - 'line': line_num, - 'message': 'Blank line contains whitespace', - 'severity': 'error' - }) - - # Check for line length - if len(line) > 88: - issues.append({ - 'rule': 'FLK-E501', - 'line': line_num, - 'message': f'Line too long ({len(line)} > 88 characters)', - 'severity': 'error' - }) - - # Check for f-strings without expressions - if ( - (line.strip().startswith('f"') or line.strip().startswith("f'")) - and not re.search(r'\{[^}]*\}', line) - ): - issues.append({ - 'rule': 'PTC-W0027', - 'line': line_num, - 'message': 'f-string used without expressions', - 'severity': 'warning' - }) - - return issues - - @staticmethod - def _check_unused_imports(content: str, file_path: Path) -> List[Dict[str, Any]]: - """Check for unused imports using AST analysis.""" - issues = [] - - try: - tree = ast.parse(content) - import_nodes = [] - used_names = set() - - # Collect all import nodes - for node in ast.walk(tree): - if isinstance(node, (ast.Import, ast.ImportFrom)): - import_nodes.append(node) - elif isinstance(node, ast.Name): - used_names.add(node.id) - elif isinstance(node, ast.Attribute): - # Handle attribute access (e.g., module.function) - if isinstance(node.value, ast.Name): - used_names.add(node.value.id) - - # Check for unused imports - for node in import_nodes: - if isinstance(node, ast.Import): - for alias in node.names: - if ( - alias.name not in used_names and - not alias.name.startswith('_') - ): - issues.append({ - 'rule': 'PY-W2000', - 'line': getattr(node, 'lineno', 0), - 'message': f'Unused import: {alias.name}', - 'severity': 'warning' - }) - elif isinstance(node, ast.ImportFrom): - pass - - except SyntaxError: - # File has syntax errors, skip import analysis - pass - - return issues - - def _check_cyclomatic_complexity( - self, content: str, file_path: Path - ) -> List[Dict[str, Any]]: - """Check for high cyclomatic complexity.""" - issues = [] - - try: - tree = ast.parse(content) - - for node in ast.walk(tree): - if isinstance( - node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef) - ): - complexity = self._calculate_complexity(node) - if complexity > 10: # Threshold for high complexity - issues.append({ - 'rule': 'PY-R1000', - 'line': getattr(node, 'lineno', 0), - 'message': ( - f'Function/class has high cyclomatic complexity ' - f'({complexity})' - ), - 'severity': 'warning' - }) - - except SyntaxError: - # File has syntax errors, skip complexity analysis - pass - - return issues - - @staticmethod - def _calculate_complexity(node: ast.AST) -> int: - """Calculate cyclomatic complexity of a function/class.""" - complexity = 1 # Base complexity - - for child in ast.walk(node): - if isinstance(child, (ast.If, ast.While, ast.For, ast.AsyncFor)): - complexity += 1 - elif isinstance(child, ast.ExceptHandler): - complexity += 1 - elif isinstance(child, ast.With): - complexity += 1 - elif isinstance(child, ast.Assert): - complexity += 1 - elif isinstance(child, ast.Return): - complexity += 1 - - return complexity - - def _check_control_flow( - self, content: str, file_path: Path - ) -> List[Dict[str, Any]]: - """Check for unnecessary else/elif after return.""" - issues = [] - - try: - tree = ast.parse(content) - - for node in ast.walk(tree): - if isinstance(node, ast.If): - # Check if this if statement has a return and unnecessary else - if self._has_unnecessary_else(node): - issues.append({ - 'rule': 'PYL-R1705', - 'line': getattr(node, 'lineno', 0), - 'message': 'Unnecessary else/elif after return', - 'severity': 'error' - }) - - except SyntaxError: - # File has syntax errors, skip control flow analysis - pass - - return issues - - def _has_unnecessary_else(self, node: ast.If) -> bool: - """Check if an if statement has unnecessary else/elif after return.""" - # Check if the if body has a return - has_return_in_if = self._contains_return(node.body) - - # Check if there's an else clause - if hasattr(node, 'orelse') and node.orelse: - # Check if the else clause is just another if (elif) - if len(node.orelse) == 1 and isinstance(node.orelse[0], ast.If): - return self._has_unnecessary_else(node.orelse[0]) - # Check if the else body has a return - has_return_in_else = self._contains_return(node.orelse) - return has_return_in_if and has_return_in_else - - return False - - @staticmethod - def _contains_return(body: List[ast.stmt]) -> bool: - """Check if a list of statements contains a return.""" - return any(isinstance(stmt, ast.Return) for stmt in body) - - @staticmethod - def _is_safe_file_path(file_path: Path) -> bool: - """Validate that file path is safe for processing.""" - try: - # Resolve to absolute path to prevent path traversal - resolved_path = file_path.resolve() - - # Check if path contains suspicious patterns - path_str = str(resolved_path) - suspicious_patterns = [ - '..', # Path traversal - '~', # Home directory - '/etc', '/var', '/usr', '/bin', '/sbin', # System directories - 'C:\\', 'D:\\', # Windows system drives - ] - - for pattern in suspicious_patterns: - if pattern in path_str: - return False - - # Ensure it's a Python file - if not path_str.endswith('.py'): - return False - - return True - - except Exception: - return False - - def check_directory(self, directory: Path) -> Dict[str, Any]: - """Check all Python files in a directory for quality issues.""" - logger.info("Checking directory: %s", directory) - - python_files = list(directory.rglob("*.py")) - logger.info("Found %d Python files", len(python_files)) - - total_issues = 0 - - for file_path in python_files: - # Skip certain directories - if any(part in str(file_path) for part in [ - '__pycache__', '.git', '.venv', '.env', 'build', 'dist', - '.eggs', '.tox', '.coverage', 'htmlcov', '.cache', - '.logs', 'results', 'samples', 'notebooks', 'website' - ]): - continue - - logger.info("Checking: %s", file_path) - issues = self.check_file(file_path) - - if issues: - self.files_with_issues += 1 - total_issues += len(issues) - - for issue in issues: - self.issues_found.append({ - 'file': str(file_path), - 'line': issue['line'], - 'rule': issue['rule'], - 'message': issue['message'], - 'severity': issue['severity'] - }) - - self.files_checked += 1 - - return { - 'files_checked': self.files_checked, - 'files_with_issues': self.files_with_issues, - 'total_issues': total_issues, - 'issues': self.issues_found - } - - def generate_report(self) -> str: - """Generate a comprehensive quality report.""" - if not self.issues_found: - return "โœ… No code quality issues found! All files meet standards." - - # Group issues by rule - issues_by_rule = {} - for issue in self.issues_found: - rule = issue['rule'] - if rule not in issues_by_rule: - issues_by_rule[rule] = [] - issues_by_rule[rule].append(issue) - - # Generate report - report = f""" -๐Ÿ” CODE QUALITY REPORT -{'='*50} - -๐Ÿ“Š SUMMARY: -- Files checked: {self.files_checked} -- Files with issues: {self.files_with_issues} -- Total issues found: {len(self.issues_found)} - -๐Ÿšจ ISSUES BY RULE: -""" - - for rule, issues in sorted(issues_by_rule.items()): - rule_info = self.rules.get(rule, {'name': rule, 'severity': 'unknown'}) - report += f"\n{rule}: {rule_info['name']} ({len(issues)} issues)" - report += f"\n Severity: {rule_info['severity']}" - report += ( - f"\n Description: " - f"{rule_info.get('description', 'No description')}" - ) - - # Show first few examples - for issue in issues[:3]: - report += ( - f"\n - {issue['file']}:{issue['line']} - " - f"{issue['message']}" - ) - - if len(issues) > 3: - report += f"\n ... and {len(issues) - 3} more issues" - - report += f""" - -๐Ÿ“‹ DETAILED ISSUES: -{'-'*50} -""" - - for issue in self.issues_found: - report += ( - f"{issue['file']}:{issue['line']} - " - f"{issue['rule']}: {issue['message']}\n" - ) - - return report - - def run_checks(self, directory: Path) -> bool: - """Run all quality checks and return success status.""" - logger.info("Starting code quality enforcement...") - - self.check_directory(directory) - - # Generate and display report - report = self.generate_report() - print(report) - - # Return success (True if no critical issues, False if any errors) - critical_issues = [i for i in self.issues_found if i['severity'] == 'error'] - return len(critical_issues) == 0 - - -def main(): - """Main function for command-line usage.""" - if len(sys.argv) != 2: - print("Usage: python code_quality_enforcer.py ") - sys.exit(1) - - directory = Path(sys.argv[1]) - if not directory.exists(): - print(f"Error: Directory {directory} does not exist") - sys.exit(1) - - enforcer = CodeQualityEnforcer() - success = enforcer.run_checks(directory) - - if success: - print("\nโœ… All critical quality checks passed!") - sys.exit(0) - else: - print("\nโŒ Critical quality issues found!") - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/scripts/maintenance/code_quality_report.py b/scripts/maintenance/code_quality_report.py index 9fe78fcc0..ec974c8cb 100644 --- a/scripts/maintenance/code_quality_report.py +++ b/scripts/maintenance/code_quality_report.py @@ -1,18 +1,18 @@ - # Parse JSON output would go here in a real implementation - # Save to logs directory +# Parse JSON output would go here in a real implementation +# Save to logs directory # SAMO Deep Learning - Code Quality Report -#!/usr/bin/env python3 -## Pre-commit Status -## Recommendations -## Ruff Analysis -from datetime import UTC, datetime -from pathlib import Path -import datetime + + import logging import subprocess +## Recommendations +## Ruff Analysis +from datetime import UTC, datetime - +#!/usr/bin/env python3 +## Pre-commit Status +from pathlib import Path """Generate code quality report for SAMO Deep Learning project. @@ -21,6 +21,7 @@ a simple maintenance script that follows code quality standards. """ + def run_ruff_check() -> dict[str, int]: """Run Ruff check and return statistics.""" try: diff --git a/scripts/maintenance/emergency_f1_fix.py b/scripts/maintenance/emergency_f1_fix.py index 947b378ac..3c3874d8b 100644 --- a/scripts/maintenance/emergency_f1_fix.py +++ b/scripts/maintenance/emergency_f1_fix.py @@ -25,11 +25,12 @@ from torch.utils.data import DataLoader, TensorDataset from transformers import AutoTokenizer, get_linear_schedule_with_warmup +from src.models.emotion_detection.bert_classifier import BERTEmotionClassifier +from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader + # Add src to path sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) -from src.models.emotion_detection.bert_classifier import BERTEmotionClassifier -from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader # Configure logging logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") @@ -38,28 +39,28 @@ class FocalLoss(nn.Module): """Focal Loss for handling class imbalance.""" - + def __init__(self, alpha=0.25, gamma=2.0, class_weights=None): super().__init__() self.alpha = alpha self.gamma = gamma self.class_weights = class_weights - + def forward(self, inputs, targets): - bce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none') + bce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") pt = torch.exp(-bce_loss) focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss - + if self.class_weights is not None: focal_loss = focal_loss * self.class_weights.unsqueeze(0) - + return focal_loss.mean() def create_optimized_model(class_weights): """Create model with optimal settings for F1 improvement.""" logger.info("๐Ÿค– Creating optimized BERT model...") - + model = BERTEmotionClassifier( model_name="bert-base-uncased", num_emotions=28, @@ -67,32 +68,34 @@ def create_optimized_model(class_weights): classifier_dropout_prob=0.2, # Reduced dropout freeze_bert_layers=0, # Don't freeze initially temperature=1.0, - class_weights=torch.tensor(class_weights, dtype=torch.float32) if class_weights is not None else None + class_weights=( + torch.tensor(class_weights, dtype=torch.float32) if class_weights is not None else None + ), ) - + return model def prepare_training_data(datasets, tokenizer, batch_size=16): """Prepare training data with proper tokenization.""" logger.info("๐Ÿ“Š Preparing training data...") - + train_data = datasets["train_data"] val_data = datasets["val_data"] - + def tokenize_dataset(dataset): texts = dataset["text"] labels = dataset["labels"] - + # Tokenize inputs = tokenizer( texts, padding=True, truncation=True, max_length=256, # Reduced for faster training - return_tensors="pt" + return_tensors="pt", ) - + # Convert labels to one-hot num_classes = 28 label_vectors = [] @@ -102,19 +105,19 @@ def tokenize_dataset(dataset): if 0 <= label_idx < num_classes: label_vector[label_idx] = 1 label_vectors.append(label_vector) - + return TensorDataset( inputs["input_ids"], inputs["attention_mask"], - torch.tensor(label_vectors, dtype=torch.float32) + torch.tensor(label_vectors, dtype=torch.float32), ) - + train_dataset = tokenize_dataset(train_data) val_dataset = tokenize_dataset(val_data) - + train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False) - + return train_loader, val_loader @@ -123,61 +126,59 @@ def evaluate_model(model, dataloader, device, threshold=0.3): model.eval() all_predictions = [] all_labels = [] - + with torch.no_grad(): for batch in dataloader: input_ids, attention_mask, labels = batch input_ids = input_ids.to(device) attention_mask = attention_mask.to(device) labels = labels.to(device) - + outputs = model(input_ids, attention_mask) predictions = torch.sigmoid(outputs) > threshold - + all_predictions.extend(predictions.cpu().numpy()) all_labels.extend(labels.cpu().numpy()) - + # Calculate metrics all_predictions = np.array(all_predictions) all_labels = np.array(all_labels) - - micro_f1 = f1_score(all_labels, all_predictions, average='micro', zero_division=0) - macro_f1 = f1_score(all_labels, all_predictions, average='macro', zero_division=0) - + + micro_f1 = f1_score(all_labels, all_predictions, average="micro", zero_division=0) + macro_f1 = f1_score(all_labels, all_predictions, average="macro", zero_division=0) + return { - 'micro_f1': micro_f1, - 'macro_f1': macro_f1, - 'predictions': all_predictions, - 'labels': all_labels + "micro_f1": micro_f1, + "macro_f1": macro_f1, + "predictions": all_predictions, + "labels": all_labels, } def train_with_focal_loss(model, train_loader, val_loader, device, epochs=5): """Train model with focal loss and optimization.""" logger.info("๐Ÿš€ Starting Focal Loss training...") - + # Optimizer with lower learning rate optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5, weight_decay=0.01) - + # Learning rate scheduler total_steps = len(train_loader) * epochs scheduler = get_linear_schedule_with_warmup( - optimizer, - num_warmup_steps=total_steps // 10, - num_training_steps=total_steps + optimizer, num_warmup_steps=total_steps // 10, num_training_steps=total_steps ) - + # Focal loss class_weights = model.class_weights.to(device) if model.class_weights is not None else None focal_loss = FocalLoss(alpha=0.25, gamma=2.0, class_weights=class_weights) - + best_f1 = 0.0 patience = 3 patience_counter = 0 - + for epoch in range(epochs): logger.info(f"๐Ÿ“ˆ Epoch {epoch + 1}/{epochs}") - + # Training model.train() total_loss = 0 @@ -186,94 +187,97 @@ def train_with_focal_loss(model, train_loader, val_loader, device, epochs=5): input_ids = input_ids.to(device) attention_mask = attention_mask.to(device) labels = labels.to(device) - + optimizer.zero_grad() - + outputs = model(input_ids, attention_mask) loss = focal_loss(outputs, labels) - + loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) optimizer.step() scheduler.step() - + total_loss += loss.item() - + if batch_idx % 50 == 0: logger.info(f" Batch {batch_idx}: Loss = {loss.item():.4f}") - + avg_loss = total_loss / len(train_loader) logger.info(f" Average Loss: {avg_loss:.4f}") - + # Validation val_results = evaluate_model(model, val_loader, device, threshold=0.3) - val_f1 = val_results['micro_f1'] - + val_f1 = val_results["micro_f1"] + logger.info(f" Validation F1: {val_f1:.4f} ({val_f1*100:.2f}%)") - + # Save best model if val_f1 > best_f1: best_f1 = val_f1 patience_counter = 0 - + # Save checkpoint checkpoint_path = Path("models/checkpoints/emergency_f1_fix.pt") checkpoint_path.parent.mkdir(parents=True, exist_ok=True) - - torch.save({ - 'model_state_dict': model.state_dict(), - 'epoch': epoch, - 'val_f1': val_f1, - 'optimizer_state_dict': optimizer.state_dict(), - }, checkpoint_path) - + + torch.save( + { + "model_state_dict": model.state_dict(), + "epoch": epoch, + "val_f1": val_f1, + "optimizer_state_dict": optimizer.state_dict(), + }, + checkpoint_path, + ) + logger.info(f" โœ… New best model saved! F1: {val_f1:.4f}") else: patience_counter += 1 if patience_counter >= patience: logger.info(f" โน๏ธ Early stopping at epoch {epoch + 1}") break - + return best_f1 def optimize_threshold(model, val_loader, device): """Optimize prediction threshold for maximum F1.""" logger.info("๐ŸŽฏ Optimizing prediction threshold...") - + model.eval() all_outputs = [] all_labels = [] - + with torch.no_grad(): for batch in val_loader: input_ids, attention_mask, labels = batch input_ids = input_ids.to(device) attention_mask = attention_mask.to(device) labels = labels.to(device) - + outputs = model(input_ids, attention_mask) probabilities = torch.sigmoid(outputs) - + all_outputs.extend(probabilities.cpu().numpy()) all_labels.extend(labels.cpu().numpy()) - + all_outputs = np.array(all_outputs) all_labels = np.array(all_labels) - + # Test different thresholds thresholds = np.arange(0.1, 0.6, 0.05) best_threshold = 0.3 best_f1 = 0.0 - + for threshold in thresholds: predictions = all_outputs > threshold - f1 = f1_score(all_labels, predictions, average='micro', zero_division=0) - + f1 = f1_score(all_labels, predictions, average="micro", zero_division=0) + if f1 > best_f1: best_f1 = f1 best_threshold = threshold - + logger.info(f" Best threshold: {best_threshold:.2f} (F1: {best_f1:.4f})") return best_threshold @@ -282,55 +286,53 @@ def emergency_f1_fix(): """Main function to fix F1 score emergency.""" logger.info("๐Ÿšจ EMERGENCY F1 FIX - SENIOR ENGINEER APPROACH") logger.info("=" * 60) - + start_time = time.time() - + try: # Load dataset logger.info("๐Ÿ“Š Loading GoEmotions dataset...") data_loader = GoEmotionsDataLoader() data_loader.download_dataset() datasets = data_loader.prepare_datasets() - + # Get class weights class_weights = datasets["class_weights"] - logger.info(f"๐Ÿ“Š Class weights computed: min={class_weights.min():.3f}, max={class_weights.max():.3f}") - + logger.info( + f"๐Ÿ“Š Class weights computed: min={class_weights.min():.3f}, max={class_weights.max():.3f}" + ) + # Create model model = create_optimized_model(class_weights) - + # Create tokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") - + # Prepare data train_loader, val_loader = prepare_training_data(datasets, tokenizer, batch_size=16) - + # Set device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) - + # Train with focal loss best_val_f1 = train_with_focal_loss(model, train_loader, val_loader, device, epochs=5) - + # Optimize threshold best_threshold = optimize_threshold(model, val_loader, device) - + # Final evaluation on test set logger.info("๐Ÿงช Final evaluation on test set...") test_data = datasets["test_data"] - + # Create test loader test_texts = test_data["text"] test_labels = test_data["labels"] - + inputs = tokenizer( - test_texts, - padding=True, - truncation=True, - max_length=256, - return_tensors="pt" + test_texts, padding=True, truncation=True, max_length=256, return_tensors="pt" ) - + # Convert labels to one-hot num_classes = 28 test_label_vectors = [] @@ -340,45 +342,50 @@ def emergency_f1_fix(): if 0 <= label_idx < num_classes: label_vector[label_idx] = 1 test_label_vectors.append(label_vector) - + test_dataset = TensorDataset( inputs["input_ids"], inputs["attention_mask"], - torch.tensor(test_label_vectors, dtype=torch.float32) + torch.tensor(test_label_vectors, dtype=torch.float32), ) test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False) - + # Evaluate with optimized threshold test_results = evaluate_model(model, test_loader, device, threshold=best_threshold) - + # Display results logger.info("๐Ÿ“Š FINAL RESULTS:") logger.info("=" * 60) - logger.info(f"Micro F1 Score: {test_results['micro_f1']:.4f} ({test_results['micro_f1']*100:.2f}%)") - logger.info(f"Macro F1 Score: {test_results['macro_f1']:.4f} ({test_results['macro_f1']*100:.2f}%)") + logger.info( + f"Micro F1 Score: {test_results['micro_f1']:.4f} ({test_results['micro_f1']*100:.2f}%)" + ) + logger.info( + f"Macro F1 Score: {test_results['macro_f1']:.4f} ({test_results['macro_f1']*100:.2f}%)" + ) logger.info(f"Best Threshold: {best_threshold:.2f}") logger.info(f"Training Time: {time.time() - start_time:.1f}s") logger.info("=" * 60) - + # Assessment target_f1 = 0.60 # 60% target for emergency fix - progress = (test_results['micro_f1'] / target_f1) * 100 - + progress = (test_results["micro_f1"] / target_f1) * 100 + logger.info(f"๐ŸŽฏ TARGET F1: {target_f1*100:.0f}%") logger.info(f"๐Ÿ“Š ACHIEVED F1: {test_results['micro_f1']*100:.2f}%") logger.info(f"๐Ÿ“ˆ PROGRESS: {progress:.1f}% of target") - - if test_results['micro_f1'] >= target_f1: + + if test_results["micro_f1"] >= target_f1: logger.info("๐ŸŽ‰ EMERGENCY TARGET ACHIEVED!") else: - gap = target_f1 - test_results['micro_f1'] + gap = target_f1 - test_results["micro_f1"] logger.info(f"๐Ÿ“‰ GAP: {gap*100:.2f} percentage points needed") - - return test_results['micro_f1'] - + + return test_results["micro_f1"] + except Exception as e: logger.error(f"โŒ Emergency F1 fix failed: {e}") import traceback + traceback.print_exc() return None @@ -389,4 +396,4 @@ def emergency_f1_fix(): logger.info("โœ… Emergency F1 fix completed successfully") else: logger.error("โŒ Emergency F1 fix failed") - sys.exit(1) \ No newline at end of file + sys.exit(1) diff --git a/scripts/maintenance/fix_all_imports_aggressive.py b/scripts/maintenance/fix_all_imports_aggressive.py index a345b4417..c83190af8 100644 --- a/scripts/maintenance/fix_all_imports_aggressive.py +++ b/scripts/maintenance/fix_all_imports_aggressive.py @@ -1,25 +1,24 @@ - # Add all needed imports - # Add imports right after the first line (usually shebang or docstring) - # Skip obvious unused imports - # Check for common undefined names - # Check what imports are needed - # Directories to fix - # Find the first import line or add at the beginning - # Fix f-strings without placeholders (convert to regular strings) - # Fix missing newline at end of file - # Fix trailing whitespace - # Fix unused imports (remove obvious ones) - # If no imports needed, return early - # If we didn't add imports yet, add them at the very beginning - # Only write if content changed - # Only write if content changed - # Split into lines -#!/usr/bin/env python3 -from pathlib import Path +# Add all needed imports +# Add imports right after the first line (usually shebang or docstring) +# Skip obvious unused imports +# Check for common undefined names +# Check what imports are needed +# Directories to fix +# Find the first import line or add at the beginning +# Fix f-strings without placeholders (convert to regular strings) +# Fix missing newline at end of file +# Fix trailing whitespace +# Fix unused imports (remove obvious ones) +# If no imports needed, return early +# If we didn't add imports yet, add them at the very beginning +# Only write if content changed +# Only write if content changed +# Split into lines import logging import re - +#!/usr/bin/env python3 +from pathlib import Path """ @@ -27,49 +26,50 @@ This addresses the extensive linting errors causing CircleCI failures. """ + def fix_file_imports_aggressive(file_path: str) -> bool: """Aggressively fix missing imports in a file.""" - with open(file_path, encoding='utf-8') as f: + with open(file_path, encoding="utf-8") as f: content = f.read() original_content = content needed_imports = set() - if 'sys.' in content or 'sys.path' in content or 'sys.exit' in content: - needed_imports.add('import sys') + if "sys." in content or "sys.path" in content or "sys.exit" in content: + needed_imports.add("import sys") - if 'os.' in content or 'os.path' in content or 'os.environ' in content: - needed_imports.add('import os') + if "os." in content or "os.path" in content or "os.environ" in content: + needed_imports.add("import os") - if 'np.' in content or 'np.ndarray' in content or 'np.array' in content: - needed_imports.add('import numpy as np') + if "np." in content or "np.ndarray" in content or "np.array" in content: + needed_imports.add("import numpy as np") - if 'json.' in content or 'json.dumps' in content or 'json.loads' in content: - needed_imports.add('import json') + if "json." in content or "json.dumps" in content or "json.loads" in content: + needed_imports.add("import json") - if 'traceback.' in content: - needed_imports.add('import traceback') + if "traceback." in content: + needed_imports.add("import traceback") - if 'time.' in content and 'import time' not in content: - needed_imports.add('import time') + if "time." in content and "import time" not in content: + needed_imports.add("import time") - if 'datetime.' in content and 'import datetime' not in content: - needed_imports.add('import datetime') + if "datetime." in content and "import datetime" not in content: + needed_imports.add("import datetime") if not needed_imports: return False - lines = content.split('\n') + lines = content.split("\n") new_lines = [] import_added = False - for _i, line in enumerate(lines): + for i, line in enumerate(lines): if i == 0 and not import_added: for imp in sorted(needed_imports): new_lines.append(imp) - new_lines.append('') # Empty line after imports + new_lines.append("") # Empty line after imports import_added = True new_lines.append(line) @@ -78,59 +78,58 @@ def fix_file_imports_aggressive(file_path: str) -> bool: new_lines = [] for imp in sorted(needed_imports): new_lines.append(imp) - new_lines.append('') # Empty line after imports + new_lines.append("") # Empty line after imports new_lines.extend(lines) - content = '\n'.join(new_lines) + content = "\n".join(new_lines) if content != original_content: - with open(file_path, 'w', encoding='utf-8') as f: + with open(file_path, "w", encoding="utf-8") as f: f.write(content) logging.info("Fixed imports in: {file_path}") return True return False + def fix_common_issues_aggressive(file_path: str) -> bool: """Aggressively fix common linting issues.""" - with open(file_path, encoding='utf-8') as f: + with open(file_path, encoding="utf-8") as f: content = f.read() original_content = content - content = re.sub(r'[ \t]+$', '', content, flags=re.MULTILINE) - - if not content.endswith('\n'): - content += '\n' + content = re.sub(r"[ \t]+$", "", content, flags=re.MULTILINE) - content = re.sub(r'"([^"]*)"', r'"\1"', content) - content = re.sub(r"'([^']*)'", r"'\1'", content) + if not content.endswith("\n"): + content += "\n" - lines = content.split('\n') + lines = content.split("\n") fixed_lines = [] for line in lines: - if any(unused in line for unused in [ - ]): + if any(unused in line for unused in []): continue fixed_lines.append(line) - content = '\n'.join(fixed_lines) + content = "\n".join(fixed_lines) if content != original_content: - with open(file_path, 'w', encoding='utf-8') as f: + with open(file_path, "w", encoding="utf-8") as f: f.write(content) logging.info("Fixed common issues in: {file_path}") return True return False + def main(): """Fix all import and linting issues aggressively.""" + logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") script_dir = Path(__file__).parent project_root = script_dir.parent - dirs_to_fix = ['src', 'tests', 'scripts'] + dirs_to_fix = ["src", "tests", "scripts"] total_fixed = 0 @@ -139,7 +138,7 @@ def main(): if not dir_path.exists(): continue - for py_file in dir_path.rglob('*.py'): + for py_file in dir_path.rglob("*.py"): try: fixed_imports = fix_file_imports_aggressive(str(py_file)) fixed_common = fix_common_issues_aggressive(str(py_file)) @@ -150,5 +149,6 @@ def main(): logging.info("\nโœ… Fixed {total_fixed} files") + if __name__ == "__main__": main() diff --git a/scripts/maintenance/fix_ci_issues.py b/scripts/maintenance/fix_ci_issues.py index 1303ecfc0..3be20dd1b 100644 --- a/scripts/maintenance/fix_ci_issues.py +++ b/scripts/maintenance/fix_ci_issues.py @@ -1,25 +1,24 @@ - # Split command for security (avoid shell=True) - # Change to project root - # Fix 1: Format code with ruff - # Fix 2: Check for any remaining formatting issues - # Fix 3: Run specific failing tests to verify fixes - # Summary -#!/usr/bin/env python3 -from pathlib import Path -from typing import Tuple +# Split command for security (avoid shell=True) +# Change to project root +# Fix 1: Format code with ruff +# Fix 2: Check for any remaining formatting issues +# Fix 3: Run specific failing tests to verify fixes +# Summary import logging import os import subprocess import sys - - +#!/usr/bin/env python3 +from pathlib import Path +from typing import Tuple """ Script to fix CI issues identified in the SAMO Deep Learning project. """ + def run_command(cmd: str, description: str) -> Tuple[bool, str]: """Run a command and return success status and output.""" logging.info("๐Ÿ”„ {description}...") diff --git a/scripts/maintenance/fix_code_quality.py b/scripts/maintenance/fix_code_quality.py index 0ff64ea22..e9b1bbd4a 100644 --- a/scripts/maintenance/fix_code_quality.py +++ b/scripts/maintenance/fix_code_quality.py @@ -9,7 +9,6 @@ import logging import re from pathlib import Path -from typing import List, Set # Configure logging logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") @@ -26,27 +25,30 @@ def __init__(self, project_root: Path): def fix_path_operations(self, content: str) -> str: """Fix path operations to use pathlib (PTH-codes).""" - if "os.path." in content or "os.makedirs" in content or "os.remove" in content: - if "from pathlib import Path" not in content: - # Add pathlib import if not present - lines = content.split("\n") - import_found = False - for i, line in enumerate(lines): - if line.strip().startswith("import ") or line.strip().startswith("from "): - if "pathlib" in line: - import_found = True - break - if not import_found and i > 0: - lines.insert(i, "from pathlib import Path") - import_found = True - break - if not import_found: - lines.insert(0, "from pathlib import Path") - content = "\n".join(lines) + if ( + "os.path." in content or "os.makedirs" in content or "os.remove" in content + ) and "from pathlib import Path" not in content: + # Add pathlib import if not present + lines = content.split("\n") + import_found = False + for i, line in enumerate(lines): + if line.strip().startswith("import ") or line.strip().startswith("from "): + if "pathlib" in line: + import_found = True + break + if not import_found and i > 0: + lines.insert(i, "from pathlib import Path") + import_found = True + break + if not import_found: + lines.insert(0, "from pathlib import Path") + content = "\n".join(lines) # Replace os.path operations with pathlib equivalents content = re.sub(r"os\.path\.join\(([^)]+)\)", r"Path(\1).as_posix()", content) - content = re.sub(r"os\.makedirs\(([^,)]+)\)", r"Path(\1).mkdir(parents=True, exist_ok=True)", content) + content = re.sub( + r"os\.makedirs\(([^,)]+)\)", r"Path(\1).mkdir(parents=True, exist_ok=True)", content + ) content = re.sub(r"os\.remove\(([^)]+)\)", r"Path(\1).unlink(missing_ok=True)", content) content = re.sub(r"os\.path\.exists\(([^)]+)\)", r"Path(\1).exists()", content) content = re.sub(r"os\.path\.isfile\(([^)]+)\)", r"Path(\1).is_file()", content) @@ -59,10 +61,10 @@ def fix_f_strings(self, content: str) -> str: # Fix f-strings without placeholders content = re.sub(r'f"([^"]*)"', r'"\1"', content) content = re.sub(r"f'([^']*)'", r"'\1'", content) - + # Fix f-strings with invalid syntax content = re.sub(r'f"([^"]*)\{([^}]*)\}([^"]*)"', r'f"\1{\2}\3"', content) - + return content def fix_import_order(self, content: str) -> str: @@ -70,16 +72,16 @@ def fix_import_order(self, content: str) -> str: lines = content.split("\n") import_lines = [] other_lines = [] - + for line in lines: if line.strip().startswith(("import ", "from ")): import_lines.append(line) else: other_lines.append(line) - + # Sort import lines import_lines.sort() - + # Reconstruct content return "\n".join(import_lines + [""] + other_lines) @@ -87,14 +89,14 @@ def fix_unused_imports(self, content: str) -> str: """Remove unused imports.""" lines = content.split("\n") filtered_lines = [] - + for line in lines: if line.strip().startswith(("import ", "from ")): # Keep all imports for now - let Ruff handle specific removals filtered_lines.append(line) else: filtered_lines.append(line) - + return "\n".join(filtered_lines) def fix_trailing_whitespace(self, content: str) -> str: diff --git a/scripts/maintenance/fix_import_paths.py b/scripts/maintenance/fix_import_paths.py index 7743b243a..d0949d017 100644 --- a/scripts/maintenance/fix_import_paths.py +++ b/scripts/maintenance/fix_import_paths.py @@ -3,74 +3,78 @@ Fix import paths after repository reorganization. This script updates common import path issues in moved scripts. """ -import re import glob +import re + def fix_import_paths_in_file(file_path): """Fix import paths in a single file.""" try: - with open(file_path, 'r', encoding='utf-8') as f: + with open(file_path, "r", encoding="utf-8") as f: content = f.read() - + original_content = content - + # Fix common import path issues replacements = [ # Fix models imports - (r'from models\.', 'from src.models.'), - (r'import models\.', 'import src.models.'), - + (r"from models\.", "from src.models."), + (r"import models\.", "import src.models."), # Fix src imports - (r'from src\.src\.', 'from src.'), - (r'import src\.src\.', 'import src.'), - + (r"from src\.src\.", "from src."), + (r"import src\.src\.", "import src."), # Fix relative imports for moved scripts - (r'from \.\.models\.', 'from src.models.'), - (r'from \.\.src\.', 'from src.'), - (r'from \.\.data\.', 'from data.'), - + (r"from \.\.models\.", "from src.models."), + (r"from \.\.src\.", "from src."), + (r"from \.\.data\.", "from data."), # Fix sys.path insertions - (r'sys\.path\.insert\(0, str\(Path\(__file__\)\.parent\.parent / "src"\)\)', - 'sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src"))'), - (r'sys\.path\.insert\(0, str\(Path\(__file__\)\.parent\.parent\.parent / "src"\)\)', - 'sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src"))'), + ( + r'sys\.path\.insert\(0, str\(Path\(__file__\)\.parent\.parent / "src"\)\)', + 'sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src"))', + ), + ( + r'sys\.path\.insert\(0, str\(Path\(__file__\)\.parent\.parent\.parent / "src"\)\)', + 'sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src"))', + ), ] - + for pattern, replacement in replacements: content = re.sub(pattern, replacement, content) - + # Only write if content changed if content != original_content: - with open(file_path, 'w', encoding='utf-8') as f: + with open(file_path, "w", encoding="utf-8") as f: f.write(content) print(f"Fixed imports in: {file_path}") return True else: print(f"No changes needed in: {file_path}") return False - + except Exception as e: print(f"Error processing {file_path}: {e}") return False + def main(): """Fix import paths in all Python files.""" print("Fixing import paths after reorganization...") - + # Get all Python files in scripts directory script_files = [] - for pattern in ['scripts/**/*.py', 'src/**/*.py']: + for pattern in ["scripts/**/*.py", "src/**/*.py"]: script_files.extend(glob.glob(pattern, recursive=True)) - + print(f"Found {len(script_files)} Python files to check") - + fixed_count = 0 for file_path in script_files: if fix_import_paths_in_file(file_path): fixed_count += 1 - + print(f"\nFixed import paths in {fixed_count} files") print("Import path fixes completed!") + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/maintenance/fix_label_mapping.py b/scripts/maintenance/fix_label_mapping.py index a7f8fcca8..9c7245963 100644 --- a/scripts/maintenance/fix_label_mapping.py +++ b/scripts/maintenance/fix_label_mapping.py @@ -3,115 +3,125 @@ Fix the label mapping issue between GoEmotions and Journal datasets. """ +import json import subprocess import sys +import pandas as pd + +from datasets import load_dataset + + def install_dependencies(): """Install required dependencies.""" print("๐Ÿ”ง Installing dependencies...") try: - subprocess.check_call([sys.executable, "-m", "pip", "install", "datasets", "pandas", "transformers"]) + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "datasets", "pandas", "transformers"] + ) print("โœ… Dependencies installed") except subprocess.CalledProcessError as e: print(f"โŒ Failed to install dependencies: {e}") return False return True + # Install dependencies first if not install_dependencies(): print("โŒ Cannot proceed without dependencies") sys.exit(1) -import json -import pandas as pd -from datasets import load_dataset def analyze_label_mapping(): """Analyze the label mapping issue.""" print("๐Ÿ” Analyzing label mapping issue...") - + # Load datasets go_emotions = load_dataset("go_emotions", "simplified") - with open('data/journal_test_dataset.json', 'r') as f: + with open("data/journal_test_dataset.json", "r") as f: journal_entries = json.load(f) journal_df = pd.DataFrame(journal_entries) - + # Analyze GoEmotions labels print("\n๐Ÿ“Š GoEmotions Analysis:") go_label_counts = {} - for example in go_emotions['train']: - if example['labels']: - for label in example['labels']: + for example in go_emotions["train"]: + if example["labels"]: + for label in example["labels"]: go_label_counts[label] = go_label_counts.get(label, 0) + 1 - + print(f"GoEmotions unique labels: {len(go_label_counts)}") print(f"GoEmotions labels: {sorted(list(go_label_counts.keys()))}") - print(f"Top 10 GoEmotions labels: {dict(sorted(go_label_counts.items(), key=lambda x: x[1], reverse=True)[:10])}") - + print( + f"Top 10 GoEmotions labels: {dict(sorted(go_label_counts.items(), key=lambda x: x[1], reverse=True)[:10])}" + ) + # Analyze Journal labels print("\n๐Ÿ“Š Journal Analysis:") - journal_label_counts = journal_df['emotion'].value_counts().to_dict() + journal_label_counts = journal_df["emotion"].value_counts().to_dict() print(f"Journal unique labels: {len(journal_label_counts)}") print(f"Journal labels: {sorted(list(journal_label_counts.keys()))}") print(f"Journal label counts: {journal_label_counts}") - + # Check for any common labels go_labels_set = set(go_label_counts.keys()) journal_labels_set = set(journal_label_counts.keys()) common_labels = go_labels_set.intersection(journal_labels_set) - + print(f"\n๐Ÿ” Common labels: {len(common_labels)}") if common_labels: print(f"Common labels: {sorted(list(common_labels))}") else: print("โŒ NO COMMON LABELS FOUND!") print("This is why we get 0 GoEmotions samples!") - + return go_label_counts, journal_label_counts + def create_emotion_mapping(): """Create a mapping between GoEmotions and Journal emotions.""" print("\n๐Ÿ”ง Creating emotion mapping...") - + # GoEmotions emotion labels (from their documentation) go_emotions_mapping = { - 'admiration': 'admiration', - 'amusement': 'happy', - 'anger': 'frustrated', - 'annoyance': 'frustrated', - 'approval': 'proud', - 'caring': 'content', - 'confusion': 'overwhelmed', - 'curiosity': 'excited', - 'desire': 'excited', - 'disappointment': 'sad', - 'disapproval': 'frustrated', - 'disgust': 'frustrated', - 'embarrassment': 'anxious', - 'excitement': 'excited', - 'fear': 'anxious', - 'gratitude': 'grateful', - 'grief': 'sad', - 'joy': 'happy', - 'love': 'content', - 'nervousness': 'anxious', - 'optimism': 'hopeful', - 'pride': 'proud', - 'realization': 'content', - 'relief': 'calm', - 'remorse': 'sad', - 'sadness': 'sad', - 'surprise': 'excited', - 'neutral': 'calm' + "admiration": "admiration", + "amusement": "happy", + "anger": "frustrated", + "annoyance": "frustrated", + "approval": "proud", + "caring": "content", + "confusion": "overwhelmed", + "curiosity": "excited", + "desire": "excited", + "disappointment": "sad", + "disapproval": "frustrated", + "disgust": "frustrated", + "embarrassment": "anxious", + "excitement": "excited", + "fear": "anxious", + "gratitude": "grateful", + "grief": "sad", + "joy": "happy", + "love": "content", + "nervousness": "anxious", + "optimism": "hopeful", + "pride": "proud", + "realization": "content", + "relief": "calm", + "remorse": "sad", + "sadness": "sad", + "surprise": "excited", + "neutral": "calm", } - + print(f"Created mapping with {len(go_emotions_mapping)} emotions") return go_emotions_mapping + def create_fixed_bulletproof_cell(): """Create a fixed bulletproof cell with proper emotion mapping.""" - - cell_code = '''# ๐Ÿš€ BULLETPROOF TRAINING CELL - FIXED LABEL MAPPING + + cell_code = """# ๐Ÿš€ BULLETPROOF TRAINING CELL - FIXED LABEL MAPPING # Runtime โ†’ Change runtime type โ†’ GPU (T4 or V100) # Kernel โ†’ Restart and run all @@ -251,30 +261,30 @@ def __init__(self, texts, labels, tokenizer, max_length=128): self.labels = labels self.tokenizer = tokenizer self.max_length = max_length - + # Validate data if len(texts) != len(labels): raise ValueError(f"Texts and labels have different lengths: {len(texts)} vs {len(labels)}") - + # Validate labels for i, label in enumerate(labels): if not isinstance(label, int) or label < 0: raise ValueError(f"Invalid label at index {i}: {label}") - + def __len__(self): return len(self.texts) - + def __getitem__(self, idx): text = self.texts[idx] label = self.labels[idx] - + # Validate inputs if not isinstance(text, str) or not text.strip(): raise ValueError(f"Invalid text at index {idx}") - + if not isinstance(label, int) or label < 0: raise ValueError(f"Invalid label at index {idx}: {label}") - + encoding = self.tokenizer( text, truncation=True, @@ -282,7 +292,7 @@ def __getitem__(self, idx): max_length=self.max_length, return_tensors='pt' ) - + return { 'input_ids': encoding['input_ids'].flatten(), 'attention_mask': encoding['attention_mask'].flatten(), @@ -293,33 +303,33 @@ def __getitem__(self, idx): class SimpleEmotionClassifier(nn.Module): def __init__(self, model_name="bert-base-uncased", num_labels=None): super().__init__() - + if num_labels is None or num_labels <= 0: raise ValueError(f"Invalid num_labels: {num_labels}") - + self.num_labels = num_labels self.bert = AutoModel.from_pretrained(model_name) self.dropout = nn.Dropout(0.3) self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels) - + print(f"โœ… Model initialized with {num_labels} labels") - + def forward(self, input_ids, attention_mask): # Validate inputs if input_ids.dim() != 2: raise ValueError(f"Expected input_ids to be 2D, got {input_ids.dim()}D") - + if attention_mask.dim() != 2: raise ValueError(f"Expected attention_mask to be 2D, got {attention_mask.dim()}D") - + outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) pooled_output = outputs.pooler_output logits = self.classifier(self.dropout(pooled_output)) - + # Validate outputs if logits.shape[-1] != self.num_labels: raise ValueError(f"Expected {self.num_labels} output classes, got {logits.shape[-1]}") - + return logits # Step 7: Setup training @@ -365,12 +375,12 @@ def forward(self, input_ids, attention_mask): for epoch in range(num_epochs): print(f"\\n๐Ÿ”„ Epoch {epoch + 1}/{num_epochs}") - + # Training model.train() total_loss = 0 num_batches = 0 - + # Train on GoEmotions print(" ๐Ÿ“š Training on GoEmotions...") for i, batch in enumerate(go_loader): @@ -379,34 +389,34 @@ def forward(self, input_ids, attention_mask): if 'input_ids' not in batch or 'attention_mask' not in batch or 'labels' not in batch: print(f"โš ๏ธ Invalid batch structure at batch {i}") continue - + # Move to device with validation input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + # Validate labels if torch.any(labels >= num_labels) or torch.any(labels < 0): print(f"โš ๏ธ Invalid labels in batch {i}: {labels}") continue - + # Forward pass optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() num_batches += 1 - + if i % 50 == 0: print(f" Batch {i}/{len(go_loader)}, Loss: {loss.item():.4f}") - + except Exception as e: print(f"โŒ Error in batch {i}: {e}") continue - + # Train on journal data print(" ๐Ÿ“ Training on journal data...") for i, batch in enumerate(journal_train_loader): @@ -414,67 +424,67 @@ def forward(self, input_ids, attention_mask): input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + if torch.any(labels >= num_labels) or torch.any(labels < 0): continue - + optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() num_batches += 1 - + if i % 10 == 0: print(f" Batch {i}/{len(journal_train_loader)}, Loss: {loss.item():.4f}") - + except Exception as e: print(f"โŒ Error in journal batch {i}: {e}") continue - + # Validation print(" ๐ŸŽฏ Validating...") model.eval() all_preds = [] all_labels = [] - + with torch.no_grad(): for batch in journal_val_loader: try: input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + outputs = model(input_ids=input_ids, attention_mask=attention_mask) preds = torch.argmax(outputs, dim=1) - + all_preds.extend(preds.cpu().numpy()) all_labels.extend(labels.cpu().numpy()) - + except Exception as e: print(f"โŒ Error in validation batch: {e}") continue - + # Calculate metrics if all_preds and all_labels: f1_macro = f1_score(all_labels, all_preds, average='macro') accuracy = accuracy_score(all_labels, all_preds) - + avg_loss = total_loss / num_batches if num_batches > 0 else 0 - + print(f" ๐Ÿ“Š Epoch {epoch + 1} Results:") print(f" Average Loss: {avg_loss:.4f}") print(f" Validation F1 (Macro): {f1_macro:.4f}") print(f" Validation Accuracy: {accuracy:.4f}") - + # Save best model if f1_macro > best_f1: best_f1 = f1_macro torch.save(model.state_dict(), 'best_simple_model.pth') print(f" ๐Ÿ’พ New best model saved! F1: {best_f1:.4f}") - + # Clear GPU cache if torch.cuda.is_available(): torch.cuda.empty_cache() @@ -504,26 +514,27 @@ def forward(self, input_ids, attention_mask): files.download('simple_training_results.json') print("\\n๐ŸŽ‰ BULLETPROOF TRAINING COMPLETED!") -print("๐Ÿ“ Files downloaded: best_simple_model.pth, simple_training_results.json")''' - +print("๐Ÿ“ Files downloaded: best_simple_model.pth, simple_training_results.json")""" + # Write to file - with open('bulletproof_training_cell_fixed.py', 'w') as f: + with open("bulletproof_training_cell_fixed.py", "w") as f: f.write(cell_code) - + print("โœ… Created fixed bulletproof training cell: bulletproof_training_cell_fixed.py") print("๐Ÿ“‹ This version has proper emotion mapping!") + if __name__ == "__main__": # Analyze the issue go_label_counts, journal_label_counts = analyze_label_mapping() - + # Create emotion mapping emotion_mapping = create_emotion_mapping() - + # Create fixed bulletproof cell create_fixed_bulletproof_cell() - + print("\n๐ŸŽฏ SUMMARY:") print("The issue was that GoEmotions uses emotion names (like 'admiration')") print("while Journal uses different emotion names (like 'proud').") - print("The fixed version maps GoEmotions emotions to Journal emotions!") \ No newline at end of file + print("The fixed version maps GoEmotions emotions to Journal emotions!") diff --git a/scripts/maintenance/fix_linting.py b/scripts/maintenance/fix_linting.py index c3566416e..a8604b677 100644 --- a/scripts/maintenance/fix_linting.py +++ b/scripts/maintenance/fix_linting.py @@ -14,36 +14,36 @@ def fix_file(file_path: str) -> None: Args: file_path: Path to the file to fix """ - with open(file_path, 'r', encoding='utf-8') as f: + with open(file_path, "r", encoding="utf-8") as f: content = f.read() original_content = content # Fix trailing whitespace - content = re.sub(r'[ \t]+$', '', content, flags=re.MULTILINE) + content = re.sub(r"[ \t]+$", "", content, flags=re.MULTILINE) # Fix missing newline at end of file - if not content.endswith('\n'): - content += '\n' + if not content.endswith("\n"): + content += "\n" # Fix f-strings without placeholders (convert to regular strings) content = re.sub(r'f"([^"]*)"', r'"\1"', content) content = re.sub(r"f'([^']*)'", r"'\1'", content) # Fix unused imports (basic removal) - lines = content.split('\n') + lines = content.split("\n") fixed_lines = [] for line in lines: # Skip obvious unused imports - if line.strip().startswith('import ') and '#' not in line: + if line.strip().startswith("import ") and "#" not in line: continue fixed_lines.append(line) - content = '\n'.join(fixed_lines) + content = "\n".join(fixed_lines) # Only write if content changed if content != original_content: - with open(file_path, 'w', encoding='utf-8') as f: + with open(file_path, "w", encoding="utf-8") as f: f.write(content) logging.info(f"Fixed: {file_path}") @@ -54,12 +54,12 @@ def main(): project_root = script_dir.parent # Directories to fix - dirs_to_fix = ['src', 'tests', 'scripts'] + dirs_to_fix = ["src", "tests", "scripts"] for dir_name in dirs_to_fix: dir_path = project_root / dir_name if dir_path.exists(): - for py_file in dir_path.rglob('*.py'): + for py_file in dir_path.rglob("*.py"): try: fix_file(str(py_file)) except Exception as e: diff --git a/scripts/maintenance/fix_linting_issues.py b/scripts/maintenance/fix_linting_issues.py index 31fc500fb..fa1b7d9dc 100644 --- a/scripts/maintenance/fix_linting_issues.py +++ b/scripts/maintenance/fix_linting_issues.py @@ -1,18 +1,17 @@ - # Remove the line containing the unused import - # Apply fixes - # Add timezone import if needed - # Files that need fixing based on the CI errors - # Fix datetime.now() calls - # Fix exception handlers that don't use the exception variable - # Fix other exception patterns - # Remove unused imports - # Replace hardcoded passwords in tests -#!/usr/bin/env python3 -from pathlib import Path +# Remove the line containing the unused import +# Apply fixes +# Add timezone import if needed +# Files that need fixing based on the CI errors +# Fix datetime.now() calls +# Fix exception handlers that don't use the exception variable +# Fix other exception patterns +# Remove unused imports +# Replace hardcoded passwords in tests import logging import re - +#!/usr/bin/env python3 +from pathlib import Path """ @@ -25,6 +24,7 @@ - Timezone issues (DTZ005) """ + def fix_unused_exception_variables(file_path: Path) -> bool: """Fix unused exception variables by using f-strings.""" content = file_path.read_text() @@ -50,17 +50,17 @@ def fix_unused_imports(file_path: Path) -> bool: original_content = content unused_imports = [ - 'import time', # in test files - 'import pytest', # in some test files - 'from unittest.mock import MagicMock', # in some test files - 'from unittest.mock import patch', # in some test files + "import time", # in test files + "import pytest", # in some test files + "from unittest.mock import MagicMock", # in some test files + "from unittest.mock import patch", # in some test files ] for unused_import in unused_imports: if unused_import in content: - lines = content.split('\n') + lines = content.split("\n") lines = [line for line in lines if unused_import not in line] - content = '\n'.join(lines) + content = "\n".join(lines) if content != original_content: file_path.write_text(content) @@ -92,19 +92,17 @@ def fix_timezone_issues(file_path: Path) -> bool: content = file_path.read_text() original_content = content - if 'datetime.now()' in content and 'from datetime import timezone' not in content: - if 'from datetime import datetime' in content: + if "datetime.now()" in content and "from datetime import timezone" not in content: + if "from datetime import datetime" in content: content = content.replace( - 'from datetime import datetime', - 'from datetime import datetime, timezone' + "from datetime import datetime", "from datetime import datetime, timezone" ) - elif 'import datetime' in content: + elif "import datetime" in content: content = content.replace( - 'import datetime', - 'import datetime\nfrom datetime import timezone' + "import datetime", "import datetime\nfrom datetime import timezone" ) - content = content.replace('datetime.now()', 'datetime.now(timezone.utc)') + content = content.replace("datetime.now()", "datetime.now(timezone.utc)") if content != original_content: file_path.write_text(content) diff --git a/scripts/maintenance/fix_linting_issues_comprehensive.py b/scripts/maintenance/fix_linting_issues_comprehensive.py index 277e82c2e..a74d1df3b 100644 --- a/scripts/maintenance/fix_linting_issues_comprehensive.py +++ b/scripts/maintenance/fix_linting_issues_comprehensive.py @@ -1,26 +1,29 @@ - # Only remove if it's clearly unused and not necessary - # Restore backup - # Check if this import is actually used - # Keep all imports for now - we'll let ruff handle unused imports - # Validate syntax - # Add logging import if not present - # Apply fixes - # Create backup - # Only write if content changed - # Read content - # Find all import lines - # Fix unused exception variables - # Fix unused loop variables - # Reconstruct with imports at top - # Replace print statements - # Simple check - can be improved - # Directories to process -#!/usr/bin/env python3 -from pathlib import Path +# Only remove if it's clearly unused and not necessary +# Restore backup +# Check if this import is actually used +# Keep all imports for now - we'll let ruff handle unused imports +# Validate syntax +# Add logging import if not present +# Apply fixes +# Create backup +# Only write if content changed +# Read content +# Find all import lines +# Fix unused exception variables +# Fix unused loop variables +# Reconstruct with imports at top +# Replace print statements +# Simple check - can be improved +# Directories to process import ast import logging import re import shutil + +#!/usr/bin/env python3 +from pathlib import Path + + """ Comprehensive Linting Fix Script for SAMO Deep Learning. @@ -44,15 +47,28 @@ """ - class ComprehensiveLintingFixer: """Comprehensive linting fixer for entire codebase.""" def __init__(self): self.necessary_imports = { - 'time', 'pytest', 'patch', 'Mock', 'json', 'logging', - 'os', 'sys', 'pathlib', 'typing', 'datetime', 'tempfile', - 'numpy', 'torch', 'whisper', 'fastapi', 'sqlalchemy' + "time", + "pytest", + "patch", + "Mock", + "json", + "logging", + "os", + "sys", + "pathlib", + "typing", + "datetime", + "tempfile", + "numpy", + "torch", + "whisper", + "fastapi", + "sqlalchemy", } self.fixed_files = [] self.errors = [] @@ -72,9 +88,11 @@ def separate_imports_and_code(self, lines: list[str]) -> tuple[list[str], list[s for line in lines: stripped = line.strip() - if (stripped.startswith('import ') or - stripped.startswith('from ') or - stripped.startswith('#')): + if ( + stripped.startswith("import ") + or stripped.startswith("from ") + or stripped.startswith("#") + ): import_lines.append(line) else: non_import_lines.append(line) @@ -87,7 +105,7 @@ def filter_imports(self, lines: list[str]) -> list[str]: for line in lines: stripped = line.strip() - if stripped.startswith('import ') or stripped.startswith('from '): + if stripped.startswith("import ") or stripped.startswith("from "): filtered_lines.append(line) else: filtered_lines.append(line) @@ -103,7 +121,7 @@ def backup_file(self, file_path: Path) -> Path: def validate_python_syntax(self, file_path: Path) -> bool: """Validate that the file has correct Python syntax.""" try: - with open(file_path, encoding='utf-8') as f: + with open(file_path, encoding="utf-8") as f: ast.parse(f.read()) return True except SyntaxError as e: @@ -112,55 +130,60 @@ def validate_python_syntax(self, file_path: Path) -> bool: def fix_import_order(self, content: str) -> str: """Fix import order by moving all imports to the top.""" - lines = content.split('\n') + lines = content.split("\n") import_lines = [] non_import_lines = [] for line in lines: stripped = line.strip() - if (stripped.startswith('import ') or - stripped.startswith('from ') or - stripped.startswith('#')): + if ( + stripped.startswith("import ") + or stripped.startswith("from ") + or stripped.startswith("#") + ): import_lines.append(line) else: non_import_lines.append(line) - return '\n'.join(import_lines + non_import_lines) + return "\n".join(import_lines + non_import_lines) def fix_unused_variables(self, content: str) -> str: """Fix unused variables by replacing with underscore.""" - content = re.sub(r'except Exception as e:', 'except Exception as e:', content) - content = re.sub(r'except Exception as e:', 'except Exception as e:', content) + content = re.sub(r"except Exception as e:", "except Exception as e:", content) + content = re.sub(r"except Exception as e:", "except Exception as e:", content) - content = re.sub(r'for (\w+) in (\w+):', r'for _\1 in \2:', content) + content = re.sub(r"for (\w+) in (\w+):", r"for _\1 in \2:", content) return content def fix_unused_imports(self, content: str) -> str: """Safely remove unused imports.""" - lines = content.split('\n') + lines = content.split("\n") filtered_lines = [] for line in lines: stripped = line.strip() - if stripped.startswith('import ') or stripped.startswith('from '): + if stripped.startswith("import ") or stripped.startswith("from "): import_name = self.extract_import_name(stripped) - if import_name and import_name not in self.necessary_imports: - if not self.is_import_used(content, import_name): - continue # Skip this line + if ( + import_name + and import_name not in self.necessary_imports + and not self.is_import_used(content, import_name) + ): + continue # Skip this line filtered_lines.append(line) - return '\n'.join(filtered_lines) + return "\n".join(filtered_lines) def extract_import_name(self, import_line: str) -> str: """Extract the main import name from an import line.""" - if import_line.startswith('import '): - return import_line.split()[1].split('.')[0] - elif import_line.startswith('from '): + if import_line.startswith("import "): + return import_line.split()[1].split(".")[0] + elif import_line.startswith("from "): parts = import_line.split() if len(parts) >= 3: - return parts[1].split('.')[0] + return parts[1].split(".")[0] return "" def is_import_used(self, content: str, import_name: str) -> bool: @@ -169,27 +192,28 @@ def is_import_used(self, content: str, import_name: str) -> bool: def fix_trailing_whitespace(self, content: str) -> str: """Remove trailing whitespace.""" - lines = content.split('\n') - return '\n'.join(line.rstrip() for line in lines) + lines = content.split("\n") + return "\n".join(line.rstrip() for line in lines) def fix_print_statements(self, content: str) -> str: """Replace print statements with logging.""" - if 'print(' in content and 'import logging' not in content: - lines = content.split('\n') + if "print(" in content and "import logging" not in content: + lines = content.split("\n") import_added = False - for _i, line in enumerate(lines): - if line.strip().startswith('import ') or line.strip().startswith('from '): - if not import_added: - lines.insert(i, 'import logging') - import_added = True - break + for i, line in enumerate(lines): + if ( + line.strip().startswith("import ") or line.strip().startswith("from ") + ) and not import_added: + lines.insert(i, "import logging") + import_added = True + break if not import_added: - lines.insert(0, 'import logging') + lines.insert(0, "import logging") - content = '\n'.join(lines) + content = "\n".join(lines) - content = re.sub(r'print\((.*?)\)', r'logging.info(\1)', content) + content = re.sub(r"print\((.*?)\)", r"logging.info(\1)", content) return content @@ -198,7 +222,7 @@ def fix_all_issues(self, file_path: Path) -> bool: try: backup_path = self.backup_file(file_path) - with open(file_path, encoding='utf-8') as f: + with open(file_path, encoding="utf-8") as f: content = f.read() original_content = content @@ -209,7 +233,7 @@ def fix_all_issues(self, file_path: Path) -> bool: content = self.fix_print_statements(content) if content != original_content: - with open(file_path, 'w', encoding='utf-8') as f: + with open(file_path, "w", encoding="utf-8") as f: f.write(content) if not self.validate_python_syntax(file_path): @@ -261,7 +285,7 @@ def main(): "src/evaluation", "src/inference", "tests", - "scripts" + "scripts", ] fixer = ComprehensiveLintingFixer() diff --git a/scripts/maintenance/fix_linting_issues_conservative.py b/scripts/maintenance/fix_linting_issues_conservative.py index a57f3b45c..dbb6b71c3 100644 --- a/scripts/maintenance/fix_linting_issues_conservative.py +++ b/scripts/maintenance/fix_linting_issues_conservative.py @@ -97,7 +97,7 @@ def fix_e402_import_order(self, content: str) -> str: result.extend(import_lines) result.append('') # Add blank line after imports result.extend(other_lines) - + return '\n'.join(result) def fix_ruf022_all_sorting(self, content: str) -> str: @@ -239,4 +239,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/maintenance/fix_missing_super_calls.py b/scripts/maintenance/fix_missing_super_calls.py new file mode 100644 index 000000000..3f54f5a13 --- /dev/null +++ b/scripts/maintenance/fix_missing_super_calls.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +""" +Script to fix missing super().__init__() calls in test classes. +""" + +import re +from pathlib import Path + + +def fix_missing_super_calls(root_dir: Path): + """Fix missing super() calls in test files.""" + test_files = list(root_dir.glob("tests/**/*.py")) + + for file_path in test_files: + if file_path.name == "__init__.py": + continue + + try: + with open(file_path, "r", encoding="utf-8") as f: + content = f.read() + + original_content = content + + # Pattern to find test classes with setUp methods that don't call super() + pattern = r'(class\s+\w+\([^)]*TestCase[^)]*\):[^}]*?def\s+setUp\s*\([^)]*\):\s*"""[^"]*"""\s*)(?!.*super\(\)\.setUp\(\))' + + def add_super_call(match): + method_start = match.group(1) + return method_start + "super().setUp()\n " + + content = re.sub(pattern, add_super_call, content, flags=re.DOTALL) + + # Also handle tearDown methods + pattern = r'(class\s+\w+\([^)]*TestCase[^)]*\):[^}]*?def\s+tearDown\s*\([^)]*\):\s*"""[^"]*"""\s*)(?!.*super\(\)\.tearDown\(\))' + + def add_super_teardown_call(match): + method_start = match.group(1) + return method_start + "super().tearDown()\n " + + content = re.sub(pattern, add_super_teardown_call, content, flags=re.DOTALL) + + if content != original_content: + with open(file_path, "w", encoding="utf-8") as f: + f.write(content) + print(f"Fixed super() calls in {file_path}") + + except Exception as e: + print(f"Error processing {file_path}: {e}") + + +def fix_specific_patterns(): + """Fix specific known patterns.""" + root_dir = Path(__file__).parent.parent.parent + + # Common test setUp patterns that need super() calls + patterns = [ + { + "file": "tests/unit/test_validation.py", + "old": 'def setUp(self):\n """Set up test fixtures."""', + "new": 'def setUp(self):\n """Set up test fixtures."""\n super().setUp()', + }, + { + "file": "tests/unit/test_emotion_detection.py", + "old": 'def setUp(self):\n """Set up test fixtures for emotion detection tests."""', + "new": 'def setUp(self):\n """Set up test fixtures for emotion detection tests."""\n super().setUp()', + }, + { + "file": "tests/unit/test_database.py", + "old": 'def setUp(self):\n """Set up test database."""', + "new": 'def setUp(self):\n """Set up test database."""\n super().setUp()', + }, + ] + + for pattern in patterns: + file_path = root_dir / pattern["file"] + if file_path.exists(): + try: + with open(file_path, "r", encoding="utf-8") as f: + content = f.read() + + if pattern["old"] in content and "super().setUp()" not in content: + content = content.replace(pattern["old"], pattern["new"]) + + with open(file_path, "w", encoding="utf-8") as f: + f.write(content) + print(f"Fixed setUp in {file_path}") + + except Exception as e: + print(f"Error fixing {file_path}: {e}") + + +if __name__ == "__main__": + root_dir = Path(__file__).parent.parent.parent + print("Fixing missing super() calls in test files...") + fix_missing_super_calls(root_dir) + fix_specific_patterns() + print("Done!") diff --git a/scripts/maintenance/fix_model_architecture_mismatch.py b/scripts/maintenance/fix_model_architecture_mismatch.py index bbfb75756..de1ae918e 100644 --- a/scripts/maintenance/fix_model_architecture_mismatch.py +++ b/scripts/maintenance/fix_model_architecture_mismatch.py @@ -9,18 +9,19 @@ import json + def fix_model_architecture(): """Fix the model architecture mismatch in the minimal notebook.""" - + # Read the existing notebook - with open('notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb', 'r') as f: + with open("notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb", "r") as f: notebook = json.load(f) - + # Find and replace the model setup cell - for cell in notebook['cells']: - if cell['cell_type'] == 'code' and 'model_name =' in ''.join(cell['source']): + for cell in notebook["cells"]: + if cell["cell_type"] == "code" and "model_name =" in "".join(cell["source"]): # Replace with fixed model setup - cell['source'] = [ + cell["source"] = [ "# Load model and tokenizer\n", "model_name = 'j-hartmann/emotion-english-distilroberta-base'\n", "print(f'๐Ÿ”ง Loading model: {model_name}')\n", @@ -62,20 +63,21 @@ def fix_model_architecture(): " model = model.to('cuda')\n", " print('โœ… Model moved to GPU')\n", "else:\n", - " print('โš ๏ธ CUDA not available, model will run on CPU')" + " print('โš ๏ธ CUDA not available, model will run on CPU')", ] break - + # Save the updated notebook - with open('notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb', 'w') as f: + with open("notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook, f, indent=2) - - print('โœ… Fixed model architecture mismatch!') - print('๐Ÿ“‹ Changes made:') - print(' โœ… Properly reconfigured classifier layer for 12 emotions') - print(' โœ… Recreated RobertaClassificationHead with correct dimensions') - print(' โœ… Initialized new classifier weights') - print(' โœ… Added detailed logging of the reconfiguration process') + + print("โœ… Fixed model architecture mismatch!") + print("๐Ÿ“‹ Changes made:") + print(" โœ… Properly reconfigured classifier layer for 12 emotions") + print(" โœ… Recreated RobertaClassificationHead with correct dimensions") + print(" โœ… Initialized new classifier weights") + print(" โœ… Added detailed logging of the reconfiguration process") + if __name__ == "__main__": - fix_model_architecture() \ No newline at end of file + fix_model_architecture() diff --git a/scripts/maintenance/fix_model_reconfiguration.py b/scripts/maintenance/fix_model_reconfiguration.py index a3dc88310..b2e40a782 100644 --- a/scripts/maintenance/fix_model_reconfiguration.py +++ b/scripts/maintenance/fix_model_reconfiguration.py @@ -10,18 +10,19 @@ import json + def fix_model_reconfiguration(): """Fix the model reconfiguration in the minimal notebook.""" - + # Read the existing notebook - with open('notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb', 'r') as f: + with open("notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb", "r") as f: notebook = json.load(f) - + # Find and replace the model setup cell - for cell in notebook['cells']: - if cell['cell_type'] == 'code' and 'model_name =' in ''.join(cell['source']): + for cell in notebook["cells"]: + if cell["cell_type"] == "code" and "model_name =" in "".join(cell["source"]): # Replace with fixed model setup - cell['source'] = [ + cell["source"] = [ "# Load model and tokenizer\n", "model_name = 'j-hartmann/emotion-english-distilroberta-base'\n", "print(f'๐Ÿ”ง Loading model: {model_name}')\n", @@ -72,21 +73,22 @@ def fix_model_reconfiguration(): " model = model.to('cuda')\n", " print('โœ… Model moved to GPU')\n", "else:\n", - " print('โš ๏ธ CUDA not available, model will run on CPU')" + " print('โš ๏ธ CUDA not available, model will run on CPU')", ] break - + # Save the updated notebook - with open('notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb', 'w') as f: + with open("notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook, f, indent=2) - - print('โœ… Fixed model reconfiguration!') - print('๐Ÿ“‹ Changes made:') - print(' โœ… Created new model with correct architecture from scratch') - print(' โœ… Used ignore_mismatched_sizes=True to handle size differences') - print(' โœ… Set problem_type to single_label_classification') - print(' โœ… Added model architecture verification test') - print(' โœ… Added detailed logging of the configuration process') + + print("โœ… Fixed model reconfiguration!") + print("๐Ÿ“‹ Changes made:") + print(" โœ… Created new model with correct architecture from scratch") + print(" โœ… Used ignore_mismatched_sizes=True to handle size differences") + print(" โœ… Set problem_type to single_label_classification") + print(" โœ… Added model architecture verification test") + print(" โœ… Added detailed logging of the configuration process") + if __name__ == "__main__": - fix_model_reconfiguration() \ No newline at end of file + fix_model_reconfiguration() diff --git a/scripts/maintenance/fix_remaining_linting.py b/scripts/maintenance/fix_remaining_linting.py index a877fe1a8..aa65f44e0 100644 --- a/scripts/maintenance/fix_remaining_linting.py +++ b/scripts/maintenance/fix_remaining_linting.py @@ -1,23 +1,26 @@ - # Fix B007: Loop control variable issues - # Fix F821: Undefined name errors - # Fix G003: Logging issues - # Fix P-series: Path issues - # Fix S-series: Import sorting issues - # Fix exception variables - # Fix loop variables that are undefined - # Fix other minor issues - # Fix undefined variables in f-strings - # Fix common undefined variables in loops - # Fix hardcoded passwords - # Fix logging statements using + instead of f-strings - # Fix unused loop variables - # Move all imports to the top - # Process all directories - # Replace os.path with pathlib - # Sort imports +# Fix B007: Loop control variable issues +# Fix F821: Undefined name errors +# Fix G003: Logging issues +# Fix P-series: Path issues +# Fix S-series: Import sorting issues +# Fix exception variables +# Fix loop variables that are undefined +# Fix other minor issues +# Fix undefined variables in f-strings +# Fix common undefined variables in loops +# Fix hardcoded passwords +# Fix logging statements using + instead of f-strings +# Fix unused loop variables +# Move all imports to the top +# Process all directories +# Replace os.path with pathlib +# Sort imports +import re + #!/usr/bin/env python3 from pathlib import Path -import re + + """ Comprehensive Linting Fix Script for SAMO Deep Learning. @@ -33,7 +36,6 @@ """ - class ComprehensiveLintingFixer: """Comprehensive linting fixer for all remaining issues.""" @@ -44,7 +46,7 @@ def __init__(self): def fix_file(self, file_path: str) -> bool: """Fix all linting issues in a single file.""" try: - with open(file_path, encoding='utf-8') as f: + with open(file_path, encoding="utf-8") as f: content = f.read() original_content = content @@ -69,9 +71,9 @@ def fix_file(self, file_path: str) -> bool: fixes_applied += other_fixes if content != original_content: - with open(file_path, 'w', encoding='utf-8') as f: + with open(file_path, "w", encoding="utf-8") as f: f.write(content) - + self.fixed_files.append(file_path) self.total_fixes += fixes_applied print(f" โœ… Fixed {fixes_applied} issues") @@ -86,13 +88,13 @@ def fix_file(self, file_path: str) -> bool: def fix_undefined_names(self, content: str) -> tuple[str, int]: """Fix F821: Undefined name errors.""" fixes = 0 - + patterns = [ - (r'for ___(\w+) in (\w+):', r'for \1 in \2:'), - (r'except Exception as e:', r'except Exception as e:'), + (r"for ___(\w+) in (\w+):", r"for \1 in \2:"), + (r"except Exception as e:", r"except Exception as e:"), (r'f"([^"]*)\{(\w+)\}([^"]*)"', r'f"\1{\2}\3"'), ] - + for pattern, replacement in patterns: new_content = re.sub(pattern, replacement, content) if new_content != content: @@ -104,38 +106,40 @@ def fix_undefined_names(self, content: str) -> tuple[str, int]: def fix_import_sorting(self, content: str) -> tuple[str, int]: """Fix S-series: Import sorting issues.""" fixes = 0 - - lines = content.split('\n') + + lines = content.split("\n") import_lines = [] non_import_lines = [] - + for line in lines: stripped = line.strip() - if (stripped.startswith('import ') or - stripped.startswith('from ') or - stripped.startswith('#')): + if ( + stripped.startswith("import ") + or stripped.startswith("from ") + or stripped.startswith("#") + ): import_lines.append(line) else: non_import_lines.append(line) - + import_lines.sort() - - new_content = '\n'.join(import_lines + non_import_lines) + + new_content = "\n".join(import_lines + non_import_lines) if new_content != content: fixes += 1 - + return new_content, fixes def fix_path_issues(self, content: str) -> tuple[str, int]: """Fix P-series: Path issues.""" fixes = 0 - + patterns = [ - (r'os\.path\.abspath\(', r'Path('), - (r'os\.path\.join\(', r'Path('), - (r'os\.path\.exists\(', r'Path('), + (r"os\.path\.abspath\(", r"Path("), + (r"os\.path\.join\(", r"Path("), + (r"os\.path\.exists\(", r"Path("), ] - + for pattern, replacement in patterns: new_content = re.sub(pattern, replacement, content) if new_content != content: @@ -147,10 +151,10 @@ def fix_path_issues(self, content: str) -> tuple[str, int]: def fix_logging_issues(self, content: str) -> tuple[str, int]: """Fix G003: Logging issues.""" fixes = 0 - + pattern = r'logging\.(info|debug|warning|error)\("([^"]*)" \+ "([^"]*)"' replacement = r'logging.\1(f"\2\3"' - + new_content = re.sub(pattern, replacement, content) if new_content != content: content = new_content @@ -161,10 +165,10 @@ def fix_logging_issues(self, content: str) -> tuple[str, int]: def fix_loop_variables(self, content: str) -> tuple[str, int]: """Fix B007: Loop control variable issues.""" fixes = 0 - - pattern = r'for (\w+), (\w+) in enumerate\((\w+)\):' - replacement = r'for _\1, \2 in enumerate(\3):' - + + pattern = r"for (\w+), (\w+) in enumerate\((\w+)\):" + replacement = r"for _\1, \2 in enumerate(\3):" + new_content = re.sub(pattern, replacement, content) if new_content != content: content = new_content @@ -175,10 +179,12 @@ def fix_loop_variables(self, content: str) -> tuple[str, int]: def fix_minor_issues(self, content: str) -> tuple[str, int]: """Fix other minor issues.""" fixes = 0 - + pattern = r'TEST_USER_PASSWORD_HASH = "test_hashed_password_123" # noqa: S105]*)"' - replacement = r'TEST_USER_PASSWORD_HASH = "test_hashed_password_123" # noqa: S105 # noqa: S105' - + replacement = ( + r'TEST_USER_PASSWORD_HASH = "test_hashed_password_123" # noqa: S105 # noqa: S105' + ) + new_content = re.sub(pattern, replacement, content) if new_content != content: content = new_content @@ -189,7 +195,7 @@ def fix_minor_issues(self, content: str) -> tuple[str, int]: def process_directory(self, directory: str) -> None: """Process all Python files in a directory.""" print(f"\n๐Ÿ”ง Processing directory: {directory}") - + for file_path in Path(directory).rglob("*.py"): if file_path.is_file(): print(f" ๐Ÿ“ {file_path}") @@ -199,18 +205,18 @@ def run(self) -> None: """Run the comprehensive linting fix.""" print("๐Ÿš€ Starting Comprehensive Linting Fix...") print("=" * 60) - + directories = ["src", "tests", "scripts"] - + for directory in directories: if Path(directory): self.process_directory(directory) - + print("\n" + "=" * 60) print("๐ŸŽ‰ COMPREHENSIVE LINTING FIX COMPLETE!") print(f"๐Ÿ“Š Files fixed: {len(self.fixed_files)}") print(f"๐Ÿ”ง Total fixes applied: {self.total_fixes}") - + if self.fixed_files: print("\nโœ… Fixed files:") for file_path in self.fixed_files: diff --git a/scripts/maintenance/fix_remaining_py38_types.py b/scripts/maintenance/fix_remaining_py38_types.py index bc9d76a19..bfff53d21 100644 --- a/scripts/maintenance/fix_remaining_py38_types.py +++ b/scripts/maintenance/fix_remaining_py38_types.py @@ -13,44 +13,42 @@ import re import sys from pathlib import Path -from typing import List, Dict, Any, Tuple +from typing import Any, Dict, List, Tuple def _fix_generic_patterns(content: str, imports_to_add: set, changes_made: list) -> str: """Fix generic type patterns (list[T], dict[K,V], set[T], tuple[T]).""" # Fix list[T] -> List[T] - list_pattern = r'\blist\[([^\]]+)\]' + list_pattern = r"\blist\[([^\]]+)\]" list_matches = re.findall(list_pattern, content) if list_matches: - content = re.sub(list_pattern, r'List[\1]', content) - imports_to_add.add('List') + content = re.sub(list_pattern, r"List[\1]", content) + imports_to_add.add("List") changes_made.append(f"list[T] -> List[T] ({len(list_matches)} instances)") # Fix dict[K, V] -> Dict[K, V] - dict_pattern = r'\bdict\[([^\]]+)\]' + dict_pattern = r"\bdict\[([^\]]+)\]" dict_matches = re.findall(dict_pattern, content) if dict_matches: - content = re.sub(dict_pattern, r'Dict[\1]', content) - imports_to_add.add('Dict') + content = re.sub(dict_pattern, r"Dict[\1]", content) + imports_to_add.add("Dict") changes_made.append(f"dict[T] -> Dict[T] ({len(dict_matches)} instances)") # Fix set[T] -> Set[T] - set_pattern = r'\bset\[([^\]]+)\]' + set_pattern = r"\bset\[([^\]]+)\]" set_matches = re.findall(set_pattern, content) if set_matches: - content = re.sub(set_pattern, r'Set[\1]', content) - imports_to_add.add('Set') + content = re.sub(set_pattern, r"Set[\1]", content) + imports_to_add.add("Set") changes_made.append(f"set[T] -> Set[T] ({len(set_matches)} instances)") # Fix tuple[T, ...] -> Tuple[T, ...] - tuple_pattern = r'\btuple\[([^\]]+)\]' + tuple_pattern = r"\btuple\[([^\]]+)\]" tuple_matches = re.findall(tuple_pattern, content) if tuple_matches: - content = re.sub(tuple_pattern, r'Tuple[\1]', content) - imports_to_add.add('Tuple') - changes_made.append( - f"tuple[T] -> Tuple[T] ({len(tuple_matches)} instances)" - ) + content = re.sub(tuple_pattern, r"Tuple[\1]", content) + imports_to_add.add("Tuple") + changes_made.append(f"tuple[T] -> Tuple[T] ({len(tuple_matches)} instances)") return content @@ -58,40 +56,34 @@ def _fix_generic_patterns(content: str, imports_to_add: set, changes_made: list) def _fix_optional_patterns(content: str, imports_to_add: set, changes_made: list) -> str: """Fix optional type patterns (A | None, None | A).""" # Fix A | None -> Optional[A] (most common case) - optional_pattern = r'([a-zA-Z_][a-zA-Z0-9_]*)\s*\|\s*None' + optional_pattern = r"([a-zA-Z_][a-zA-Z0-9_]*)\s*\|\s*None" optional_matches = re.findall(optional_pattern, content) if optional_matches: - content = re.sub(optional_pattern, r'Optional[\1]', content) - imports_to_add.add('Optional') - changes_made.append( - f"A | None -> Optional[A] ({len(optional_matches)} instances)" - ) + content = re.sub(optional_pattern, r"Optional[\1]", content) + imports_to_add.add("Optional") + changes_made.append(f"A | None -> Optional[A] ({len(optional_matches)} instances)") # Fix None | A -> Optional[A] - optional_pattern2 = r'None\s*\|\s*([a-zA-Z_][a-zA-Z0-9_]*)' + optional_pattern2 = r"None\s*\|\s*([a-zA-Z_][a-zA-Z0-9_]*)" optional_matches2 = re.findall(optional_pattern2, content) if optional_matches2: - content = re.sub(optional_pattern2, r'Optional[\1]', content) - imports_to_add.add('Optional') - changes_made.append( - f"None | A -> Optional[A] ({len(optional_matches2)} instances)" - ) + content = re.sub(optional_pattern2, r"Optional[\1]", content) + imports_to_add.add("Optional") + changes_made.append(f"None | A -> Optional[A] ({len(optional_matches2)} instances)") return content def _fix_union_patterns(content: str, imports_to_add: set, changes_made: list) -> str: """Fix union type patterns (A | B -> Union[A, B]).""" - union_pattern = r'([a-zA-Z_][a-zA-Z0-9_]*)\s*\|\s*([a-zA-Z_][a-zA-Z0-9_]*)' + union_pattern = r"([a-zA-Z_][a-zA-Z0-9_]*)\s*\|\s*([a-zA-Z_][a-zA-Z0-9_]*)" union_matches = re.findall(union_pattern, content) if union_matches: # Filter out matches that are likely not type annotations filtered_matches = [] for left, right in union_matches: # Skip if it looks like a bitwise operation in code - type_names = [ - 'None', 'str', 'int', 'float', 'bool', 'list', 'dict', 'set', 'tuple' - ] + type_names = ["None", "str", "int", "float", "bool", "list", "dict", "set", "tuple"] if not (left in type_names or right in type_names): continue filtered_matches.append((left, right)) @@ -99,14 +91,12 @@ def _fix_union_patterns(content: str, imports_to_add: set, changes_made: list) - if filtered_matches: # Replace the filtered matches for left, right in filtered_matches: - if left != 'None' and right != 'None': - pattern = f'\\b{re.escape(left)}\\s*\\|\\s*{re.escape(right)}\\b' - replacement = f'Union[{left}, {right}]' + if left != "None" and right != "None": + pattern = f"\\b{re.escape(left)}\\s*\\|\\s*{re.escape(right)}\\b" + replacement = f"Union[{left}, {right}]" content = re.sub(pattern, replacement, content) - imports_to_add.add('Union') - changes_made.append( - f"{left} | {right} -> Union[{left}, {right}]" - ) + imports_to_add.add("Union") + changes_made.append(f"{left} | {right} -> Union[{left}, {right}]") return content @@ -117,40 +107,31 @@ def _add_typing_imports(content: str, imports_to_add: set, dry_run: bool) -> str return content # Find existing typing imports - typing_import_match = re.search(r'from typing import ([^\n]+)', content) + typing_import_match = re.search(r"from typing import ([^\n]+)", content) if typing_import_match: existing_imports = typing_import_match.group(1).strip() # Parse existing imports to avoid duplicates - existing_set = {imp.strip() for imp in existing_imports.split(',')} + existing_set = {imp.strip() for imp in existing_imports.split(",")} combined_imports = sorted(existing_set | imports_to_add) new_import_line = f'from typing import {", ".join(combined_imports)}' # Replace the existing import line - content = re.sub( - r'from typing import ([^\n]+)', - new_import_line, - content - ) + content = re.sub(r"from typing import ([^\n]+)", new_import_line, content) else: # Find last import line lines = content.splitlines() last_import_line = -1 for i, line in enumerate(lines): - if (line.strip().startswith('import ') or - line.strip().startswith('from ')): + if line.strip().startswith("import ") or line.strip().startswith("from "): last_import_line = i if last_import_line >= 0: - import_line = ( - f"from typing import {', '.join(sorted(imports_to_add))}" - ) + import_line = f"from typing import {', '.join(sorted(imports_to_add))}" lines.insert(last_import_line + 1, import_line) else: - import_line = ( - f"from typing import {', '.join(sorted(imports_to_add))}" - ) + import_line = f"from typing import {', '.join(sorted(imports_to_add))}" lines.insert(0, import_line) - content = '\n'.join(lines) + content = "\n".join(lines) return content @@ -158,7 +139,7 @@ def _add_typing_imports(content: str, imports_to_add: set, dry_run: bool) -> str def fix_file(file_path: Path, dry_run: bool = False) -> Dict[str, Any]: """Fix Python 3.8 compatibility issues in a single file.""" try: - with open(file_path, 'r', encoding='utf-8') as f: + with open(file_path, "r", encoding="utf-8") as f: content = f.read() original_content = content @@ -175,18 +156,18 @@ def fix_file(file_path: Path, dry_run: bool = False) -> Dict[str, Any]: # Write back to file if changes were made if content != original_content and not dry_run: - with open(file_path, 'w', encoding='utf-8') as f: + with open(file_path, "w", encoding="utf-8") as f: f.write(content) return { - 'file': str(file_path), - 'changes': changes_made, - 'imports_added': list(imports_to_add), - 'modified': content != original_content + "file": str(file_path), + "changes": changes_made, + "imports_added": list(imports_to_add), + "modified": content != original_content, } except Exception as e: - return {'file': str(file_path), 'error': str(e), 'modified': False} + return {"file": str(file_path), "error": str(e), "modified": False} def _parse_arguments() -> Tuple[Path, bool]: @@ -196,7 +177,7 @@ def _parse_arguments() -> Tuple[Path, bool]: sys.exit(1) directory = Path(sys.argv[1]) - dry_run = '--dry-run' in sys.argv + dry_run = "--dry-run" in sys.argv return directory, dry_run @@ -224,9 +205,9 @@ def _process_single_file(file_path: Path, dry_run: bool) -> Dict[str, Any]: print(f"Processing: {file_path}") result = fix_file(file_path, dry_run=dry_run) - if 'error' in result: + if "error" in result: print(f" โŒ Error: {result['error']}") - elif result['modified']: + elif result["modified"]: print(f" โœ… Modified: {', '.join(result['changes'])}") print(f" Imports added: {', '.join(result['imports_added'])}") else: @@ -245,8 +226,8 @@ def _process_all_files(python_files: List[Path], dry_run: bool) -> Tuple[List[Di result = _process_single_file(file_path, dry_run) results.append(result) - if result.get('modified', False): - total_changes += len(result.get('changes', [])) + if result.get("modified", False): + total_changes += len(result.get("changes", [])) return results, total_changes @@ -257,12 +238,9 @@ def _print_summary(results: List[Dict[str, Any]], total_changes: int, dry_run: b print("SUMMARY") print("=" * 50) - modified = [r for r in results if r.get('modified', False)] - errors = [r for r in results if 'error' in r] - no_changes = [ - r for r in results - if not r.get('modified', False) and 'error' not in r - ] + modified = [r for r in results if r.get("modified", False)] + errors = [r for r in results if "error" in r] + no_changes = [r for r in results if not r.get("modified", False) and "error" not in r] print(f"Files processed: {len(results)}") print(f"Modified: {len(modified)}") @@ -284,8 +262,8 @@ def _print_summary(results: List[Dict[str, Any]], total_changes: int, dry_run: b def find_python_files(directory: Path) -> List[Path]: """Find all Python files in a directory recursively.""" python_files = [] - for item in directory.rglob('*.py'): - if not any(part.startswith('.') for part in item.parts): + for item in directory.rglob("*.py"): + if not any(part.startswith(".") for part in item.parts): python_files.append(item) return python_files @@ -310,5 +288,5 @@ def main(): _print_summary(results, total_changes, dry_run) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/scripts/maintenance/fix_staticmethod_parameters.py b/scripts/maintenance/fix_staticmethod_parameters.py new file mode 100644 index 000000000..1b410fd7b --- /dev/null +++ b/scripts/maintenance/fix_staticmethod_parameters.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python3 +""" +Script to fix methods decorated with @staticmethod that incorrectly have 'self' parameters. +""" + +import re +from pathlib import Path + + +def fix_staticmethod_parameters(file_path: Path): + """Fix @staticmethod methods that incorrectly have 'self' parameters.""" + try: + with open(file_path, "r", encoding="utf-8") as f: + content = f.read() + + original_content = content + + # Pattern to find @staticmethod followed by method definition with self parameter + # This pattern matches: + # @staticmethod + # def method_name(self, other_params): + pattern = r"(@staticmethod\s*\n\s*def\s+\w+\s*\(\s*)self\s*,?\s*" + + def fix_self_parameter(match): + """Remove 'self' parameter from @staticmethod methods.""" + prefix = match.group(1) + # Remove 'self,' or just 'self' if it's the only parameter + return prefix + + # Fix methods with self as first parameter + content = re.sub(pattern, fix_self_parameter, content, flags=re.MULTILINE) + + # Also handle case where self is the only parameter + pattern2 = r"(@staticmethod\s*\n\s*def\s+\w+\s*\(\s*)self\s*(\)\s*:)" + content = re.sub(pattern2, r"\1\2", content, flags=re.MULTILINE) + + if content != original_content: + with open(file_path, "w", encoding="utf-8") as f: + f.write(content) + print(f"Fixed @staticmethod parameters in {file_path}") + return True + + except Exception as e: + print(f"Error processing {file_path}: {e}") + + return False + + +def process_files(root_dir: Path): + """Process Python files to fix @staticmethod parameters.""" + fixed_count = 0 + + for pattern in ["**/*.py"]: + for file_path in root_dir.glob(pattern): + if file_path.name == "__init__.py": + continue + + # Skip certain directories + if any(part in str(file_path) for part in [".git", "__pycache__", ".pytest_cache"]): + continue + + if fix_staticmethod_parameters(file_path): + fixed_count += 1 + + return fixed_count + + +if __name__ == "__main__": + root_dir = Path(__file__).parent.parent.parent + print("Fixing @staticmethod methods with incorrect 'self' parameters...") + + fixed_count = process_files(root_dir) + print(f"Fixed {fixed_count} files") + print("Done!") diff --git a/scripts/maintenance/fix_threshold_tuning.py b/scripts/maintenance/fix_threshold_tuning.py index 925725d70..946c434a4 100644 --- a/scripts/maintenance/fix_threshold_tuning.py +++ b/scripts/maintenance/fix_threshold_tuning.py @@ -1,20 +1,21 @@ - # Create trainer and load dataset - # Initialize the model with class weights - # Load trained model - # Prepare dataset - # Test much lower thresholds -# Add src to path -# Configure logging -#!/usr/bin/env python3 -from src.models.emotion_detection.bert_classifier import evaluate_emotion_classifier -from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer -from pathlib import Path +# Create trainer and load dataset +# Initialize the model with class weights +# Load trained model +# Prepare dataset +# Test much lower thresholds + import logging import sys -import torch +from pathlib import Path +import torch +# Configure logging +#!/usr/bin/env python3 +from src.models.emotion_detection.bert_classifier import evaluate_emotion_classifier +# Add src to path +from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer """Fix Threshold Tuning for Better F1 Scores. diff --git a/scripts/maintenance/improve_model_f1_fixed.py b/scripts/maintenance/improve_model_f1_fixed.py index afbdd427d..7d85130f1 100644 --- a/scripts/maintenance/improve_model_f1_fixed.py +++ b/scripts/maintenance/improve_model_f1_fixed.py @@ -1,61 +1,63 @@ - # Test loading the checkpoint - # Additional training with focal loss - # Apply class weights if provided - # Calculate binary cross entropy loss - # Calculate class weights - # Calculate focal loss - # Calculate focal weight - # Check if target achieved - # Check if target achieved - # Convert logits to probabilities - # Create focal loss - # Create or load model - # Create trainer for focal loss fine-tuning - # Evaluate final model - # For now, save the best individual model - # IMPORTANT: Disable dev mode to use full dataset - # Load dataset - # Model 1: Standard configuration - # Model 2: Different learning rate - # Model 3: With focal loss - # Note: This will be handled in the trainer initialization - # Save model - # Save model - # Simple ensemble prediction (average of predictions) - # Train fresh model with extended epochs and full dataset - # Train multiple models with different configurations - # Apply selected technique - # Create data loader - # Create model with optimal settings - # Create trainer with development mode disabled for better results - # Evaluate - # Find valid checkpoint (if any) - # Report results - # Set device - # Train model on full dataset - # Update output path +# Test loading the checkpoint +# Additional training with focal loss +# Apply class weights if provided +# Calculate binary cross entropy loss +# Calculate class weights +# Calculate focal loss +# Calculate focal weight +# Check if target achieved +# Check if target achieved +# Convert logits to probabilities +# Create focal loss +# Create or load model +# Create trainer for focal loss fine-tuning +# Evaluate final model +# For now, save the best individual model +# IMPORTANT: Disable dev mode to use full dataset +# Load dataset +# Model 1: Standard configuration +# Model 2: Different learning rate +# Model 3: With focal loss +# Note: This will be handled in the trainer initialization +# Save model +# Save model +# Simple ensemble prediction (average of predictions) +# Train fresh model with extended epochs and full dataset +# Train multiple models with different configurations +# Apply selected technique +# Create data loader +# Create model with optimal settings +# Create trainer with development mode disabled for better results +# Evaluate +# Find valid checkpoint (if any) +# Report results +# Set device +# Train model on full dataset +# Update output path + +# Add src to path +import argparse +import logging + # Add src to path # Configure logging +import sys +import time + # Constants #!/usr/bin/env python3 from pathlib import Path -import sys +from typing import Optional -# Add src to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) +import torch +import torch.nn.functional as F +from torch import nn from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer -from torch import nn -from typing import Optional -import argparse -import logging -import time -import torch -import torch.nn.functional as F - +sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) """ diff --git a/scripts/maintenance/infer_mapping_and_eval.py b/scripts/maintenance/infer_mapping_and_eval.py index 9922c9506..be17d348c 100644 --- a/scripts/maintenance/infer_mapping_and_eval.py +++ b/scripts/maintenance/infer_mapping_and_eval.py @@ -1,11 +1,13 @@ import os + import numpy as np import torch +from scipy.optimize import linear_sum_assignment +from sklearn.metrics import accuracy_score, f1_score from tqdm import tqdm +from transformers import AutoModelForSequenceClassification, AutoTokenizer + from datasets import load_dataset -from transformers import AutoTokenizer, AutoModelForSequenceClassification -from sklearn.metrics import f1_score, accuracy_score -from scipy.optimize import linear_sum_assignment MODEL_ID = os.getenv("MODEL_ID", "0xmnrv/samo") TOKEN = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN") @@ -53,7 +55,7 @@ def predict_probs(texts): P_chunks = [] for i in tqdm(range(0, len(ds), BATCH)): - P_chunks.append(predict_probs(ds[i:i + BATCH]["text"])) + P_chunks.append(predict_probs(ds[i : i + BATCH]["text"])) P = np.concatenate(P_chunks, axis=0) # (N, num_labels) @@ -115,7 +117,8 @@ def evaluate(th): # Optional: write corrected config.json with inferred labels in model-index order if os.getenv("WRITE_CONFIG", "0") == "1": - from transformers import AutoConfig + pass + cfg = mdl.config id2label = {int(mi): ds_names[dj] for mi, dj in mapping} for i in range(M): diff --git a/scripts/maintenance/metrics_test.py b/scripts/maintenance/metrics_test.py index 74a5624f9..48656904c 100644 --- a/scripts/maintenance/metrics_test.py +++ b/scripts/maintenance/metrics_test.py @@ -2,12 +2,14 @@ # pip install -U transformers datasets scikit-learn torch tqdm huggingface_hub import os + import numpy as np import torch +from sklearn.metrics import accuracy_score, f1_score from tqdm import tqdm +from transformers import AutoModelForSequenceClassification, AutoTokenizer + from datasets import load_dataset -from transformers import AutoTokenizer, AutoModelForSequenceClassification -from sklearn.metrics import f1_score, accuracy_score MODEL_ID = os.getenv("MODEL_ID", "0xmnrv/samo") TOKEN = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN") @@ -23,11 +25,7 @@ def norm(s: str) -> str: # 1) Load model + tokenizer (private repos require token) tok = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=True, token=TOKEN) -mdl = ( - AutoModelForSequenceClassification.from_pretrained(MODEL_ID, token=TOKEN) - .to(DEVICE) - .eval() -) +mdl = AutoModelForSequenceClassification.from_pretrained(MODEL_ID, token=TOKEN).to(DEVICE).eval() cfg = mdl.config num_labels = int(getattr(cfg, "num_labels", len(getattr(cfg, "id2label", {})) or 28)) @@ -85,16 +83,12 @@ def norm(s: str) -> str: kept_model_indices = [ds_to_model[i] for i in kept_ds_indices] else: if num_labels == len(ds_names): - print( - "Low mapping coverage; identity mapping (assumes same order)." - ) + print("Low mapping coverage; identity mapping (assumes same order).") kept_ds_indices = list(range(num_labels)) kept_model_indices = list(range(num_labels)) else: m = min(num_labels, len(ds_names)) - print( - f"Low mapping coverage; min-dim identity mapping ({m} labels)." - ) + print(f"Low mapping coverage; min-dim identity mapping ({m} labels).") kept_ds_indices = list(range(m)) kept_model_indices = list(range(m)) @@ -136,7 +130,7 @@ def predict_probs(batch_texts): all_probs_full, all_true = [], [] for i in tqdm(range(0, len(val), BATCH)): - batch = val[i:i + BATCH] + batch = val[i : i + BATCH] batch_probs = predict_probs(batch["text"]) # predictions for this batch all_probs_full.append(batch_probs) all_true.append(np.stack(batch["y"])) diff --git a/scripts/maintenance/quick_label_fix.py b/scripts/maintenance/quick_label_fix.py index 8fab9044a..4f030f300 100644 --- a/scripts/maintenance/quick_label_fix.py +++ b/scripts/maintenance/quick_label_fix.py @@ -5,67 +5,75 @@ """ import json +import pickle + import pandas as pd -from datasets import load_dataset from sklearn.preprocessing import LabelEncoder -import pickle + +from datasets import load_dataset + def quick_label_fix(): """Quick fix for label mismatch issues.""" print("๐Ÿ”ง Applying quick label fix...") - + # Load datasets go_emotions = load_dataset("go_emotions", "simplified") - - with open('data/journal_test_dataset.json', 'r') as f: + + with open("data/journal_test_dataset.json", "r") as f: journal_entries = json.load(f) journal_df = pd.DataFrame(journal_entries) - + # Get all unique labels go_labels = set() - for example in go_emotions['train']: - if example['labels']: - go_labels.update(example['labels']) - - journal_labels = set(journal_df['emotion'].unique()) - + for example in go_emotions["train"]: + if example["labels"]: + go_labels.update(example["labels"]) + + journal_labels = set(journal_df["emotion"].unique()) + # Use only common labels to avoid mismatches common_labels = sorted(list(go_labels.intersection(journal_labels))) - + if not common_labels: print("โš ๏ธ No common labels found! Using all labels...") common_labels = sorted(list(go_labels.union(journal_labels))) - + print(f"๐Ÿ“Š Using {len(common_labels)} labels: {common_labels}") - + # Create label encoder label_encoder = LabelEncoder() label_encoder.fit(common_labels) - + # Create mappings label_to_id = {label: idx for idx, label in enumerate(label_encoder.classes_)} id_to_label = {idx: label for label, idx in label_to_id.items()} - + # Save fixed encoder - with open('fixed_label_encoder.pkl', 'wb') as f: + with open("fixed_label_encoder.pkl", "wb") as f: pickle.dump(label_encoder, f) - + # Save mappings - with open('label_mappings.json', 'w') as f: - json.dump({ - 'label_to_id': label_to_id, - 'id_to_label': id_to_label, - 'num_labels': len(label_encoder.classes_), - 'classes': label_encoder.classes_.tolist() - }, f, indent=2) - + with open("label_mappings.json", "w") as f: + json.dump( + { + "label_to_id": label_to_id, + "id_to_label": id_to_label, + "num_labels": len(label_encoder.classes_), + "classes": label_encoder.classes_.tolist(), + }, + f, + indent=2, + ) + print(f"โœ… Fixed label encoder saved!") print(f"๐Ÿ“Š Use num_labels={len(label_encoder.classes_)} in your model") print(f"๐Ÿ“Š Label encoder: fixed_label_encoder.pkl") print(f"๐Ÿ“Š Mappings: label_mappings.json") - + return len(label_encoder.classes_) + if __name__ == "__main__": num_labels = quick_label_fix() - print(f"\n๐ŸŽ‰ Quick fix completed! Use num_labels={num_labels}") \ No newline at end of file + print(f"\n๐ŸŽ‰ Quick fix completed! Use num_labels={num_labels}") diff --git a/scripts/maintenance/repo_inventory.py b/scripts/maintenance/repo_inventory.py index b812feaae..cd7c9896b 100644 --- a/scripts/maintenance/repo_inventory.py +++ b/scripts/maintenance/repo_inventory.py @@ -5,15 +5,15 @@ - Scans for references to candidate paths - Configurable via configs/repo_inventory.json and CLI flags """ -import os -import json import argparse +import json +import os import subprocess import time from functools import lru_cache from pathlib import Path from shutil import which -from typing import List, Dict, Any +from typing import Any, Dict, List ROOT = Path(__file__).resolve().parents[2] LOGS = ROOT / ".logs" @@ -80,11 +80,7 @@ def list_all_files() -> List[Path]: continue # Include regular files and symlinks-to-files; skip vanished/dirs try: - if p.is_file() or ( - p.is_symlink() - and p.exists() - and p.resolve().is_file() - ): + if p.is_file() or (p.is_symlink() and p.exists() and p.resolve().is_file()): files.append(p) except OSError: continue diff --git a/scripts/maintenance/typehint_codemod.py b/scripts/maintenance/typehint_codemod.py index 100b035ac..b95cb270f 100644 --- a/scripts/maintenance/typehint_codemod.py +++ b/scripts/maintenance/typehint_codemod.py @@ -17,11 +17,12 @@ import ast import sys from pathlib import Path -from typing import List, Dict, Any, Optional, Tuple +from typing import Any, Dict, List, Optional, Tuple # Try to import astor for Python 3.8 compatibility try: import astor + def ast_to_source(node): """Convert AST node to source code using astor library. @@ -32,6 +33,7 @@ def ast_to_source(node): str: Source code representation of the node """ return astor.to_source(node) + except ImportError: # Fallback for Python 3.8 without astor def ast_to_source(node): @@ -75,12 +77,14 @@ def visit_AnnAssign(self, node): if node.annotation: new_annotation = self._convert_type_hint(node.annotation) if new_annotation != node.annotation: - self.changes.append({ - 'type': 'annotation', - 'node': node, - 'old': node.annotation, - 'new': new_annotation - }) + self.changes.append( + { + "type": "annotation", + "node": node, + "old": node.annotation, + "new": new_annotation, + } + ) self.generic_visit(node) def visit_arg(self, node): @@ -88,26 +92,19 @@ def visit_arg(self, node): if node.annotation: new_annotation = self._convert_type_hint(node.annotation) if new_annotation != node.annotation: - self.changes.append({ - 'type': 'arg', - 'node': node, - 'old': node.annotation, - 'new': new_annotation - }) + self.changes.append( + {"type": "arg", "node": node, "old": node.annotation, "new": new_annotation} + ) self.generic_visit(node) - def visit_FunctionDef(self, node): """Visit function definitions.""" if node.returns: new_returns = self._convert_type_hint(node.returns) if new_returns != node.returns: - self.changes.append({ - 'type': 'returns', - 'node': node, - 'old': node.returns, - 'new': new_returns - }) + self.changes.append( + {"type": "returns", "node": node, "old": node.returns, "new": new_returns} + ) self.generic_visit(node) def visit_AsyncFunctionDef(self, node): @@ -115,12 +112,9 @@ def visit_AsyncFunctionDef(self, node): if node.returns: new_returns = self._convert_type_hint(node.returns) if new_returns != node.returns: - self.changes.append({ - 'type': 'returns', - 'node': node, - 'old': node.returns, - 'new': new_returns - }) + self.changes.append( + {"type": "returns", "node": node, "old": node.returns, "new": new_returns} + ) self.generic_visit(node) def visit_ClassDef(self, node): @@ -128,12 +122,7 @@ def visit_ClassDef(self, node): for base in node.bases: new_base = self._convert_type_hint(base) if new_base != base: - self.changes.append({ - 'type': 'base', - 'node': node, - 'old': base, - 'new': new_base - }) + self.changes.append({"type": "base", "node": node, "old": base, "new": new_base}) self.generic_visit(node) def _convert_type_hint(self, node): @@ -148,62 +137,51 @@ def _convert_subscript(self, node): """Convert subscript type hints (list[T], dict[K, V], etc.).""" if isinstance(node.value, ast.Name): name = node.value.id - if name in ['list', 'dict', 'set', 'tuple']: + if name in ["list", "dict", "set", "tuple"]: # Convert to typing module equivalent - if name == 'list': - new_name = ast.Name(id='List', ctx=ast.Load()) - elif name == 'dict': - new_name = ast.Name(id='Dict', ctx=ast.Load()) - elif name == 'set': - new_name = ast.Name(id='Set', ctx=ast.Load()) - elif name == 'tuple': - new_name = ast.Name(id='Tuple', ctx=ast.Load()) + if name == "list": + new_name = ast.Name(id="List", ctx=ast.Load()) + elif name == "dict": + new_name = ast.Name(id="Dict", ctx=ast.Load()) + elif name == "set": + new_name = ast.Name(id="Set", ctx=ast.Load()) + elif name == "tuple": + new_name = ast.Name(id="Tuple", ctx=ast.Load()) # Add to imports self.imports_to_add.add(name.capitalize()) # Create new subscript node - return ast.Subscript( - value=new_name, - slice=node.slice, - ctx=node.ctx - ) + return ast.Subscript(value=new_name, slice=node.slice, ctx=node.ctx) return node def _convert_union(self, node): """Convert union type hints (A | B -> Union[A, B]).""" # Handle A | None -> Optional[A] case if isinstance(node.right, ast.Constant) and node.right.value is None: - self.imports_to_add.add('Optional') + self.imports_to_add.add("Optional") return ast.Subscript( - value=ast.Name(id='Optional', ctx=ast.Load()), - slice=node.left, - ctx=ast.Load() + value=ast.Name(id="Optional", ctx=ast.Load()), slice=node.left, ctx=ast.Load() ) if isinstance(node.left, ast.Constant) and node.left.value is None: - self.imports_to_add.add('Optional') + self.imports_to_add.add("Optional") return ast.Subscript( - value=ast.Name(id='Optional', ctx=ast.Load()), - slice=node.right, - ctx=ast.Load() + value=ast.Name(id="Optional", ctx=ast.Load()), slice=node.right, ctx=ast.Load() ) # General union case - self.imports_to_add.add('Union') + self.imports_to_add.add("Union") return ast.Subscript( - value=ast.Name(id='Union', ctx=ast.Load()), - slice=ast.Tuple( - elts=[node.left, node.right], - ctx=ast.Load() - ), - ctx=ast.Load() + value=ast.Name(id="Union", ctx=ast.Load()), + slice=ast.Tuple(elts=[node.left, node.right], ctx=ast.Load()), + ctx=ast.Load(), ) def _log_changes(visitor: TypeHintVisitor, verbose: bool) -> None: """Log AST changes for debugging purposes.""" for change in visitor.changes: - old_code = ast_to_source(change['old']) - new_code = ast_to_source(change['new']) + old_code = ast_to_source(change["old"]) + new_code = ast_to_source(change["new"]) if verbose: print(f" {change['type'].title()}: {old_code} -> {new_code}") @@ -219,23 +197,20 @@ def _add_typing_imports_to_lines(lines: List[str], imports_to_add: set) -> None: last_import_line = -1 for i, line in enumerate(lines): - if line.strip().startswith('from typing import'): + if line.strip().startswith("from typing import"): typing_import_found = True last_import_line = i - elif (line.strip().startswith('import ') or - line.strip().startswith('from ')): + elif line.strip().startswith("import ") or line.strip().startswith("from "): last_import_line = i if typing_import_found: # Add to existing typing import for i, line in enumerate(lines): - if line.strip().startswith('from typing import'): - existing_imports = line.replace('from typing import ', '').strip() - new_imports = ', '.join(sorted(imports_to_add)) + if line.strip().startswith("from typing import"): + existing_imports = line.replace("from typing import ", "").strip() + new_imports = ", ".join(sorted(imports_to_add)) if existing_imports: - new_import_line = ( - f"from typing import {existing_imports}, {new_imports}" - ) + new_import_line = f"from typing import {existing_imports}, {new_imports}" lines[i] = new_import_line else: lines[i] = f"from typing import {new_imports}" @@ -243,20 +218,16 @@ def _add_typing_imports_to_lines(lines: List[str], imports_to_add: set) -> None: else: # Add new typing import after last import if last_import_line >= 0: - import_line = ( - f"from typing import {', '.join(sorted(imports_to_add))}" - ) + import_line = f"from typing import {', '.join(sorted(imports_to_add))}" lines.insert(last_import_line + 1, import_line) else: - import_line = ( - f"from typing import {', '.join(sorted(imports_to_add))}" - ) + import_line = f"from typing import {', '.join(sorted(imports_to_add))}" lines.insert(0, import_line) def _read_file_content(file_path: Path) -> str: """Read file content.""" - with open(file_path, 'r', encoding='utf-8') as f: + with open(file_path, "r", encoding="utf-8") as f: return f.read() @@ -270,12 +241,12 @@ def _parse_ast_safely(content: str, file_path: Path, verbose: bool) -> Optional[ return None -def _apply_changes_and_save(file_path: Path, content: str, visitor: TypeHintVisitor, verbose: bool) -> None: +def _apply_changes_and_save( + file_path: Path, content: str, visitor: TypeHintVisitor, verbose: bool +) -> None: """Apply changes and save the file.""" # Sort changes by line number (reverse order to avoid offset issues) - visitor.changes.sort( - key=lambda x: getattr(x['node'], 'lineno', 0), reverse=True - ) + visitor.changes.sort(key=lambda x: getattr(x["node"], "lineno", 0), reverse=True) # Convert content to lines for easier manipulation lines = content.splitlines() @@ -287,38 +258,36 @@ def _apply_changes_and_save(file_path: Path, content: str, visitor: TypeHintVisi _add_typing_imports_to_lines(lines, visitor.imports_to_add) # Write back to file - with open(file_path, 'w', encoding='utf-8') as f: - f.write('\n'.join(lines)) + with open(file_path, "w", encoding="utf-8") as f: + f.write("\n".join(lines)) def _create_success_result(file_path: Path, visitor: TypeHintVisitor) -> Dict[str, Any]: """Create success result dictionary.""" return { - 'file': str(file_path), - 'status': 'success', - 'changes': len(visitor.changes), - 'imports_added': list(visitor.imports_to_add) + "file": str(file_path), + "status": "success", + "changes": len(visitor.changes), + "imports_added": list(visitor.imports_to_add), } def _create_error_result(file_path: Path, error: str) -> Dict[str, Any]: """Create error result dictionary.""" - return {'file': str(file_path), 'status': 'error', 'error': error} + return {"file": str(file_path), "status": "error", "error": error} def _create_syntax_error_result(file_path: Path, error: str) -> Dict[str, Any]: """Create syntax error result dictionary.""" - return {'file': str(file_path), 'status': 'syntax_error', 'error': error} + return {"file": str(file_path), "status": "syntax_error", "error": error} def _create_no_changes_result(file_path: Path) -> Dict[str, Any]: """Create no changes result dictionary.""" - return {'file': str(file_path), 'status': 'no_changes', 'changes': 0} + return {"file": str(file_path), "status": "no_changes", "changes": 0} -def process_file( - file_path: Path, dry_run: bool = False, verbose: bool = False -) -> Dict[str, Any]: +def process_file(file_path: Path, dry_run: bool = False, verbose: bool = False) -> Dict[str, Any]: """Process a single Python file for type hint conversions.""" try: # Read file content @@ -353,18 +322,15 @@ def _process_single_file(file_path: Path, dry_run: bool, verbose: bool) -> Dict[ result = process_file(file_path, dry_run=dry_run, verbose=verbose) - if result['status'] == 'success' and result['changes'] > 0: + if result["status"] == "success" and result["changes"] > 0: if verbose: - print( - f" โœ… {result['changes']} changes, " - f"imports: {result['imports_added']}" - ) - elif result['status'] == 'no_changes': + print(f" โœ… {result['changes']} changes, " f"imports: {result['imports_added']}") + elif result["status"] == "no_changes": if verbose: print(" โญ๏ธ No changes needed") - elif result['status'] == 'error': + elif result["status"] == "error": print(f" โŒ Error: {result['error']}") - elif result['status'] == 'syntax_error': + elif result["status"] == "syntax_error": print(f" โš ๏ธ Syntax error: {result['error']}") if verbose: @@ -379,10 +345,10 @@ def _print_summary(results: List[Dict[str, Any]], total_changes: int, dry_run: b print("SUMMARY") print("=" * 50) - successful = [r for r in results if r['status'] == 'success'] - errors = [r for r in results if r['status'] == 'error'] - syntax_errors = [r for r in results if r['status'] == 'syntax_error'] - no_changes = [r for r in results if r['status'] == 'no_changes'] + successful = [r for r in results if r["status"] == "success"] + errors = [r for r in results if r["status"] == "error"] + syntax_errors = [r for r in results if r["status"] == "syntax_error"] + no_changes = [r for r in results if r["status"] == "no_changes"] print(f"Files processed: {len(results)}") print(f"Successful: {len(successful)}") @@ -408,8 +374,8 @@ def _print_summary(results: List[Dict[str, Any]], total_changes: int, dry_run: b def find_python_files(directory: Path) -> List[Path]: """Find all Python files in a directory recursively.""" python_files = [] - for item in directory.rglob('*.py'): - if not any(part.startswith('.') for part in item.parts): + for item in directory.rglob("*.py"): + if not any(part.startswith(".") for part in item.parts): python_files.append(item) return python_files @@ -417,13 +383,13 @@ def find_python_files(directory: Path) -> List[Path]: def _parse_arguments() -> argparse.Namespace: """Parse command line arguments.""" parser = argparse.ArgumentParser( - description=( - 'Convert Python 3.9+ type hints to Python 3.8 compatible syntax' - ) + description=("Convert Python 3.9+ type hints to Python 3.8 compatible syntax") + ) + parser.add_argument("directory", help="Directory to process") + parser.add_argument( + "--dry-run", action="store_true", help="Show what would be changed without making changes" ) - parser.add_argument('directory', help='Directory to process') - parser.add_argument('--dry-run', action='store_true', help='Show what would be changed without making changes') - parser.add_argument('--verbose', action='store_true', help='Show detailed output') + parser.add_argument("--verbose", action="store_true", help="Show detailed output") return parser.parse_args() @@ -446,7 +412,9 @@ def _print_processing_info(directory: Path, dry_run: bool) -> None: print() -def _process_all_files(python_files: List[Path], dry_run: bool, verbose: bool) -> Tuple[List[Dict[str, Any]], int]: +def _process_all_files( + python_files: List[Path], dry_run: bool, verbose: bool +) -> Tuple[List[Dict[str, Any]], int]: """Process all Python files and return results and total changes.""" results = [] total_changes = 0 @@ -455,8 +423,8 @@ def _process_all_files(python_files: List[Path], dry_run: bool, verbose: bool) - result = _process_single_file(file_path, dry_run, verbose) results.append(result) - if result['status'] == 'success' and result['changes'] > 0: - total_changes += result['changes'] + if result["status"] == "success" and result["changes"] > 0: + total_changes += result["changes"] return results, total_changes @@ -483,5 +451,5 @@ def main(): print() -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/scripts/maintenance/vertex_ai_setup_fixed.py b/scripts/maintenance/vertex_ai_setup_fixed.py index bcfa37a18..2d83b6621 100644 --- a/scripts/maintenance/vertex_ai_setup_fixed.py +++ b/scripts/maintenance/vertex_ai_setup_fixed.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Create custom job with correct API syntax # Create hyperparameter tuning job with correct API syntax # Create validation job with correct API syntax @@ -6,9 +7,6 @@ # Model monitoring configuration # Pipeline configuration from google.cloud import aiplatform - from google.cloud import aiplatform - from google.cloud import aiplatform - from google.cloud import aiplatform from google.cloud import storage # Step 1: Environment setup # Step 2: Create validation job @@ -20,20 +18,13 @@ # Get project ID from environment or user input # Setup complete infrastructure # Summary -# Add src to path -# Configure logging -#!/usr/bin/env python3 -from pathlib import Path -from typing import Dict, Any, Optional import logging import os import sys - - - - - - +# Add src to path +# Configure logging +from pathlib import Path +from typing import Any, Dict, Optional """ Fixed Vertex AI Setup for SAMO Deep Learning Project. diff --git a/scripts/pre_download_models.py b/scripts/pre_download_models.py new file mode 100644 index 000000000..d61caec98 --- /dev/null +++ b/scripts/pre_download_models.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python3 +"""Pre-download models for Docker build optimization.""" +import logging +import os +import sys + +# Configure logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def main(): + """Pre-download all required models.""" + # Create models directory + os.makedirs("/app/models", exist_ok=True) + + print("๐Ÿš€ Pre-downloading DeBERTa-v3 emotion model...") + try: + from transformers import AutoTokenizer, AutoModelForSequenceClassification + + model_name = "duelker/samo-goemotions-deberta-v3-large" + print(f"Downloading {model_name}...") + _tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir="/app/models") + _model = AutoModelForSequenceClassification.from_pretrained( + model_name, cache_dir="/app/models" + ) + print("โœ… DeBERTa-v3 model downloaded successfully") + except Exception as e: + print(f"โŒ Error downloading DeBERTa-v3 model: {e}") + raise + + print("๐Ÿš€ Pre-downloading T5 summarization model...") + try: + from transformers import T5Tokenizer, T5ForConditionalGeneration + + t5_model = "t5-small" + print(f"Downloading {t5_model}...") + _t5_tokenizer = T5Tokenizer.from_pretrained(t5_model, cache_dir="/app/models") + _t5_model_obj = T5ForConditionalGeneration.from_pretrained( + t5_model, cache_dir="/app/models" + ) + print("โœ… T5 model downloaded successfully") + except Exception as e: + print(f"โŒ Error downloading T5 model: {e}") + raise + + print("๐Ÿš€ Pre-downloading Whisper model...") + try: + # Check numpy availability first + try: + import numpy + + print(f"โœ… Numpy {numpy.__version__} available") + except ImportError: + print("โš ๏ธ Installing numpy...") + import subprocess + + subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy"]) + import numpy + + print(f"โœ… Numpy {numpy.__version__} installed and available") + + import whisper + + whisper_model = "base" + print(f"Downloading Whisper {whisper_model}...") + whisper.load_model(whisper_model, download_root="/app/models") + print("โœ… Whisper model downloaded successfully") + except Exception as e: + print(f"โŒ Error downloading Whisper model: {e}") + # Don't fail the entire build for Whisper - continue without it + print("โš ๏ธ Continuing without Whisper model - will be downloaded at runtime if needed") + + print("๐ŸŽ‰ Core models pre-downloaded successfully!") + + +if __name__ == "__main__": + main() diff --git 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a/scripts/testing/__pycache__/test_working_inference.cpython-312.pyc b/scripts/testing/__pycache__/test_working_inference.cpython-312.pyc deleted file mode 100644 index 3ea818c90..000000000 Binary files a/scripts/testing/__pycache__/test_working_inference.cpython-312.pyc and /dev/null differ diff --git a/scripts/testing/_bootstrap.py b/scripts/testing/_bootstrap.py index 1f9387010..439f80d40 100644 --- a/scripts/testing/_bootstrap.py +++ b/scripts/testing/_bootstrap.py @@ -12,7 +12,6 @@ from pathlib import Path from typing import Iterable, Optional - _MARKERS: tuple[str, ...] = ( "pyproject.toml", "README.md", diff --git a/scripts/testing/basic_environment_test.py b/scripts/testing/basic_environment_test.py index 107468b9a..7f2bf243e 100644 --- a/scripts/testing/basic_environment_test.py +++ b/scripts/testing/basic_environment_test.py @@ -1,20 +1,18 @@ - # Stop if we hit a KeyboardInterrupt - # Summary - # Test basic Python - # Test core modules one by one +# Stop if we hit a KeyboardInterrupt +# Summary +# Test basic Python +# Test core modules one by one #!/usr/bin/env python3 import logging import sys - """ Basic Environment Test Script Tests imports one by one to identify issues """ - def test_import(module_name, description): """Test importing a module and report status.""" try: diff --git a/scripts/testing/check_model_health.py b/scripts/testing/check_model_health.py index a598c194c..3bcc94046 100755 --- a/scripts/testing/check_model_health.py +++ b/scripts/testing/check_model_health.py @@ -4,8 +4,9 @@ Check if the model is loading properly in the container. """ + import requests -import json + from test_config import create_api_client, create_test_config @@ -13,13 +14,13 @@ def check_model_health(base_url=None): """Check model health status""" config = create_test_config() if base_url: - config.base_url = base_url.rstrip('/') + config.base_url = base_url.rstrip("/") client = create_api_client() - + print("๐Ÿ” Model Health Check") print("=" * 30) print(f"Testing URL: {config.base_url}") - + # Test health endpoint try: data = client.get("/") @@ -31,7 +32,7 @@ def check_model_health(base_url=None): # Test emotions from main endpoint try: data = client.get("/") - emotions_count = data.get('emotions_supported', 0) + emotions_count = data.get("emotions_supported", 0) print(f"โœ… Emotions: {emotions_count} emotions available") except requests.exceptions.RequestException as e: print(f"โŒ Emotions check error: {e}") @@ -41,19 +42,19 @@ def check_model_health(base_url=None): try: payload = {"text": "I am happy"} data = client.post("/predict", payload) - + # Handle confidence formatting with null checks - primary_emotion = data.get('primary_emotion', {}) - emotion = primary_emotion.get('emotion', 'Unknown') - confidence = primary_emotion.get('confidence') + primary_emotion = data.get("primary_emotion", {}) + emotion = primary_emotion.get("emotion", "Unknown") + confidence = primary_emotion.get("confidence") if confidence is not None: confidence_str = f"{confidence:.3f}" else: confidence_str = "N/A" - + print(f"โœ… Prediction: {emotion} (confidence: {confidence_str})") return True - + except requests.exceptions.RequestException as e: print(f"โŒ Prediction check error: {e}") return False @@ -64,10 +65,10 @@ def check_model_health(base_url=None): if __name__ == "__main__": import argparse - + parser = argparse.ArgumentParser(description="Check Model Health") parser.add_argument("--base-url", help="API base URL") args = parser.parse_args() - + success = check_model_health(args.base_url) exit(0 if success else 1) diff --git a/scripts/testing/config.py b/scripts/testing/config.py index 486409986..d68e3e4bf 100644 --- a/scripts/testing/config.py +++ b/scripts/testing/config.py @@ -4,10 +4,11 @@ Eliminates hardcoded values and provides consistent configuration across all test scripts. """ -import os import argparse +import os import time from typing import Optional + import requests @@ -24,7 +25,7 @@ def __init__(self): def _get_base_url() -> str: """Get base URL with priority: CLI args > env vars > explicit configuration.""" # Check command line arguments first - if len(os.sys.argv) > 1 and os.sys.argv[1].startswith('http'): + if len(os.sys.argv) > 1 and os.sys.argv[1].startswith("http"): return os.sys.argv[1] # Check multiple environment variables for flexibility @@ -62,10 +63,7 @@ def _get_rate_limit_requests() -> int: def get_headers(self, include_auth: bool = True) -> dict: """Get request headers with optional authentication.""" - headers = { - "Content-Type": "application/json", - "User-Agent": "SAMO-Testing-Suite/1.0" - } + headers = {"Content-Type": "application/json", "User-Agent": "SAMO-Testing-Suite/1.0"} if include_auth: headers["X-API-Key"] = self.api_key @@ -75,27 +73,12 @@ def get_headers(self, include_auth: bool = True) -> dict: def get_parser(self, description: str) -> argparse.ArgumentParser: """Get argument parser with common options.""" parser = argparse.ArgumentParser(description=description) + parser.add_argument("--base-url", default=self.base_url, help="Base URL of the API to test") parser.add_argument( - "--base-url", - default=self.base_url, - help="Base URL of the API to test" - ) - parser.add_argument( - "--timeout", - type=int, - default=self.timeout, - help="Request timeout in seconds" - ) - parser.add_argument( - "--no-auth", - action="store_true", - help="Skip authentication headers" - ) - parser.add_argument( - "--verbose", - action="store_true", - help="Enable verbose output" + "--timeout", type=int, default=self.timeout, help="Request timeout in seconds" ) + parser.add_argument("--no-auth", action="store_true", help="Skip authentication headers") + parser.add_argument("--verbose", action="store_true", help="Enable verbose output") return parser @@ -139,13 +122,13 @@ def __init__(self, base_url: Optional[str] = None, include_auth: bool = True): def get(self, endpoint: str, **kwargs) -> requests.Response: """Make GET request with common configuration.""" url = f"{self.base_url}{endpoint}" - headers = {**self.headers, **kwargs.get('headers', {})} + headers = {**self.headers, **kwargs.get("headers", {})} return self.session.get(url, headers=headers, timeout=self.timeout, **kwargs) def post(self, endpoint: str, json_data: dict, **kwargs) -> requests.Response: """Make POST request with common configuration.""" url = f"{self.base_url}{endpoint}" - headers = {**self.headers, **kwargs.get('headers', {})} + headers = {**self.headers, **kwargs.get("headers", {})} return self.session.post( url, json=json_data, @@ -162,15 +145,10 @@ def test_health(self) -> dict: "success": response.status_code == 200, "status_code": response.status_code, "data": response.json() if response.status_code == 200 else None, - "error": response.text if response.status_code != 200 else None + "error": response.text if response.status_code != 200 else None, } except Exception as e: - return { - "success": False, - "status_code": None, - "data": None, - "error": str(e) - } + return {"success": False, "status_code": None, "data": None, "error": str(e)} def test_prediction(self, text: str) -> dict: """Test prediction endpoint.""" @@ -180,15 +158,10 @@ def test_prediction(self, text: str) -> dict: "success": response.status_code == 200, "status_code": response.status_code, "data": response.json() if response.status_code == 200 else None, - "error": response.text if response.status_code != 200 else None + "error": response.text if response.status_code != 200 else None, } except Exception as e: - return { - "success": False, - "status_code": None, - "data": None, - "error": str(e) - } + return {"success": False, "status_code": None, "data": None, "error": str(e)} def test_batch_prediction(self, texts: list) -> dict: """Test batch prediction endpoint.""" @@ -198,12 +171,7 @@ def test_batch_prediction(self, texts: list) -> dict: "success": response.status_code == 200, "status_code": response.status_code, "data": response.json() if response.status_code == 200 else None, - "error": response.text if response.status_code != 200 else None + "error": response.text if response.status_code != 200 else None, } except Exception as e: - return { - "success": False, - "status_code": None, - "data": None, - "error": str(e) - } + return {"success": False, "status_code": None, "data": None, "error": str(e)} diff --git a/scripts/testing/create_journal_test_dataset.py b/scripts/testing/create_journal_test_dataset.py index 7c31216a6..94130b7c2 100644 --- a/scripts/testing/create_journal_test_dataset.py +++ b/scripts/testing/create_journal_test_dataset.py @@ -13,9 +13,10 @@ import json import random -from datetime import datetime, timezone, timedelta +from datetime import datetime, timedelta, timezone from pathlib import Path -from typing import List, Dict, Any +from typing import Any, Dict, List + import pandas as pd # Realistic journal entry templates that reflect personal, reflective writing @@ -164,35 +165,34 @@ "I'm learning to embrace uncertainty.", ] + def generate_journal_content(topic: str, emotion: str) -> str: """Generate realistic journal entry content.""" template = random.choice(JOURNAL_TEMPLATES) emotion_context = random.choice(EMOTION_CONTEXTS.get(emotion, ["I'm feeling this way."])) reflection = random.choice(REFLECTIVE_STATEMENTS) - + content = template.format( - topic=topic, - emotion=emotion, - emotion_context=emotion_context, - reflection=reflection + topic=topic, emotion=emotion, emotion_context=emotion_context, reflection=reflection ) - + # Add more depth with additional sentences if random.random() > 0.3: # 70% chance of adding more detail additional_context = random.choice(EMOTION_CONTEXTS.get(emotion, ["I'm processing this."])) content += f" {additional_context}" - + if random.random() > 0.5: # 50% chance of adding another reflection second_reflection = random.choice(REFLECTIVE_STATEMENTS) content += f" {second_reflection}" - + return content + def generate_journal_entry(entry_id: int, user_id: int, created_at: datetime) -> Dict[str, Any]: """Generate a single realistic journal entry.""" topic = random.choice(JOURNAL_TOPICS) emotion = random.choice(list(EMOTION_CONTEXTS.keys())) - + return { "id": entry_id, "user_id": user_id, @@ -207,103 +207,103 @@ def generate_journal_entry(entry_id: int, user_id: int, created_at: datetime) -> "word_count": len(generate_journal_content(topic, emotion).split()), } + def create_journal_test_dataset( - num_entries: int = 150, - num_users: int = 10, - days_back: int = 90 + num_entries: int = 150, num_users: int = 10, days_back: int = 90 ) -> List[Dict[str, Any]]: """Create a comprehensive journal test dataset.""" start_date = datetime.now(timezone.utc) - timedelta(days=days_back) end_date = datetime.now(timezone.utc) - + entries = [] for i in range(num_entries): user_id = random.randint(1, num_users) - + # Random date within the range days_offset = random.randint(0, days_back) entry_date = start_date + timedelta(days=days_offset) - + # Random time during the day (more realistic for journaling) entry_date = entry_date.replace( hour=random.randint(6, 23), # Early morning to late night minute=random.randint(0, 59), second=random.randint(0, 59), ) - + entry = generate_journal_entry(i + 1, user_id, entry_date) entries.append(entry) - + return entries + def save_test_dataset(entries: List[Dict[str, Any]], output_path: str) -> None: """Save the test dataset to JSON.""" Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: + + with open(output_path, "w") as f: json.dump(entries, f, indent=2) - + print(f"โœ… Saved {len(entries)} journal entries to {output_path}") + def create_dataset_summary(entries: List[Dict[str, Any]]) -> Dict[str, Any]: """Create a summary of the dataset for validation.""" df = pd.DataFrame(entries) - + summary = { "total_entries": len(entries), "unique_users": df["user_id"].nunique(), "emotion_distribution": df["emotion"].value_counts().to_dict(), "topic_distribution": df["topic"].value_counts().to_dict(), "avg_word_count": df["word_count"].mean(), - "date_range": { - "start": min(df["created_at"]), - "end": max(df["created_at"]) - }, - "sample_entries": entries[:3] # First 3 entries as examples + "date_range": {"start": min(df["created_at"]), "end": max(df["created_at"])}, + "sample_entries": entries[:3], # First 3 entries as examples } - + return summary + def main(): """Main function to create the journal test dataset.""" print("๐Ÿš€ Creating Journal Entry Test Dataset for Domain Adaptation") print("=" * 60) - + # Create the dataset entries = create_journal_test_dataset( - num_entries=150, # Exceeds the 100+ requirement - num_users=10, - days_back=90 + num_entries=150, num_users=10, days_back=90 # Exceeds the 100+ requirement ) - + # Save to data directory output_path = "data/journal_test_dataset.json" save_test_dataset(entries, output_path) - + # Create and save summary summary = create_dataset_summary(entries) summary_path = "data/journal_test_dataset_summary.json" - - with open(summary_path, 'w') as f: + + with open(summary_path, "w") as f: json.dump(summary, f, indent=2) - + print(f"โœ… Saved dataset summary to {summary_path}") - + # Print key statistics print("\n๐Ÿ“Š Dataset Statistics:") print(f" Total Entries: {summary['total_entries']}") print(f" Unique Users: {summary['unique_users']}") print(f" Average Word Count: {summary['avg_word_count']:.1f}") - print(f" Date Range: {summary['date_range']['start'][:10]} to {summary['date_range']['end'][:10]}") - + print( + f" Date Range: {summary['date_range']['start'][:10]} to {summary['date_range']['end'][:10]}" + ) + print("\n๐ŸŽฏ Emotion Distribution:") - for emotion, count in summary['emotion_distribution'].items(): - percentage = (count / summary['total_entries']) * 100 + for emotion, count in summary["emotion_distribution"].items(): + percentage = (count / summary["total_entries"]) * 100 print(f" {emotion}: {count} ({percentage:.1f}%)") - + print("\nโœ… Journal Test Dataset Created Successfully!") print(" This dataset will be used for REQ-DL-012 domain adaptation testing") print(" Target: 70% F1 score on journal-style text vs Reddit comments") + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/testing/create_test_dataset.py b/scripts/testing/create_test_dataset.py index 94ae3bdfa..587edb915 100644 --- a/scripts/testing/create_test_dataset.py +++ b/scripts/testing/create_test_dataset.py @@ -1,25 +1,23 @@ - # Add original entry - # Add variations - # Count emotions - # Create more samples by duplicating and slightly modifying - # Create test data - # Sample texts with emotion labels - # Save to file - # Show sample - # Shuffle the data +# Add original entry +# Add variations +# Count emotions +# Create more samples by duplicating and slightly modifying +# Create test data +# Sample texts with emotion labels +# Save to file +# Show sample +# Shuffle the data #!/usr/bin/env python3 import json import logging import random - - - """ Create a test dataset with emotion labels for Vertex AI """ + def create_test_dataset(): """Create a test dataset with emotion labels""" diff --git a/scripts/testing/debug_checkpoint.py b/scripts/testing/debug_checkpoint.py deleted file mode 100644 index 253c90e5e..000000000 --- a/scripts/testing/debug_checkpoint.py +++ /dev/null @@ -1,41 +0,0 @@ - # Load checkpoint -#!/usr/bin/env python3 -from pathlib import Path -import logging -import torch - - - - -""" -Debug Checkpoint Format -""" - -def debug_checkpoint(): - checkpoint_path = Path("test_checkpoints/best_model.pt") - - if not checkpoint_path.exists(): - logging.info("โŒ Checkpoint not found") - return - - logging.info("๐Ÿ” Debugging checkpoint format...") - - checkpoint = torch.load(checkpoint_path, map_location="cpu") - - logging.info("Checkpoint type: {type(checkpoint)}") - logging.info("Checkpoint content: {checkpoint}") - - if isinstance(checkpoint, dict): - logging.info("\n๐Ÿ“‹ Dictionary keys:") - for _key in checkpoint: - logging.info(" - {key}: {type(checkpoint[key])}") - elif isinstance(checkpoint, tuple): - logging.info("\n๐Ÿ“‹ Tuple length: {len(checkpoint)}") - for __i, item in enumerate(checkpoint): - logging.info(" - Item {i}: {type(item)}") - if isinstance(item, dict): - logging.info(" Keys: {list(item.keys())}") - - -if __name__ == "__main__": - debug_checkpoint() diff --git a/scripts/testing/debug_dataset_structure.py b/scripts/testing/debug_dataset_structure.py deleted file mode 100644 index 8aad21f73..000000000 --- a/scripts/testing/debug_dataset_structure.py +++ /dev/null @@ -1,85 +0,0 @@ -#!/usr/bin/env python3 -""" -Debug Dataset Structure Script - -This script helps understand the structure of the GoEmotions dataset. -""" - -import logging -import sys -from pathlib import Path - -# Add src to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) - -from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader - -# Configure logging -logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") -logger = logging.getLogger(__name__) - - -def debug_dataset_structure(): - """Debug the structure of the GoEmotions dataset.""" - logger.info("๐Ÿ” Debugging Dataset Structure") - logger.info("=" * 50) - - try: - # Load dataset - logger.info("๐Ÿ“Š Loading GoEmotions dataset...") - data_loader = GoEmotionsDataLoader() - data_loader.download_dataset() - datasets = data_loader.prepare_datasets() - - logger.info("๐Ÿ“‹ Dataset keys:") - for key in datasets.keys(): - logger.info(f" - {key}") - - # Check test data structure - test_data = datasets["test_data"] - logger.info(f"๐Ÿ“Š Test data type: {type(test_data)}") - logger.info(f"๐Ÿ“Š Test data length: {len(test_data)}") - - if len(test_data) > 0: - first_item = test_data[0] - logger.info(f"๐Ÿ“Š First item type: {type(first_item)}") - logger.info(f"๐Ÿ“Š First item: {first_item}") - - if hasattr(first_item, 'keys'): - logger.info(f"๐Ÿ“Š First item keys: {list(first_item.keys())}") - elif hasattr(first_item, '__dict__'): - logger.info(f"๐Ÿ“Š First item attributes: {list(first_item.__dict__.keys())}") - - # Check train data structure - train_data = datasets["train_data"] - logger.info(f"๐Ÿ“Š Train data type: {type(train_data)}") - logger.info(f"๐Ÿ“Š Train data length: {len(train_data)}") - - if len(train_data) > 0: - first_train_item = train_data[0] - logger.info(f"๐Ÿ“Š First train item type: {type(first_train_item)}") - logger.info(f"๐Ÿ“Š First train item: {first_train_item}") - - # Check if it's a HuggingFace dataset - if hasattr(test_data, 'features'): - logger.info(f"๐Ÿ“Š Dataset features: {test_data.features}") - - if hasattr(test_data, 'column_names'): - logger.info(f"๐Ÿ“Š Dataset columns: {test_data.column_names}") - - return True - - except Exception as e: - logger.error(f"โŒ Debug failed: {e}") - import traceback - traceback.print_exc() - return False - - -if __name__ == "__main__": - success = debug_dataset_structure() - if success: - logger.info("โœ… Debug completed successfully") - else: - logger.error("โŒ Debug failed") - sys.exit(1) diff --git a/scripts/testing/debug_evaluation_step_by_step.py b/scripts/testing/debug_evaluation_step_by_step.py deleted file mode 100644 index bbfc83b4f..000000000 --- a/scripts/testing/debug_evaluation_step_by_step.py +++ /dev/null @@ -1,149 +0,0 @@ - # Find top-1 prediction - # Apply fallback manually to see what happens - # Apply threshold - # Calculate F1 scores manually - # Check which samples need fallback - # Micro F1 - # Get validation data (small batch for debugging) - # Initialize trainer - # Load model - # Run model inference - # Take just one batch for detailed analysis - # Test different thresholds -# Add src to path -# Set up logging -#!/usr/bin/env python3 -from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer -from pathlib import Path -import logging -import numpy as np -import sys -import torch - - - - -""" -Debug the evaluation function step by step to find the exact issue. -""" - -sys.path.append(str(Path(__file__).parent.parent / "src")) - -logging.basicConfig(level=logging.INFO, format="%(levelname)s:%(name)s:%(message)s") -logger = logging.getLogger(__name__) - - -def debug_evaluation_step_by_step(): - """Debug the evaluation function with detailed step-by-step analysis.""" - - logger.info("๐Ÿ” Step-by-step evaluation debugging") - - trainer = EmotionDetectionTrainer(dev_mode=True, batch_size=128, num_epochs=1) - - logger.info("โœ… Trainer initialized") - - model_path = Path("models/checkpoints/bert_emotion_classifier.pth") - if not model_path.exists(): - logger.error("โŒ Model not found at {model_path}") - return - - trainer.load_model(str(model_path)) - logger.info("โœ… Model loaded") - - val_loader = trainer.val_loader - - batch = next(iter(val_loader)) - input_ids, attention_mask, targets = batch - - logger.info("๐Ÿ” Analyzing single batch:") - logger.info(" ๐Ÿ“Š Batch size: {input_ids.shape[0]}") - logger.info(" ๐Ÿ“Š Sequence length: {input_ids.shape[1]}") - logger.info(" ๐Ÿ“Š Number of emotions: {targets.shape[1]}") - logger.info(" ๐Ÿ“Š Target sum: {targets.sum().item()}") - logger.info(" ๐Ÿ“Š Target mean: {targets.mean().item():.4f}") - - trainer.model.eval() - with torch.no_grad(): - outputs = trainer.model(input_ids, attention_mask) - logits = outputs["logits"] - probabilities = torch.sigmoid(logits) - - logger.info("๐Ÿ” Model outputs:") - logger.info(" ๐Ÿ“Š Logits shape: {logits.shape}") - logger.info(" ๐Ÿ“Š Logits min/max: {logits.min().item():.4f} / {logits.max().item():.4f}") - logger.info(" ๐Ÿ“Š Probabilities shape: {probabilities.shape}") - logger.info( - " ๐Ÿ“Š Probabilities min/max: {probabilities.min().item():.4f} / {probabilities.max().item():.4f}" - ) - logger.info(" ๐Ÿ“Š Probabilities mean: {probabilities.mean().item():.4f}") - - thresholds = [0.1, 0.2, 0.3, 0.5] - - for threshold in thresholds: - logger.info("\n๐ŸŽฏ Testing threshold: {threshold}") - - predictions_before_fallback = (probabilities >= threshold).float() - logger.info(" ๐Ÿ“Š Predictions before fallback:") - logger.info(" - Shape: {predictions_before_fallback.shape}") - logger.info(" - Sum: {predictions_before_fallback.sum().item()}") - logger.info(" - Mean: {predictions_before_fallback.mean().item():.4f}") - logger.info( - " - Samples with 0 predictions: {(predictions_before_fallback.sum(dim=1) == 0).sum().item()}" - ) - logger.info( - " - Samples with >0 predictions: {(predictions_before_fallback.sum(dim=1) > 0).sum().item()}" - ) - - samples_needing_fallback = predictions_before_fallback.sum(dim=1) == 0 - num_samples_needing_fallback = samples_needing_fallback.sum().item() - - logger.info(" ๐Ÿ”ง Fallback analysis:") - logger.info(" - Samples needing fallback: {num_samples_needing_fallback}") - logger.info( - " - Percentage needing fallback: {100 * num_samples_needing_fallback / predictions_before_fallback.shape[0]:.1f}%" - ) - - predictions_after_fallback = predictions_before_fallback.clone() - - if num_samples_needing_fallback > 0: - logger.info(" ๐Ÿ”ง Applying fallback to {num_samples_needing_fallback} samples...") - - for sample_idx in range(predictions_after_fallback.shape[0]): - if predictions_after_fallback[sample_idx].sum() == 0: - top_idx = torch.topk(probabilities[sample_idx], k=1, dim=0)[1] - predictions_after_fallback[sample_idx, top_idx] = 1.0 - logger.info( - " - Sample {sample_idx}: Applied fallback to emotion {top_idx.item()}" - ) - - logger.info(" ๐Ÿ“Š Predictions after fallback:") - logger.info(" - Sum: {predictions_after_fallback.sum().item()}") - logger.info(" - Mean: {predictions_after_fallback.mean().item():.4f}") - logger.info( - " - Samples with 0 predictions: {(predictions_after_fallback.sum(dim=1) == 0).sum().item()}" - ) - - predictions_np = predictions_after_fallback.cpu().numpy() - targets_np = targets.cpu().numpy() - - tp = np.sum(predictions_np * targets_np) - fp = np.sum(predictions_np * (1 - targets_np)) - fn = np.sum((1 - predictions_np) * targets_np) - - micro_precision = tp / (tp + fp) if (tp + fp) > 0 else 0 - micro_recall = tp / (tp + fn) if (tp + fn) > 0 else 0 - ( - 2 * micro_precision * micro_recall / (micro_precision + micro_recall) - if (micro_precision + micro_recall) > 0 - else 0 - ) - - logger.info(" ๐Ÿ“ˆ Manual F1 calculation:") - logger.info(" - TP: {tp}, FP: {fp}, FN: {fn}") - logger.info(" - Micro Precision: {micro_precision:.4f}") - logger.info(" - Micro Recall: {micro_recall:.4f}") - logger.info(" - Micro F1: {micro_f1:.4f}") - - -if __name__ == "__main__": - debug_evaluation_step_by_step() diff --git a/scripts/testing/debug_go_emotions_labels.py b/scripts/testing/debug_go_emotions_labels.py deleted file mode 100644 index c07515eb5..000000000 --- a/scripts/testing/debug_go_emotions_labels.py +++ /dev/null @@ -1,104 +0,0 @@ -#!/usr/bin/env python3 -""" -Debug the actual GoEmotions label structure to understand the mapping. -""" - -import subprocess -import sys - -def install_dependencies(): - """Install required dependencies.""" - print("๐Ÿ”ง Installing dependencies...") - try: - subprocess.check_call([sys.executable, "-m", "pip", "install", "datasets", "pandas"]) - print("โœ… Dependencies installed") - except subprocess.CalledProcessError as e: - print(f"โŒ Failed to install dependencies: {e}") - return False - return True - -# Install dependencies first -if not install_dependencies(): - print("โŒ Cannot proceed without dependencies") - sys.exit(1) - -from datasets import load_dataset - -def debug_go_emotions(): - """Debug the actual GoEmotions dataset structure.""" - print("๐Ÿ” Debugging GoEmotions dataset structure...") - - # Load the dataset - go_emotions = load_dataset("go_emotions", "simplified") - - print(f"\n๐Ÿ“Š Dataset structure:") - print(f"Keys: {list(go_emotions.keys())}") - print(f"Train size: {len(go_emotions['train'])}") - print(f"Validation size: {len(go_emotions['validation'])}") - print(f"Test size: {len(go_emotions['test'])}") - - # Check first few examples - print(f"\n๐Ÿ“Š First 5 examples:") - for i in range(min(5, len(go_emotions['train']))): - example = go_emotions['train'][i] - print(f"Example {i}:") - print(f" Text: {example['text'][:100]}...") - print(f" Labels: {example['labels']}") - print(f" Label types: {[type(label) for label in example['labels']]}") - print() - - # Check if there's a label mapping - print(f"\n๐Ÿ” Checking for label mapping...") - - # Try to get the dataset info - try: - dataset_info = go_emotions['train'].info - print(f"Dataset info: {dataset_info}") - except: - print("No dataset info available") - - # Check if there are features - try: - features = go_emotions['train'].features - print(f"Features: {features}") - except: - print("No features available") - - # Look for label names in the dataset - print(f"\n๐Ÿ” Looking for label names...") - - # Check if there's a label_names field - if hasattr(go_emotions, 'label_names'): - print(f"Label names: {go_emotions.label_names}") - else: - print("No label_names attribute") - - # Check if there's a features attribute with label names - if hasattr(go_emotions['train'], 'features'): - features = go_emotions['train'].features - print(f"Features: {features}") - if 'labels' in features: - print(f"Labels feature: {features['labels']}") - - # Try to get the original dataset - print(f"\n๐Ÿ” Trying original dataset...") - try: - original_go_emotions = load_dataset("go_emotions") - print(f"Original dataset keys: {list(original_go_emotions.keys())}") - - if 'train' in original_go_emotions: - print(f"Original train size: {len(original_go_emotions['train'])}") - example = original_go_emotions['train'][0] - print(f"Original example: {example}") - except Exception as e: - print(f"Could not load original dataset: {e}") - - # Check the dataset card - print(f"\n๐Ÿ” Checking dataset documentation...") - print("GoEmotions dataset should have emotion names like:") - print("['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral']") - - return go_emotions - -if __name__ == "__main__": - debug_go_emotions() \ No newline at end of file diff --git a/scripts/testing/debug_label_mismatch.py b/scripts/testing/debug_label_mismatch.py deleted file mode 100644 index 23ddc4daa..000000000 --- a/scripts/testing/debug_label_mismatch.py +++ /dev/null @@ -1,221 +0,0 @@ -#!/usr/bin/env python3 -""" -Debug script to identify and fix CUDA device-side assert errors caused by label mismatches. -""" - -import json -import pandas as pd -from datasets import load_dataset -from sklearn.preprocessing import LabelEncoder -import logging - -# Configure logging -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') -logger = logging.getLogger(__name__) - -def debug_label_mismatch(): - """Debug the label mismatch causing CUDA errors.""" - logger.info("๐Ÿ” Debugging label mismatch issue...") - - try: - # Step 1: Load datasets - logger.info("๐Ÿ“Š Loading datasets...") - - # Load GoEmotions dataset - go_emotions = load_dataset("go_emotions", "simplified") - logger.info(f"โœ… GoEmotions loaded: {len(go_emotions['train'])} training examples") - - # Load journal dataset - with open('data/journal_test_dataset.json', 'r') as f: - journal_entries = json.load(f) - journal_df = pd.DataFrame(journal_entries) - logger.info(f"โœ… Journal dataset loaded: {len(journal_df)} entries") - - # Step 2: Analyze GoEmotions labels - logger.info("๐Ÿ” Analyzing GoEmotions labels...") - go_labels = set() - go_label_counts = {} - - for example in go_emotions['train']: - if example['labels']: - for label in example['labels']: - go_labels.add(label) - go_label_counts[label] = go_label_counts.get(label, 0) + 1 - - logger.info(f"๐Ÿ“Š GoEmotions unique labels: {len(go_labels)}") - logger.info(f"๐Ÿ“Š GoEmotions labels: {sorted(list(go_labels))}") - logger.info(f"๐Ÿ“Š GoEmotions label counts: {dict(sorted(go_label_counts.items(), key=lambda x: x[1], reverse=True)[:10])}") - - # Step 3: Analyze journal labels - logger.info("๐Ÿ” Analyzing journal labels...") - journal_labels = set(journal_df['emotion'].unique()) - journal_label_counts = journal_df['emotion'].value_counts().to_dict() - - logger.info(f"๐Ÿ“Š Journal unique labels: {len(journal_labels)}") - logger.info(f"๐Ÿ“Š Journal labels: {sorted(list(journal_labels))}") - logger.info(f"๐Ÿ“Š Journal label counts: {journal_label_counts}") - - # Step 4: Check for label mismatches - logger.info("๐Ÿ” Checking for label mismatches...") - - # Find labels that exist in one dataset but not the other - go_only = go_labels - journal_labels - journal_only = journal_labels - go_labels - common_labels = go_labels.intersection(journal_labels) - - logger.info(f"๐Ÿ“Š Labels only in GoEmotions: {sorted(list(go_only))}") - logger.info(f"๐Ÿ“Š Labels only in Journal: {sorted(list(journal_only))}") - logger.info(f"๐Ÿ“Š Common labels: {sorted(list(common_labels))}") - - if go_only: - logger.warning(f"โš ๏ธ {len(go_only)} labels only in GoEmotions - may cause issues") - if journal_only: - logger.warning(f"โš ๏ธ {len(journal_only)} labels only in Journal - may cause issues") - - # Step 5: Create unified label encoder - logger.info("๐Ÿงฌ Creating unified label encoder...") - - # Option 1: Use only common labels (safer) - if len(common_labels) > 0: - all_labels = sorted(list(common_labels)) - logger.info(f"๐Ÿ“Š Using only common labels: {len(all_labels)} labels") - else: - # Option 2: Use all labels (may cause issues) - all_labels = sorted(list(go_labels.union(journal_labels))) - logger.warning(f"โš ๏ธ No common labels found! Using all labels: {len(all_labels)}") - - label_encoder = LabelEncoder() - label_encoder.fit(all_labels) - num_labels = len(label_encoder.classes_) - - logger.info(f"๐Ÿ“Š Final num_labels: {num_labels}") - logger.info(f"๐Ÿ“Š Encoded classes: {label_encoder.classes_}") - - # Step 6: Test label encoding - logger.info("๐Ÿงช Testing label encoding...") - - # Test GoEmotions encoding - go_encoded = [] - go_encoding_errors = [] - - for i, example in enumerate(go_emotions['train'][:100]): # Test first 100 - if example['labels']: - try: - # Take first label for simplicity - label = example['labels'][0] - if label in label_encoder.classes_: - encoded = label_encoder.transform([label])[0] - go_encoded.append(encoded) - else: - go_encoding_errors.append(f"Label '{label}' not in encoder classes") - except Exception as e: - go_encoding_errors.append(f"Error encoding label '{label}': {e}") - - # Test journal encoding - journal_encoded = [] - journal_encoding_errors = [] - - for i, emotion in enumerate(journal_df['emotion'][:100]): # Test first 100 - try: - if emotion in label_encoder.classes_: - encoded = label_encoder.transform([emotion])[0] - journal_encoded.append(encoded) - else: - journal_encoding_errors.append(f"Label '{emotion}' not in encoder classes") - except Exception as e: - journal_encoding_errors.append(f"Error encoding label '{emotion}': {e}") - - # Report encoding results - if go_encoded: - logger.info(f"โœ… GoEmotions encoding successful: {len(go_encoded)} samples") - logger.info(f"๐Ÿ“Š GoEmotions label range: {min(go_encoded)} to {max(go_encoded)}") - if go_encoding_errors: - logger.error(f"โŒ GoEmotions encoding errors: {len(go_encoding_errors)}") - for error in go_encoding_errors[:5]: # Show first 5 errors - logger.error(f" - {error}") - - if journal_encoded: - logger.info(f"โœ… Journal encoding successful: {len(journal_encoded)} samples") - logger.info(f"๐Ÿ“Š Journal label range: {min(journal_encoded)} to {max(journal_encoded)}") - if journal_encoding_errors: - logger.error(f"โŒ Journal encoding errors: {len(journal_encoding_errors)}") - for error in journal_encoding_errors[:5]: # Show first 5 errors - logger.error(f" - {error}") - - # Step 7: Validate label ranges - logger.info("๐Ÿ” Validating label ranges...") - - expected_range = list(range(num_labels)) - go_range = list(range(min(go_encoded), max(go_encoded) + 1)) if go_encoded else [] - journal_range = list(range(min(journal_encoded), max(journal_encoded) + 1)) if journal_encoded else [] - - logger.info(f"๐Ÿ“Š Expected range: {expected_range}") - logger.info(f"๐Ÿ“Š GoEmotions range: {go_range}") - logger.info(f"๐Ÿ“Š Journal range: {journal_range}") - - # Check for out-of-bounds labels - go_out_of_bounds = [label for label in go_encoded if label < 0 or label >= num_labels] - journal_out_of_bounds = [label for label in journal_encoded if label < 0 or label >= num_labels] - - if go_out_of_bounds: - logger.error(f"โŒ GoEmotions has {len(go_out_of_bounds)} out-of-bounds labels") - if journal_out_of_bounds: - logger.error(f"โŒ Journal has {len(journal_out_of_bounds)} out-of-bounds labels") - - # Step 8: Provide recommendations - logger.info("๐Ÿ’ก Recommendations:") - - if go_encoding_errors or journal_encoding_errors: - logger.info("1. ๐Ÿ”ง Use only common labels between datasets") - logger.info("2. ๐Ÿ”ง Filter out samples with non-common labels") - logger.info("3. ๐Ÿ”ง Create a more robust label mapping") - else: - logger.info("1. โœ… Label encoding looks good!") - logger.info("2. โœ… Proceed with training using the unified label encoder") - - # Step 9: Create fixed label encoder - logger.info("๐Ÿ”ง Creating fixed label encoder...") - - # Save the working label encoder - import pickle - with open('fixed_label_encoder.pkl', 'wb') as f: - pickle.dump(label_encoder, f) - - # Create label mappings - label_to_id = {label: idx for idx, label in enumerate(label_encoder.classes_)} - id_to_label = {idx: label for label, idx in label_to_id.items()} - - # Save mappings - with open('label_mappings.json', 'w') as f: - json.dump({ - 'label_to_id': label_to_id, - 'id_to_label': id_to_label, - 'num_labels': num_labels, - 'classes': label_encoder.classes_.tolist() - }, f, indent=2) - - logger.info("โœ… Fixed label encoder saved:") - logger.info(" - fixed_label_encoder.pkl") - logger.info(" - label_mappings.json") - - return { - 'num_labels': num_labels, - 'label_encoder': label_encoder, - 'label_to_id': label_to_id, - 'id_to_label': id_to_label, - 'go_encoding_errors': len(go_encoding_errors), - 'journal_encoding_errors': len(journal_encoding_errors) - } - - except Exception as e: - logger.error(f"โŒ Debugging failed: {e}") - return None - -if __name__ == "__main__": - result = debug_label_mismatch() - if result: - print(f"\n๐ŸŽ‰ Debugging completed successfully!") - print(f"๐Ÿ“Š Use num_labels={result['num_labels']} in your model") - print(f"๐Ÿ“Š Label encoder saved as 'fixed_label_encoder.pkl'") - else: - print(f"\nโŒ Debugging failed!") \ No newline at end of file diff --git a/scripts/testing/debug_model_loading.py b/scripts/testing/debug_model_loading.py index b44fa92ee..fc9c5e5b4 100644 --- a/scripts/testing/debug_model_loading.py +++ b/scripts/testing/debug_model_loading.py @@ -4,10 +4,10 @@ Get detailed information about why the model is not loading properly. """ -import requests import json -import time -import argparse + +import requests + from test_config import create_api_client, create_test_config @@ -15,12 +15,12 @@ def debug_model_loading(): """Debug the model loading issues""" config = create_test_config() client = create_api_client() - + print("๐Ÿ” Debugging Model Loading Issues") print("=" * 50) print(f"Testing URL: {config.base_url}") print(f"API Key: {config.api_key[:20]}...") - + # Test model status with API key print("\n1. Testing model status with API key...") try: @@ -31,7 +31,7 @@ def debug_model_loading(): print(" ๐Ÿ” Unauthorized - API key mismatch") else: print(f" โŒ Model status error: {e}") - + # Test security status print("\n2. Testing security status...") try: @@ -50,7 +50,7 @@ def debug_model_loading(): print(f" โŒ Prediction error: {e}") except ValueError as e: print(f" โŒ Invalid response format: {e}") - + # Test batch prediction print("\n4. Testing batch prediction...") try: @@ -61,7 +61,7 @@ def debug_model_loading(): print(f" โŒ Batch prediction error: {e}") except ValueError as e: print(f" โŒ Invalid response format: {e}") - + # Test with different input formats print("\n5. Testing different input formats...") test_cases = [ diff --git a/scripts/testing/debug_rate_limiter.py b/scripts/testing/debug_rate_limiter.py index 13feead62..3ad4265df 100644 --- a/scripts/testing/debug_rate_limiter.py +++ b/scripts/testing/debug_rate_limiter.py @@ -7,11 +7,13 @@ # pylint: disable=protected-access -import sys import os -sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src')) +import sys -from src.api_rate_limiter import TokenBucketRateLimiter, RateLimitConfig # noqa: E402 +from src.api_rate_limiter import RateLimitConfig # noqa: E402 +from src.api_rate_limiter import TokenBucketRateLimiter + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "src")) def debug_rate_limiter(): @@ -49,10 +51,7 @@ def debug_rate_limiter(): print(f"Buckets after second request: {rate_limiter.buckets}") # Check what's in the bucket for this client - client_key = ( - meta1.get("client_key") - or rate_limiter._get_client_key(client_ip, user_agent) - ) + client_key = meta1.get("client_key") or rate_limiter._get_client_key(client_ip, user_agent) print(f"\n๐Ÿ”‘ Client key: {client_key}") print(f"Bucket value for client: {rate_limiter.buckets[client_key]}") print(f"Last refill time for client: {rate_limiter.last_refill[client_key]}") diff --git a/scripts/testing/debug_rate_limiter_test.py b/scripts/testing/debug_rate_limiter_test.py index 0519ecba6..e69de29bb 100644 --- a/scripts/testing/debug_rate_limiter_test.py +++ b/scripts/testing/debug_rate_limiter_test.py @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/scripts/testing/debug_state_dict.py b/scripts/testing/debug_state_dict.py index a786e7bcc..ce51e9e00 100644 --- a/scripts/testing/debug_state_dict.py +++ b/scripts/testing/debug_state_dict.py @@ -1,16 +1,17 @@ - # Load checkpoint -#!/usr/bin/env python3 -from pathlib import Path +# Load checkpoint import logging -import torch +#!/usr/bin/env python3 +from pathlib import Path +import torch """ Debug Model State Dict Structure """ + def debug_state_dict(): checkpoint_path = Path("test_checkpoints/best_model.pt") diff --git a/scripts/testing/direct_evaluation_test.py b/scripts/testing/direct_evaluation_test.py index 84ff5ef2d..9f4eb1190 100644 --- a/scripts/testing/direct_evaluation_test.py +++ b/scripts/testing/direct_evaluation_test.py @@ -1,29 +1,28 @@ - # Apply sigmoid to get probabilities - # Apply threshold - # Calculate F1 manually - # Check if any samples have zero predictions - # Check what type of output we get - # Convert to numpy for metrics calculation - # Count expected predictions - # Get model output - # Test threshold application - # Get one batch from validation data - # Initialize trainer - # Load model - # Move to device - # Run model inference - # Unpack batch data -# Add src to path -#!/usr/bin/env python3 -from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer -from pathlib import Path +# Apply sigmoid to get probabilities +# Apply threshold +# Calculate F1 manually +# Check if any samples have zero predictions +# Check what type of output we get +# Convert to numpy for metrics calculation +# Count expected predictions +# Get model output +# Test threshold application +# Get one batch from validation data +# Initialize trainer +# Load model +# Move to device +# Run model inference +# Unpack batch data import logging -import numpy as np import sys -import torch - +from pathlib import Path +import numpy as np +import torch +# Add src to path +#!/usr/bin/env python3 +from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer """ Direct test of evaluation logic to find and fix the bug. diff --git a/scripts/testing/final_temperature_test.py b/scripts/testing/final_temperature_test.py index e1ae8d786..f9b700ae1 100644 --- a/scripts/testing/final_temperature_test.py +++ b/scripts/testing/final_temperature_test.py @@ -7,17 +7,20 @@ import sys from pathlib import Path +import numpy as np import torch +from sklearn.metrics import f1_score from torch.utils.data import DataLoader from transformers import AutoTokenizer -import numpy as np + +from src.models.emotion_detection.bert_classifier import ( + EmotionDataset, + create_bert_emotion_classifier, +) # Add src to path sys.path.append(str(Path.cwd() / "src")) -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier, EmotionDataset -from sklearn.metrics import f1_score - def final_temperature_test(): """Run final temperature scaling test.""" @@ -77,10 +80,10 @@ def final_temperature_test(): # Create simple test data logging.info("๐Ÿ“ Creating test data...") - + # Create emotion labels (simplified for testing) emotion_labels = ["joy", "sadness", "anger", "fear"] - + # Create simple test data test_texts = [ "I am so happy today!", @@ -90,9 +93,9 @@ def final_temperature_test(): "I feel great about everything!", "This is disappointing.", "I'm furious with you!", - "I'm terrified of the dark." + "I'm terrified of the dark.", ] - + test_labels = [ [1, 0, 0, 0], # joy [0, 1, 0, 0], # sadness @@ -106,63 +109,67 @@ def final_temperature_test(): # Create tokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") - + # Create dataset dataset = EmotionDataset(test_texts, test_labels, tokenizer, max_length=128) dataloader = DataLoader(dataset, batch_size=4, shuffle=False) # Test different temperatures temperatures = [0.5, 1.0, 1.5, 2.0] - + logging.info("๐Ÿงช Testing temperature scaling...") - + for temp in temperatures: logging.info(f"\n๐ŸŒก๏ธ Temperature: {temp}") - + # Set temperature model.temperature = temp - + all_predictions = [] all_labels = [] - + with torch.no_grad(): for batch in dataloader: input_ids = batch["input_ids"].to(device) attention_mask = batch["attention_mask"].to(device) labels = batch["labels"].to(device) - + # Run evaluation outputs = model(input_ids, attention_mask) probabilities = torch.sigmoid(outputs / temp) - + # Apply threshold predictions = (probabilities > 0.5).float() - + # Convert to numpy for sklearn all_predictions.append(predictions.cpu().numpy()) all_labels.append(labels.cpu().numpy()) - + # Concatenate results all_predictions = np.concatenate(all_predictions, axis=0) all_labels = np.concatenate(all_labels, axis=0) - + # Calculate metrics - micro_f1 = f1_score(all_labels, all_predictions, average='micro', zero_division=0) - macro_f1 = f1_score(all_labels, all_predictions, average='macro', zero_division=0) - + micro_f1 = f1_score(all_labels, all_predictions, average="micro", zero_division=0) + macro_f1 = f1_score(all_labels, all_predictions, average="macro", zero_division=0) + logging.info(f" Micro F1: {micro_f1:.4f}") logging.info(f" Macro F1: {macro_f1:.4f}") - + # Show some predictions logging.info(" Sample predictions:") for i in range(min(3, len(test_texts))): - pred_emotions = [emotion_labels[j] for j, pred in enumerate(all_predictions[i]) if pred > 0.5] - true_emotions = [emotion_labels[j] for j, true in enumerate(all_labels[i]) if true > 0.5] + pred_emotions = [ + emotion_labels[j] for j, pred in enumerate(all_predictions[i]) if pred > 0.5 + ] + true_emotions = [ + emotion_labels[j] for j, true in enumerate(all_labels[i]) if true > 0.5 + ] logging.info(f" Text: {test_texts[i]}") logging.info(f" Predicted: {pred_emotions}") logging.info(f" True: {true_emotions}") logging.info(f" Raw probs: {probabilities[i].cpu().numpy()}") - + logging.info("โœ… Temperature scaling test completed!") diff --git a/scripts/testing/hf_serverless_smoke.py b/scripts/testing/hf_serverless_smoke.py index e776c8dba..9dbf325ea 100644 --- a/scripts/testing/hf_serverless_smoke.py +++ b/scripts/testing/hf_serverless_smoke.py @@ -1,8 +1,8 @@ #!/usr/bin/env python3 -import os import json -import time +import os import sys +import time from typing import List, Tuple import requests diff --git a/scripts/testing/local_validation_debug.py b/scripts/testing/local_validation_debug.py index 65ac63e13..b410b0ec2 100644 --- a/scripts/testing/local_validation_debug.py +++ b/scripts/testing/local_validation_debug.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Check per-class distribution # Count positive labels # Analyze first few examples @@ -22,30 +23,20 @@ from src.models.emotion_detection.bert_classifier import WeightedBCELoss from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier from src.models.emotion_detection.dataset_loader import create_goemotions_loader - from src.models.emotion_detection.dataset_loader import create_goemotions_loader import pandas as pd import torch - import torch - import torch import torch.nn.functional as F import transformers # Run all validations # Run validations # Summary +import logging +import sys # Add src to path # Configure logging -#!/usr/bin/env python3 from pathlib import Path -import logging -import numpy as np -import sys - - - - - - +import numpy as np """ Local Validation and Debug Script for SAMO Deep Learning. diff --git a/scripts/testing/mega_comprehensive_model_test.py b/scripts/testing/mega_comprehensive_model_test.py index 7ae040331..a96ac3285 100644 --- a/scripts/testing/mega_comprehensive_model_test.py +++ b/scripts/testing/mega_comprehensive_model_test.py @@ -7,26 +7,28 @@ covering every aspect of performance, robustness, bias, and real-world scenarios. """ -import os -import torch -import numpy as np import json +import os import random -from transformers import AutoTokenizer, AutoModelForSequenceClassification -from datetime import datetime from collections import Counter, defaultdict +from datetime import datetime + +import numpy as np +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + # import matplotlib.pyplot as plt # Not needed for this test # import seaborn as sns # Not needed for this test class MegaComprehensiveModelTester: """Mega comprehensive model testing framework.""" - + def __init__(self, model_path="deployment/models/default"): self.model_path = model_path self.tokenizer = None self.model = None self.emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] - + # Test results storage self.test_results = { 'basic_tests': {}, @@ -39,44 +41,44 @@ def __init__(self, model_path="deployment/models/default"): 'confidence_analysis': {}, 'error_analysis': {} } - + def load_model(self): """Load the model and tokenizer.""" print("๐Ÿ”ง LOADING MODEL FOR MEGA TESTING") print("=" * 60) - + try: self.tokenizer = AutoTokenizer.from_pretrained(self.model_path) self.model = AutoModelForSequenceClassification.from_pretrained(self.model_path) - + if torch.cuda.is_available(): self.model = self.model.to('cuda') print("โœ… Model moved to GPU") else: print("โš ๏ธ CUDA not available, using CPU") - + print("โœ… Model loaded successfully for mega testing") return True - + except Exception as e: print(f"โŒ Failed to load model: {e}") return False - + def predict_emotion(self, text): """Make a prediction with confidence.""" inputs = self.tokenizer(text, return_tensors='pt', truncation=True, padding=True) if torch.cuda.is_available(): inputs = {k: v.to('cuda') for k, v in inputs.items()} - + with torch.no_grad(): outputs = self.model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_label = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_label].item() - + # Get all probabilities for analysis all_probs = probabilities[0].cpu().numpy() - + # Get predicted emotion name if predicted_label in self.model.config.id2label: predicted_emotion = self.model.config.id2label[predicted_label] @@ -84,14 +86,14 @@ def predict_emotion(self, text): predicted_emotion = self.model.config.id2label[str(predicted_label)] else: predicted_emotion = f"unknown_{predicted_label}" - + return predicted_emotion, confidence, all_probs - + def test_basic_functionality(self): """Test basic model functionality.""" print("\n๐Ÿงช BASIC FUNCTIONALITY TESTS") print("=" * 60) - + basic_test_cases = [ # Direct emotion statements ("I am happy", "happy"), @@ -106,7 +108,7 @@ def test_basic_functionality(self): ("I feel overwhelmed", "overwhelmed"), ("I am proud", "proud"), ("I feel tired", "tired"), - + # With context ("I am happy today", "happy"), ("I feel sad about the news", "sad"), @@ -121,101 +123,101 @@ def test_basic_functionality(self): ("I am proud of my work", "proud"), ("I feel tired after exercise", "tired") ] - + correct = 0 confidences = [] - + for i, (text, expected) in enumerate(basic_test_cases, 1): predicted, confidence, _ = self.predict_emotion(text) is_correct = predicted == expected if is_correct: correct += 1 confidences.append(confidence) - + status = "โœ…" if is_correct else "โŒ" print(f"{status} {i:2d}. \"{text}\" โ†’ {predicted} (expected: {expected}) [conf: {confidence:.3f}]") - + accuracy = correct / len(basic_test_cases) * 100 avg_confidence = np.mean(confidences) - + self.test_results['basic_tests'] = { 'accuracy': accuracy, 'avg_confidence': avg_confidence, 'total_tests': len(basic_test_cases), 'correct': correct } - + print(f"\n๐Ÿ“Š Basic Test Results: {accuracy:.2f}% accuracy, {avg_confidence:.3f} avg confidence") - + def test_edge_cases(self): """Test edge cases and unusual inputs.""" print("\n๐Ÿ” EDGE CASES AND UNUSUAL INPUTS") print("=" * 60) - + edge_cases = [ # Very short inputs ("Happy", "happy"), ("Sad", "sad"), ("Excited!", "excited"), ("Anxious?", "anxious"), - + # Very long inputs ("I am feeling incredibly happy and joyful and ecstatic and delighted and pleased and satisfied and content and cheerful and glad and thrilled and overjoyed and elated and jubilant and euphoric and blissful and radiant and beaming and glowing and sparkling and wonderful", "happy"), - + # Mixed emotions ("I am happy but also a bit sad", "happy"), # Should pick dominant emotion ("I feel excited yet anxious", "excited"), ("I am grateful but tired", "grateful"), - + # Ambiguous cases ("I feel okay", "content"), # Neutral should map to content ("I am fine", "content"), ("Not bad", "content"), - + # Intensifiers ("I am EXTREMELY happy", "happy"), ("I feel SO sad", "sad"), ("I am REALLY excited", "excited"), ("I feel VERY anxious", "anxious"), - + # Negations ("I am not happy", "sad"), # Should detect negative emotion ("I don't feel excited", "content"), ("I am not calm", "anxious"), - + # Questions ("Am I happy?", "happy"), ("Why am I sad?", "sad"), ("Should I be excited?", "excited"), - + # Emojis and symbols ("I am happy ๐Ÿ˜Š", "happy"), ("I feel sad :(", "sad"), ("I am excited!!!", "excited"), ("I feel anxious...", "anxious"), - + # Capitalization variations ("I AM HAPPY", "happy"), ("i am sad", "sad"), ("I Am Excited", "excited"), ("i FEEL anxious", "anxious"), - + # Repetition ("Happy happy happy", "happy"), ("Sad sad sad sad", "sad"), ("Excited excited", "excited"), - + # Numbers and special characters ("I am happy 123", "happy"), ("I feel sad @#$%", "sad"), ("I am excited (really!)", "excited"), - + # Empty or minimal ("", "content"), # Should default to something (" ", "content"), ("...", "content") ] - + results = [] for text, expected in edge_cases: predicted, confidence, _ = self.predict_emotion(text) @@ -227,11 +229,11 @@ def test_edge_cases(self): 'confidence': confidence, 'correct': is_correct }) - + correct = sum(1 for r in results if r['correct']) accuracy = correct / len(results) * 100 avg_confidence = np.mean([r['confidence'] for r in results]) - + self.test_results['edge_cases'] = { 'accuracy': accuracy, 'avg_confidence': avg_confidence, @@ -239,28 +241,28 @@ def test_edge_cases(self): 'correct': correct, 'details': results } - + print(f"๐Ÿ“Š Edge Case Results: {accuracy:.2f}% accuracy, {avg_confidence:.3f} avg confidence") print(f" Correct: {correct}/{len(results)}") - + def test_stress_conditions(self): """Test model under stress conditions.""" print("\n๐Ÿ’ช STRESS TESTS") print("=" * 60) - + # Generate random noise text random_texts = [] for _ in range(20): words = ['the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by'] random_text = ' '.join(random.choices(words, k=random.randint(5, 15))) random_texts.append(random_text) - + # Generate very long texts long_texts = [] for _ in range(10): long_text = "I am feeling " + "very " * random.randint(10, 30) + "happy today because " + "of many reasons " * random.randint(5, 15) long_texts.append(long_text) - + # Generate texts with special characters special_char_texts = [ "I am happy @#$%^&*()", @@ -272,9 +274,9 @@ def test_stress_conditions(self): "I am proud ๐ŸŽ‰๐ŸŽŠ๐ŸŽˆ๐ŸŽ‚๐ŸŽ", "I feel tired ๐Ÿ’ค๐Ÿ˜ด๐Ÿ›๏ธ" ] - + all_stress_tests = random_texts + long_texts + special_char_texts - + results = [] for text in all_stress_tests: try: @@ -293,10 +295,10 @@ def test_stress_conditions(self): 'success': False, 'error': str(e) }) - + successful = sum(1 for r in results if r['success']) avg_confidence = np.mean([r['confidence'] for r in results if r['success']]) - + self.test_results['stress_tests'] = { 'success_rate': successful / len(results) * 100, 'avg_confidence': avg_confidence, @@ -304,15 +306,15 @@ def test_stress_conditions(self): 'successful': successful, 'details': results } - + print(f"๐Ÿ“Š Stress Test Results: {successful/len(results)*100:.2f}% success rate, {avg_confidence:.3f} avg confidence") print(f" Successful: {successful}/{len(results)}") - + def test_bias_analysis(self): """Analyze model for bias across different inputs.""" print("\nโš–๏ธ BIAS ANALYSIS") print("=" * 60) - + # Test with different sentence structures structures = [ "I am {emotion}", @@ -326,9 +328,9 @@ def test_bias_analysis(self): "I am so {emotion}", "I am really {emotion}" ] - + bias_results = defaultdict(list) - + for structure in structures: for emotion in self.emotions: text = structure.format(emotion=emotion) @@ -340,32 +342,32 @@ def test_bias_analysis(self): 'confidence': confidence, 'correct': predicted == emotion }) - + # Analyze bias emotion_accuracies = {} emotion_confidences = {} emotion_predictions = defaultdict(Counter) - + for emotion, results in bias_results.items(): correct = sum(1 for r in results if r['correct']) accuracy = correct / len(results) * 100 avg_confidence = np.mean([r['confidence'] for r in results]) - + emotion_accuracies[emotion] = accuracy emotion_confidences[emotion] = avg_confidence - + # Count what this emotion was predicted as for r in results: emotion_predictions[emotion][r['predicted']] += 1 - + # Find most/least accurate emotions most_accurate = max(emotion_accuracies.items(), key=lambda x: x[1]) least_accurate = min(emotion_accuracies.items(), key=lambda x: x[1]) - + # Find most/least confident emotions most_confident = max(emotion_confidences.items(), key=lambda x: x[1]) least_confident = min(emotion_confidences.items(), key=lambda x: x[1]) - + self.test_results['bias_analysis'] = { 'emotion_accuracies': emotion_accuracies, 'emotion_confidences': emotion_confidences, @@ -377,7 +379,7 @@ def test_bias_analysis(self): 'overall_accuracy': np.mean(list(emotion_accuracies.values())), 'overall_confidence': np.mean(list(emotion_confidences.values())) } - + print(f"๐Ÿ“Š Bias Analysis Results:") print(f" Overall accuracy: {np.mean(list(emotion_accuracies.values())):.2f}%") print(f" Overall confidence: {np.mean(list(emotion_confidences.values())):.3f}") @@ -385,12 +387,12 @@ def test_bias_analysis(self): print(f" Least accurate: {least_accurate[0]} ({least_accurate[1]:.2f}%)") print(f" Most confident: {most_confident[0]} ({most_confident[1]:.3f})") print(f" Least confident: {least_confident[0]} ({least_confident[1]:.3f})") - + def test_robustness(self): """Test model robustness to variations.""" print("\n๐Ÿ›ก๏ธ ROBUSTNESS TESTS") print("=" * 60) - + base_texts = [ "I am happy today", "I feel sad about the news", @@ -405,10 +407,10 @@ def test_robustness(self): "I am proud of my work", "I feel tired after exercise" ] - + # Test with different tokenization lengths robustness_results = [] - + for base_text in base_texts: # Test with truncation for max_length in [10, 20, 50, 100, 200]: @@ -416,18 +418,18 @@ def test_robustness(self): inputs = self.tokenizer(base_text, return_tensors='pt', truncation=True, max_length=max_length, padding=True) if torch.cuda.is_available(): inputs = {k: v.to('cuda') for k, v in inputs.items()} - + with torch.no_grad(): outputs = self.model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_label = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_label].item() - + if predicted_label in self.model.config.id2label: predicted_emotion = self.model.config.id2label[predicted_label] else: predicted_emotion = f"unknown_{predicted_label}" - + robustness_results.append({ 'base_text': base_text, 'max_length': max_length, @@ -444,10 +446,10 @@ def test_robustness(self): 'success': False, 'error': str(e) }) - + successful = sum(1 for r in robustness_results if r['success']) avg_confidence = np.mean([r['confidence'] for r in robustness_results if r['success']]) - + self.test_results['robustness_tests'] = { 'success_rate': successful / len(robustness_results) * 100, 'avg_confidence': avg_confidence, @@ -455,15 +457,15 @@ def test_robustness(self): 'successful': successful, 'details': robustness_results } - + print(f"๐Ÿ“Š Robustness Test Results: {successful/len(robustness_results)*100:.2f}% success rate, {avg_confidence:.3f} avg confidence") print(f" Successful: {successful}/{len(robustness_results)}") - + def test_real_world_scenarios(self): """Test with real-world scenarios.""" print("\n๐ŸŒ REAL-WORLD SCENARIOS") print("=" * 60) - + real_world_cases = [ # Social media posts ("Just got promoted! Can't believe it!", "excited"), @@ -478,7 +480,7 @@ def test_real_world_scenarios(self): ("Frustrated with the slow internet", "frustrated"), ("Hopeful about the new project", "hopeful"), ("Happy to see old friends", "happy"), - + # Journal entries ("Today I reflected on my journey and felt proud of how far I've come", "proud"), ("The uncertainty of the future is making me anxious", "anxious"), @@ -492,7 +494,7 @@ def test_real_world_scenarios(self): ("Feeling sad about the loss of a loved one", "sad"), ("I'm calm and at peace with myself", "calm"), ("I'm happy with the progress I've made", "happy"), - + # Customer service scenarios ("I'm frustrated with the poor service I received", "frustrated"), ("I'm grateful for the quick resolution", "grateful"), @@ -506,7 +508,7 @@ def test_real_world_scenarios(self): ("I'm sad that I had to go through this", "sad"), ("I'm calm now that everything is sorted", "calm"), ("I'm happy with the outcome", "happy"), - + # Work scenarios ("I'm excited about the new project assignment", "excited"), ("I'm anxious about the upcoming deadline", "anxious"), @@ -521,11 +523,11 @@ def test_real_world_scenarios(self): ("I'm calm during the presentation", "calm"), ("I'm happy with the recognition", "happy") ] - + correct = 0 confidences = [] predictions_by_emotion = defaultdict(list) - + for text, expected in real_world_cases: predicted, confidence, _ = self.predict_emotion(text) is_correct = predicted == expected @@ -538,10 +540,10 @@ def test_real_world_scenarios(self): 'confidence': confidence, 'correct': is_correct }) - + accuracy = correct / len(real_world_cases) * 100 avg_confidence = np.mean(confidences) - + # Analyze performance by emotion in real-world scenarios emotion_performance = {} for emotion, cases in predictions_by_emotion.items(): @@ -554,7 +556,7 @@ def test_real_world_scenarios(self): 'total_cases': len(cases), 'correct': emotion_correct } - + self.test_results['real_world_scenarios'] = { 'accuracy': accuracy, 'avg_confidence': avg_confidence, @@ -562,34 +564,34 @@ def test_real_world_scenarios(self): 'correct': correct, 'emotion_performance': emotion_performance } - + print(f"๐Ÿ“Š Real-World Results: {accuracy:.2f}% accuracy, {avg_confidence:.3f} avg confidence") print(f" Correct: {correct}/{len(real_world_cases)}") - + # Show worst performing emotions worst_emotions = sorted(emotion_performance.items(), key=lambda x: x[1]['accuracy'])[:3] print(f" Worst performing emotions: {', '.join([f'{e[0]} ({e[1]['accuracy']:.1f}%)' for e in worst_emotions])}") - + def analyze_confidence_distribution(self): """Analyze confidence distribution across all tests.""" print("\n๐Ÿ“Š CONFIDENCE ANALYSIS") print("=" * 60) - + # Collect all confidence scores from previous tests all_confidences = [] - + # From basic tests if 'basic_tests' in self.test_results: all_confidences.extend([0.8, 0.9, 0.95]) # Representative values - + # From edge cases if 'edge_cases' in self.test_results: all_confidences.extend([r['confidence'] for r in self.test_results['edge_cases']['details']]) - + # From real-world scenarios if 'real_world_scenarios' in self.test_results: all_confidences.extend([0.85, 0.92, 0.88]) # Representative values - + if all_confidences: confidence_stats = { 'mean': np.mean(all_confidences), @@ -602,9 +604,9 @@ def analyze_confidence_distribution(self): 'low_confidence': sum(1 for c in all_confidences if c < 0.5), 'total': len(all_confidences) } - + self.test_results['confidence_analysis'] = confidence_stats - + print(f"๐Ÿ“Š Confidence Distribution:") print(f" Mean: {confidence_stats['mean']:.3f}") print(f" Median: {confidence_stats['median']:.3f}") @@ -613,27 +615,27 @@ def analyze_confidence_distribution(self): print(f" High confidence (โ‰ฅ0.8): {confidence_stats['high_confidence']}/{confidence_stats['total']} ({confidence_stats['high_confidence']/confidence_stats['total']*100:.1f}%)") print(f" Medium confidence (0.5-0.8): {confidence_stats['medium_confidence']}/{confidence_stats['total']} ({confidence_stats['medium_confidence']/confidence_stats['total']*100:.1f}%)") print(f" Low confidence (<0.5): {confidence_stats['low_confidence']}/{confidence_stats['total']} ({confidence_stats['low_confidence']/confidence_stats['total']*100:.1f}%)") - + def generate_comprehensive_report(self): """Generate a comprehensive test report.""" print("\n๐Ÿ“‹ MEGA COMPREHENSIVE TEST REPORT") print("=" * 80) - + # Calculate overall metrics total_tests = 0 total_correct = 0 all_confidences = [] - + for test_type, results in self.test_results.items(): if 'accuracy' in results: total_tests += results.get('total_tests', 0) total_correct += results.get('correct', 0) if 'avg_confidence' in results: all_confidences.append(results['avg_confidence']) - + overall_accuracy = total_correct / total_tests * 100 if total_tests > 0 else 0 overall_confidence = np.mean(all_confidences) if all_confidences else 0 - + # Generate report report = { 'timestamp': datetime.now().isoformat(), @@ -651,14 +653,14 @@ def generate_comprehensive_report(self): 'deployment_ready': overall_accuracy >= 80 and overall_confidence >= 0.6 } } - + # Save report report_path = f"test_reports/mega_comprehensive_test_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" os.makedirs("test_reports", exist_ok=True) - + with open(report_path, 'w') as f: json.dump(report, f, indent=2) - + # Print summary print(f"๐ŸŽฏ OVERALL PERFORMANCE SUMMARY") print(f" Total Tests: {total_tests}") @@ -667,11 +669,11 @@ def generate_comprehensive_report(self): print(f" Model Status: {report['summary']['model_status']}") print(f" Confidence Status: {report['summary']['confidence_status']}") print(f" Deployment Ready: {'โœ… YES' if report['summary']['deployment_ready'] else 'โŒ NO'}") - + print(f"\n๐Ÿ“ Detailed report saved to: {report_path}") - + return report - + def run_all_tests(self): """Run all comprehensive tests.""" print("๐Ÿš€ STARTING MEGA COMPREHENSIVE MODEL TESTING") @@ -680,11 +682,11 @@ def run_all_tests(self): print(f"๐ŸŽฏ Emotions: {', '.join(self.emotions)}") print(f"โฐ Started at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") print() - + # Load model if not self.load_model(): return False - + # Run all test suites self.test_basic_functionality() self.test_edge_cases() @@ -693,20 +695,20 @@ def run_all_tests(self): self.test_robustness() self.test_real_world_scenarios() self.analyze_confidence_distribution() - + # Generate comprehensive report report = self.generate_comprehensive_report() - + print(f"\n๐ŸŽ‰ MEGA COMPREHENSIVE TESTING COMPLETE!") print("=" * 80) - + return report def main(): """Main function to run mega comprehensive testing.""" tester = MegaComprehensiveModelTester() report = tester.run_all_tests() - + if report: print(f"\nโœ… Testing completed successfully!") print(f"๐Ÿ“Š Final Results:") @@ -718,4 +720,4 @@ def main(): print(f"\nโŒ Testing failed!") if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/testing/mega_test_summary.py b/scripts/testing/mega_test_summary.py index 2954387f4..a8c9a62ab 100644 --- a/scripts/testing/mega_test_summary.py +++ b/scripts/testing/mega_test_summary.py @@ -6,25 +6,28 @@ This script displays the results from the mega comprehensive testing that was completed. """ + def display_mega_test_results(): """Display the mega comprehensive test results.""" - + print("๐ŸŽ‰ MEGA COMPREHENSIVE TEST RESULTS SUMMARY") print("=" * 80) print("๐Ÿ“ Model Tested: deployment/models/default") - print("๐ŸŽฏ Emotions: anxious, calm, content, excited, frustrated, grateful, happy, hopeful, overwhelmed, proud, sad, tired") + print( + "๐ŸŽฏ Emotions: anxious, calm, content, excited, frustrated, grateful, happy, hopeful, overwhelmed, proud, sad, tired" + ) print() - + print("๐Ÿ“Š TEST SUITE RESULTS") print("=" * 50) - + # Basic Functionality Tests print("๐Ÿงช BASIC FUNCTIONALITY TESTS") print(" โœ… Accuracy: 100.00% (24/24)") print(" โœ… Average Confidence: 0.965 (96.5%)") print(" โœ… All basic emotion expressions correctly identified") print() - + # Edge Cases Tests print("๐Ÿ” EDGE CASES AND UNUSUAL INPUTS") print(" โœ… Accuracy: 81.58% (31/38)") @@ -32,7 +35,7 @@ def display_mega_test_results(): print(" โœ… Handles short inputs, long inputs, mixed emotions, negations, questions") print(" โœ… Handles emojis, symbols, capitalization variations, special characters") print() - + # Stress Tests print("๐Ÿ’ช STRESS TESTS") print(" โœ… Success Rate: 100.00% (38/38)") @@ -40,7 +43,7 @@ def display_mega_test_results(): print(" โœ… Handles random noise text, very long texts, special characters") print(" โœ… No crashes or errors under stress conditions") print() - + # Bias Analysis print("โš–๏ธ BIAS ANALYSIS") print(" โœ… Overall Accuracy: 100.00%") @@ -50,7 +53,7 @@ def display_mega_test_results(): print(" โœ… Least Confident: content (0.951)") print(" โœ… No significant bias detected") print() - + # Robustness Tests print("๐Ÿ›ก๏ธ ROBUSTNESS TESTS") print(" โœ… Success Rate: 100.00% (60/60)") @@ -58,7 +61,7 @@ def display_mega_test_results(): print(" โœ… Handles different tokenization lengths (10-200 tokens)") print(" โœ… Consistent performance across input variations") print() - + # Real-World Scenarios print("๐ŸŒ REAL-WORLD SCENARIOS") print(" โœ… Accuracy: 93.75% (45/48)") @@ -66,7 +69,7 @@ def display_mega_test_results(): print(" โœ… Tested: Social media posts, journal entries, customer service, work scenarios") print(" โš ๏ธ Minor issues with: excited, grateful, hopeful (75% accuracy each)") print() - + # Confidence Analysis print("๐Ÿ“Š CONFIDENCE ANALYSIS") print(" โœ… Mean Confidence: 0.839 (83.9%)") @@ -76,10 +79,10 @@ def display_mega_test_results(): print(" โœ… Low Confidence (<0.5): 11.4% of predictions") print(" โœ… Confidence Range: 0.134 - 0.971") print() - + print("๐ŸŽฏ OVERALL PERFORMANCE ASSESSMENT") print("=" * 50) - + print("๐Ÿ† EXCELLENT PERFORMANCE ACROSS ALL METRICS:") print() print("โœ… BASIC FUNCTIONALITY: PERFECT (100% accuracy)") @@ -111,10 +114,10 @@ def display_mega_test_results(): print(" - Handles social media, journal entries, work scenarios") print(" - Minor issues with 3 emotions (excited, grateful, hopeful)") print() - + print("๐Ÿš€ DEPLOYMENT READINESS ASSESSMENT") print("=" * 50) - + print("โœ… DEPLOYMENT STATUS: FULLY READY") print() print("๐ŸŽฏ STRENGTHS:") @@ -144,5 +147,6 @@ def display_mega_test_results(): print(" Your comprehensive model has passed the most rigorous testing possible!") print(" It's ready for production deployment with confidence.") + if __name__ == "__main__": - display_mega_test_results() \ No newline at end of file + display_mega_test_results() diff --git a/scripts/testing/minimal_eval_test.py b/scripts/testing/minimal_eval_test.py index e7c117d07..d627392dd 100644 --- a/scripts/testing/minimal_eval_test.py +++ b/scripts/testing/minimal_eval_test.py @@ -1,13 +1,7 @@ - # Apply threshold (this is the exact line from our evaluation function) - # Check fallback logic - # Count how many should be above threshold - # Create probabilities similar to what we observed - # Create synthetic data matching what we observed - # min: 0.1150, max: 0.9119, mean: 0.4681 #!/usr/bin/env python3 import logging -import torch +import torch """ Minimal test of evaluation logic to isolate the bug. @@ -33,8 +27,8 @@ def test_evaluation_logic(): " Probabilities min/max/mean: {probabilities.min():.4f}/{probabilities.max():.4f}/{probabilities.mean():.4f}" ) - (probabilities >= threshold).sum().item() - batch_size * num_emotions + above_threshold_count = (probabilities >= threshold).sum().item() + total_predictions = batch_size * num_emotions print( " Expected above threshold: {expected_above_threshold}/{total_positions} ({100*expected_above_threshold/total_positions:.1f}%)" diff --git a/scripts/testing/minimal_test.py b/scripts/testing/minimal_test.py index 6a7ae522e..36980b087 100644 --- a/scripts/testing/minimal_test.py +++ b/scripts/testing/minimal_test.py @@ -11,10 +11,11 @@ import torch +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier + # Add src to path sys.path.append(str(Path.cwd() / "src")) -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier # Configure logging logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") diff --git a/scripts/testing/quick_f1_test.py b/scripts/testing/quick_f1_test.py index fa2326c0d..9a738eb29 100644 --- a/scripts/testing/quick_f1_test.py +++ b/scripts/testing/quick_f1_test.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Configuration 1: Standard training with full dataset # Evaluate model # Initialize model with class weights @@ -6,19 +7,16 @@ # Save the model # Train model import traceback +import logging +import sys # Add src to path # Configure logging -#!/usr/bin/env python3 from pathlib import Path -from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer -import logging -import sys -import torch -import traceback - - +import torch +from src.models.emotion_detection.training_pipeline import \ + EmotionDetectionTrainer """ Quick F1 Score Test and Improvement diff --git a/scripts/testing/quick_focal_test.py b/scripts/testing/quick_focal_test.py index 71be2e785..963887ed2 100644 --- a/scripts/testing/quick_focal_test.py +++ b/scripts/testing/quick_focal_test.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Simple focal loss implementation # Add src to path # Add src to path @@ -12,16 +13,10 @@ import torch import torch.nn.functional as F # Summary -# Configure logging -#!/usr/bin/env python3 -from pathlib import Path import logging import sys - - - - - +# Configure logging +from pathlib import Path """ Quick Focal Loss Test diff --git a/scripts/testing/quick_temperature_test.py b/scripts/testing/quick_temperature_test.py index 728edecb8..0886f3551 100644 --- a/scripts/testing/quick_temperature_test.py +++ b/scripts/testing/quick_temperature_test.py @@ -1,19 +1,15 @@ - # Quick evaluation - # Update temperature - # Initialize trainer with dev_mode - # Load model - # Test temperatures -#!/usr/bin/env python3 -from src.models.emotion_detection.bert_classifier import evaluate_emotion_classifier -from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer -from pathlib import Path +# Quick evaluation +# Update temperature +# Initialize trainer with dev_mode +# Load model +# Test temperatures import logging import sys +from pathlib import Path - - - - +#!/usr/bin/env python3 +from src.models.emotion_detection.bert_classifier import evaluate_emotion_classifier +from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer """ Quick Temperature Scaling Test. @@ -21,6 +17,7 @@ sys.path.append(str(Path.cwd() / "src")) + def quick_temperature_test(): logging.info("๐ŸŒก๏ธ Quick Temperature Scaling Test") diff --git a/scripts/testing/run_api_rate_limiter_tests.py b/scripts/testing/run_api_rate_limiter_tests.py index 6a3600753..34a7aed40 100644 --- a/scripts/testing/run_api_rate_limiter_tests.py +++ b/scripts/testing/run_api_rate_limiter_tests.py @@ -11,15 +11,13 @@ import contextlib import os -import pytest import sys import tempfile +import pytest + # DRY bootstrap -from scripts.testing._bootstrap import ( - ensure_project_root_on_sys_path, - configure_basic_logging, -) +from scripts.testing._bootstrap import configure_basic_logging, ensure_project_root_on_sys_path # Configure logging and path project_root = ensure_project_root_on_sys_path() @@ -35,10 +33,12 @@ sys.exit(1) # Create a temporary pytest configuration to avoid conflicts with pyproject.toml - with tempfile.NamedTemporaryFile(mode='w', suffix='.ini', delete=False) as f: - f.write("""[pytest] + with tempfile.NamedTemporaryFile(mode="w", suffix=".ini", delete=False) as f: + f.write( + """[pytest] addopts = --cov=src.api_rate_limiter --cov-report=term-missing --cov-fail-under=50 -v --tb=short -""") +""" + ) temp_config = f.name try: diff --git a/scripts/testing/setup_model_testing.py b/scripts/testing/setup_model_testing.py index eeed16839..774fee908 100644 --- a/scripts/testing/setup_model_testing.py +++ b/scripts/testing/setup_model_testing.py @@ -3,22 +3,20 @@ Setup script for testing the emotion detection model. """ -import os import json +import os import shutil + def check_model_files(): """Check if required model files exist.""" print("๐Ÿ” Checking for model files...") - - required_files = { - 'model': 'best_simple_model.pth', - 'results': 'simple_training_results.json' - } - + + required_files = {"model": "best_simple_model.pth", "results": "simple_training_results.json"} + missing_files = [] existing_files = {} - + for file_type, filename in required_files.items(): if os.path.exists(filename): size = os.path.getsize(filename) @@ -27,13 +25,14 @@ def check_model_files(): else: missing_files.append(file_type) print(f"โŒ {file_type.capitalize()}: {filename} - MISSING") - + return existing_files, missing_files + def create_mock_results(): """Create mock results file for testing if missing.""" print("\n๐Ÿ”ง Creating mock results file for testing...") - + # Mock results based on our training mock_results = { "best_f1": 0.6692, @@ -42,8 +41,18 @@ def create_mock_results(): "go_samples": 43410, "journal_samples": 150, "all_emotions": [ - "anxious", "calm", "content", "excited", "frustrated", - "grateful", "happy", "hopeful", "overwhelmed", "proud", "sad", "tired" + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", ], "emotion_mapping": { "joy": "happy", @@ -72,85 +81,144 @@ def create_mock_results(): "realization": "content", "relief": "calm", "remorse": "sad", - "neutral": "calm" - } + "neutral": "calm", + }, } - - with open('simple_training_results.json', 'w') as f: + + with open("simple_training_results.json", "w") as f: json.dump(mock_results, f, indent=2) - + print("โœ… Created mock results file: simple_training_results.json") -def find_model_file(): + +def find_model_file(min_size_bytes: int = 0): """Find the model file in common locations.""" print("\n๐Ÿ” Searching for model file...") - + search_locations = [ "best_simple_model.pth", "best_focal_model.pth", # Fallback os.path.expanduser("~/Downloads/best_simple_model.pth"), os.path.expanduser("~/Desktop/best_simple_model.pth"), - os.path.expanduser("~/best_simple_model.pth") + os.path.expanduser("~/best_simple_model.pth"), ] - + for location in search_locations: if os.path.exists(location): size = os.path.getsize(location) + if size < min_size_bytes: + print( + f"โš ๏ธ Skipping {location} - too small ({size:,} bytes < {min_size_bytes:,} bytes)" + ) + continue + print(f"โœ… Found model: {location} ({size:,} bytes)") - + # Copy to current directory if not already here if location != "best_simple_model.pth": shutil.copy2(location, "best_simple_model.pth") - print(f"โœ… Copied to: best_simple_model.pth") - + print("โœ… Copied to: best_simple_model.pth") + return True - + print("โŒ Model file not found in common locations") return False + def setup_testing(): """Main setup function.""" print("๐Ÿš€ SETTING UP MODEL TESTING") print("=" * 50) - + # Check existing files existing_files, missing_files = check_model_files() - - # Find model file if missing - if 'model' in missing_files: - if not find_model_file(): - print("\nโŒ Cannot proceed without model file!") - print("๐Ÿ“‹ Please download best_simple_model.pth from Colab and place it in this directory") - return False - + + # Define minimum size for model files (10KB) + min_size_bytes = 10 * 1024 + + # Check if model file is missing or too small + model_missing = "model" in missing_files + model_exists = os.path.exists("best_simple_model.pth") + + if model_exists: + model_size = os.path.getsize("best_simple_model.pth") + model_too_small = model_size < min_size_bytes + else: + model_too_small = False + + needs_model = model_missing or model_too_small + + if needs_model and not find_model_file(min_size_bytes=min_size_bytes): + print("\nโŒ Cannot proceed without model file!") + print("๐Ÿ“‹ Please download best_simple_model.pth and place it in this directory") + return False + # Create mock results if missing - if 'results' in missing_files: + if "results" in missing_files: create_mock_results() - + print("\nโœ… Setup complete! Ready for testing.") return True + +def test_model_loading(): + """Test loading the model file to verify it's valid.""" + if not os.path.exists("best_simple_model.pth"): + return False + + print("โœ… Model file exists") + + # Try to load safely with weights_only when supported + import torch + + try: + # Try with weights_only=True for security (PyTorch 1.13+) + checkpoint = torch.load("best_simple_model.pth", weights_only=True, map_location="cpu") + print("โœ… Model loaded with weights_only=True (secure)") + except TypeError: + # Fall back to map_location-only if weights_only not supported + checkpoint = torch.load("best_simple_model.pth", map_location="cpu") + print("โœ… Model loaded with map_location only (fallback)") + + # Safely inspect the loaded object + try: + if isinstance(checkpoint, dict): + # Handle dictionary checkpoint + if "state_dict" in checkpoint: + state_dict = checkpoint["state_dict"] + layer_count = len(state_dict.keys()) + print(f"โœ… Model checkpoint loaded with {layer_count} layers (from state_dict)") + else: + layer_count = len(checkpoint.keys()) + print(f"โœ… Model checkpoint loaded with {layer_count} layers (from dict keys)") + elif hasattr(checkpoint, "state_dict"): + # Handle torch.nn.Module + state_dict = checkpoint.state_dict() + layer_count = len(state_dict.keys()) + print(f"โœ… Model checkpoint loaded with {layer_count} layers (from Module.state_dict)") + elif hasattr(checkpoint, "parameters"): + # Fallback to parameters count + param_count = len(list(checkpoint.parameters())) + print(f"โœ… Model checkpoint loaded with {param_count} parameters") + else: + print("โœ… Model checkpoint loaded (unknown structure)") + except Exception as e: + print(f"โš ๏ธ Could not determine model structure: {e}") + print("โœ… Model checkpoint loaded (structure inspection failed)") + + return True + + def run_quick_test(): """Run a quick test to verify everything works.""" print("\n๐Ÿงช Running quick test...") - + try: - import torch - import transformers - from sklearn.preprocessing import LabelEncoder - + pass + print("โœ… All required libraries available") - - # Test model loading - if os.path.exists('best_simple_model.pth'): - print("โœ… Model file exists") - - # Try to load a small part to verify it's valid - checkpoint = torch.load('best_simple_model.pth', map_location='cpu') - print(f"โœ… Model checkpoint loaded with {len(checkpoint)} layers") - - return True - + return test_model_loading() + except ImportError as e: print(f"โŒ Missing library: {e}") print("๐Ÿ“‹ Install with: pip install torch transformers scikit-learn") @@ -159,10 +227,11 @@ def run_quick_test(): print(f"โŒ Test failed: {e}") return False + if __name__ == "__main__": if setup_testing(): run_quick_test() print("\n๐ŸŽ‰ Ready to test the model!") print("๐Ÿ“‹ Run: python scripts/test_emotion_model.py") else: - print("\nโŒ Setup failed. Please check the issues above.") \ No newline at end of file + print("\nโŒ Setup failed. Please check the issues above.") diff --git a/scripts/testing/simple_loss_debug.py b/scripts/testing/simple_loss_debug.py index 04fc42d7b..baf00e1e3 100644 --- a/scripts/testing/simple_loss_debug.py +++ b/scripts/testing/simple_loss_debug.py @@ -1,22 +1,20 @@ - # Analyze loss pattern - # Check training logs - # Common causes of 0.0000 loss - # Create test script - # Look for training log files - # Scenario 1: Normal case - # Scenario 2: All zeros - # Scenario 3: All ones - # Scenario 4: Perfect predictions - # Scenario 5: Very small logits - # Suggest debugging steps - # Summary +# Analyze loss pattern +# Check training logs +# Common causes of 0.0000 loss +# Create test script +# Look for training log files +# Scenario 1: Normal case +# Scenario 2: All zeros +# Scenario 3: All ones +# Scenario 4: Perfect predictions +# Scenario 5: Very small logits +# Suggest debugging steps +# Summary +import logging + # Configure logging #!/usr/bin/env python3 from pathlib import Path -import logging - - - """ Simple Loss Debug Script for SAMO Deep Learning. @@ -40,7 +38,7 @@ def analyze_loss_pattern(): "5. **Loss function bug** - Incorrect loss calculation", "6. **Data loading issue** - Empty or corrupted batches", "7. **Model architecture issue** - Model produces constant outputs", - "8. **Numerical precision** - Loss is very small but not exactly 0" + "8. **Numerical precision** - Loss is very small but not exactly 0", ] logger.info("๐Ÿ“‹ Possible causes of 0.0000 loss:") @@ -54,11 +52,7 @@ def check_training_logs(): """Check for patterns in training logs.""" logger.info("๐Ÿ” Checking training log patterns...") - log_patterns = [ - "*.log", - "logs/*.log", - ".logs/*.log" - ] + log_patterns = ["*.log", "logs/*.log", ".logs/*.log"] found_logs = [] for pattern in log_patterns: @@ -87,7 +81,7 @@ def suggest_debugging_steps(): "5. **Check model outputs** - Verify model produces varied predictions", "6. **Test loss function** - Manually compute loss on sample data", "7. **Check for NaN/Inf** - Look for numerical instability", - "8. **Verify data loading** - Ensure batches contain valid data" + "8. **Verify data loading** - Ensure batches contain valid data", ] logger.info("๐Ÿ“‹ Recommended debugging steps:") @@ -163,9 +157,9 @@ def main(): create_test_script() - logger.info("\n" + "="*60) + logger.info("\n" + "=" * 60) logger.info("๐Ÿ“‹ SIMPLE DEBUG SUMMARY") - logger.info("="*60) + logger.info("=" * 60) logger.info("๐ŸŽฏ Most likely causes of 0.0000 loss:") logger.info(" 1. All labels are zero (most common)") diff --git a/scripts/testing/simple_model_test.py b/scripts/testing/simple_model_test.py index 265b0b8f1..fcbe29b5f 100644 --- a/scripts/testing/simple_model_test.py +++ b/scripts/testing/simple_model_test.py @@ -6,17 +6,18 @@ import json import os + def test_model_files(): """Test if model files exist and are valid.""" print("๐Ÿงช SIMPLE MODEL TEST") print("=" * 50) - + # Check model file model_file = "best_simple_model.pth" if os.path.exists(model_file): size = os.path.getsize(model_file) print(f"โœ… Model file: {model_file} ({size:,} bytes)") - + # Check if it's a reasonable size (should be ~400MB+) if size > 100_000_000: # 100MB print("โœ… Model file size looks good!") @@ -25,107 +26,114 @@ def test_model_files(): else: print(f"โŒ Model file missing: {model_file}") return False - + # Check results file results_file = "simple_training_results.json" if os.path.exists(results_file): size = os.path.getsize(results_file) print(f"โœ… Results file: {results_file} ({size:,} bytes)") - + # Try to load and parse try: - with open(results_file, 'r') as f: + with open(results_file, "r") as f: results = json.load(f) - + print(f"โœ… Results file is valid JSON") print(f"๐Ÿ“Š F1 Score: {results.get('best_f1', 'N/A')}") print(f"๐Ÿ“Š Emotions: {len(results.get('all_emotions', []))}") - + except json.JSONDecodeError: print("โŒ Results file is not valid JSON") return False else: print(f"โŒ Results file missing: {results_file}") return False - + return True + def test_python_environment(): """Test Python environment and libraries.""" print("\n๐Ÿ”ง Testing Python Environment:") print("-" * 30) - + # Test basic imports try: import sys + print(f"โœ… Python version: {sys.version}") except ImportError: print("โŒ Cannot import sys") return False - + # Test JSON try: - import json + json.dumps({"test": "data"}) print("โœ… JSON module available") - except ImportError: - print("โŒ JSON module not available") + except Exception as e: + print(f"โŒ JSON module error: {e}") return False - + # Test OS try: - import os + os.getcwd() print("โœ… OS module available") - except ImportError: - print("โŒ OS module not available") + except Exception as e: + print(f"โŒ OS module error: {e}") return False - + return True + def suggest_next_steps(): """Suggest next steps for testing.""" print("\n๐Ÿ“‹ NEXT STEPS:") print("=" * 30) - + print("1. ๐Ÿ Python Environment:") print(" - You're using Python 3.8.6 but libraries are in Python 3.11") print(" - Options:") print(" a) Use: python3.11 scripts/test_emotion_model.py") - print(" b) Install libraries in current Python: pip3 install torch transformers scikit-learn") + print( + " b) Install libraries in current Python: pip3 install torch transformers scikit-learn" + ) print(" c) Create virtual environment") - + print("\n2. ๐Ÿงช Model Testing:") print(" - Once Python is fixed, run: python scripts/test_emotion_model.py") print(" - This will test the model with sample journal entries") - + print("\n3. ๐Ÿ“Š Dataset Expansion:") print(" - Run: python scripts/expand_journal_dataset.py") print(" - This will create 1000+ balanced samples") - + print("\n4. ๐Ÿš€ Retraining:") print(" - Use expanded dataset to retrain") print(" - Expect 75-85% F1 score!") + def main(): """Main test function.""" print("๐Ÿš€ SIMPLE MODEL TESTING") print("=" * 50) - + # Test files files_ok = test_model_files() - + # Test environment env_ok = test_python_environment() - + print(f"\n๐Ÿ“Š Test Results:") print(f" Files: {'โœ…' if files_ok else 'โŒ'}") print(f" Environment: {'โœ…' if env_ok else 'โŒ'}") - + if files_ok and env_ok: print("\n๐ŸŽ‰ All tests passed! Ready for full testing.") else: print("\nโš ๏ธ Some issues found. Check above.") - + suggest_next_steps() + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/testing/simple_rate_limiter_test.py b/scripts/testing/simple_rate_limiter_test.py index 0519ecba6..e69de29bb 100644 --- a/scripts/testing/simple_rate_limiter_test.py +++ b/scripts/testing/simple_rate_limiter_test.py @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/scripts/testing/simple_temperature_test.py b/scripts/testing/simple_temperature_test.py index b7b4c7372..bab6ab008 100644 --- a/scripts/testing/simple_temperature_test.py +++ b/scripts/testing/simple_temperature_test.py @@ -11,10 +11,14 @@ import torch +from src.models.emotion_detection.bert_classifier import ( + create_bert_emotion_classifier, + evaluate_emotion_classifier, +) + # Add src to path sys.path.append(str(Path.cwd() / "src")) -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier, evaluate_emotion_classifier # Configure logging logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") @@ -60,13 +64,13 @@ def simple_temperature_test(): # Test different temperatures temperatures = [0.5, 1.0, 1.5, 2.0] - + for temp in temperatures: logger.info(f"๐Ÿ“Š Testing temperature: {temp}") - + # Set model temperature model.temperature = temp - + # Evaluate model try: results = evaluate_emotion_classifier( @@ -74,11 +78,11 @@ def simple_temperature_test(): tokenizer=tokenizer, texts=test_texts, labels=test_labels, - device=device + device=device, ) - + logger.info(f" Temperature {temp}: F1 = {results.get('f1_score', 'N/A'):.4f}") - + except Exception as e: logger.warning(f" Temperature {temp}: Error - {e}") diff --git a/scripts/testing/simple_temperature_test_local.py b/scripts/testing/simple_temperature_test_local.py index 33a15fbea..c50c8e4e1 100644 --- a/scripts/testing/simple_temperature_test_local.py +++ b/scripts/testing/simple_temperature_test_local.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Apply threshold # Calculate macro F1 # Calculate metrics @@ -19,20 +20,16 @@ # Load sample data # Set device # Test different temperatures -#!/usr/bin/env python3 -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier, EmotionDataset -from pathlib import Path -from torch.utils.data import DataLoader import json import logging import sys -import torch - - - - +from pathlib import Path +import torch +from torch.utils.data import DataLoader +from src.models.emotion_detection.bert_classifier import ( + EmotionDataset, create_bert_emotion_classifier) """ Simple Temperature Scaling Test - Using Local Sample Data. diff --git a/scripts/testing/simple_test.py b/scripts/testing/simple_test.py index 602e51681..37ed19d2f 100644 --- a/scripts/testing/simple_test.py +++ b/scripts/testing/simple_test.py @@ -1,18 +1,13 @@ +#!/usr/bin/env python3 # Create loader # Get first example # Try different ways to access from src.models.emotion_detection.dataset_loader import create_goemotions_loader import traceback -# Add src to path -#!/usr/bin/env python3 -from pathlib import Path import logging import sys -import traceback - - - - +# Add src to path +from pathlib import Path """ Simple test to understand the dataset object type. diff --git a/scripts/testing/simple_threshold_test.py b/scripts/testing/simple_threshold_test.py index abb069628..24d8b174f 100644 --- a/scripts/testing/simple_threshold_test.py +++ b/scripts/testing/simple_threshold_test.py @@ -1,22 +1,14 @@ - # Apply fallback logic - # Apply threshold to get predictions - # Check for samples with no predictions - # Count probabilities above threshold - # Create probabilities with similar distribution to what we observed - # Create synthetic probability data that matches what we saw in debug output - # Test threshold application - # mean=0.4681, min=0.1150, max=0.9119 #!/usr/bin/env python3 import logging -import torch - +import torch """ Simple test to isolate the threshold application bug. """ + def test_threshold_application(): """Test threshold application with synthetic data.""" @@ -38,13 +30,15 @@ def test_threshold_application(): logging.info("\n๐ŸŽฏ Applying threshold: {threshold}") above_threshold = probabilities >= threshold - above_threshold.sum().item() - batch_size * num_emotions + above_threshold_count = above_threshold.sum().item() + total_predictions = batch_size * num_emotions logging.info("๐Ÿ“Š Threshold analysis:") logging.info(" - Total positions: {total_positions}") logging.info(" - Positions >= {threshold}: {num_above_threshold}") - logging.info(" - Percentage >= {threshold}: {100 * num_above_threshold / total_positions:.1f}%") + logging.info( + " - Percentage >= {threshold}: {100 * num_above_threshold / total_positions:.1f}%" + ) predictions = (probabilities >= threshold).float() diff --git a/scripts/testing/smoke_local.py b/scripts/testing/smoke_local.py index dcc2c9272..079cdf1b9 100644 --- a/scripts/testing/smoke_local.py +++ b/scripts/testing/smoke_local.py @@ -13,6 +13,7 @@ """ import argparse +import asyncio # ensure available for ws async path import io import json import os @@ -20,15 +21,15 @@ import wave from contextlib import suppress from datetime import datetime, timedelta, timezone -from typing import Optional, Callable +from typing import Callable, Optional import numpy as np import requests -import asyncio # ensure available for ws async path # WebSocket client: prefer websocket-client if available; fallback to websockets (async) try: import websocket # type: ignore + WEBSOCKET_BACKEND = "websocket-client" except Exception: websocket = None # type: ignore @@ -140,11 +141,7 @@ def phase_auth_login_refresh_logout( json={"username": "tester@example.com", "password": "secret123"}, timeout=10, ) - data = ( - r.json() - if r.headers.get("content-type", "").startswith("application/json") - else {} - ) + data = r.json() if r.headers.get("content-type", "").startswith("application/json") else {} access_token = data.get("access_token") refresh_token = data.get("refresh_token") p("/auth/login", r.status_code, "token" if access_token else r.text[:60]) @@ -174,9 +171,7 @@ def phase_auth_login_refresh_logout( ) new = ( r.json() - if r.headers.get("content-type", "").startswith( - "application/json" - ) + if r.headers.get("content-type", "").startswith("application/json") else {} ) access_token = new.get("access_token", access_token) @@ -364,10 +359,7 @@ def phase_websocket( ) -> None: """Attempt a minimal WS exchange if HTTPS โ†’ WSS; otherwise print skipped.""" if base_url.startswith("https://"): - ws_url = ( - url("/ws/realtime").replace("https://", "wss://") - + f"?token={elevated}" - ) + ws_url = url("/ws/realtime").replace("https://", "wss://") + f"?token={elevated}" else: ws_url = None if ws_url and websocket is not None and WEBSOCKET_BACKEND == "websocket-client": @@ -388,6 +380,7 @@ def phase_websocket( except Exception as exc: p("WS /ws/realtime", None, f"error: {exc}") elif ws_url and WEBSOCKET_BACKEND == "websockets" and websockets is not None: + async def ws_run(): """Minimal WS flow using websockets client for smoke tests.""" try: diff --git a/scripts/testing/standalone_focal_test.py b/scripts/testing/standalone_focal_test.py index 8b7773728..1aedb71ec 100644 --- a/scripts/testing/standalone_focal_test.py +++ b/scripts/testing/standalone_focal_test.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Create a simple BERT classifier # Create a simple classifier head # Load a small subset for testing @@ -10,17 +11,12 @@ # Create synthetic data # Setup device # Configure logging -#!/usr/bin/env python3 -from torch import nn import logging import sys + import torch import torch.nn.functional as F - - - - """ Standalone Focal Loss Test diff --git a/scripts/testing/test_api_startup.py b/scripts/testing/test_api_startup.py index 0519ecba6..e69de29bb 100644 --- a/scripts/testing/test_api_startup.py +++ b/scripts/testing/test_api_startup.py @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/scripts/testing/test_calibration.py b/scripts/testing/test_calibration.py index 4d07a1362..7c8cff54a 100755 --- a/scripts/testing/test_calibration.py +++ b/scripts/testing/test_calibration.py @@ -1,31 +1,34 @@ - # Get predictions - # Process labels - # Tokenize - # Calculate metrics - # Check if F1 score meets target - # Create model - # Create tokenizer - # Load checkpoint - # Load model - # Load validation data - # Process validation data - # Set optimal temperature -# Add src to path -# Configure logging -# Constants -#!/usr/bin/env python3 -from pathlib import Path -from sklearn.metrics import f1_score -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier -from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader -from transformers import AutoTokenizer +# Get predictions +# Process labels +# Tokenize +# Calculate metrics +# Check if F1 score meets target +# Create model +# Create tokenizer +# Load checkpoint +# Load model +# Load validation data +# Process validation data +# Set optimal temperature + + import logging import os import sys -import torch +# Constants +#!/usr/bin/env python3 +from pathlib import Path +import torch +# Add src to path +# Configure logging +from sklearn.metrics import f1_score +from transformers import AutoTokenizer + +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier +from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader """ Test Model Calibration diff --git a/scripts/testing/test_calibration_fixed.py b/scripts/testing/test_calibration_fixed.py index 136a591a5..855921cda 100644 --- a/scripts/testing/test_calibration_fixed.py +++ b/scripts/testing/test_calibration_fixed.py @@ -1,29 +1,31 @@ - # Try to load the checkpoint - # Create new model - # Get predictions - # Load existing model - # Tokenize - # Calculate metrics - # Check if F1 score meets target - # Convert to numpy arrays - # Create simple labels (one emotion per text) - # Create test data - # Create tokenizer - # Find valid checkpoint - # Process test data - # Set optimal temperature -# Configure logging +# Try to load the checkpoint +# Create new model +# Get predictions +# Load existing model +# Tokenize +# Calculate metrics +# Check if F1 score meets target +# Convert to numpy arrays +# Create simple labels (one emotion per text) +# Create test data +# Create tokenizer +# Find valid checkpoint +# Process test data +# Set optimal temperature + +import logging +import sys + # Constants #!/usr/bin/env python3 from pathlib import Path -from sklearn.metrics import f1_score -from transformers import AutoTokenizer, AutoModel -import logging + import numpy as np -import sys import torch - +# Configure logging +from sklearn.metrics import f1_score +from transformers import AutoModel, AutoTokenizer """ Fixed Model Calibration Test diff --git a/scripts/testing/test_cloud_run_api_endpoints.py b/scripts/testing/test_cloud_run_api_endpoints.py index 19782a9a2..54e5a27da 100644 --- a/scripts/testing/test_cloud_run_api_endpoints.py +++ b/scripts/testing/test_cloud_run_api_endpoints.py @@ -4,26 +4,29 @@ Tests the deployed SAMO Emotion Detection API for functionality, security, and performance. """ -import requests -import json -import time -import sys -import os import argparse -from typing import Dict, Any, List +import json import logging +import os +import sys +import time +from typing import Any, Dict + +import requests + from test_config import create_api_client, create_test_config # Configure logging -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) + class CloudRunAPITester: def __init__(self, base_url: str = None): config = create_test_config() self.base_url = base_url or config.base_url self.client = create_api_client() - + # Test data self.test_texts = [ "I am feeling really happy today!", @@ -35,39 +38,36 @@ def __init__(self, base_url: str = None): "I'm so grateful for your help.", "This is disgusting and revolting.", "I'm feeling optimistic about the future.", - "This is really confusing and puzzling." + "This is really confusing and puzzling.", ] def test_health_endpoint(self) -> Dict[str, Any]: """Test the health/status endpoint""" logger.info("Testing health endpoint...") - + try: data = self.client.get("/") logger.info(f"Health endpoint response: {data}") - + # Validate expected fields for minimal API required_fields = ["status", "service", "version", "emotions_supported"] if missing_fields := [field for field in required_fields if field not in data]: return { "success": False, "error": f"Missing required fields: {missing_fields}", - "response": data + "response": data, } - + return { "success": True, "status": data.get("status"), "version": data.get("version"), "service": data.get("service"), - "emotions_supported": data.get("emotions_supported", 0) + "emotions_supported": data.get("emotions_supported", 0), } - + except requests.exceptions.RequestException as e: - return { - "success": False, - "error": f"Health endpoint failed: {str(e)}" - } + return {"success": False, "error": f"Health endpoint failed: {str(e)}"} def _validate_emotion_response(self, data: Dict[str, Any]) -> Dict[str, Any]: """Validate emotion detection response structure""" @@ -75,20 +75,20 @@ def _validate_emotion_response(self, data: Dict[str, Any]) -> Dict[str, Any]: return { "success": False, "error": "Missing primary_emotion field in emotion detection response", - "response": data + "response": data, } - + # Check if emotions were detected primary_emotion = data.get("primary_emotion", {}) emotion = primary_emotion.get("emotion", "") confidence = primary_emotion.get("confidence", 0) - + return { "success": True, "emotion_detected": bool(emotion), "confidence": confidence, "emotion": emotion, - "response_time": 0.0 # Will be measured in performance test + "response_time": 0.0, # Will be measured in performance test } def _create_test_payload(self, text: str = None) -> Dict[str, str]: @@ -100,63 +100,58 @@ def _create_test_payload(self, text: str = None) -> Dict[str, str]: def test_emotion_detection_endpoint(self) -> Dict[str, Any]: """Test the emotion detection endpoint""" logger.info("Testing emotion detection endpoint...") - + try: payload = self._create_test_payload() data = self.client.post("/predict", payload) logger.info(f"Emotion detection response: {data}") - + return self._validate_emotion_response(data) - + except requests.exceptions.RequestException as e: - return { - "success": False, - "error": f"Emotion detection failed: {str(e)}" - } + return {"success": False, "error": f"Emotion detection failed: {str(e)}"} def test_model_loading(self) -> Dict[str, Any]: """Test if models are properly loaded""" logger.info("Testing model loading...") - + # Test multiple emotion detection requests to verify model loading results = [] - + for i, text in enumerate(self.test_texts[:3]): # Test first 3 texts try: payload = {"text": text} data = self.client.post("/predict", payload) - - results.append({ - "text_index": i, - "success": True, - "emotion_detected": bool(data.get("primary_emotion", {}).get("emotion")), - "confidence": data.get("primary_emotion", {}).get("confidence", 0), - "response_time": 0.0 # Will be measured in performance test - }) - + + results.append( + { + "text_index": i, + "success": True, + "emotion_detected": bool(data.get("primary_emotion", {}).get("emotion")), + "confidence": data.get("primary_emotion", {}).get("confidence", 0), + "response_time": 0.0, # Will be measured in performance test + } + ) + except Exception as e: - results.append({ - "text_index": i, - "success": False, - "error": str(e) - }) - + results.append({"text_index": i, "success": False, "error": str(e)}) + # Analyze results - models are loaded if all requests succeeded successful_requests = [r for r in results if r["success"]] models_loaded = len(successful_requests) == len(results) - + return { "success": models_loaded, "total_tests": len(results), "successful_tests": len(successful_requests), "models_loaded": models_loaded, - "results": results + "results": results, } def test_invalid_inputs(self) -> Dict[str, Any]: """Test invalid input handling""" logger.info("Testing invalid inputs...") - + invalid_test_cases = [ {"text": ""}, # Empty text {"invalid": "field"}, # Missing text field @@ -165,9 +160,9 @@ def test_invalid_inputs(self) -> Dict[str, Any]: {}, # Empty payload None, # None payload ] - + results = [] - + for i, test_case in enumerate(invalid_test_cases): try: if test_case is None: @@ -175,90 +170,78 @@ def test_invalid_inputs(self) -> Dict[str, Any]: data = self.client.post("/predict", {}) else: data = self.client.post("/predict", test_case) - + # If we get here, the request succeeded (which might be unexpected) - results.append({ - "test_case": i, - "input": test_case, - "success": True, - "unexpected": True, - "response": data - }) - + results.append( + { + "test_case": i, + "input": test_case, + "success": True, + "unexpected": True, + "response": data, + } + ) + except requests.exceptions.RequestException as e: # Expected failure for invalid inputs - results.append({ - "test_case": i, - "input": test_case, - "success": False, - "expected": True, - "error": str(e) - }) + results.append( + { + "test_case": i, + "input": test_case, + "success": False, + "expected": True, + "error": str(e), + } + ) except Exception as e: - results.append({ - "test_case": i, - "input": test_case, - "success": False, - "error": str(e) - }) - + results.append( + {"test_case": i, "input": test_case, "success": False, "error": str(e)} + ) + # Count expected vs unexpected results expected_failures = [r for r in results if r.get("expected", False)] unexpected_successes = [r for r in results if r.get("unexpected", False)] - + return { "success": len(expected_failures) > 0, # At least some inputs should be rejected "total_tests": len(results), "expected_failures": len(expected_failures), "unexpected_successes": len(unexpected_successes), - "results": results + "results": results, } def test_security_features(self) -> Dict[str, Any]: """Test security features like rate limiting and authentication""" logger.info("Testing security features...") - + # Test rate limiting by making multiple rapid requests logger.info("Testing rate limiting...") config = create_test_config() rate_limit_requests = config.get_rate_limit_requests() - + rapid_requests = [] for i in range(rate_limit_requests): try: payload = {"text": f"Test request {i}"} data = self.client.post("/predict", payload) - rapid_requests.append({ - "request": i, - "success": True, - "status": "success" - }) + rapid_requests.append({"request": i, "success": True, "status": "success"}) except requests.exceptions.RequestException as e: if "429" in str(e): - rapid_requests.append({ - "request": i, - "success": False, - "status": "rate_limited", - "error": str(e) - }) + rapid_requests.append( + {"request": i, "success": False, "status": "rate_limited", "error": str(e)} + ) else: - rapid_requests.append({ - "request": i, - "success": False, - "status": "error", - "error": str(e) - }) + rapid_requests.append( + {"request": i, "success": False, "status": "error", "error": str(e)} + ) except Exception as e: - rapid_requests.append({ - "request": i, - "success": False, - "status": "error", - "error": str(e) - }) - + rapid_requests.append( + {"request": i, "success": False, "status": "error", "error": str(e)} + ) + # Check if any requests were rate limited (429 status) rate_limited = any(r.get("status") == "rate_limited" for r in rapid_requests) - + # Test security headers logger.info("Testing security headers...") try: @@ -267,52 +250,46 @@ def test_security_features(self) -> Dict[str, Any]: # This would need to be done with raw requests if needed security_headers = { "tested": True, - "note": "Headers checked via raw requests if needed" + "note": "Headers checked via raw requests if needed", } - + except Exception as e: security_headers = {"error": str(e)} - + # For minimal API, consider security test successful if rate limiting works or if no rate limiting is implemented # (since our minimal API doesn't have advanced security features) success = True # Consider successful for minimal API - + return { "success": success, "rate_limiting_tested": True, "security_headers_tested": security_headers.get("tested", False), - "note": "Minimal API - basic security features only" + "note": "Minimal API - basic security features only", } def test_performance(self) -> Dict[str, Any]: """Test API performance metrics""" logger.info("Testing performance...") - + performance_results = [] - + for i, text in enumerate(self.test_texts[:5]): # Test first 5 texts try: payload = {"text": text} start_time = time.time() data = self.client.post("/predict", payload) end_time = time.time() - - performance_results.append({ - "request": i, - "response_time": end_time - start_time, - "success": True - }) - + + performance_results.append( + {"request": i, "response_time": end_time - start_time, "success": True} + ) + except Exception as e: - performance_results.append({ - "request": i, - "error": str(e), - "success": False - }) - + performance_results.append({"request": i, "error": str(e), "success": False}) + # Calculate performance metrics successful_requests = [r for r in performance_results if r["success"]] - + if successful_requests: response_times = [r["response_time"] for r in successful_requests] avg_response_time = sum(response_times) / len(response_times) @@ -320,10 +297,12 @@ def test_performance(self) -> Dict[str, Any]: min_response_time = min(response_times) else: avg_response_time = max_response_time = min_response_time = 0 - - success_rate = len(successful_requests) / len(performance_results) if performance_results else 0 + + success_rate = ( + len(successful_requests) / len(performance_results) if performance_results else 0 + ) success = success_rate >= 0.8 # Consider successful if 80%+ requests succeed - + return { "success": success, "total_requests": len(performance_results), @@ -332,19 +311,15 @@ def test_performance(self) -> Dict[str, Any]: "avg_response_time": avg_response_time, "max_response_time": max_response_time, "min_response_time": min_response_time, - "results": performance_results + "results": performance_results, } def run_comprehensive_test(self) -> Dict[str, Any]: """Run all tests and generate comprehensive report""" logger.info("Starting comprehensive API testing...") - - test_results = { - "timestamp": time.time(), - "base_url": self.base_url, - "tests": {} - } - + + test_results = {"timestamp": time.time(), "base_url": self.base_url, "tests": {}} + # Run all tests test_results["tests"]["health"] = self.test_health_endpoint() test_results["tests"]["emotion_detection"] = self.test_emotion_detection_endpoint() @@ -352,10 +327,10 @@ def run_comprehensive_test(self) -> Dict[str, Any]: test_results["tests"]["invalid_inputs"] = self.test_invalid_inputs() test_results["tests"]["security"] = self.test_security_features() test_results["tests"]["performance"] = self.test_performance() - + # Generate summary test_results["summary"] = self.generate_summary(test_results["tests"]) - + return test_results @staticmethod @@ -365,21 +340,23 @@ def generate_summary(tests: Dict[str, Any]) -> Dict[str, Any]: "overall_success": True, "passed_tests": 0, "failed_tests": 0, - "critical_issues": [] + "critical_issues": [], } - + for test_name, result in tests.items(): if isinstance(result, dict) and result.get("success", False): summary["passed_tests"] += 1 else: summary["failed_tests"] += 1 if test_name in ["health", "model_loading"]: - summary["critical_issues"].append(f"{test_name}: {result.get('error', 'Unknown error')}") - + summary["critical_issues"].append( + f"{test_name}: {result.get('error', 'Unknown error')}" + ) + # Check for critical failures if summary["failed_tests"] > 0: summary["overall_success"] = False - + return summary @@ -389,65 +366,67 @@ def main(): parser = argparse.ArgumentParser(description="Test SAMO Cloud Run API") parser.add_argument("--base-url", help="API base URL") args = parser.parse_args() - + config = create_test_config() base_url = args.base_url or config.base_url - + print("๐Ÿงช SAMO Cloud Run API Testing") print("=" * 50) print(f"Testing URL: {base_url}") print() - + # Create tester instance tester = CloudRunAPITester(base_url) - + # Run comprehensive test results = tester.run_comprehensive_test() - + # Print results print("๐Ÿ“Š Test Results Summary") print("=" * 50) - + summary = results["summary"] print(f"Overall Success: {'โœ… PASS' if summary['overall_success'] else 'โŒ FAIL'}") print(f"Tests Passed: {summary['passed_tests']}") print(f"Tests Failed: {summary['failed_tests']}") - + if summary["critical_issues"]: print("\n๐Ÿšจ Critical Issues:") for issue in summary["critical_issues"]: print(f" - {issue}") - + # Print detailed results print("\n๐Ÿ“‹ Detailed Results:") print("-" * 30) - + for test_name, result in results["tests"].items(): - status = "โœ… PASS" if isinstance(result, dict) and result.get("success", False) else "โŒ FAIL" + status = ( + "โœ… PASS" if isinstance(result, dict) and result.get("success", False) else "โŒ FAIL" + ) print(f"{test_name.upper()}: {status}") - + if isinstance(result, dict): if "error" in result: print(f" Error: {result['error']}") elif test_name == "performance" and "avg_response_time" in result: print(f" Avg Response Time: {result['avg_response_time']:.3f}s") print(f" Success Rate: {result['success_rate']:.1%}") - + # Save results to file output_file = "test_reports/cloud_run_api_test_results.json" try: os.makedirs("test_reports", exist_ok=True) - - with open(output_file, 'w') as f: + + with open(output_file, "w") as f: json.dump(results, f, indent=2) print(f"\n๐Ÿ’พ Results saved to: {output_file}") - + except Exception as e: print(f"\nโš ๏ธ Could not save results: {e}") - + # Exit with appropriate code sys.exit(0 if summary["overall_success"] else 1) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/testing/test_comprehensive_model.py b/scripts/testing/test_comprehensive_model.py index 34e7bf62b..ab5ed86c9 100644 --- a/scripts/testing/test_comprehensive_model.py +++ b/scripts/testing/test_comprehensive_model.py @@ -7,66 +7,83 @@ and compares it with the fallback model to verify the improvements. """ -import os -import torch -from transformers import AutoTokenizer, AutoModelForSequenceClassification import json +import os from datetime import datetime +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + + def test_comprehensive_model(): """Test the comprehensive model thoroughly.""" - + print("๐Ÿงช COMPREHENSIVE MODEL TESTING") print("=" * 60) print("๐Ÿ“ Testing model from: deployment/models/default") print() - + # Define paths comprehensive_model_path = "deployment/models/default" fallback_model_path = "deployment/models/model_1_fallback" - + # 1. Load comprehensive model print("๐Ÿ”ง LOADING COMPREHENSIVE MODEL") print("-" * 40) - + try: tokenizer = AutoTokenizer.from_pretrained(comprehensive_model_path) model = AutoModelForSequenceClassification.from_pretrained(comprehensive_model_path) - + if torch.cuda.is_available(): - model = model.to('cuda') + model = model.to("cuda") print("โœ… Model moved to GPU") else: print("โš ๏ธ CUDA not available, using CPU") - + print("โœ… Comprehensive model loaded successfully") - + except Exception as e: print(f"โŒ Failed to load comprehensive model: {e}") return - + # 2. Analyze configuration print(f"\n๐Ÿ“‹ COMPREHENSIVE MODEL CONFIGURATION") print("-" * 40) - + print(f"Model type: {model.config.model_type}") - print(f"Architecture: {model.config.architectures[0] if model.config.architectures else 'Unknown'}") + print( + f"Architecture: {model.config.architectures[0] if model.config.architectures else 'Unknown'}" + ) print(f"Hidden layers: {model.config.num_hidden_layers}") print(f"Hidden size: {model.config.hidden_size}") print(f"Number of labels: {model.config.num_labels}") print(f"Problem type: {model.config.problem_type}") - + if model.config.id2label: print(f"id2label: {model.config.id2label}") if model.config.label2id: print(f"label2id: {model.config.label2id}") - + # 3. Verify emotion classes print(f"\n๐ŸŽฏ EMOTION CLASSES VERIFICATION") print("-" * 40) - - expected_emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] - + + expected_emotions = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", + ] + if model.config.id2label: actual_emotions = [] for i in range(len(model.config.id2label)): @@ -76,10 +93,10 @@ def test_comprehensive_model(): actual_emotions.append(model.config.id2label[str(i)]) else: actual_emotions.append(f"unknown_{i}") - + print(f"Expected emotions: {expected_emotions}") print(f"Actual emotions: {actual_emotions}") - + if actual_emotions == expected_emotions: print("โœ… Emotion classes match expected!") else: @@ -88,31 +105,31 @@ def test_comprehensive_model(): else: print("โŒ No id2label found in model config") return - + # 4. Test model architecture print(f"\n๐Ÿ—๏ธ MODEL ARCHITECTURE TEST") print("-" * 40) - - test_input = tokenizer("I feel happy today", return_tensors='pt', truncation=True, padding=True) + + test_input = tokenizer("I feel happy today", return_tensors="pt", truncation=True, padding=True) if torch.cuda.is_available(): - test_input = {k: v.to('cuda') for k, v in test_input.items()} - + test_input = {k: v.to("cuda") for k, v in test_input.items()} + with torch.no_grad(): test_output = model(**test_input) output_shape = test_output.logits.shape print(f"Output logits shape: {output_shape}") print(f"Expected shape: [1, {len(expected_emotions)}]") - + if output_shape[1] == len(expected_emotions): print("โœ… Model architecture is correct!") else: print(f"โŒ Model architecture mismatch: {output_shape[1]} != {len(expected_emotions)}") return - + # 5. Comprehensive inference test print(f"\n๐Ÿงช COMPREHENSIVE INFERENCE TEST") print("-" * 40) - + # Test cases covering all emotions with various intensities and contexts test_cases = [ # Basic emotion expressions @@ -128,7 +145,6 @@ def test_comprehensive_model(): ("I am proud of my accomplishments.", "proud"), ("I feel sad about the loss.", "sad"), ("I am tired from working all day.", "tired"), - # More complex expressions ("This situation is making me extremely anxious and worried.", "anxious"), ("I feel completely overwhelmed by all the responsibilities.", "overwhelmed"), @@ -149,7 +165,6 @@ def test_comprehensive_model(): ("I feel really tired after working all day.", "tired"), ("I am sad about the recent loss.", "sad"), ("This excites me about the possibilities ahead.", "excited"), - # Edge cases and variations ("I'm a bit nervous about tomorrow.", "anxious"), ("Feeling peaceful and relaxed.", "calm"), @@ -162,29 +177,29 @@ def test_comprehensive_model(): ("Too much to handle right now.", "overwhelmed"), ("Really pleased with my progress.", "proud"), ("Feeling down today.", "sad"), - ("Exhausted from the long day.", "tired") + ("Exhausted from the long day.", "tired"), ] - + correct_predictions = 0 total_confidence = 0.0 confidence_scores = [] - + print("Testing each emotion class:") print() - + for i, (text, expected_emotion) in enumerate(test_cases, 1): # Tokenize input - inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True) + inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) if torch.cuda.is_available(): - inputs = {k: v.to('cuda') for k, v in inputs.items()} - + inputs = {k: v.to("cuda") for k, v in inputs.items()} + # Get prediction with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_label = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_label].item() - + # Get predicted emotion name if predicted_label in model.config.id2label: predicted_emotion = model.config.id2label[predicted_label] @@ -192,7 +207,7 @@ def test_comprehensive_model(): predicted_emotion = model.config.id2label[str(predicted_label)] else: predicted_emotion = f"unknown_{predicted_label}" - + # Check if prediction is correct is_correct = predicted_emotion == expected_emotion if is_correct: @@ -200,70 +215,78 @@ def test_comprehensive_model(): status = "โœ…" else: status = "โŒ" - + total_confidence += confidence confidence_scores.append(confidence) - - print(f"{status} {i:2d}. \"{text}\"") - print(f" Expected: {expected_emotion:<12} | Predicted: {predicted_emotion:<12} | Confidence: {confidence:.3f}") + + print(f'{status} {i:2d}. "{text}"') + print( + f" Expected: {expected_emotion:<12} | Predicted: {predicted_emotion:<12} | Confidence: {confidence:.3f}" + ) print() - + # 6. Performance analysis print(f"\n๐Ÿ“Š PERFORMANCE ANALYSIS") print("-" * 40) - + accuracy = correct_predictions / len(test_cases) * 100 average_confidence = total_confidence / len(test_cases) min_confidence = min(confidence_scores) max_confidence = max(confidence_scores) - + print(f"Accuracy: {accuracy:.2f}% ({correct_predictions}/{len(test_cases)})") print(f"Average confidence: {average_confidence:.3f}") print(f"Confidence range: {min_confidence:.3f} - {max_confidence:.3f}") - print(f"High confidence predictions (โ‰ฅ0.8): {sum(1 for c in confidence_scores if c >= 0.8)}/{len(test_cases)}") - print(f"Medium confidence predictions (0.5-0.8): {sum(1 for c in confidence_scores if 0.5 <= c < 0.8)}/{len(test_cases)}") - print(f"Low confidence predictions (<0.5): {sum(1 for c in confidence_scores if c < 0.5)}/{len(test_cases)}") - + print( + f"High confidence predictions (โ‰ฅ0.8): {sum(1 for c in confidence_scores if c >= 0.8)}/{len(test_cases)}" + ) + print( + f"Medium confidence predictions (0.5-0.8): {sum(1 for c in confidence_scores if 0.5 <= c < 0.8)}/{len(test_cases)}" + ) + print( + f"Low confidence predictions (<0.5): {sum(1 for c in confidence_scores if c < 0.5)}/{len(test_cases)}" + ) + # 7. Compare with fallback model print(f"\n๐Ÿ”„ COMPARISON WITH FALLBACK MODEL") print("-" * 40) - + try: fallback_tokenizer = AutoTokenizer.from_pretrained(fallback_model_path) fallback_model = AutoModelForSequenceClassification.from_pretrained(fallback_model_path) - + if torch.cuda.is_available(): - fallback_model = fallback_model.to('cuda') - + fallback_model = fallback_model.to("cuda") + # Test same cases on fallback model fallback_correct = 0 fallback_confidence = 0.0 - + for text, expected_emotion in test_cases[:12]: # Test first 12 cases - inputs = fallback_tokenizer(text, return_tensors='pt', truncation=True, padding=True) + inputs = fallback_tokenizer(text, return_tensors="pt", truncation=True, padding=True) if torch.cuda.is_available(): - inputs = {k: v.to('cuda') for k, v in inputs.items()} - + inputs = {k: v.to("cuda") for k, v in inputs.items()} + with torch.no_grad(): outputs = fallback_model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_label = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_label].item() - + if predicted_label in fallback_model.config.id2label: predicted_emotion = fallback_model.config.id2label[predicted_label] elif str(predicted_label) in fallback_model.config.id2label: predicted_emotion = fallback_model.config.id2label[str(predicted_label)] else: predicted_emotion = f"unknown_{predicted_label}" - + if predicted_emotion == expected_emotion: fallback_correct += 1 fallback_confidence += confidence - + fallback_accuracy = fallback_correct / 12 * 100 fallback_avg_confidence = fallback_confidence / 12 - + print(f"Comprehensive Model (36 cases):") print(f" Accuracy: {accuracy:.2f}%") print(f" Average confidence: {average_confidence:.3f}") @@ -272,26 +295,28 @@ def test_comprehensive_model(): print(f" Accuracy: {fallback_accuracy:.2f}%") print(f" Average confidence: {fallback_avg_confidence:.3f}") print() - + if accuracy > fallback_accuracy: improvement = accuracy - fallback_accuracy print(f"โœ… Comprehensive model shows {improvement:.2f}% improvement in accuracy!") else: print(f"โš ๏ธ Fallback model performed better by {fallback_accuracy - accuracy:.2f}%") - + if average_confidence > fallback_avg_confidence: conf_improvement = average_confidence - fallback_avg_confidence print(f"โœ… Comprehensive model shows {conf_improvement:.3f} improvement in confidence!") else: - print(f"โš ๏ธ Fallback model has higher confidence by {fallback_avg_confidence - average_confidence:.3f}") - + print( + f"โš ๏ธ Fallback model has higher confidence by {fallback_avg_confidence - average_confidence:.3f}" + ) + except Exception as e: print(f"โš ๏ธ Could not compare with fallback model: {e}") - + # 8. Configuration persistence verification print(f"\n๐Ÿ” CONFIGURATION PERSISTENCE VERIFICATION") print("-" * 40) - + # Check if all critical configuration is preserved config_checks = [ ("num_labels", model.config.num_labels == 12), @@ -299,32 +324,32 @@ def test_comprehensive_model(): ("id2label", model.config.id2label is not None), ("label2id", model.config.label2id is not None), ("model_type", model.config.model_type == "roberta"), - ("num_hidden_layers", model.config.num_hidden_layers == 6) # DistilRoBERTa + ("num_hidden_layers", model.config.num_hidden_layers == 6), # DistilRoBERTa ] - + all_checks_passed = True for check_name, check_result in config_checks: status = "โœ…" if check_result else "โŒ" print(f"{status} {check_name}: {check_result}") if not check_result: all_checks_passed = False - + if all_checks_passed: print("โœ… Configuration persistence verified!") else: print("โŒ Configuration persistence issues detected!") - + # 9. Final assessment print(f"\n๐ŸŽฏ FINAL ASSESSMENT") print("-" * 40) - + print("Configuration Status:") if all_checks_passed: print("โœ… Configuration persistence verified") print("โœ… Model should work correctly in deployment") else: print("โŒ Configuration persistence issues") - + print("\nPerformance Status:") if accuracy >= 90: print("โœ… Excellent performance (โ‰ฅ90% accuracy)") @@ -334,7 +359,7 @@ def test_comprehensive_model(): print("โš ๏ธ Acceptable performance (โ‰ฅ70% accuracy)") else: print("โŒ Poor performance (<70% accuracy)") - + print("\nConfidence Status:") if average_confidence >= 0.8: print("โœ… High confidence predictions") @@ -344,52 +369,57 @@ def test_comprehensive_model(): print("โš ๏ธ Moderate confidence predictions") else: print("โŒ Low confidence predictions") - + # 10. Summary print(f"\n๐Ÿ“‹ SUMMARY") print("-" * 40) - + print("โœ… Comprehensive model loads successfully") print("โœ… Architecture is correct (DistilRoBERTa)") print("โœ… Emotion classes are properly configured") print("โœ… Inference works correctly") print(f"๐Ÿ“Š Test accuracy: {accuracy:.2f}%") print(f"๐Ÿ“Š Average confidence: {average_confidence:.3f}") - + if all_checks_passed: print("โœ… Configuration persistence verified") print("โœ… Model ready for deployment!") else: print("โŒ Configuration persistence issues need attention") - + # 11. Update model metadata print(f"\n๐Ÿ“ UPDATING MODEL METADATA") print("-" * 40) - + metadata_path = os.path.join(comprehensive_model_path, "model_metadata.json") if os.path.exists(metadata_path): try: - with open(metadata_path, 'r') as f: + with open(metadata_path, "r") as f: metadata = json.load(f) - + # Update with test results metadata["created_date"] = datetime.now().isoformat() metadata["performance"]["test_accuracy"] = f"{accuracy:.2f}%" metadata["performance"]["average_confidence"] = f"{average_confidence:.3f}" - metadata["performance"]["confidence_range"] = f"{min_confidence:.3f} - {max_confidence:.3f}" + metadata["performance"][ + "confidence_range" + ] = f"{min_confidence:.3f} - {max_confidence:.3f}" metadata["status"] = "ready" - metadata["notes"] = f"Comprehensive model tested successfully. Accuracy: {accuracy:.2f}%, Confidence: {average_confidence:.3f}" - - with open(metadata_path, 'w') as f: + metadata["notes"] = ( + f"Comprehensive model tested successfully. Accuracy: {accuracy:.2f}%, Confidence: {average_confidence:.3f}" + ) + + with open(metadata_path, "w") as f: json.dump(metadata, f, indent=2) - + print("โœ… Model metadata updated with test results") - + except Exception as e: print(f"โš ๏ธ Could not update metadata: {e}") - + print(f"\n๐ŸŽ‰ COMPREHENSIVE MODEL TESTING COMPLETE!") print("=" * 60) + if __name__ == "__main__": - test_comprehensive_model() \ No newline at end of file + test_comprehensive_model() diff --git a/scripts/testing/test_config.py b/scripts/testing/test_config.py index 15e3f2bd4..c347f7494 100644 --- a/scripts/testing/test_config.py +++ b/scripts/testing/test_config.py @@ -4,37 +4,39 @@ Provides consistent configuration for all testing scripts. """ -import os import argparse +import os import secrets from typing import Optional class TestConfig: """Centralized configuration for all testing scripts""" - + def __init__(self, base_url: Optional[str] = None, api_key: Optional[str] = None): self.base_url = base_url or self._get_base_url() self.api_key = api_key or self._get_api_key() - + def _get_base_url(self) -> str: """Get base URL from environment or command line arguments""" # Priority: CLI arg > environment variable > default parser = argparse.ArgumentParser(add_help=False) - parser.add_argument('--base-url', help='API base URL') + parser.add_argument("--base-url", help="API base URL") args, _ = parser.parse_known_args() - + if args.base_url: - return args.base_url.rstrip('/') - + return args.base_url.rstrip("/") + # Check multiple environment variables for flexibility - env_url = (os.environ.get("API_BASE_URL") or - os.environ.get("CLOUD_RUN_API_URL") or - os.environ.get("MODEL_API_BASE_URL")) - + env_url = ( + os.environ.get("API_BASE_URL") + or os.environ.get("CLOUD_RUN_API_URL") + or os.environ.get("MODEL_API_BASE_URL") + ) + if env_url: - return env_url.rstrip('/') - + return env_url.rstrip("/") + # If no URL is provided, raise an error to force explicit configuration raise ValueError( "No API base URL provided. Please set one of:\n" @@ -43,24 +45,21 @@ def _get_base_url(self) -> str: " - MODEL_API_BASE_URL environment variable\n" " - --base-url command line argument" ) - + def _get_api_key(self) -> str: """Get API key from environment or generate securely""" # Priority: environment variable > secure generation api_key = os.environ.get("API_KEY") if api_key: return api_key - + # Fallback: generate a secure random key return f"samo-admin-key-{secrets.token_urlsafe(32)}" - + def get_headers(self) -> dict: """Get standard headers for API requests""" - return { - "X-API-Key": self.api_key, - "Content-Type": "application/json" - } - + return {"X-API-Key": self.api_key, "Content-Type": "application/json"} + def get_rate_limit_requests(self) -> int: """Get number of requests for rate limiting tests""" return int(os.environ.get("RATE_LIMIT_REQUESTS", "10")) @@ -68,19 +67,19 @@ def get_rate_limit_requests(self) -> int: class APIClient: """Centralized API client with consistent error handling""" - + def __init__(self, config: TestConfig): self.config = config self.base_url = config.base_url self.headers = config.get_headers() - + def get(self, endpoint: str, **kwargs) -> dict: """Make GET request with consistent error handling""" import requests - + url = f"{self.base_url}/{endpoint.lstrip('/')}" - headers = {**self.headers, **kwargs.get('headers', {})} - + headers = {**self.headers, **kwargs.get("headers", {})} + try: response = requests.get(url, headers=headers, timeout=30, **kwargs) response.raise_for_status() @@ -89,14 +88,14 @@ def get(self, endpoint: str, **kwargs) -> dict: raise requests.exceptions.RequestException(f"GET {endpoint} failed: {str(e)}") except ValueError as e: raise ValueError(f"Invalid JSON response from {endpoint}: {str(e)}") - + def post(self, endpoint: str, data: dict, **kwargs) -> dict: """Make POST request with consistent error handling""" import requests - + url = f"{self.base_url}/{endpoint.lstrip('/')}" - headers = {**self.headers, **kwargs.get('headers', {})} - + headers = {**self.headers, **kwargs.get("headers", {})} + try: response = requests.post(url, json=data, headers=headers, timeout=30, **kwargs) response.raise_for_status() @@ -115,4 +114,4 @@ def create_test_config() -> TestConfig: def create_api_client() -> APIClient: """Factory function to create API client""" config = create_test_config() - return APIClient(config) \ No newline at end of file + return APIClient(config) diff --git a/scripts/testing/test_domain_adaptation.py b/scripts/testing/test_domain_adaptation.py index 3a603bba8..ac2bc4a88 100644 --- a/scripts/testing/test_domain_adaptation.py +++ b/scripts/testing/test_domain_adaptation.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Apply threshold and get predicted emotions # Predict # Sort by confidence @@ -23,18 +24,14 @@ # Performance analysis # Save samples for testing from src.models.emotion_detection.bert_classifier import BERTEmotionClassifier -# Set up logging -#!/usr/bin/env python3 -from pathlib import Path -from transformers import AutoTokenizer import argparse import json import logging -import torch - - - +# Set up logging +from pathlib import Path +import torch +from transformers import AutoTokenizer """Domain Adaptation Testing for SAMO Deep Learning. diff --git a/scripts/testing/test_e2e_simple.py b/scripts/testing/test_e2e_simple.py index 0519ecba6..e69de29bb 100644 --- a/scripts/testing/test_e2e_simple.py +++ b/scripts/testing/test_e2e_simple.py @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/scripts/testing/test_emotion_model.py b/scripts/testing/test_emotion_model.py index bfcbb0c21..27977ac3f 100644 --- a/scripts/testing/test_emotion_model.py +++ b/scripts/testing/test_emotion_model.py @@ -4,37 +4,40 @@ """ import json + +import numpy as np import torch import torch.nn as nn -from transformers import AutoModel, AutoTokenizer from sklearn.preprocessing import LabelEncoder -import numpy as np +from transformers import AutoModel, AutoTokenizer + def load_trained_model(): """Load the trained emotion detection model.""" print("๐Ÿ”ง Loading trained model...") - + # Load model weights - model_path = 'best_simple_model.pth' + model_path = "best_simple_model.pth" model = SimpleEmotionClassifier(model_name="bert-base-uncased", num_labels=12) - model.load_state_dict(torch.load(model_path, map_location='cpu')) + model.load_state_dict(torch.load(model_path, map_location="cpu")) model.eval() - + # Load tokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") - + # Load label encoder - with open('simple_training_results.json', 'r') as f: + with open("simple_training_results.json", "r") as f: results = json.load(f) - + # Create label encoder from results - all_emotions = results.get('all_emotions', []) + all_emotions = results.get("all_emotions", []) label_encoder = LabelEncoder() label_encoder.fit(all_emotions) - + print(f"โœ… Model loaded with {len(label_encoder.classes_)} emotions: {label_encoder.classes_}") return model, tokenizer, label_encoder + class SimpleEmotionClassifier(nn.Module): def __init__(self, model_name="bert-base-uncased", num_labels=None): super().__init__() @@ -42,49 +45,47 @@ def __init__(self, model_name="bert-base-uncased", num_labels=None): self.bert = AutoModel.from_pretrained(model_name) self.dropout = nn.Dropout(0.3) self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels) - + def forward(self, input_ids, attention_mask): outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) pooled_output = outputs.pooler_output logits = self.classifier(self.dropout(pooled_output)) return logits -def predict_emotion(text, model, tokenizer, label_encoder, device='cpu'): + +def predict_emotion(text, model, tokenizer, label_encoder, device="cpu"): """Predict emotion for a given text.""" model.to(device) - + # Tokenize input encoding = tokenizer( - text, - truncation=True, - padding='max_length', - max_length=128, - return_tensors='pt' + text, truncation=True, padding="max_length", max_length=128, return_tensors="pt" ) - + # Move to device - input_ids = encoding['input_ids'].to(device) - attention_mask = encoding['attention_mask'].to(device) - + input_ids = encoding["input_ids"].to(device) + attention_mask = encoding["attention_mask"].to(device) + # Predict with torch.no_grad(): outputs = model(input_ids=input_ids, attention_mask=attention_mask) probabilities = torch.softmax(outputs, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + # Get emotion label emotion = label_encoder.inverse_transform([predicted_class])[0] - + return emotion, confidence, probabilities[0].cpu().numpy() + def test_model(): """Test the model with sample journal entries.""" print("๐Ÿงช Testing emotion detection model...") - + # Load model model, tokenizer, label_encoder = load_trained_model() - + # Sample journal entries for testing test_entries = [ "I'm feeling really happy today! Everything is going well.", @@ -98,18 +99,18 @@ def test_model(): "I feel calm and peaceful right now.", "I'm hopeful that things will get better.", "I'm tired and need some rest.", - "I'm content with how things are going." + "I'm content with how things are going.", ] - + print("\n๐Ÿ“Š Testing Results:") print("=" * 80) - + for i, text in enumerate(test_entries, 1): emotion, confidence, all_probs = predict_emotion(text, model, tokenizer, label_encoder) - + print(f"\n{i}. Text: {text}") print(f" Predicted: {emotion} (confidence: {confidence:.3f})") - + # Show top 3 predictions top_indices = np.argsort(all_probs)[-3:][::-1] print(" Top 3 predictions:") @@ -117,29 +118,31 @@ def test_model(): prob = all_probs[idx] emotion_name = label_encoder.inverse_transform([idx])[0] print(f" - {emotion_name}: {prob:.3f}") - + print("\nโœ… Model testing completed!") + def analyze_performance(): """Analyze model performance on validation data.""" print("\n๐Ÿ“ˆ Performance Analysis:") print("=" * 40) - + # Load results - with open('simple_training_results.json', 'r') as f: + with open("simple_training_results.json", "r") as f: results = json.load(f) - + print(f"Final F1 Score: {results['best_f1']:.4f}") print(f"Target Achieved: {results['target_achieved']}") print(f"Number of Labels: {results['num_labels']}") print(f"GoEmotions Samples: {results['go_samples']}") print(f"Journal Samples: {results['journal_samples']}") - + # Show emotion mapping print(f"\nEmotion Mapping Used:") - for go_emotion, journal_emotion in results['emotion_mapping'].items(): + for go_emotion, journal_emotion in results["emotion_mapping"].items(): print(f" {go_emotion} โ†’ {journal_emotion}") + if __name__ == "__main__": test_model() - analyze_performance() \ No newline at end of file + analyze_performance() diff --git a/scripts/testing/test_final_inference.py b/scripts/testing/test_final_inference.py index 949c4313f..ce0606222 100644 --- a/scripts/testing/test_final_inference.py +++ b/scripts/testing/test_final_inference.py @@ -4,23 +4,25 @@ Uses public RoBERTa tokenizer to avoid authentication issues """ -import torch import json -from transformers import AutoTokenizer, AutoModelForSequenceClassification from pathlib import Path +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + + def test_final_inference(): """Test inference with public RoBERTa tokenizer""" - + print("๐Ÿงช FINAL INFERENCE TEST") print("=" * 50) - + # Check if model files exist - model_dir = Path(__file__).parent.parent / 'deployment' / 'model' - required_files = ['config.json', 'model.safetensors', 'training_args.bin'] - + model_dir = Path(__file__).parent.parent / "deployment" / "model" + required_files = ["config.json", "model.safetensors", "training_args.bin"] + print(f"๐Ÿ“ Checking model directory: {model_dir}") - + missing_files = [] for file in required_files: file_path = model_dir / file @@ -29,47 +31,57 @@ def test_final_inference(): else: print(f"โŒ Missing: {file}") missing_files.append(file) - + if missing_files: print(f"\nโŒ Missing required files: {missing_files}") return False - + print(f"\nโœ… All model files found!") - + try: # Load the model config to understand the architecture - with open(model_dir / 'config.json', 'r') as f: + with open(model_dir / "config.json", "r") as f: config = json.load(f) - + print(f"๐Ÿ”ง Model type: {config.get('model_type', 'unknown')}") print(f"๐Ÿ“Š Number of labels: {len(config.get('id2label', {}))}") - + # Define the emotion mapping based on your training # This should match the order from your training emotion_mapping = [ - 'anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', - 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired' + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", ] - + print(f"๐ŸŽฏ Emotion mapping: {emotion_mapping}") - + # Use a public RoBERTa tokenizer instead of the private one base_model_name = "roberta-base" # Public model, no authentication needed print(f"๐Ÿ”ง Loading public tokenizer: {base_model_name}") - + tokenizer = AutoTokenizer.from_pretrained(base_model_name) - + # Load the fine-tuned model print(f"๐Ÿ”ง Loading fine-tuned model from: {model_dir}") model = AutoModelForSequenceClassification.from_pretrained(str(model_dir)) - - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) model.eval() - + print(f"โœ… Model loaded successfully!") print(f"๐ŸŽฏ Device: {device}") - + # Test texts test_texts = [ "I'm feeling really happy today!", @@ -81,32 +93,29 @@ def test_final_inference(): "I'm feeling sad and lonely today.", "I'm excited about the new opportunities.", "I feel calm and peaceful right now.", - "I'm hopeful that things will get better." + "I'm hopeful that things will get better.", ] - + print(f"\n๐Ÿ“Š Testing predictions:") print("-" * 50) - + for i, text in enumerate(test_texts, 1): try: # Tokenize input - inputs = tokenizer( - text, - truncation=True, - padding=True, - return_tensors='pt' - ).to(device) - + inputs = tokenizer(text, truncation=True, padding=True, return_tensors="pt").to( + device + ) + # Get predictions with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + # Map to emotion name predicted_emotion = emotion_mapping[predicted_class] - + # Get top 3 predictions top3_indices = torch.topk(probabilities[0], 3).indices top3_predictions = [] @@ -114,68 +123,80 @@ def test_final_inference(): emotion = emotion_mapping[idx.item()] conf = probabilities[0][idx].item() top3_predictions.append((emotion, conf)) - + print(f"{i:2d}. Text: {text}") print(f" Predicted: {predicted_emotion} (confidence: {confidence:.3f})") print(f" Top 3 predictions:") for emotion, conf in top3_predictions: print(f" - {emotion}: {conf:.3f}") print() - + except Exception as e: print(f"{i:2d}. Text: {text}") print(f" Error: {e}") print() - + print("๐ŸŽ‰ Final inference test completed successfully!") return True - + except Exception as e: print(f"โŒ Error during inference: {e}") import traceback + traceback.print_exc() return False + def test_simple_prediction(): """Simple test with just one prediction""" - + print("๐Ÿงช SIMPLE PREDICTION TEST") print("=" * 50) - + try: - model_dir = Path(__file__).parent.parent / 'deployment' / 'model' - + model_dir = Path(__file__).parent.parent / "deployment" / "model" + # Use public RoBERTa tokenizer tokenizer = AutoTokenizer.from_pretrained("roberta-base") model = AutoModelForSequenceClassification.from_pretrained(str(model_dir)) - - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) model.eval() - + # Emotion mapping emotion_mapping = [ - 'anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', - 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired' + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", ] - + # Test one text text = "I'm feeling really happy today!" print(f"๐Ÿ“ Testing: {text}") - - inputs = tokenizer(text, truncation=True, padding=True, return_tensors='pt').to(device) - + + inputs = tokenizer(text, truncation=True, padding=True, return_tensors="pt").to(device) + with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + predicted_emotion = emotion_mapping[predicted_class] - + print(f"๐ŸŽฏ Predicted: {predicted_emotion}") print(f"๐Ÿ“Š Confidence: {confidence:.3f}") - + # Show top 3 top3_indices = torch.topk(probabilities[0], 3).indices print(f"\n๐Ÿ† Top 3 predictions:") @@ -183,30 +204,31 @@ def test_simple_prediction(): emotion = emotion_mapping[idx.item()] conf = probabilities[0][idx].item() print(f" {i+1}. {emotion}: {conf:.3f}") - + print(f"\n๐ŸŽ‰ Simple prediction test completed!") return True - + except Exception as e: print(f"โŒ Error: {e}") return False + if __name__ == "__main__": print("๐Ÿš€ EMOTION DETECTION - FINAL TEST") print("=" * 60) - + # Try the full test first print("\n1๏ธโƒฃ Testing full inference...") success = test_final_inference() - + if not success: print("\n2๏ธโƒฃ Trying simple prediction test...") test_simple_prediction() - + if success: print(f"\n๐ŸŽ‰ SUCCESS! Your 99.54% F1 score model is working!") print(f"๐Ÿ“‹ Next steps:") print(f" - Deploy with: cd deployment && ./deploy.sh") print(f" - API will be available at: http://localhost:5000") else: - print(f"\nโŒ Tests failed. Check the error messages above.") \ No newline at end of file + print(f"\nโŒ Tests failed. Check the error messages above.") diff --git a/scripts/testing/test_fixed_evaluation.py b/scripts/testing/test_fixed_evaluation.py index f23807598..c0e1bc577 100644 --- a/scripts/testing/test_fixed_evaluation.py +++ b/scripts/testing/test_fixed_evaluation.py @@ -1,20 +1,21 @@ - # Create trainer - # Load trained model - # Prepare data and model - # Success criteria - # Test different thresholds with fixed evaluation -# Add src to path -# Configure logging -#!/usr/bin/env python3 -from src.models.emotion_detection.bert_classifier import evaluate_emotion_classifier -from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer -from pathlib import Path +# Create trainer +# Load trained model +# Prepare data and model +# Success criteria +# Test different thresholds with fixed evaluation + import logging import sys -import torch +from pathlib import Path +import torch +# Configure logging +#!/usr/bin/env python3 +from src.models.emotion_detection.bert_classifier import evaluate_emotion_classifier +# Add src to path +from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer """Test Fixed Evaluation Function. @@ -69,9 +70,9 @@ def main(): ) macro_f1 = metrics["macro_f1"] - metrics["micro_f1"] + micro_f1 = metrics["micro_f1"] - logger.info(" ๐Ÿ“Š Macro F1: {macro_f1:.4f} | Micro F1: {micro_f1:.4f}") + logger.info(f" ๐Ÿ“Š Macro F1: {macro_f1:.4f} | Micro F1: {micro_f1:.4f}") best_f1 = max(best_f1, macro_f1) diff --git a/scripts/testing/test_fixed_inference.py b/scripts/testing/test_fixed_inference.py index 2ed7ab6e7..411f5191b 100644 --- a/scripts/testing/test_fixed_inference.py +++ b/scripts/testing/test_fixed_inference.py @@ -4,23 +4,25 @@ Handles missing tokenizer and generic labels """ -import torch import json -from transformers import AutoTokenizer, AutoModelForSequenceClassification from pathlib import Path +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + + def test_fixed_inference(): """Test inference with missing tokenizer and generic labels""" - + print("๐Ÿงช FIXED INFERENCE TEST") print("=" * 50) - + # Check if model files exist - model_dir = Path(__file__).parent.parent / 'deployment' / 'model' - required_files = ['config.json', 'model.safetensors', 'training_args.bin'] - + model_dir = Path(__file__).parent.parent / "deployment" / "model" + required_files = ["config.json", "model.safetensors", "training_args.bin"] + print(f"๐Ÿ“ Checking model directory: {model_dir}") - + missing_files = [] for file in required_files: file_path = model_dir / file @@ -29,47 +31,57 @@ def test_fixed_inference(): else: print(f"โŒ Missing: {file}") missing_files.append(file) - + if missing_files: print(f"\nโŒ Missing required files: {missing_files}") return False - + print(f"\nโœ… All model files found!") - + try: # Load the model config to understand the architecture - with open(model_dir / 'config.json', 'r') as f: + with open(model_dir / "config.json", "r") as f: config = json.load(f) - + print(f"๐Ÿ”ง Model type: {config.get('model_type', 'unknown')}") print(f"๐Ÿ“Š Number of labels: {len(config.get('id2label', {}))}") - + # Define the emotion mapping based on your training # This should match the order from your training emotion_mapping = [ - 'anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', - 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired' + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", ] - + print(f"๐ŸŽฏ Emotion mapping: {emotion_mapping}") - + # Load the base model tokenizer (since the fine-tuned one wasn't saved) base_model_name = "j-hartmann/emotion-english-distilroberta-base" print(f"๐Ÿ”ง Loading base tokenizer: {base_model_name}") - + tokenizer = AutoTokenizer.from_pretrained(base_model_name) - + # Load the fine-tuned model print(f"๐Ÿ”ง Loading fine-tuned model from: {model_dir}") model = AutoModelForSequenceClassification.from_pretrained(str(model_dir)) - - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) model.eval() - + print(f"โœ… Model loaded successfully!") print(f"๐ŸŽฏ Device: {device}") - + # Test texts test_texts = [ "I'm feeling really happy today!", @@ -81,32 +93,29 @@ def test_fixed_inference(): "I'm feeling sad and lonely today.", "I'm excited about the new opportunities.", "I feel calm and peaceful right now.", - "I'm hopeful that things will get better." + "I'm hopeful that things will get better.", ] - + print(f"\n๐Ÿ“Š Testing predictions:") print("-" * 50) - + for i, text in enumerate(test_texts, 1): try: # Tokenize input - inputs = tokenizer( - text, - truncation=True, - padding=True, - return_tensors='pt' - ).to(device) - + inputs = tokenizer(text, truncation=True, padding=True, return_tensors="pt").to( + device + ) + # Get predictions with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + # Map to emotion name predicted_emotion = emotion_mapping[predicted_class] - + # Get top 3 predictions top3_indices = torch.topk(probabilities[0], 3).indices top3_predictions = [] @@ -114,38 +123,40 @@ def test_fixed_inference(): emotion = emotion_mapping[idx.item()] conf = probabilities[0][idx].item() top3_predictions.append((emotion, conf)) - + print(f"{i:2d}. Text: {text}") print(f" Predicted: {predicted_emotion} (confidence: {confidence:.3f})") print(f" Top 3 predictions:") for emotion, conf in top3_predictions: print(f" - {emotion}: {conf:.3f}") print() - + except Exception as e: print(f"{i:2d}. Text: {text}") print(f" Error: {e}") print() - + print("๐ŸŽ‰ Fixed inference test completed successfully!") return True - + except Exception as e: print(f"โŒ Error during inference: {e}") import traceback + traceback.print_exc() return False + if __name__ == "__main__": print("๐Ÿš€ EMOTION DETECTION - FIXED TEST") print("=" * 60) - + success = test_fixed_inference() - + if success: print(f"\n๐ŸŽ‰ SUCCESS! Your 99.54% F1 score model is working!") print(f"๐Ÿ“‹ Next steps:") print(f" - Deploy with: cd deployment && ./deploy.sh") print(f" - API will be available at: http://localhost:5000") else: - print(f"\nโŒ Test failed. Check the error messages above.") \ No newline at end of file + print(f"\nโŒ Test failed. Check the error messages above.") diff --git a/scripts/testing/test_local_inference.py b/scripts/testing/test_local_inference.py index a5f33ddb1..1db417bec 100644 --- a/scripts/testing/test_local_inference.py +++ b/scripts/testing/test_local_inference.py @@ -6,14 +6,18 @@ import sys -from scripts.testing._bootstrap import ensure_project_root_on_sys_path, ensure_path, configure_basic_logging +from scripts.testing._bootstrap import ( + configure_basic_logging, + ensure_path, + ensure_project_root_on_sys_path, +) # Ensure project root and logging PROJECT_ROOT = ensure_project_root_on_sys_path() logger = configure_basic_logging() # Add the deployment directory to the path -DEPLOYMENT_DIR = PROJECT_ROOT / 'deployment' +DEPLOYMENT_DIR = PROJECT_ROOT / "deployment" ensure_path(DEPLOYMENT_DIR) @@ -23,8 +27,8 @@ def test_local_inference(): logger.info("=" * 50) # Check if model files exist - model_dir = DEPLOYMENT_DIR / 'model' - required_files = ['config.json', 'model.safetensors', 'training_args.bin'] + model_dir = DEPLOYMENT_DIR / "model" + required_files = ["config.json", "model.safetensors", "training_args.bin"] logger.info("๐Ÿ“ Checking model directory: %s", model_dir) @@ -55,7 +59,7 @@ def test_local_inference(): "I'm feeling sad and lonely today.", "I'm excited about the new opportunities.", "I feel calm and peaceful right now.", - "I'm hopeful that things will get better." + "I'm hopeful that things will get better.", ] try: @@ -81,4 +85,4 @@ def test_local_inference(): if __name__ == "__main__": success = test_local_inference() - sys.exit(0 if success else 1) \ No newline at end of file + sys.exit(0 if success else 1) diff --git a/scripts/testing/test_loss_scenarios.py b/scripts/testing/test_loss_scenarios.py index b3b8abbf5..33e659a2c 100644 --- a/scripts/testing/test_loss_scenarios.py +++ b/scripts/testing/test_loss_scenarios.py @@ -1,20 +1,20 @@ - # Scenario 1: Normal case - # Scenario 2: All zeros - # Scenario 3: All ones - # Scenario 4: Perfect predictions - # Scenario 5: Very small logits +# Scenario 1: Normal case +# Scenario 2: All zeros +# Scenario 3: All ones +# Scenario 4: Perfect predictions +# Scenario 5: Very small logits #!/usr/bin/env python3 import logging + import torch import torch.nn.functional as F - - """ Simple Test Script for Loss Debugging """ + def test_bce_loss(): """Test BCE loss with different scenarios.""" logging.info("๐Ÿงช Testing BCE Loss Scenarios...") @@ -49,5 +49,6 @@ def test_bce_loss(): F.binary_cross_entropy_with_logits(logits, labels) logging.info("Small logits - Loss: {loss.item():.6f}") + if __name__ == "__main__": test_bce_loss() diff --git a/scripts/testing/test_model_status.py b/scripts/testing/test_model_status.py index 9a3d0e467..f4c32b1bc 100644 --- a/scripts/testing/test_model_status.py +++ b/scripts/testing/test_model_status.py @@ -4,8 +4,10 @@ Get detailed information about model loading status and any errors. """ -import requests import argparse + +import requests + from test_config import create_api_client, create_test_config @@ -28,7 +30,7 @@ def test_emotions_endpoint(client): print("\n2. Testing emotions from main endpoint...") try: data = client.get("/") - emotions_count = data.get('emotions_supported', 0) + emotions_count = data.get("emotions_supported", 0) print(f" โœ… Emotions: {emotions_count} emotions available") return True except requests.exceptions.RequestException as e: @@ -41,8 +43,8 @@ def test_model_status_endpoint(client): print("\n3. Testing model status from main endpoint...") try: data = client.get("/") - model_type = data.get('model_type', 'Unknown') - service = data.get('service', 'Unknown') + model_type = data.get("model_type", "Unknown") + service = data.get("service", "Unknown") print(f" โœ… Model Type: {model_type}") print(f" โœ… Service: {service}") return True @@ -68,26 +70,26 @@ def test_model_status(base_url=None): """Test the model status endpoint""" config = create_test_config() if base_url: - config.base_url = base_url.rstrip('/') + config.base_url = base_url.rstrip("/") client = create_api_client() - + print("๐Ÿ” Testing Model Status") print("=" * 40) print(f"Testing URL: {config.base_url}") - + # Run all tests health_success = test_health_endpoint(client) emotions_success = test_emotions_endpoint(client) model_status_success = test_model_status_endpoint(client) prediction_success = test_prediction_endpoint(client) - + # Summary print("\n๐Ÿ“Š Test Summary:") print(f" Health: {'โœ…' if health_success else 'โŒ'}") print(f" Emotions: {'โœ…' if emotions_success else 'โŒ'}") print(f" Model Status: {'โœ…' if model_status_success else 'โŒ'}") print(f" Prediction: {'โœ…' if prediction_success else 'โŒ'}") - + return health_success and emotions_success and prediction_success @@ -96,10 +98,10 @@ def main(): parser = argparse.ArgumentParser(description="Test Model Status Endpoint") parser.add_argument("--base-url", help="API base URL") args = parser.parse_args() - + success = test_model_status(args.base_url) exit(0 if success else 1) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/testing/test_new_trained_model.py b/scripts/testing/test_new_trained_model.py index d21c77746..d25f11d1a 100644 --- a/scripts/testing/test_new_trained_model.py +++ b/scripts/testing/test_new_trained_model.py @@ -4,25 +4,31 @@ ====================== Tests the newly trained model from Colab with proper verification """ -import torch from pathlib import Path -from transformers import AutoTokenizer, AutoModelForSequenceClassification + +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + def test_new_trained_model(): """Test the newly trained model from Colab""" - + print("๐Ÿงช TESTING NEW TRAINED MODEL") print("=" * 50) - + # Model directory - model_dir = Path(__file__).parent.parent / 'deployment' / 'model' - + model_dir = Path(__file__).parent.parent / "deployment" / "model" + # Check for required files required_files = [ - 'config.json', 'model.safetensors', 'training_args.bin', - 'tokenizer.json', 'tokenizer_config.json', 'vocab.json' + "config.json", + "model.safetensors", + "training_args.bin", + "tokenizer.json", + "tokenizer_config.json", + "vocab.json", ] - + print("๐Ÿ“ Checking model files...") for file in required_files: file_path = model_dir / file @@ -31,15 +37,15 @@ def test_new_trained_model(): else: print(f"โŒ Missing: {file}") return False - + print("\n๐Ÿ”ง Loading model...") try: # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained(str(model_dir)) model = AutoModelForSequenceClassification.from_pretrained(str(model_dir)) - + print("โœ… Model loaded successfully!") - + # Check model configuration print(f"\n๐Ÿ“Š Model Configuration:") print(f" Model type: {model.config.model_type}") @@ -48,12 +54,25 @@ def test_new_trained_model(): print(f" Hidden size: {model.config.hidden_size}") print(f" Number of labels: {model.config.num_labels}") print(f" Labels: {model.config.id2label}") - + # Define emotion mapping - emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] - + emotions = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", + ] + print(f"\n๐ŸŽฏ Testing predictions...") - + # Test examples test_examples = [ "I am feeling really happy today!", @@ -67,43 +86,45 @@ def test_new_trained_model(): "I feel calm and peaceful.", "I am excited about the new opportunity.", "I feel content with my life.", - "I am hopeful for the future." + "I am hopeful for the future.", ] - + model.eval() correct = 0 - + for text in test_examples: # Tokenize - inputs = tokenizer(text, return_tensors='pt', truncation=True, max_length=128) - + inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128) + # Predict with torch.no_grad(): outputs = model(**inputs) predictions = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(predictions, dim=1).item() confidence = predictions[0][predicted_class].item() - + predicted_emotion = emotions[predicted_class] - + # Find expected emotion expected_emotion = None for emotion in emotions: if emotion in text.lower(): expected_emotion = emotion break - + if expected_emotion and predicted_emotion == expected_emotion: correct += 1 status = "โœ…" else: status = "โŒ" - - print(f"{status} \"{text}\" โ†’ {predicted_emotion} (expected: {expected_emotion}, confidence: {confidence:.3f})") - + + print( + f'{status} "{text}" โ†’ {predicted_emotion} (expected: {expected_emotion}, confidence: {confidence:.3f})' + ) + accuracy = correct / len(test_examples) print(f"\n๐Ÿ“Š Test Accuracy: {accuracy:.1%} ({correct}/{len(test_examples)})") - + # Test on some edge cases print(f"\n๐Ÿงช Testing edge cases...") edge_cases = [ @@ -111,20 +132,20 @@ def test_new_trained_model(): "This is amazing!", "I'm so disappointed.", "Everything is going well.", - "I'm exhausted." + "I'm exhausted.", ] - + for text in edge_cases: - inputs = tokenizer(text, return_tensors='pt', truncation=True, max_length=128) + inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128) with torch.no_grad(): outputs = model(**inputs) predictions = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(predictions, dim=1).item() confidence = predictions[0][predicted_class].item() - + predicted_emotion = emotions[predicted_class] - print(f" \"{text}\" โ†’ {predicted_emotion} (confidence: {confidence:.3f})") - + print(f' "{text}" โ†’ {predicted_emotion} (confidence: {confidence:.3f})') + # Overall assessment print(f"\n๐ŸŽฏ MODEL ASSESSMENT:") if accuracy >= 0.8: @@ -135,21 +156,22 @@ def test_new_trained_model(): print("โš ๏ธ FAIR: Model needs improvement but is functional") else: print("โŒ POOR: Model needs significant improvement") - + print(f"\n๐Ÿ“‹ Next steps:") print(f" 1. Model is ready for local testing") print(f" 2. Can be deployed to API server") print(f" 3. Consider retraining tomorrow for better results") - + return True - + except Exception as e: print(f"โŒ Error testing model: {str(e)}") return False + if __name__ == "__main__": success = test_new_trained_model() if success: print("\n๐ŸŽ‰ Model testing completed successfully!") else: - print("\nโŒ Model testing failed!") \ No newline at end of file + print("\nโŒ Model testing failed!") diff --git a/scripts/testing/test_new_trained_model_comprehensive.py b/scripts/testing/test_new_trained_model_comprehensive.py index 75a6a5cb8..58787c11a 100644 --- a/scripts/testing/test_new_trained_model_comprehensive.py +++ b/scripts/testing/test_new_trained_model_comprehensive.py @@ -10,28 +10,31 @@ 4. Comparison with expected behavior """ -import torch -import numpy as np -from transformers import AutoTokenizer, AutoModelForSequenceClassification import warnings -warnings.filterwarnings('ignore') + +import numpy as np +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + +warnings.filterwarnings("ignore") + def test_new_trained_model(): """Comprehensive test of the newly trained model.""" - + print("๐Ÿงช COMPREHENSIVE MODEL TESTING") print("=" * 50) - + # Model path model_path = "deployment/model" - + print(f"๐Ÿ“ Testing model from: {model_path}") print() - + # 1. Load the model and tokenizer print("๐Ÿ”ง LOADING MODEL AND TOKENIZER") print("-" * 40) - + try: tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForSequenceClassification.from_pretrained(model_path) @@ -39,26 +42,41 @@ def test_new_trained_model(): except Exception as e: print(f"โŒ Error loading model: {str(e)}") return - + # 2. Check configuration print("\n๐Ÿ“‹ CONFIGURATION ANALYSIS") print("-" * 40) - + print(f"Model type: {model.config.model_type}") - print(f"Architecture: {model.config.architectures[0] if model.config.architectures else 'Not specified'}") + print( + f"Architecture: {model.config.architectures[0] if model.config.architectures else 'Not specified'}" + ) print(f"Hidden layers: {model.config.num_hidden_layers}") print(f"Hidden size: {model.config.hidden_size}") print(f"Number of labels: {getattr(model.config, 'num_labels', 'NOT SET')}") print(f"Problem type: {getattr(model.config, 'problem_type', 'NOT SET')}") print(f"id2label: {model.config.id2label}") print(f"label2id: {model.config.label2id}") - + # 3. Verify emotion classes print("\n๐ŸŽฏ EMOTION CLASSES VERIFICATION") print("-" * 40) - - expected_emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] - + + expected_emotions = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", + ] + if model.config.id2label: # Handle both string and integer keys actual_emotions = [] @@ -69,39 +87,41 @@ def test_new_trained_model(): actual_emotions.append(model.config.id2label[str(i)]) else: actual_emotions.append(f"unknown_{i}") - + print(f"Expected emotions: {expected_emotions}") print(f"Actual emotions: {actual_emotions}") - + if actual_emotions == expected_emotions: print("โœ… Emotion classes match expected!") else: print("โŒ Emotion classes don't match expected!") else: print("โŒ No id2label found in config!") - + # 4. Test model architecture print("\n๐Ÿ—๏ธ MODEL ARCHITECTURE TEST") print("-" * 40) - + # Test with a sample input - test_input = tokenizer("I feel happy today", return_tensors='pt', truncation=True, padding=True) - + test_input = tokenizer("I feel happy today", return_tensors="pt", truncation=True, padding=True) + with torch.no_grad(): outputs = model(**test_input) logits = outputs.logits print(f"Output logits shape: {logits.shape}") print(f"Expected shape: [1, {len(expected_emotions)}]") - + if logits.shape[1] == len(expected_emotions): print("โœ… Model architecture is correct!") else: - print(f"โŒ Model architecture mismatch! Expected {len(expected_emotions)}, got {logits.shape[1]}") - + print( + f"โŒ Model architecture mismatch! Expected {len(expected_emotions)}, got {logits.shape[1]}" + ) + # 5. Comprehensive inference test print("\n๐Ÿงช COMPREHENSIVE INFERENCE TEST") print("-" * 40) - + test_cases = [ "I feel anxious about the presentation.", "I am feeling calm and peaceful.", @@ -114,22 +134,22 @@ def test_new_trained_model(): "I am feeling overwhelmed with tasks.", "I am proud of my accomplishments.", "I feel sad about the loss.", - "I am tired from working all day." + "I am tired from working all day.", ] - + print("Testing each emotion class:") print() - + results = [] for i, test_case in enumerate(test_cases): - inputs = tokenizer(test_case, return_tensors='pt', truncation=True, padding=True) - + inputs = tokenizer(test_case, return_tensors="pt", truncation=True, padding=True) + with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_label = torch.argmax(outputs.logits, dim=1).item() confidence = probabilities[0][predicted_label].item() - + # Handle both string and integer keys if predicted_label in model.config.id2label: predicted_emotion = model.config.id2label[predicted_label] @@ -138,70 +158,76 @@ def test_new_trained_model(): else: predicted_emotion = f"unknown_{predicted_label}" expected_emotion = expected_emotions[i] - + result = { - 'input': test_case, - 'expected': expected_emotion, - 'predicted': predicted_emotion, - 'confidence': confidence, - 'correct': predicted_emotion == expected_emotion + "input": test_case, + "expected": expected_emotion, + "predicted": predicted_emotion, + "confidence": confidence, + "correct": predicted_emotion == expected_emotion, } results.append(result) - - status = "โœ…" if result['correct'] else "โŒ" + + status = "โœ…" if result["correct"] else "โŒ" print(f"{status} {i+1:2d}. \"{test_case[:50]}{'...' if len(test_case) > 50 else ''}\"") - print(f" Expected: {expected_emotion:12s} | Predicted: {predicted_emotion:12s} | Confidence: {confidence:.3f}") + print( + f" Expected: {expected_emotion:12s} | Predicted: {predicted_emotion:12s} | Confidence: {confidence:.3f}" + ) print() - + # 6. Performance analysis print("๐Ÿ“Š PERFORMANCE ANALYSIS") print("-" * 40) - - correct_predictions = sum(1 for r in results if r['correct']) + + correct_predictions = sum(1 for r in results if r["correct"]) total_predictions = len(results) accuracy = correct_predictions / total_predictions - avg_confidence = np.mean([r['confidence'] for r in results]) - + avg_confidence = np.mean([r["confidence"] for r in results]) + print(f"Accuracy: {accuracy:.2%} ({correct_predictions}/{total_predictions})") print(f"Average confidence: {avg_confidence:.3f}") - + # 7. Configuration persistence verification print("\n๐Ÿ” CONFIGURATION PERSISTENCE VERIFICATION") print("-" * 40) - + config_issues = [] - + # Check if num_labels is set - if not hasattr(model.config, 'num_labels') or model.config.num_labels is None: + if not hasattr(model.config, "num_labels") or model.config.num_labels is None: config_issues.append("num_labels is not set") - + # Check if problem_type is set - if not hasattr(model.config, 'problem_type') or model.config.problem_type is None: + if not hasattr(model.config, "problem_type") or model.config.problem_type is None: config_issues.append("problem_type is not set") - + # Check if id2label is properly formatted if not model.config.id2label: config_issues.append("id2label is missing") elif len(model.config.id2label) != len(expected_emotions): - config_issues.append(f"id2label has wrong length: {len(model.config.id2label)} vs {len(expected_emotions)}") - + config_issues.append( + f"id2label has wrong length: {len(model.config.id2label)} vs {len(expected_emotions)}" + ) + # Check if label2id is properly formatted if not model.config.label2id: config_issues.append("label2id is missing") elif len(model.config.label2id) != len(expected_emotions): - config_issues.append(f"label2id has wrong length: {len(model.config.label2id)} vs {len(expected_emotions)}") - + config_issues.append( + f"label2id has wrong length: {len(model.config.label2id)} vs {len(expected_emotions)}" + ) + if config_issues: print("โŒ Configuration issues found:") for issue in config_issues: print(f" - {issue}") else: print("โœ… Configuration persistence verified!") - + # 8. Final assessment print("\n๐ŸŽฏ FINAL ASSESSMENT") print("-" * 40) - + print("Configuration Status:") if config_issues: print("โŒ Configuration persistence issues detected") @@ -209,7 +235,7 @@ def test_new_trained_model(): else: print("โœ… Configuration persistence verified") print("โœ… Model should work correctly in deployment") - + print(f"\nPerformance Status:") if accuracy >= 0.8: print("โœ… Excellent performance (โ‰ฅ80% accuracy)") @@ -217,7 +243,7 @@ def test_new_trained_model(): print("โœ… Good performance (โ‰ฅ60% accuracy)") else: print("โŒ Poor performance (<60% accuracy)") - + print(f"\nConfidence Status:") if avg_confidence >= 0.7: print("โœ… High confidence predictions") @@ -225,31 +251,32 @@ def test_new_trained_model(): print("โš ๏ธ Moderate confidence predictions") else: print("โŒ Low confidence predictions") - + # 9. Summary print("\n๐Ÿ“‹ SUMMARY") print("-" * 40) - + print(f"โœ… Model loads successfully") print(f"โœ… Architecture is correct (DistilRoBERTa)") print(f"โœ… Emotion classes are properly configured") print(f"โœ… Inference works correctly") print(f"๐Ÿ“Š Test accuracy: {accuracy:.2%}") print(f"๐Ÿ“Š Average confidence: {avg_confidence:.3f}") - + if config_issues: print(f"โš ๏ธ Configuration issues: {len(config_issues)}") print(" Consider using the comprehensive notebook for better configuration persistence") else: print(f"โœ… Configuration persistence verified") print("โœ… Model ready for deployment!") - + return { - 'accuracy': accuracy, - 'avg_confidence': avg_confidence, - 'config_issues': config_issues, - 'results': results + "accuracy": accuracy, + "avg_confidence": avg_confidence, + "config_issues": config_issues, + "results": results, } + if __name__ == "__main__": - test_new_trained_model() \ No newline at end of file + test_new_trained_model() diff --git a/scripts/testing/test_numpy_compatibility.py b/scripts/testing/test_numpy_compatibility.py index de68fb00c..9ea2d9f49 100644 --- a/scripts/testing/test_numpy_compatibility.py +++ b/scripts/testing/test_numpy_compatibility.py @@ -3,35 +3,40 @@ Test script to verify numpy compatibility fix for transformers. """ -import sys import logging +import sys # Configure logging -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) + def test_numpy_compatibility(): """Test numpy compatibility with transformers.""" logger.info("๐Ÿงช Testing numpy compatibility...") - + try: # Test 1: Basic numpy import import numpy as np + logger.info(f"โœ… Numpy version: {np.__version__}") - + # Test 2: Check for broadcast_to function - if hasattr(np.lib.stride_tricks, 'broadcast_to'): + if hasattr(np.lib.stride_tricks, "broadcast_to"): logger.info("โœ… broadcast_to function exists") else: logger.warning("โš ๏ธ broadcast_to function missing, applying fix...") + def broadcast_to(array, shape): return np.broadcast_arrays(array, np.empty(shape))[0] + np.lib.stride_tricks.broadcast_to = broadcast_to logger.info("โœ… broadcast_to function added") - + # Test 3: Test transformers import try: - from transformers import AutoModel, AutoTokenizer + from transformers import AutoTokenizer + logger.info("โœ… Transformers import successful") except ImportError as e: if "broadcast_to" in str(e): @@ -40,7 +45,7 @@ def broadcast_to(array, shape): else: logger.error(f"โŒ Other transformers import error: {e}") return False - + # Test 4: Test basic transformers functionality try: tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") @@ -48,15 +53,16 @@ def broadcast_to(array, shape): except Exception as e: logger.error(f"โŒ Tokenizer loading failed: {e}") return False - + logger.info("๐ŸŽ‰ All numpy compatibility tests passed!") return True - + except Exception as e: logger.error(f"โŒ Test failed: {e}") return False + if __name__ == "__main__": success = test_numpy_compatibility() if not success: - sys.exit(1) \ No newline at end of file + sys.exit(1) diff --git a/scripts/testing/test_phase3_cloud_run_optimization.py b/scripts/testing/test_phase3_cloud_run_optimization.py deleted file mode 100644 index d063ecd45..000000000 --- a/scripts/testing/test_phase3_cloud_run_optimization.py +++ /dev/null @@ -1,589 +0,0 @@ -#!/usr/bin/env python3 -""" -Phase 3 Cloud Run Optimization Test Suite -Comprehensive testing for Cloud Run optimization components using enhanced test approach -""" - -import os -import sys -import yaml -import json -import time -from pathlib import Path -from typing import Dict, Any, List, Optional -import unittest -from unittest.mock import patch -import logging - -# Add src to path for imports -sys.path.insert(0, str(Path(__file__).parent.parent.parent / 'src')) - -class Phase3CloudRunOptimizationTest(unittest.TestCase): - """Comprehensive test suite for Phase 3 Cloud Run optimization""" - - def setUp(self): - """Set up test environment""" - # Get the project root directory (2 levels up from scripts/testing) - self.project_root = Path(__file__).parent.parent.parent - self.cloud_run_dir = self.project_root / "deployment" / "cloud-run" - - # Alternative path calculation for when running from scripts/testing - if not self.cloud_run_dir.exists(): - # When running from scripts/testing, use relative path - self.cloud_run_dir = Path("../../deployment/cloud-run").resolve() - - # Ensure the cloud-run directory exists - self.assertTrue(self.cloud_run_dir.exists(), f"Cloud Run directory not found: {self.cloud_run_dir}") - - # Set up logging for tests - logging.basicConfig(level=logging.INFO) - self.logger = logging.getLogger(__name__) - - self.maxDiff = None - - # Test configuration - self.test_config = { - 'environment': 'test', - 'memory_limit_mb': 1024, - 'cpu_limit': 1, - 'max_instances': 5, - 'min_instances': 1, - 'concurrency': 40, - 'timeout_seconds': 180, - 'health_check_interval': 30, - 'graceful_shutdown_timeout': 15 - } - - def test_01_cloudbuild_yaml_structure(self): - """Test Cloud Build YAML structure and validation""" - print("๐Ÿ” Testing Cloud Build YAML structure...") - - cloudbuild_path = self.cloud_run_dir / 'cloudbuild.yaml' - self.assertTrue(cloudbuild_path.exists(), "cloudbuild.yaml should exist") - - with open(cloudbuild_path, 'r') as f: - config = yaml.safe_load(f) - - # Validate required fields - required_fields = ['steps', 'images', 'timeout'] - self._assert_all_fields_present(config, required_fields) - - # Validate steps structure - steps = config['steps'] - self.assertIsInstance(steps, list, "Steps should be a list") - self.assertGreater(len(steps), 0, "Should have at least one step") - - # Validate each step has required fields - self._assert_all_steps_valid(steps) - - # Validate timeout format - timeout = config['timeout'] - self.assertIsInstance(timeout, str, "Timeout should be a string") - self.assertTrue(timeout.endswith('s'), "Timeout should end with 's'") - - print("โœ… Cloud Build YAML structure validation passed") - - def _assert_all_fields_present(self, config, required_fields): - """Helper method to check all required fields are present""" - missing_fields = [field for field in required_fields if field not in config] - if missing_fields: - self.fail(f"Missing required fields: {', '.join(missing_fields)}") - - def _assert_all_steps_valid(self, steps): - """Helper method to validate all steps""" - invalid_steps = [] - for i, step in enumerate(steps): - if 'name' not in step or 'args' not in step: - invalid_steps.append(f"Step {i}") - - if invalid_steps: - self.fail(f"Invalid steps: {', '.join(invalid_steps)}") - - def test_02_health_monitor_functionality(self): - """Test health monitor functionality and metrics collection""" - print("๐Ÿ” Testing health monitor functionality...") - - # Import health monitor - sys.path.insert(0, str(self.cloud_run_dir)) - try: - from health_monitor import HealthMonitor, HealthMetrics - except ImportError as e: - if 'psutil' in str(e): - self.skipTest("psutil not available in test environment") - raise - - # Test health monitor initialization - monitor = HealthMonitor() - self.assertIsNotNone(monitor, "Health monitor should initialize") - self.assertFalse(monitor.is_shutting_down, "Should not be shutting down initially") - self.assertEqual(monitor.active_requests, 0, "Should start with 0 active requests") - - # Test system metrics - metrics = monitor.get_system_metrics() - self._test_required_metrics(metrics) - - # Test request tracking - monitor.request_started() - self.assertEqual(monitor.active_requests, 1, "Should track request start") - - monitor.request_completed() - self.assertEqual(monitor.active_requests, 0, "Should track request completion") - - # Test edge case: multiple rapid requests - self._test_multiple_requests(monitor) - - # Test edge case: negative requests (should not go below 0) - monitor.request_completed() - self.assertEqual(monitor.active_requests, 0, "Should not go below 0 active requests") - - print("โœ… Health monitor functionality tests passed") - - def _test_required_metrics(self, metrics): - """Helper method to test required metrics""" - required_metrics = ['memory_usage_mb', 'cpu_usage_percent', 'memory_percent', 'uptime_seconds'] - missing_metrics = [metric for metric in required_metrics if metric not in metrics] - if missing_metrics: - self.fail(f"Missing metrics: {', '.join(missing_metrics)}") - - # Check all metrics are numeric - non_numeric_metrics = [metric for metric in required_metrics if not isinstance(metrics[metric], (int, float))] - if non_numeric_metrics: - self.fail(f"Non-numeric metrics: {', '.join(non_numeric_metrics)}") - - def _test_multiple_requests(self, monitor): - """Helper method to test multiple requests""" - # Add 10 requests - for i in range(10): - monitor.request_started() - self.assertEqual(monitor.active_requests, 10, "Should handle multiple requests") - - # Complete 10 requests - for i in range(10): - monitor.request_completed() - self.assertEqual(monitor.active_requests, 0, "Should handle multiple completions") - - def test_03_environment_config_validation(self): - """Test environment configuration validation and edge cases""" - print("๐Ÿ” Testing environment configuration validation...") - - # Import config - sys.path.insert(0, str(self.cloud_run_dir)) - from config import EnvironmentConfig - - # Test production configuration - with patch.dict(os.environ, {'ENVIRONMENT': 'production'}): - config = EnvironmentConfig() - self.assertEqual(config.environment, 'production', "Should load production environment") - - # Test configuration validation - config.validate_config() # Should not raise exception for valid config - - # Test resource limits - cloud_config = config.config - self.assertGreaterEqual(cloud_config.memory_limit_mb, 512, "Memory should be >= 512MB") - self.assertLessEqual(cloud_config.memory_limit_mb, 8192, "Memory should be <= 8GB") - self.assertGreaterEqual(cloud_config.cpu_limit, 1, "CPU should be >= 1") - self.assertLessEqual(cloud_config.cpu_limit, 8, "CPU should be <= 8") - - # Test staging configuration - with patch.dict(os.environ, {'ENVIRONMENT': 'staging'}): - config = EnvironmentConfig() - self.assertEqual(config.environment, 'staging', "Should load staging environment") - config.validate_config() # Should not raise exception for valid config - - # Test development configuration - with patch.dict(os.environ, {'ENVIRONMENT': 'development'}): - config = EnvironmentConfig() - self.assertEqual(config.environment, 'development', "Should load development environment") - config.validate_config() # Should not raise exception for valid config - - # Test edge case: invalid environment - with patch.dict(os.environ, {'ENVIRONMENT': 'invalid'}): - config = EnvironmentConfig() - self.assertEqual(config.environment, 'invalid', "Should load invalid environment") - # Should still be valid as it falls back to development defaults - - print("โœ… Environment configuration validation tests passed") - - def test_04_dockerfile_optimization(self): - """Test Dockerfile optimization and security features""" - print("๐Ÿ” Testing Dockerfile optimization...") - - dockerfile_path = self.cloud_run_dir / 'Dockerfile.secure' - self.assertTrue(dockerfile_path.exists(), "Dockerfile.secure should exist") - - with open(dockerfile_path, 'r') as f: - content = f.read() - - # Test security features - self._test_security_features(content) - - # Test Cloud Run optimizations - self._test_cloud_run_features(content) - - # Test resource optimization - self._test_optimization_features(content) - - print("โœ… Dockerfile optimization tests passed") - - def _test_security_features(self, content): - """Helper method to test security features""" - security_features = [ - 'FROM --platform=linux/amd64', # Platform targeting - 'USER appuser', # Non-root user - 'HEALTHCHECK', # Health check - '--no-cache-dir', # No cache for security - 'PYTHONHASHSEED=random', # Random hash seed - 'PIP_DISABLE_PIP_VERSION_CHECK=1' # Disable pip version check - ] - - missing_features = [feature for feature in security_features if feature not in content] - if missing_features: - self.fail(f"Missing security features: {', '.join(missing_features)}") - - def _test_cloud_run_features(self, content): - """Helper method to test Cloud Run features""" - cloud_run_features = [ - 'EXPOSE 8080', # Cloud Run port - '--bind :$PORT', # Dynamic port binding - '--workers 1', # Single worker for Cloud Run - '--timeout 0', # Cloud Run handles timeouts - '--keep-alive 5' # Keep-alive optimization - ] - - missing_features = [feature for feature in cloud_run_features if feature not in content] - if missing_features: - self.fail(f"Missing Cloud Run features: {', '.join(missing_features)}") - - def _test_optimization_features(self, content): - """Helper method to test optimization features""" - optimization_features = [ - '--max-requests 1000', # Request recycling - '--max-requests-jitter 100', # Jitter for load distribution - '--access-logfile -', # Structured logging - '--error-logfile -' # Error logging - ] - - missing_features = [feature for feature in optimization_features if feature not in content] - if missing_features: - self.fail(f"Missing optimization features: {', '.join(missing_features)}") - - def test_05_requirements_security(self): - """Test requirements.txt security and version pinning""" - print("๐Ÿ” Testing requirements security...") - - requirements_path = self.cloud_run_dir / 'requirements_secure.txt' - self.assertTrue(requirements_path.exists(), "requirements_secure.txt should exist") - - with open(requirements_path, 'r') as f: - content = f.read() - - # Test required dependencies (updated to match actual requirements format) - required_deps = [ - 'flask==', # Web framework (exact version pinning) - 'gunicorn==', # WSGI server - 'psutil==', # System monitoring - 'requests==', # HTTP client - 'prometheus-client==' # Metrics - ] - - missing_deps = [dep for dep in required_deps if dep not in content] - if missing_deps: - self.fail(f"Missing required dependencies: {', '.join(missing_deps)}") - - # Test version pinning (dependencies should have == for exact versions) - lines = content.split('\n') - unpinned_deps = [] - for line in lines: - line = line.strip() - if (line and not line.startswith('#') and - '==' not in line and '>=' not in line and '<=' not in line): - unpinned_deps.append(line) - - if unpinned_deps: - self.fail(f"Unpinned dependencies: {', '.join(unpinned_deps)}") - - print("โœ… Requirements security tests passed") - - def test_06_auto_scaling_configuration(self): - """Test auto-scaling configuration and validation""" - print("๐Ÿ” Testing auto-scaling configuration...") - - cloudbuild_path = self.cloud_run_dir / 'cloudbuild.yaml' - with open(cloudbuild_path, 'r') as f: - config = yaml.safe_load(f) - - # Find Cloud Run deployment step - deploy_step = self._find_deploy_step(config) - self.assertIsNotNone(deploy_step, "Should have Cloud Run deployment step") - - # Get args from the step - args = deploy_step.get('args', []) - self.assertIsInstance(args, list, "Args should be a list") - self.assertGreater(len(args), 0, "Should have deployment arguments") - - # Test auto-scaling parameters (Cloud Build format: --param=value) - scaling_params = [ - '--max-instances=10', - '--min-instances=1', - '--concurrency=80' - ] - - missing_params = [param for param in scaling_params if param not in args] - if missing_params: - self.fail(f"Missing auto-scaling parameters: {', '.join(missing_params)}") - - # Test resource allocation (Cloud Build format: --param=value) - resource_params = [ - '--memory=2Gi', - '--cpu=2' - ] - - missing_resource_params = [param for param in resource_params if param not in args] - if missing_resource_params: - self.fail(f"Missing resource parameters: {', '.join(missing_resource_params)}") - - print("โœ… Auto-scaling configuration tests passed") - - def _find_deploy_step(self, config): - """Helper method to find deployment step""" - for step in config['steps']: - if 'gcr.io/google.com/cloudsdktool/cloud-sdk' in step.get('name', ''): - return step - return None - - def test_07_health_check_integration(self): - """Test health check integration and monitoring""" - print("๐Ÿ” Testing health check integration...") - - # Test health check endpoint configuration - cloudbuild_path = self.cloud_run_dir / 'cloudbuild.yaml' - with open(cloudbuild_path, 'r') as f: - config = yaml.safe_load(f) - - # Check for health check environment variables - deploy_step = self._find_deploy_step(config) - self.assertIsNotNone(deploy_step, "Should have deployment step") - - args = deploy_step['args'] - - # Test health check environment variables (updated to match actual format) - health_vars = [ - 'HEALTH_CHECK_INTERVAL=30', - 'GRACEFUL_SHUTDOWN_TIMEOUT=30', - 'ENABLE_HEALTH_CHECKS=true' - ] - - # Check if the environment variables are set in any --set-env-vars argument - env_vars_found = 0 - for arg in args: - if arg.startswith('--set-env-vars='): - for var in health_vars: - if var in arg: - env_vars_found += 1 - - self.assertGreaterEqual(env_vars_found, 2, f"Should have at least 2 health check environment variables, found {env_vars_found}") - - print("โœ… Health check integration tests passed") - - def test_08_configuration_edge_cases(self): - """Test configuration edge cases and error handling""" - print("๐Ÿ” Testing configuration edge cases...") - - sys.path.insert(0, str(self.cloud_run_dir)) - from config import EnvironmentConfig - - # Test invalid memory limits - with patch.dict(os.environ, { - 'ENVIRONMENT': 'production', - 'MEMORY_LIMIT_MB': '100' # Too low - }): - config = EnvironmentConfig() - # Should still be valid as it uses defaults - - # Test invalid CPU limits - with patch.dict(os.environ, { - 'ENVIRONMENT': 'production', - 'CPU_LIMIT': '10' # Too high - }): - config = EnvironmentConfig() - # Should still be valid as it uses defaults - - # Test invalid timeout - with patch.dict(os.environ, { - 'ENVIRONMENT': 'production', - 'TIMEOUT_SECONDS': '1000' # Too high - }): - config = EnvironmentConfig() - # Should still be valid as it uses defaults - - # Test empty environment variables - with patch.dict(os.environ, { - 'ENVIRONMENT': 'production', - 'MEMORY_LIMIT_MB': '', - 'CPU_LIMIT': '', - 'MAX_INSTANCES': '' - }): - config = EnvironmentConfig() - config.validate_config() # Should not raise exception for valid config - - print("โœ… Configuration edge case tests passed") - - def test_09_performance_metrics(self): - """Test performance metrics and monitoring""" - print("๐Ÿ” Testing performance metrics...") - - sys.path.insert(0, str(self.cloud_run_dir)) - try: - from health_monitor import HealthMonitor - except ImportError as e: - if 'psutil' in str(e): - self.skipTest("psutil not available in test environment") - raise - - monitor = HealthMonitor() - - # Test metrics collection - metrics = monitor.get_comprehensive_health() - - required_metrics = [ - 'status', 'timestamp', 'uptime_seconds', - 'system', 'models', 'api', 'requests' - ] - - missing_metrics = [metric for metric in required_metrics if metric not in metrics] - if missing_metrics: - self.fail(f"Missing performance metrics: {', '.join(missing_metrics)}") - - # Test system metrics structure - system_metrics = metrics['system'] - system_required = ['memory_usage_mb', 'cpu_usage_percent', 'memory_percent'] - - missing_system_metrics = [metric for metric in system_required if metric not in system_metrics] - if missing_system_metrics: - self.fail(f"Missing system metrics: {', '.join(missing_system_metrics)}") - - # Check all system metrics are numeric - non_numeric_system_metrics = [metric for metric in system_required if not isinstance(system_metrics[metric], (int, float))] - if non_numeric_system_metrics: - self.fail(f"Non-numeric system metrics: {', '.join(non_numeric_system_metrics)}") - - # Test request metrics - request_metrics = metrics['requests'] - self.assertIn('active', request_metrics, "Should track active requests") - self.assertIn('total_processed', request_metrics, "Should track total processed requests") - - print("โœ… Performance metrics tests passed") - - def test_10_yaml_parsing_validation(self): - """Test YAML parsing and validation using enhanced test approach""" - print("๐Ÿ” Testing YAML parsing and validation...") - - # Test Cloud Build YAML parsing - cloudbuild_path = self.cloud_run_dir / 'cloudbuild.yaml' - with open(cloudbuild_path, 'r') as f: - config = yaml.safe_load(f) - - # Validate YAML structure using enhanced approach - self._validate_yaml_structure(config, 'cloudbuild.yaml') - - # Test configuration serialization - sys.path.insert(0, str(self.cloud_run_dir)) - from config import EnvironmentConfig - - config_obj = EnvironmentConfig('production') - config_dict = config_obj.to_dict() - - # Convert to YAML and back to test serialization - yaml_str = yaml.dump(config_dict, default_flow_style=False) - parsed_config = yaml.safe_load(yaml_str) - - self.assertEqual(config_dict, parsed_config, "YAML serialization should be reversible") - - print("โœ… YAML parsing validation tests passed") - - def _validate_yaml_structure(self, config: Dict[str, Any], filename: str): - """Enhanced YAML structure validation""" - # Validate top-level structure - self.assertIsInstance(config, dict, f"{filename} should be a dictionary") - - # Validate required top-level keys - if filename == 'cloudbuild.yaml': - required_keys = ['steps', 'images'] - missing_keys = [key for key in required_keys if key not in config] - if missing_keys: - self.fail(f"{filename} missing required keys: {', '.join(missing_keys)}") - - # Validate nested structures - if 'steps' in config: - self.assertIsInstance(config['steps'], list, "Steps should be a list") - invalid_steps = [] - for i, step in enumerate(config['steps']): - if not isinstance(step, dict): - invalid_steps.append(f"Step {i} should be a dictionary") - elif 'name' not in step or 'args' not in step: - invalid_steps.append(f"Step {i} missing required fields") - - if invalid_steps: - self.fail(f"Invalid steps: {', '.join(invalid_steps)}") - -def run_phase3_tests(): - """Run all Phase 3 Cloud Run optimization tests""" - print("๐Ÿš€ Starting Phase 3 Cloud Run Optimization Test Suite") - print("=" * 60) - - # Create test suite - suite = unittest.TestLoader().loadTestsFromTestCase(Phase3CloudRunOptimizationTest) - - # Run tests - runner = unittest.TextTestRunner(verbosity=2) - result = runner.run(suite) - - # Generate test report - test_report = { - 'phase': 'Phase 3 - Cloud Run Optimization', - 'total_tests': result.testsRun, - 'failures': len(result.failures), - 'errors': len(result.errors), - 'success_rate': ((result.testsRun - len(result.failures) - len(result.errors)) / result.testsRun) * 100, - 'timestamp': time.strftime('%Y-%m-%d %H:%M:%S'), - 'test_details': [] - } - - # Add test details - for test, traceback in result.failures: - test_report['test_details'].append({ - 'test': test._testMethodName, - 'status': 'FAILED', - 'error': traceback - }) - - for test, traceback in result.errors: - test_report['test_details'].append({ - 'test': test._testMethodName, - 'status': 'ERROR', - 'error': traceback - }) - - # Save test report - report_path = Path(__file__).parent / 'phase3_test_report.json' - with open(report_path, 'w') as f: - json.dump(test_report, f, indent=2) - - print("\n" + "=" * 60) - print("๐Ÿ“Š Phase 3 Test Results:") - print(f" Total Tests: {test_report['total_tests']}") - print(f" Failures: {test_report['failures']}") - print(f" Errors: {test_report['errors']}") - print(f" Success Rate: {test_report['success_rate']:.1f}%") - print(f" Report saved to: {report_path}") - - if result.wasSuccessful(): - print("โœ… All Phase 3 tests passed!") - return True - print("โŒ Some Phase 3 tests failed!") - return False - -if __name__ == '__main__': - success = run_phase3_tests() - sys.exit(0 if success else 1) \ No newline at end of file diff --git a/scripts/testing/test_phase3_cloud_run_optimization_fixed.py b/scripts/testing/test_phase3_cloud_run_optimization_fixed.py deleted file mode 100644 index a846c6b2b..000000000 --- a/scripts/testing/test_phase3_cloud_run_optimization_fixed.py +++ /dev/null @@ -1,376 +0,0 @@ -#!/usr/bin/env python3 -""" -Phase 3 Cloud Run Optimization Test Suite - Fixed Version -Comprehensive testing for Cloud Run optimization components without loops/conditionals -""" -import sys -import yaml -from pathlib import Path -from typing import Dict, Any, List, Optional -import unittest - -# Add src to path for imports -sys.path.insert(0, str(Path(__file__).parent.parent.parent / 'src')) - -class Phase3CloudRunOptimizationTestFixed(unittest.TestCase): - """Fixed test suite for Phase 3 Cloud Run optimization - no loops/conditionals""" - - def setUp(self): - """Set up test environment""" - self.test_dir = Path(__file__).parent - self.cloud_run_dir = self.test_dir.parent.parent / 'deployment' / 'cloud-run' - self.maxDiff = None - - # Test configuration - self.test_config = { - 'environment': 'test', - 'memory_limit_mb': 1024, - 'cpu_limit': 1, - 'max_instances': 5, - 'min_instances': 1, - 'concurrency': 40, - 'timeout_seconds': 180, - 'health_check_interval': 30, - 'graceful_shutdown_timeout': 15 - } - - def test_01_cloudbuild_yaml_structure(self): - """Test Cloud Build YAML structure and validation - no loops""" - print("๐Ÿ” Testing Cloud Build YAML structure...") - - cloudbuild_path = self.cloud_run_dir / 'cloudbuild.yaml' - self.assertTrue(cloudbuild_path.exists(), "cloudbuild.yaml should exist") - - with open(cloudbuild_path, 'r') as f: - config = yaml.safe_load(f) - - # Validate required fields - individual assertions instead of loop - self.assertIn('steps', config, "Missing required field: steps") - self.assertIn('images', config, "Missing required field: images") - self.assertIn('timeout', config, "Missing required field: timeout") - - # Validate steps structure - steps = config['steps'] - self.assertIsInstance(steps, list, "Steps should be a list") - self.assertGreater(len(steps), 0, "Should have at least one step") - - # Validate first step has required fields - if len(steps) > 0: - first_step = steps[0] - self.assertIn('name', first_step, "First step missing 'name' field") - self.assertIn('args', first_step, "First step missing 'args' field") - - # Validate timeout format - timeout = config['timeout'] - self.assertIsInstance(timeout, str, "Timeout should be a string") - self.assertTrue(timeout.endswith('s'), "Timeout should end with 's'") - - print("โœ… Cloud Build YAML structure validation passed") - - def test_02_health_monitor_initialization(self): - """Test health monitor initialization - no conditionals""" - print("๐Ÿ” Testing health monitor initialization...") - - # Import health monitor with graceful fallback - sys.path.insert(0, str(self.cloud_run_dir)) - try: - from health_monitor import HealthMonitor, HealthMetrics - except ImportError: - self.skipTest("Health monitor not available in test environment") - - # Test health monitor initialization - monitor = HealthMonitor() - self.assertIsNotNone(monitor, "Health monitor should initialize") - self.assertFalse(monitor.is_shutting_down, "Should not be shutting down initially") - self.assertEqual(monitor.active_requests, 0, "Should start with 0 active requests") - - print("โœ… Health monitor initialization passed") - - def test_03_system_metrics_structure(self): - """Test system metrics structure - no loops""" - print("๐Ÿ” Testing system metrics structure...") - - sys.path.insert(0, str(self.cloud_run_dir)) - try: - from health_monitor import HealthMonitor - except ImportError: - self.skipTest("Health monitor not available in test environment") - - monitor = HealthMonitor() - metrics = monitor.get_system_metrics() - - # Individual assertions instead of loop - self.assertIn('memory_usage_mb', metrics, "Missing metric: memory_usage_mb") - self.assertIn('cpu_usage_percent', metrics, "Missing metric: cpu_usage_percent") - self.assertIn('memory_percent', metrics, "Missing metric: memory_percent") - self.assertIn('uptime_seconds', metrics, "Missing metric: uptime_seconds") - - # Validate metric types - self.assertIsInstance(metrics['memory_usage_mb'], (int, float), "memory_usage_mb should be numeric") - self.assertIsInstance(metrics['cpu_usage_percent'], (int, float), "cpu_usage_percent should be numeric") - self.assertIsInstance(metrics['memory_percent'], (int, float), "memory_percent should be numeric") - self.assertIsInstance(metrics['uptime_seconds'], (int, float), "uptime_seconds should be numeric") - - print("โœ… System metrics structure validation passed") - - def test_04_request_tracking(self): - """Test request tracking functionality - no loops""" - print("๐Ÿ” Testing request tracking...") - - sys.path.insert(0, str(self.cloud_run_dir)) - try: - from health_monitor import HealthMonitor - except ImportError: - self.skipTest("Health monitor not available in test environment") - - monitor = HealthMonitor() - - # Test single request tracking - monitor.request_started() - self.assertEqual(monitor.active_requests, 1, "Should track single request start") - - monitor.request_completed() - self.assertEqual(monitor.active_requests, 0, "Should track single request completion") - - print("โœ… Request tracking validation passed") - - def test_05_environment_config_validation(self): - """Test environment configuration validation - no loops""" - print("๐Ÿ” Testing environment configuration...") - - config_path = self.cloud_run_dir / 'config.py' - self.assertTrue(config_path.exists(), "config.py should exist") - - with open(config_path, 'r') as f: - content = f.read() - - # Check for required configuration elements - required_elements = [ - 'class Config', - 'def __init__', - 'environment', - 'memory_limit_mb', - 'cpu_limit' - ] - - # Individual assertions instead of loop - self.assertIn('class Config', content, "Missing Config class") - self.assertIn('def __init__', content, "Missing __init__ method") - self.assertIn('environment', content, "Missing environment configuration") - self.assertIn('memory_limit_mb', content, "Missing memory_limit_mb configuration") - self.assertIn('cpu_limit', content, "Missing cpu_limit configuration") - - print("โœ… Environment configuration validation passed") - - def test_06_dockerfile_optimization(self): - """Test Dockerfile optimization features - no loops""" - print("๐Ÿ” Testing Dockerfile optimization...") - - dockerfile_path = self.cloud_run_dir / 'Dockerfile.secure' - self.assertTrue(dockerfile_path.exists(), "Dockerfile.secure should exist") - - with open(dockerfile_path, 'r') as f: - content = f.read() - - # Check for optimization features - optimization_features = [ - 'FROM python:3.9-slim', - 'WORKDIR /app', - 'COPY requirements_secure.txt', - 'RUN pip install', - 'EXPOSE 8080', - 'HEALTHCHECK' - ] - - # Individual assertions instead of loop - self.assertIn('FROM python:3.9-slim', content, "Missing Python base image") - self.assertIn('WORKDIR /app', content, "Missing working directory") - self.assertIn('COPY requirements_secure.txt', content, "Missing requirements copy") - self.assertIn('RUN pip install', content, "Missing pip install") - self.assertIn('EXPOSE 8080', content, "Missing port exposure") - self.assertIn('HEALTHCHECK', content, "Missing health check") - - print("โœ… Dockerfile optimization validation passed") - - def test_07_requirements_security(self): - """Test requirements security - no loops""" - print("๐Ÿ” Testing requirements security...") - - requirements_path = self.cloud_run_dir / 'requirements_secure.txt' - self.assertTrue(requirements_path.exists(), "requirements_secure.txt should exist") - - with open(requirements_path, 'r') as f: - content = f.read() - - # Check for required dependencies - required_dependencies = [ - 'flask', - 'torch', - 'transformers', - 'numpy', - 'scikit-learn', - 'gunicorn', - 'cryptography', - 'bcrypt', - 'redis', - 'psutil', - 'prometheus-client', - 'requests', - 'fastapi' - ] - - # Individual assertions instead of loop - self.assertIn('flask', content, "Missing Flask dependency") - self.assertIn('torch', content, "Missing PyTorch dependency") - self.assertIn('transformers', content, "Missing Transformers dependency") - self.assertIn('numpy', content, "Missing NumPy dependency") - self.assertIn('scikit-learn', content, "Missing Scikit-learn dependency") - self.assertIn('gunicorn', content, "Missing Gunicorn dependency") - self.assertIn('cryptography', content, "Missing Cryptography dependency") - self.assertIn('bcrypt', content, "Missing bcrypt dependency") - self.assertIn('redis', content, "Missing Redis dependency") - self.assertIn('psutil', content, "Missing psutil dependency") - self.assertIn('prometheus-client', content, "Missing prometheus-client dependency") - self.assertIn('requests', content, "Missing requests dependency") - self.assertIn('fastapi', content, "Missing FastAPI dependency") - - print("โœ… Requirements security validation passed") - - def test_08_auto_scaling_configuration(self): - """Test auto-scaling configuration - no loops""" - print("๐Ÿ” Testing auto-scaling configuration...") - - cloudbuild_path = self.cloud_run_dir / 'cloudbuild.yaml' - self.assertTrue(cloudbuild_path.exists(), "cloudbuild.yaml should exist") - - with open(cloudbuild_path, 'r') as f: - config = yaml.safe_load(f) - - # Get deployment step - deployment_step = None - for step in config['steps']: - if 'gcloud' in step.get('name', '') and 'run' in step.get('args', []): - deployment_step = step - break - - self.assertIsNotNone(deployment_step, "Should have deployment step") - - args = deployment_step['args'] - args_str = ' '.join(args) - - # Check for auto-scaling parameters - self.assertIn('--max-instances', args_str, "Missing max-instances parameter") - self.assertIn('--min-instances', args_str, "Missing min-instances parameter") - self.assertIn('--concurrency', args_str, "Missing concurrency parameter") - self.assertIn('--memory', args_str, "Missing memory parameter") - self.assertIn('--cpu', args_str, "Missing cpu parameter") - - print("โœ… Auto-scaling configuration validation passed") - - def test_09_health_check_integration(self): - """Test health check integration - no loops""" - print("๐Ÿ” Testing health check integration...") - - cloudbuild_path = self.cloud_run_dir / 'cloudbuild.yaml' - self.assertTrue(cloudbuild_path.exists(), "cloudbuild.yaml should exist") - - with open(cloudbuild_path, 'r') as f: - config = yaml.safe_load(f) - - # Get deployment step - deployment_step = None - for step in config['steps']: - if 'gcloud' in step.get('name', '') and 'run' in step.get('args', []): - deployment_step = step - break - - self.assertIsNotNone(deployment_step, "Should have deployment step") - - args = deployment_step['args'] - args_str = ' '.join(args) - - # Check for health and monitoring environment variables - self.assertIn('HEALTH_CHECK_INTERVAL', args_str, "Missing health check interval") - self.assertIn('GRACEFUL_SHUTDOWN_TIMEOUT', args_str, "Missing graceful shutdown timeout") - self.assertIn('ENABLE_MONITORING', args_str, "Missing monitoring enablement") - self.assertIn('ENABLE_HEALTH_CHECKS', args_str, "Missing health checks enablement") - - print("โœ… Health check integration validation passed") - - def test_10_yaml_parsing_validation(self): - """Test YAML parsing validation - no loops""" - print("๐Ÿ” Testing YAML parsing validation...") - - cloudbuild_path = self.cloud_run_dir / 'cloudbuild.yaml' - self.assertTrue(cloudbuild_path.exists(), "cloudbuild.yaml should exist") - - # Test YAML parsing - with open(cloudbuild_path, 'r') as f: - config = yaml.safe_load(f) - - # Validate basic structure - self.assertIsInstance(config, dict, "Config should be a dictionary") - self.assertIn('steps', config, "Should have steps") - self.assertIn('images', config, "Should have images") - self.assertIn('timeout', config, "Should have timeout") - - # Validate steps is a list - steps = config['steps'] - self.assertIsInstance(steps, list, "Steps should be a list") - - # Validate images is a list - images = config['images'] - self.assertIsInstance(images, list, "Images should be a list") - - print("โœ… YAML parsing validation passed") - -def run_phase3_tests_fixed(): - """Run all Phase 3 tests with fixed approach""" - print("๐Ÿš€ RUNNING PHASE 3 CLOUD RUN OPTIMIZATION TESTS (FIXED VERSION)") - print("=" * 70) - - # Create test suite - loader = unittest.TestLoader() - suite = loader.loadTestsFromTestCase(Phase3CloudRunOptimizationTestFixed) - - # Run tests - runner = unittest.TextTestRunner(verbosity=2) - result = runner.run(suite) - - # Print summary - print("\n" + "=" * 70) - print("๐Ÿ“Š PHASE 3 TEST RESULTS SUMMARY") - print("=" * 70) - print(f"Tests run: {result.testsRun}") - print(f"Failures: {len(result.failures)}") - print(f"Errors: {len(result.errors)}") - print(f"Skipped: {len(result.skipped)}") - - if result.failures: - print("\nโŒ FAILURES:") - for test, traceback in result.failures: - print(f" - {test}: {traceback.split('AssertionError:')[-1].strip()}") - - if result.errors: - print("\nโŒ ERRORS:") - for test, traceback in result.errors: - print(f" - {test}: {traceback.split('Exception:')[-1].strip()}") - - if result.skipped: - print("\nโš ๏ธ SKIPPED:") - for test, reason in result.skipped: - print(f" - {test}: {reason}") - - success = len(result.failures) == 0 and len(result.errors) == 0 - if success: - print("\n๐ŸŽ‰ ALL PHASE 3 TESTS PASSED!") - print("โœ… Cloud Run optimization is ready for deployment") - else: - print("\nโŒ SOME PHASE 3 TESTS FAILED!") - print("Please fix the issues before proceeding with deployment") - - return success - -if __name__ == "__main__": - success = run_phase3_tests_fixed() - sys.exit(0 if success else 1) \ No newline at end of file diff --git a/scripts/testing/test_phase4_vertex_ai_automation.py b/scripts/testing/test_phase4_vertex_ai_automation.py deleted file mode 100644 index 47072f532..000000000 --- a/scripts/testing/test_phase4_vertex_ai_automation.py +++ /dev/null @@ -1,493 +0,0 @@ -#!/usr/bin/env python3 -""" -Phase 4: Vertex AI Deployment Automation Test Suite -Comprehensive testing for Phase 4 Vertex AI automation features -""" -import sys -from pathlib import Path -from typing import Dict, Any, List, Optional -import unittest - -# Add src to path for imports -sys.path.insert(0, str(Path(__file__).parent.parent.parent / 'src')) - -class Phase4VertexAIAutomationTest(unittest.TestCase): - """Comprehensive test suite for Phase 4 Vertex AI automation""" - - def setUp(self): - """Set up test environment""" - self.test_dir = Path(__file__).parent - self.deployment_dir = self.test_dir.parent.parent / 'deployment' - self.vertex_ai_script = self.deployment_dir / 'vertex_ai_phase4_automation.py' - self.maxDiff = None - - # Test configuration - self.test_config = { - 'project_id': 'test-project-123', - 'region': 'us-central1', - 'model_name': 'test-emotion-detection', - 'endpoint_name': 'test-endpoint', - 'machine_type': 'n1-standard-2', - 'min_replicas': 1, - 'max_replicas': 5, - 'cost_budget': 50.0 - } - - def test_01_script_structure(self): - """Test Phase 4 automation script structure""" - print("๐Ÿ” Testing Phase 4 automation script structure...") - - self.assertTrue(self.vertex_ai_script.exists(), "Vertex AI automation script should exist") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for required classes and methods - required_elements = [ - 'class DeploymentConfig', - 'class VertexAIPhase4Automation', - 'def check_prerequisites', - 'def generate_model_version', - 'def create_deployment_package', - 'def build_and_push_image', - 'def create_vertex_ai_model', - 'def deploy_model_to_endpoint', - 'def setup_monitoring_and_alerting', - 'def setup_cost_monitoring', - 'def rollback_deployment', - 'def setup_ab_testing', - 'def get_performance_metrics', - 'def cleanup_old_versions', - 'def run_full_deployment' - ] - - for element in required_elements: - self.assertIn(element, content, f"Missing required element: {element}") - - print("โœ… Phase 4 automation script structure validation passed") - - def test_02_deployment_config_dataclass(self): - """Test DeploymentConfig dataclass structure""" - print("๐Ÿ” Testing DeploymentConfig dataclass...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for dataclass import and usage - self.assertIn('from dataclasses import dataclass', content, "Missing dataclass import") - self.assertIn('@dataclass', content, "Missing dataclass decorator") - - # Check for required configuration fields - required_fields = [ - 'project_id: str', - 'region: str', - 'model_name: str', - 'endpoint_name: str', - 'machine_type: str', - 'min_replicas: int', - 'max_replicas: int', - 'cost_budget: float' - ] - - for field in required_fields: - self.assertIn(field, content, f"Missing required field: {field}") - - print("โœ… DeploymentConfig dataclass validation passed") - - def test_03_prerequisites_checking(self): - """Test prerequisites checking functionality""" - print("๐Ÿ” Testing prerequisites checking...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for prerequisite checks - prerequisite_checks = [ - 'gcloud CLI', - 'Authentication', - 'Project Configuration', - 'Vertex AI API', - 'Cloud Monitoring API', - 'Cloud Logging API', - 'Artifact Registry', - 'IAM Permissions' - ] - - for check in prerequisite_checks: - self.assertIn(check, content, f"Missing prerequisite check: {check}") - - # Check for individual check methods - check_methods = [ - '_check_gcloud', - '_check_authentication', - '_check_project', - '_check_vertex_ai_api', - '_check_monitoring_api', - '_check_logging_api', - '_check_artifact_registry', - '_check_iam_permissions' - ] - - for method in check_methods: - self.assertIn(f'def {method}', content, f"Missing check method: {method}") - - print("โœ… Prerequisites checking validation passed") - - def test_04_model_versioning(self): - """Test model versioning functionality""" - print("๐Ÿ” Testing model versioning...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for version generation - self.assertIn('def generate_model_version', content, "Missing version generation method") - self.assertIn('datetime.now().strftime', content, "Missing timestamp generation") - self.assertIn('git rev-parse', content, "Missing git commit hash") - - # Check for version format - self.assertIn('v{timestamp}_{git_hash}', content, "Missing version format") - - print("โœ… Model versioning validation passed") - - def test_05_deployment_package_creation(self): - """Test deployment package creation""" - print("๐Ÿ” Testing deployment package creation...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for deployment package creation - self.assertIn('def create_deployment_package', content, "Missing deployment package creation") - self.assertIn('deployment/vertex_ai/{version}', content, "Missing versioned directory structure") - self.assertIn('Dockerfile', content, "Missing Dockerfile creation") - self.assertIn('version_metadata.json', content, "Missing version metadata") - - # Check for required files - required_files = [ - 'model/', - 'requirements.txt', - 'predict.py', - 'Dockerfile', - 'version_metadata.json' - ] - - for file in required_files: - self.assertIn(file, content, f"Missing required file: {file}") - - print("โœ… Deployment package creation validation passed") - - def test_06_docker_image_handling(self): - """Test Docker image building and pushing""" - print("๐Ÿ” Testing Docker image handling...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for Docker operations - self.assertIn('def build_and_push_image', content, "Missing Docker image handling") - self.assertIn('gcloud auth configure-docker', content, "Missing Docker authentication") - self.assertIn('docker build', content, "Missing Docker build") - self.assertIn('docker push', content, "Missing Docker push") - - # Check for image URI format - self.assertIn('gcr.io/{self.config.project_id}', content, "Missing image URI format") - - print("โœ… Docker image handling validation passed") - - def test_07_vertex_ai_model_creation(self): - """Test Vertex AI model creation""" - print("๐Ÿ” Testing Vertex AI model creation...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for model creation - self.assertIn('def create_vertex_ai_model', content, "Missing model creation method") - self.assertIn('gcloud ai models upload', content, "Missing model upload command") - self.assertIn('--container-image-uri', content, "Missing container image URI") - self.assertIn('--container-predict-route', content, "Missing predict route") - self.assertIn('--container-health-route', content, "Missing health route") - - print("โœ… Vertex AI model creation validation passed") - - def test_08_endpoint_deployment(self): - """Test endpoint deployment functionality""" - print("๐Ÿ” Testing endpoint deployment...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for endpoint deployment - self.assertIn('def deploy_model_to_endpoint', content, "Missing endpoint deployment method") - self.assertIn('gcloud ai endpoints deploy-model', content, "Missing endpoint deployment command") - self.assertIn('--traffic-split', content, "Missing traffic split") - self.assertIn('--machine-type', content, "Missing machine type") - self.assertIn('--min-replica-count', content, "Missing min replica count") - self.assertIn('--max-replica-count', content, "Missing max replica count") - - print("โœ… Endpoint deployment validation passed") - - def test_09_monitoring_and_alerting(self): - """Test monitoring and alerting setup""" - print("๐Ÿ” Testing monitoring and alerting...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for monitoring setup - self.assertIn('def setup_monitoring_and_alerting', content, "Missing monitoring setup method") - self.assertIn('monitoring_policy.json', content, "Missing monitoring policy") - self.assertIn('gcloud alpha monitoring policies create', content, "Missing monitoring policy creation") - - # Check for alert conditions - self.assertIn('High Error Rate', content, "Missing error rate monitoring") - self.assertIn('High Latency', content, "Missing latency monitoring") - - print("โœ… Monitoring and alerting validation passed") - - def test_10_cost_monitoring(self): - """Test cost monitoring setup""" - print("๐Ÿ” Testing cost monitoring...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for cost monitoring - self.assertIn('def setup_cost_monitoring', content, "Missing cost monitoring method") - self.assertIn('budget_config.json', content, "Missing budget configuration") - self.assertIn('gcloud billing budgets create', content, "Missing budget creation") - - # Check for budget thresholds - self.assertIn('thresholdPercent', content, "Missing budget thresholds") - - print("โœ… Cost monitoring validation passed") - - def test_11_rollback_capabilities(self): - """Test rollback capabilities""" - print("๐Ÿ” Testing rollback capabilities...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for rollback functionality - self.assertIn('def rollback_deployment', content, "Missing rollback method") - self.assertIn('deployment_history', content, "Missing deployment history") - self.assertIn('gcloud ai endpoints deploy-model', content, "Missing rollback deployment") - - print("โœ… Rollback capabilities validation passed") - - def test_12_ab_testing_support(self): - """Test A/B testing support""" - print("๐Ÿ” Testing A/B testing support...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for A/B testing - self.assertIn('def setup_ab_testing', content, "Missing A/B testing method") - self.assertIn('version_a', content, "Missing version A parameter") - self.assertIn('version_b', content, "Missing version B parameter") - self.assertIn('traffic_split', content, "Missing traffic split") - - print("โœ… A/B testing support validation passed") - - def test_13_performance_metrics(self): - """Test performance metrics collection""" - print("๐Ÿ” Testing performance metrics...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for performance metrics - self.assertIn('def get_performance_metrics', content, "Missing performance metrics method") - self.assertIn('gcloud ai endpoints describe', content, "Missing endpoint description") - self.assertIn('gcloud ai models list', content, "Missing model listing") - - print("โœ… Performance metrics validation passed") - - def test_14_cleanup_functionality(self): - """Test cleanup functionality""" - print("๐Ÿ” Testing cleanup functionality...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for cleanup - self.assertIn('def cleanup_old_versions', content, "Missing cleanup method") - self.assertIn('keep_versions', content, "Missing version retention") - self.assertIn('gcloud ai models delete', content, "Missing model deletion") - - print("โœ… Cleanup functionality validation passed") - - def test_15_full_deployment_workflow(self): - """Test full deployment workflow""" - print("๐Ÿ” Testing full deployment workflow...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for full deployment workflow - self.assertIn('def run_full_deployment', content, "Missing full deployment method") - - # Check for workflow steps - workflow_steps = [ - 'check_prerequisites', - 'generate_model_version', - 'create_deployment_package', - 'build_and_push_image', - 'create_vertex_ai_model', - 'deploy_model_to_endpoint', - 'setup_monitoring_and_alerting', - 'setup_cost_monitoring', - 'get_performance_metrics', - 'cleanup_old_versions', - '_save_deployment_summary' - ] - - for step in workflow_steps: - self.assertIn(step, content, f"Missing workflow step: {step}") - - print("โœ… Full deployment workflow validation passed") - - def test_16_error_handling(self): - """Test error handling and logging""" - print("๐Ÿ” Testing error handling...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for error handling - self.assertIn('import logging', content, "Missing logging import") - self.assertIn('logger = logging.getLogger', content, "Missing logger setup") - self.assertIn('try:', content, "Missing try blocks") - self.assertIn('except', content, "Missing except blocks") - self.assertIn('logger.error', content, "Missing error logging") - self.assertIn('logger.warning', content, "Missing warning logging") - - print("โœ… Error handling validation passed") - - def test_17_configuration_management(self): - """Test configuration management""" - print("๐Ÿ” Testing configuration management...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for configuration management - self.assertIn('DeploymentConfig', content, "Missing deployment configuration") - self.assertIn('project_id', content, "Missing project ID configuration") - self.assertIn('region', content, "Missing region configuration") - self.assertIn('machine_type', content, "Missing machine type configuration") - self.assertIn('min_replicas', content, "Missing min replicas configuration") - self.assertIn('max_replicas', content, "Missing max replicas configuration") - self.assertIn('cost_budget', content, "Missing cost budget configuration") - - print("โœ… Configuration management validation passed") - - def test_18_security_features(self): - """Test security features""" - print("๐Ÿ” Testing security features...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for security features - self.assertIn('subprocess.run', content, "Missing subprocess usage") - self.assertIn('capture_output=True', content, "Missing output capture") - self.assertIn('text=True', content, "Missing text mode") - self.assertIn('check=True', content, "Missing error checking") - - print("โœ… Security features validation passed") - - def test_19_documentation_and_logging(self): - """Test documentation and logging""" - print("๐Ÿ” Testing documentation and logging...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for documentation - self.assertIn('"""', content, "Missing docstrings") - self.assertIn('Phase 4: Vertex AI Deployment Automation', content, "Missing module docstring") - self.assertIn('Enhanced Vertex AI deployment', content, "Missing class docstring") - - # Check for logging - self.assertIn('logger.info', content, "Missing info logging") - self.assertIn('print(', content, "Missing print statements") - - print("โœ… Documentation and logging validation passed") - - def test_20_main_function(self): - """Test main function""" - print("๐Ÿ” Testing main function...") - - with open(self.vertex_ai_script, 'r') as f: - content = f.read() - - # Check for main function - self.assertIn('def main():', content, "Missing main function") - self.assertIn('if __name__ == "__main__":', content, "Missing main guard") - self.assertIn('gcloud config get-value project', content, "Missing project ID retrieval") - self.assertIn('DeploymentConfig(', content, "Missing configuration creation") - self.assertIn('VertexAIPhase4Automation(', content, "Missing automation instance creation") - self.assertIn('run_full_deployment()', content, "Missing deployment execution") - - print("โœ… Main function validation passed") - -def run_phase4_tests(): - """Run all Phase 4 tests""" - print("๐Ÿš€ RUNNING PHASE 4 VERTEX AI AUTOMATION TESTS") - print("=" * 70) - - # Create test suite - loader = unittest.TestLoader() - suite = loader.loadTestsFromTestCase(Phase4VertexAIAutomationTest) - - # Run tests - runner = unittest.TextTestRunner(verbosity=2) - result = runner.run(suite) - - # Print summary - print("\n" + "=" * 70) - print("๐Ÿ“Š PHASE 4 TEST RESULTS SUMMARY") - print("=" * 70) - print(f"Tests run: {result.testsRun}") - print(f"Failures: {len(result.failures)}") - print(f"Errors: {len(result.errors)}") - print(f"Skipped: {len(result.skipped)}") - - if result.failures: - print("\nโŒ FAILURES:") - for test, traceback in result.failures: - print(f" - {test}: {traceback.split('AssertionError:')[-1].strip()}") - - if result.errors: - print("\nโŒ ERRORS:") - for test, traceback in result.errors: - print(f" - {test}: {traceback.split('Exception:')[-1].strip()}") - - if result.skipped: - print("\nโš ๏ธ SKIPPED:") - for test, reason in result.skipped: - print(f" - {test}: {reason}") - - success = len(result.failures) == 0 and len(result.errors) == 0 - if success: - print("\n๐ŸŽ‰ ALL PHASE 4 TESTS PASSED!") - print("โœ… Vertex AI automation is ready for deployment") - print("\n๐Ÿ“‹ Phase 4 Features Validated:") - print(" โœ… Automated model versioning and deployment") - print(" โœ… Rollback capabilities and A/B testing support") - print(" โœ… Model performance monitoring and alerting") - print(" โœ… Cost optimization and resource management") - print(" โœ… Comprehensive testing and validation") - else: - print("\nโŒ SOME PHASE 4 TESTS FAILED!") - print("Please fix the issues before proceeding with deployment") - - return success - -if __name__ == "__main__": - success = run_phase4_tests() - sys.exit(0 if success else 1) \ No newline at end of file diff --git a/scripts/testing/test_pr4_integration.py b/scripts/testing/test_pr4_integration.py index 761e39b50..6a245687e 100644 --- a/scripts/testing/test_pr4_integration.py +++ b/scripts/testing/test_pr4_integration.py @@ -6,35 +6,37 @@ implemented in PR #4 are properly integrated and functional. """ -import sys -import yaml -import subprocess import shutil +import subprocess +import sys from pathlib import Path -from typing import Dict, Any +from typing import Any, Dict + +import yaml + class PR4IntegrationTester: """Integration tester for PR #4 security and documentation enhancements.""" - + def __init__(self): self.project_root = Path(__file__).parent.parent.parent self.security_config_path = self.project_root / "configs" / "security.yaml" self.openapi_spec_path = self.project_root / "docs" / "api" / "openapi.yaml" self.requirements_path = self.project_root / "requirements.txt" self.test_results = [] - + def run_all_tests(self) -> Dict[str, Any]: """Run all integration tests for PR #4.""" print("๐Ÿ” Running PR #4 Integration Tests...") - + tests = [ self.test_security_configuration, self.test_openapi_specification, self.test_dependencies_security, self.test_documentation_completeness, - self.test_security_scanning_tools + self.test_security_scanning_tools, ] - + for test in tests: try: result = test() @@ -46,13 +48,13 @@ def run_all_tests(self) -> Dict[str, Any]: "name": test.__name__, "passed": False, "message": f"Test failed with exception: {str(e)}", - "details": str(e) + "details": str(e), } self.test_results.append(error_result) print(f"โŒ FAIL {test.__name__}: {str(e)}") - + return self.generate_summary() - + def test_security_configuration(self) -> Dict[str, Any]: """Test that security configuration is valid and complete.""" if not self.security_config_path.exists(): @@ -60,50 +62,50 @@ def test_security_configuration(self) -> Dict[str, Any]: "name": "Security Configuration", "passed": False, "message": "Security configuration file not found", - "details": f"Expected: {self.security_config_path}" + "details": f"Expected: {self.security_config_path}", } - + try: - with open(self.security_config_path, 'r', encoding='utf-8') as f: + with open(self.security_config_path, "r", encoding="utf-8") as f: config = yaml.safe_load(f) - + # Check required sections - required_sections = ['api', 'security_headers', 'logging', 'environment'] + required_sections = ["api", "security_headers", "logging", "environment"] missing_sections = [section for section in required_sections if section not in config] - + if missing_sections: return { "name": "Security Configuration", "passed": False, "message": f"Missing required sections: {missing_sections}", - "details": f"Found sections: {list(config.keys())}" + "details": f"Found sections: {list(config.keys())}", } - + # Check API security settings - api_config = config.get('api', {}) - if not api_config.get('rate_limiting', {}).get('enabled'): + api_config = config.get("api", {}) + if not api_config.get("rate_limiting", {}).get("enabled"): return { "name": "Security Configuration", "passed": False, "message": "Rate limiting not enabled in API configuration", - "details": "Rate limiting is required for production security" + "details": "Rate limiting is required for production security", } - + return { "name": "Security Configuration", "passed": True, "message": "Security configuration is valid and complete", - "details": f"All {len(required_sections)} required sections present" + "details": f"All {len(required_sections)} required sections present", } - + except yaml.YAMLError as e: return { "name": "Security Configuration", "passed": False, "message": f"Invalid YAML in security configuration: {str(e)}", - "details": str(e) + "details": str(e), } - + def test_openapi_specification(self) -> Dict[str, Any]: """Test that OpenAPI specification is valid and complete.""" if not self.openapi_spec_path.exists(): @@ -111,58 +113,58 @@ def test_openapi_specification(self) -> Dict[str, Any]: "name": "OpenAPI Specification", "passed": False, "message": "OpenAPI specification file not found", - "details": f"Expected: {self.openapi_spec_path}" + "details": f"Expected: {self.openapi_spec_path}", } - + try: - with open(self.openapi_spec_path, 'r') as f: + with open(self.openapi_spec_path, "r") as f: spec = yaml.safe_load(f) - + # Check OpenAPI version - if spec.get('openapi') != '3.1.0': + if spec.get("openapi") != "3.1.0": return { "name": "OpenAPI Specification", "passed": False, "message": "OpenAPI version should be 3.1.0", - "details": f"Found version: {spec.get('openapi')}" + "details": f"Found version: {spec.get('openapi')}", } - + # Check required sections - required_sections = ['info', 'paths', 'components'] + required_sections = ["info", "paths", "components"] missing_sections = [section for section in required_sections if section not in spec] - + if missing_sections: return { "name": "OpenAPI Specification", "passed": False, "message": f"Missing required sections: {missing_sections}", - "details": f"Found sections: {list(spec.keys())}" + "details": f"Found sections: {list(spec.keys())}", } - + # Check security definitions - if 'security' not in spec: + if "security" not in spec: return { "name": "OpenAPI Specification", "passed": False, "message": "Security definitions missing", - "details": "API security should be documented" + "details": "API security should be documented", } - + return { "name": "OpenAPI Specification", "passed": True, "message": "OpenAPI specification is valid and complete", - "details": f"Version {spec.get('openapi')} with all required sections" + "details": f"Version {spec.get('openapi')} with all required sections", } - + except yaml.YAMLError as e: return { "name": "OpenAPI Specification", "passed": False, "message": f"Invalid YAML in OpenAPI specification: {str(e)}", - "details": str(e) + "details": str(e), } - + def test_dependencies_security(self) -> Dict[str, Any]: """Test that dependencies are secure and up-to-date.""" if not self.requirements_path.exists(): @@ -170,25 +172,25 @@ def test_dependencies_security(self) -> Dict[str, Any]: "name": "Dependencies Security", "passed": False, "message": "Requirements file not found", - "details": f"Expected: {self.requirements_path}" + "details": f"Expected: {self.requirements_path}", } - + try: - with open(self.requirements_path, 'r') as f: + with open(self.requirements_path, "r") as f: requirements = f.read() - + # Check for security scanning tools - security_tools = ['bandit', 'safety'] + security_tools = ["bandit", "safety"] missing_tools = [tool for tool in security_tools if tool not in requirements] - + if missing_tools: return { "name": "Dependencies Security", "passed": False, "message": f"Missing security scanning tools: {missing_tools}", - "details": "Security tools are required for vulnerability scanning" + "details": "Security tools are required for vulnerability scanning", } - + # Check for critical security packages # The list of critical security packages is loaded from security.yaml under the 'critical_packages' key. # These packages are considered critical because: @@ -196,182 +198,190 @@ def test_dependencies_security(self) -> Dict[str, Any]: # - certifi: Ensures up-to-date CA certificates for secure HTTPS connections. # - urllib3: Secure HTTP client with robust TLS/SSL support. try: - with open(self.security_config_path, 'r') as secf: + with open(self.security_config_path, "r") as secf: security_config = yaml.safe_load(secf) - critical_packages = security_config.get('critical_packages', ['cryptography', 'certifi', 'urllib3']) - if 'critical_packages' not in security_config: - print("โš ๏ธ Warning: 'critical_packages' not found in security.yaml, using default list.") + critical_packages = security_config.get( + "critical_packages", ["cryptography", "certifi", "urllib3"] + ) + if "critical_packages" not in security_config: + print( + "โš ๏ธ Warning: 'critical_packages' not found in security.yaml, using default list." + ) except Exception as e: - print(f"โš ๏ธ Warning: Could not read security.yaml for critical_packages: {str(e)}. Using default list.") - critical_packages = ['cryptography', 'certifi', 'urllib3'] + print( + f"โš ๏ธ Warning: Could not read security.yaml for critical_packages: {str(e)}. Using default list." + ) + critical_packages = ["cryptography", "certifi", "urllib3"] missing_critical = [pkg for pkg in critical_packages if pkg not in requirements] - + if missing_critical: return { "name": "Dependencies Security", "passed": False, "message": f"Missing critical security packages: {missing_critical}", - "details": "Critical security packages are required" + "details": "Critical security packages are required", } - + return { "name": "Dependencies Security", "passed": True, "message": "Dependencies include required security packages", - "details": f"All {len(security_tools)} security tools and {len(critical_packages)} critical packages present" + "details": f"All {len(security_tools)} security tools and {len(critical_packages)} critical packages present", } - + except Exception as e: return { "name": "Dependencies Security", "passed": False, "message": f"Error reading requirements file: {str(e)}", - "details": str(e) + "details": str(e), } - + def test_documentation_completeness(self) -> Dict[str, Any]: """Test that documentation is complete and accessible.""" required_docs = [ "docs/deployment/PRODUCTION_DEPLOYMENT_GUIDE.md", "CONTRIBUTING.md", - "docs/monster-pr-8-breakdown-strategy.md" + "docs/monster-pr-8-breakdown-strategy.md", ] - + missing_docs = [] for doc_path in required_docs: if not (self.project_root / doc_path).exists(): missing_docs.append(doc_path) - + if missing_docs: return { "name": "Documentation Completeness", "passed": False, "message": f"Missing required documentation: {missing_docs}", - "details": "All required documentation should be present" + "details": "All required documentation should be present", } - + return { "name": "Documentation Completeness", "passed": True, "message": "All required documentation is present", - "details": f"Found {len(required_docs)} required documentation files" + "details": f"Found {len(required_docs)} required documentation files", } - + def test_security_scanning_tools(self) -> Dict[str, Any]: """Test that security scanning tools are available and functional.""" try: # Test bandit availability - bandit_path = shutil.which('bandit') + bandit_path = shutil.which("bandit") if not bandit_path: return { "name": "Security Scanning Tools", "passed": False, "message": "Bandit security scanner not found in PATH", - "details": "Install bandit: pip install bandit" + "details": "Install bandit: pip install bandit", } - result = subprocess.run([bandit_path, '--version'], - capture_output=True, text=True, timeout=30) + result = subprocess.run( + [bandit_path, "--version"], capture_output=True, text=True, timeout=30 + ) if result.returncode != 0: return { "name": "Security Scanning Tools", "passed": False, "message": "Bandit security scanner not available", - "details": f"Bandit error: {result.stderr}" + "details": f"Bandit error: {result.stderr}", } - + # Test safety availability - safety_path = shutil.which('safety') + safety_path = shutil.which("safety") if safety_path is None: return { "name": "Security Scanning Tools", "passed": False, "message": "Safety vulnerability scanner not found in PATH", - "details": "Install safety and ensure it is in a secure location" + "details": "Install safety and ensure it is in a secure location", } - result = subprocess.run([safety_path, '--version'], - capture_output=True, text=True, timeout=30) + result = subprocess.run( + [safety_path, "--version"], capture_output=True, text=True, timeout=30 + ) if result.returncode != 0: return { "name": "Security Scanning Tools", "passed": False, "message": "Safety vulnerability scanner not available", - "details": f"Safety error: {result.stderr}" + "details": f"Safety error: {result.stderr}", } - + return { "name": "Security Scanning Tools", "passed": True, "message": "Security scanning tools are available and functional", - "details": "Bandit and Safety scanners are working" + "details": "Bandit and Safety scanners are working", } - + except subprocess.TimeoutExpired: return { "name": "Security Scanning Tools", "passed": False, "message": "Security scanning tools timed out", - "details": "Tools may not be properly installed" + "details": "Tools may not be properly installed", } except FileNotFoundError: return { "name": "Security Scanning Tools", "passed": False, "message": "Security scanning tools not found", - "details": "Install bandit and safety: pip install bandit safety" + "details": "Install bandit and safety: pip install bandit safety", } - + def generate_summary(self) -> Dict[str, Any]: """Generate test summary and recommendations.""" total_tests = len(self.test_results) passed_tests = sum(1 for result in self.test_results if result["passed"]) failed_tests = total_tests - passed_tests - + summary = { "total_tests": total_tests, "passed": passed_tests, "failed": failed_tests, "success_rate": (passed_tests / total_tests) * 100 if total_tests > 0 else 0, "results": self.test_results, - "recommendations": [] + "recommendations": [], } - + # Generate recommendations based on failures if failed_tests > 0: - summary["recommendations"].append( - f"Fix {failed_tests} failing tests before proceeding" - ) - + summary["recommendations"].append(f"Fix {failed_tests} failing tests before proceeding") + if summary["success_rate"] < 100: summary["recommendations"].append( "Complete integration testing before claiming PR #4 is ready" ) - + return summary + def main(): """Main function to run PR #4 integration tests.""" tester = PR4IntegrationTester() summary = tester.run_all_tests() - - print("\n" + "="*60) + + print("\n" + "=" * 60) print("๐Ÿ“Š PR #4 Integration Test Summary") - print("="*60) + print("=" * 60) print(f"Total Tests: {summary['total_tests']}") print(f"Passed: {summary['passed']}") print(f"Failed: {summary['failed']}") print(f"Success Rate: {summary['success_rate']:.1f}%") - - if summary['recommendations']: + + if summary["recommendations"]: print("\n๐Ÿ”ง Recommendations:") - for rec in summary['recommendations']: + for rec in summary["recommendations"]: print(f" - {rec}") - - if summary['failed'] > 0: + + if summary["failed"] > 0: print("\nโŒ PR #4 is NOT ready for submission") sys.exit(1) else: print("\nโœ… PR #4 integration tests passed!") print("Ready for final review and submission") + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/testing/test_pr5_cicd_integration.py b/scripts/testing/test_pr5_cicd_integration.py index 064d4032a..099d78f21 100644 --- a/scripts/testing/test_pr5_cicd_integration.py +++ b/scripts/testing/test_pr5_cicd_integration.py @@ -6,22 +6,24 @@ """ import os -import sys -import yaml import subprocess +import sys from pathlib import Path +import yaml + + def test_yaml_syntax(): """Test that the CircleCI config YAML is valid.""" print("๐Ÿ” Testing CircleCI YAML syntax...") - + config_path = Path(".circleci/config.yml") if not config_path.exists(): print("โŒ CircleCI config file not found") return False - + try: - with open(config_path, 'r') as f: + with open(config_path, "r") as f: yaml.safe_load(f) print("โœ… CircleCI YAML syntax is valid") return True @@ -29,6 +31,7 @@ def test_yaml_syntax(): print(f"โŒ YAML syntax error: {e}") return False + def test_conda_environment_setup(): """Test that conda environment setup configuration is valid (FAST VERSION).""" print("๐Ÿ” Testing conda environment setup (fast validation)...") @@ -39,10 +42,11 @@ def test_conda_environment_setup(): if os.path.exists(conda_path): conda_cmd = [conda_path] else: - conda_cmd = ['conda'] # fallback to PATH - - result = subprocess.run(conda_cmd + ['--version'], - capture_output=True, text=True, timeout=10) + conda_cmd = ["conda"] # fallback to PATH + + result = subprocess.run( + conda_cmd + ["--version"], capture_output=True, text=True, timeout=10 + ) if result.returncode != 0: print("โŒ Conda not available") return False @@ -54,53 +58,53 @@ def test_conda_environment_setup(): return False # Validate environment.yml structure - with open(env_path, 'r') as f: + with open(env_path, "r") as f: env_yaml = yaml.safe_load(f) - + # Check required fields - if 'name' not in env_yaml: + if "name" not in env_yaml: print("โŒ environment.yml missing 'name' field") return False - - if 'dependencies' not in env_yaml: + + if "dependencies" not in env_yaml: print("โŒ environment.yml missing 'dependencies' field") return False - - dependencies = env_yaml.get('dependencies', []) + + dependencies = env_yaml.get("dependencies", []) if not dependencies: print("โŒ environment.yml has no dependencies") return False - + # Check for key packages import re + found_packages = [] for dep in dependencies: if isinstance(dep, str): - package_name = re.split(r'[=<>~,]+', dep)[0].strip() - if package_name != 'python': + package_name = re.split(r"[=<>~,]+", dep)[0].strip() + if package_name != "python": found_packages.append(package_name) - + if not found_packages: print("โŒ No valid packages found in environment.yml") return False - + print(f"โœ… Found {len(found_packages)} packages in environment.yml") print(f"โœ… Conda environment setup validation passed (fast mode)") return True - + except Exception as e: print(f"โŒ Conda environment test failed: {e}") return False + def test_critical_fixes(): """Test that critical CircleCI fixes are applied using YAML parsing.""" - import yaml - print("๐Ÿ” Testing critical CircleCI fixes...") config_path = Path(".circleci/config.yml") try: - with open(config_path, 'r') as f: + with open(config_path, "r") as f: config = yaml.safe_load(f) except Exception as e: print(f"โŒ Failed to load config: {e}") @@ -181,24 +185,20 @@ def test_critical_fixes(): return all_fixes_present + def test_pipeline_structure(): """Test that the pipeline structure is correct, including handling malformed or incomplete configs.""" print("๐Ÿ” Testing pipeline structure...") config_path = Path(".circleci/config.yml") try: - with open(config_path, 'r') as f: + with open(config_path, "r") as f: config = yaml.safe_load(f) except Exception as e: print(f"โŒ Failed to load config: {e}") return False - required_components = [ - "executors", - "commands", - "jobs", - "workflows" - ] + required_components = ["executors", "commands", "jobs", "workflows"] all_components_present = True if not isinstance(config, dict): @@ -214,6 +214,7 @@ def test_pipeline_structure(): return all_components_present + def test_pipeline_structure_edge_cases(): """Test pipeline structure with missing sections and malformed YAML (edge cases).""" print("๐Ÿ” Testing pipeline structure edge cases...") @@ -225,12 +226,7 @@ def test_pipeline_structure_edge_cases(): "jobs": {}, # "workflows" missing } - required_components = [ - "executors", - "commands", - "jobs", - "workflows" - ] + required_components = ["executors", "commands", "jobs", "workflows"] missing_count = 0 for component in required_components: if component not in incomplete_config: @@ -241,60 +237,65 @@ def test_pipeline_structure_edge_cases(): malformed_configs = [None, [], "not_a_dict"] for idx, malformed in enumerate(malformed_configs): if not isinstance(malformed, dict): - print(f"โœ… Malformed config case {idx+1}: {repr(malformed)} correctly identified as invalid") + print( + f"โœ… Malformed config case {idx+1}: {repr(malformed)} correctly identified as invalid" + ) else: - print(f"โŒ Malformed config case {idx+1}: {repr(malformed)} incorrectly identified as valid") + print( + f"โŒ Malformed config case {idx+1}: {repr(malformed)} incorrectly identified as valid" + ) return True + def test_job_dependencies(): """Test that job dependencies are properly configured with order verification.""" print("๐Ÿ” Testing job dependencies...") - + config_path = Path(".circleci/config.yml") try: - with open(config_path, 'r') as f: + with open(config_path, "r") as f: config = yaml.safe_load(f) except Exception as e: print(f"โŒ Failed to load config: {e}") return False - - workflows = config.get('workflows', {}) + + workflows = config.get("workflows", {}) if not workflows: print("โŒ No workflows found") return False - + main_workflow = None for workflow_name, workflow_config in workflows.items(): - if workflow_name == 'samo-ci-cd': + if workflow_name == "samo-ci-cd": main_workflow = workflow_config break - + if not main_workflow: print("โŒ Main workflow 'samo-ci-cd' not found") return False - - jobs = main_workflow.get('jobs', []) + + jobs = main_workflow.get("jobs", []) if not jobs: print("โŒ No jobs in main workflow") return False - + print(f"โœ… Found {len(jobs)} jobs in main workflow") - + # Verify job dependency order and relationships job_names = [] job_dependencies = {} - + for job in jobs: if isinstance(job, dict): # Job with configuration job_name = list(job.keys())[0] job_config = job[job_name] job_names.append(job_name) - + # Check for dependencies - if 'requires' in job_config: - job_dependencies[job_name] = job_config['requires'] + if "requires" in job_config: + job_dependencies[job_name] = job_config["requires"] print(f"โœ… Job '{job_name}' has dependencies: {job_config['requires']}") else: job_dependencies[job_name] = [] @@ -304,7 +305,7 @@ def test_job_dependencies(): job_names.append(job) job_dependencies[job] = [] print(f"โœ… Job '{job}' has no dependencies (runs first)") - + # Verify dependency relationships are valid all_deps_valid = True for job_name, deps in job_dependencies.items(): @@ -312,10 +313,10 @@ def test_job_dependencies(): if dep not in job_names: print(f"โŒ Job '{job_name}' depends on '{dep}' which doesn't exist") all_deps_valid = False - + if all_deps_valid: print("โœ… All job dependencies reference valid jobs") - + # Check for circular dependencies (basic check) has_circular = False for job_name, deps in job_dependencies.items(): @@ -323,47 +324,45 @@ def test_job_dependencies(): if job_name in job_dependencies.get(dep, []): print(f"โŒ Circular dependency detected: {job_name} โ†” {dep}") has_circular = True - + if not has_circular: print("โœ… No circular dependencies detected") - + return all_deps_valid and not has_circular + def test_environment_variables(): """Test that environment variables are properly configured.""" print("๐Ÿ” Testing environment variables...") - + config_path = Path(".circleci/config.yml") try: - with open(config_path, 'r') as f: + with open(config_path, "r") as f: config = yaml.safe_load(f) except Exception as e: print(f"โŒ Failed to load config: {e}") return False - + # Check for hardcoded conda paths that should be abstracted content = "" try: - with open(config_path, 'r') as f: + with open(config_path, "r") as f: content = f.read() except Exception as e: print(f"โŒ Failed to read config content: {e}") return False - - hardcoded_paths = [ - "$HOME/miniconda/bin/conda", - "~/miniconda/bin/conda" - ] - + + hardcoded_paths = ["$HOME/miniconda/bin/conda", "~/miniconda/bin/conda"] + found_hardcoded = False for path in hardcoded_paths: if path in content: print(f"โš ๏ธ Found hardcoded conda path: {path}") found_hardcoded = True - + if not found_hardcoded: print("โœ… No hardcoded conda paths found") - + # Check for environment variable usage env_vars = ["$CIRCLE_WORKING_DIRECTORY", "$HOME", "$PATH"] found_env_vars = 0 @@ -371,17 +370,18 @@ def test_environment_variables(): if var in content: found_env_vars += 1 print(f"โœ… Found environment variable usage: {var}") - + if found_env_vars > 0: print(f"โœ… Found {found_env_vars} environment variables in use") - + return True + def main(): """Run all PR #5 CI/CD integration tests.""" print("๐Ÿ” Running PR #5 CI/CD Integration Tests...") print("=" * 60) - + tests = [ ("YAML Syntax", test_yaml_syntax), ("Conda Environment Setup", test_conda_environment_setup), @@ -391,10 +391,10 @@ def main(): ("Job Dependencies", test_job_dependencies), ("Environment Variables", test_environment_variables), ] - + passed = 0 total = len(tests) - + for test_name, test_func in tests: print(f"\n๐Ÿ“‹ {test_name}") print("-" * 40) @@ -406,7 +406,7 @@ def main(): print(f"โŒ {test_name} FAILED") except Exception as e: print(f"โŒ {test_name} ERROR: {e}") - + print("\n" + "=" * 60) print("๐Ÿ“Š PR #5 CI/CD Integration Test Summary") print("=" * 60) @@ -414,16 +414,17 @@ def main(): print(f"Passed: {passed}") print(f"Failed: {total - passed}") print(f"Success Rate: {(passed/total)*100:.1f}%") - + if passed == total: print("\nโœ… PR #5 CI/CD pipeline is ready for testing!") print("Ready for CircleCI validation") else: print(f"\nโŒ PR #5 needs {total - passed} fixes before testing") print("Please address the failing tests above") - + return passed == total + if __name__ == "__main__": success = main() - sys.exit(0 if success else 1) \ No newline at end of file + sys.exit(0 if success else 1) diff --git a/scripts/testing/test_rate_limiter_fix.py b/scripts/testing/test_rate_limiter_fix.py index 9d0d72339..f85394a16 100644 --- a/scripts/testing/test_rate_limiter_fix.py +++ b/scripts/testing/test_rate_limiter_fix.py @@ -1,17 +1,18 @@ #!/usr/bin/env python3 """Test script to verify rate limiter fix.""" -from unittest.mock import MagicMock import asyncio import sys import time +from unittest.mock import MagicMock -from scripts.testing._bootstrap import ensure_project_root_on_sys_path, configure_basic_logging +from scripts.testing._bootstrap import configure_basic_logging, ensure_project_root_on_sys_path +from src.api_rate_limiter import RateLimitConfig # noqa: E402 +from src.api_rate_limiter import TokenBucketRateLimiter # Ensure project root and logging PROJECT_ROOT = ensure_project_root_on_sys_path() logger = configure_basic_logging() -from src.api_rate_limiter import TokenBucketRateLimiter, RateLimitConfig # noqa: E402 async def test_token_refill_logic(): """Test the token refill logic manually.""" @@ -20,9 +21,9 @@ async def test_token_refill_logic(): config = RateLimitConfig( burst_size=5, requests_per_minute=10, - rapid_fire_threshold=1000, # avoid rapid-fire trigger - sustained_rate_threshold=100000, # avoid sustained-rate trigger - enable_user_agent_analysis=False, # disable UA analysis for test + rapid_fire_threshold=1000, # avoid rapid-fire trigger + sustained_rate_threshold=100000, # avoid sustained-rate trigger + enable_user_agent_analysis=False, # disable UA analysis for test enable_request_pattern_analysis=False, # disable pattern analysis ) rate_limiter = TokenBucketRateLimiter(config) @@ -60,6 +61,7 @@ async def test_token_refill_logic(): return allowed_final + if __name__ == "__main__": success = asyncio.run(test_token_refill_logic()) sys.exit(0 if success else 1) diff --git a/scripts/testing/test_rate_limiter_no_threading.py b/scripts/testing/test_rate_limiter_no_threading.py index 0519ecba6..e69de29bb 100644 --- a/scripts/testing/test_rate_limiter_no_threading.py +++ b/scripts/testing/test_rate_limiter_no_threading.py @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/scripts/testing/test_temperature_scaling.py b/scripts/testing/test_temperature_scaling.py index fa96e33a5..76a53de41 100644 --- a/scripts/testing/test_temperature_scaling.py +++ b/scripts/testing/test_temperature_scaling.py @@ -1,26 +1,26 @@ - # Calculate predictions per sample (overprediction metric) - # Evaluate with current temperature - # This is approximated from the debug output - # Track best result - # Update model temperature - # Display all results - # Initialize trainer - # Load trained model - # Provide recommendations - # Save results for CircleCI - # Test different temperatures -# Add src to path -# Set up logging -#!/usr/bin/env python3 -from src.models.emotion_detection.bert_classifier import evaluate_emotion_classifier -from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer -from pathlib import Path +# Calculate predictions per sample (overprediction metric) +# Evaluate with current temperature +# This is approximated from the debug output +# Track best result +# Update model temperature +# Display all results +# Initialize trainer +# Load trained model +# Provide recommendations +# Save results for CircleCI +# Test different temperatures + import json import logging import sys +from pathlib import Path +# Set up logging +#!/usr/bin/env python3 +from src.models.emotion_detection.bert_classifier import evaluate_emotion_classifier - +# Add src to path +from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer """ Temperature Scaling Test for BERT Emotion Classifier. diff --git a/scripts/testing/test_vertex_setup.py b/scripts/testing/test_vertex_setup.py index 5e85a4605..ad81d6d05 100644 --- a/scripts/testing/test_vertex_setup.py +++ b/scripts/testing/test_vertex_setup.py @@ -26,10 +26,10 @@ def test_vertex_setup(): config_dir = Path("configs/vertex_ai") if config_dir.exists(): logger.info(f"โœ… Configuration directory exists: {config_dir}") - + config_files = list(config_dir.glob("*.json")) logger.info(f"โœ… Found {len(config_files)} configuration files") - + for config_file in config_files: logger.info(f" - {config_file.name}") else: @@ -39,10 +39,10 @@ def test_vertex_setup(): data_dir = Path("data/vertex_ai") if data_dir.exists(): logger.info(f"โœ… Data directory exists: {data_dir}") - + data_files = list(data_dir.glob("*.json")) logger.info(f"โœ… Found {len(data_files)} data files") - + for data_file in data_files: logger.info(f" - {data_file.name}") else: diff --git a/scripts/testing/test_voice_pipeline.py b/scripts/testing/test_voice_pipeline.py index 0c007bee9..25ba14f6d 100644 --- a/scripts/testing/test_voice_pipeline.py +++ b/scripts/testing/test_voice_pipeline.py @@ -5,18 +5,22 @@ This script tests the complete voice-first pipeline including audio recording, transcription, and emotion detection. """ -from pathlib import Path import logging -import numpy as np import sys +from pathlib import Path + +import numpy as np import torch +from src.models.emotion_detection.training_pipeline import ( + create_bert_emotion_classifier, +) # noqa: E402 + # Ensure project root is on sys.path PROJECT_ROOT = Path(__file__).resolve().parents[2] if str(PROJECT_ROOT) not in sys.path: sys.path.insert(0, str(PROJECT_ROOT)) -from src.models.emotion_detection.training_pipeline import create_bert_emotion_classifier # noqa: E402 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) @@ -36,10 +40,14 @@ def test_whisper_transcription(): try: import whisper # Local import to handle optional dependency + model = whisper.load_model("base") logger.info("โœ… Whisper model loaded successfully") logger.info(" โ€ข Model: %s", getattr(model, "name", "base")) - logger.info(" โ€ข Parameters: %s", getattr(getattr(model, "dims", object()), "n_text_state", "unknown")) + logger.info( + " โ€ข Parameters: %s", + getattr(getattr(model, "dims", object()), "n_text_state", "unknown"), + ) logger.info(" โ€ข Transcription test: Simulated audio processing") logger.info(" โ€ข Expected output: Text transcription") @@ -96,6 +104,7 @@ def test_voice_emotion_features(): try: import librosa # Local import to handle optional dependency + sample_rate = 16000 duration = 3 samples = int(sample_rate * duration) diff --git a/scripts/testing/test_working_inference.py b/scripts/testing/test_working_inference.py index 986e59ffd..2c152efef 100644 --- a/scripts/testing/test_working_inference.py +++ b/scripts/testing/test_working_inference.py @@ -4,23 +4,25 @@ Uses public roberta-base tokenizer and maps generic labels to emotions """ -import torch import json -from transformers import AutoTokenizer, AutoModelForSequenceClassification from pathlib import Path +import torch +from transformers import AutoModelForSequenceClassification, AutoTokenizer + + def test_working_inference(): """Test inference with public roberta-base tokenizer""" - + print("๐Ÿงช WORKING INFERENCE TEST") print("=" * 50) - + # Check if model files exist - model_dir = Path(__file__).parent.parent / 'deployment' / 'model' - required_files = ['config.json', 'model.safetensors', 'training_args.bin'] - + model_dir = Path(__file__).parent.parent / "deployment" / "model" + required_files = ["config.json", "model.safetensors", "training_args.bin"] + print(f"๐Ÿ“ Checking model directory: {model_dir}") - + missing_files = [] for file in required_files: file_path = model_dir / file @@ -29,131 +31,161 @@ def test_working_inference(): else: print(f"โŒ Missing: {file}") missing_files.append(file) - + if missing_files: print(f"\nโŒ Missing files: {missing_files}") return False - + print("\nโœ… All model files found!") - + # Load config to understand the model - with open(model_dir / 'config.json', 'r') as f: + with open(model_dir / "config.json", "r") as f: config = json.load(f) - + print(f"๐Ÿ”ง Model type: {config.get('model_type', 'unknown')}") print(f"๐Ÿ“Š Number of labels: {len(config.get('id2label', {}))}") - + # Define emotion mapping based on your training order - emotion_mapping = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] + emotion_mapping = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", + ] print(f"๐ŸŽฏ Emotion mapping: {emotion_mapping}") - + try: print(f"\n๐Ÿ”ง Loading public tokenizer: roberta-base") tokenizer = AutoTokenizer.from_pretrained("roberta-base") - + print(f"๐Ÿ”ง Loading model from: {model_dir}") model = AutoModelForSequenceClassification.from_pretrained(str(model_dir)) - - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) model.eval() - + print(f"โœ… Model loaded successfully on {device}") - + # Test texts test_texts = [ "I'm feeling really happy today!", "I'm so frustrated with this project.", "I feel anxious about the presentation.", "I'm grateful for all the support.", - "I'm feeling overwhelmed with tasks." + "I'm feeling overwhelmed with tasks.", ] - + print(f"\n๐Ÿงช Testing inference...") print("=" * 50) - + for i, text in enumerate(test_texts, 1): print(f"\n{i}. Text: {text}") - + # Tokenize - inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True) + inputs = tokenizer( + text, return_tensors="pt", truncation=True, max_length=512, padding=True + ) inputs = {k: v.to(device) for k, v in inputs.items()} - + # Predict with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - + # Map to emotion name emotion = emotion_mapping[predicted_class] - + print(f" Predicted: {emotion} (confidence: {confidence:.3f})") - + print(f"\nโœ… Inference test completed successfully!") return True - + except Exception as e: print(f"\nโŒ Error during inference: {str(e)}") return False + def test_simple_inference(): """Simple inference test as fallback""" - + print("\n๐Ÿงช SIMPLE INFERENCE TEST") print("=" * 50) - + try: - model_dir = Path(__file__).parent.parent / 'deployment' / 'model' - + model_dir = Path(__file__).parent.parent / "deployment" / "model" + print(f"๐Ÿ”ง Loading tokenizer and model from: {model_dir}") - + # Use roberta-base tokenizer tokenizer = AutoTokenizer.from_pretrained("roberta-base") model = AutoModelForSequenceClassification.from_pretrained(str(model_dir)) - - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) model.eval() - + # Simple test text = "I'm feeling happy today!" inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512) inputs = {k: v.to(device) for k, v in inputs.items()} - + with torch.no_grad(): outputs = model(**inputs) probabilities = torch.softmax(outputs.logits, dim=1) predicted_class = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_class].item() - - emotion_mapping = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired'] + + emotion_mapping = [ + "anxious", + "calm", + "content", + "excited", + "frustrated", + "grateful", + "happy", + "hopeful", + "overwhelmed", + "proud", + "sad", + "tired", + ] emotion = emotion_mapping[predicted_class] - + print(f"โœ… Simple test successful!") print(f" Text: {text}") print(f" Predicted: {emotion} (confidence: {confidence:.3f})") return True - + except Exception as e: print(f"โŒ Error during simple inference: {str(e)}") return False + if __name__ == "__main__": print("๐Ÿš€ EMOTION DETECTION - WORKING TEST") print("=" * 60) - + # Try the full test first print("\n1๏ธโƒฃ Testing full inference...") success = test_working_inference() - + if not success: print("\n2๏ธโƒฃ Trying simple inference test...") success = test_simple_inference() - + if success: print(f"\n๐ŸŽ‰ SUCCESS! Your 99.54% F1 score model is working!") print(f"๐Ÿ“Š Ready for deployment!") else: - print(f"\nโŒ Test failed. Check the error messages above.") \ No newline at end of file + print(f"\nโŒ Test failed. Check the error messages above.") diff --git a/scripts/training/SAMO_Colab_Setup.py b/scripts/training/SAMO_Colab_Setup.py index 955cc7c44..bf4f486ab 100644 --- a/scripts/training/SAMO_Colab_Setup.py +++ b/scripts/training/SAMO_Colab_Setup.py @@ -11,43 +11,46 @@ """ import os -import sys import subprocess +import sys from typing import Optional + def print_header() -> None: """Print setup header.""" + def check_gpu() -> Optional[bool]: """Check GPU availability.""" try: import torch + gpu_available = torch.cuda.is_available() - + if gpu_available: torch.cuda.get_device_name(0) torch.cuda.get_device_properties(0).total_memory / 1e9 else: pass - + return True except ImportError: return False + def clone_repository() -> Optional[bool]: """Clone SAMO repository.""" try: # Clone repository - subprocess.run([ - "git", "clone", "https://github.com/uelkerd/SAMO--DL.git" - ], check=True) - + subprocess.run(["git", "clone", "https://github.com/uelkerd/SAMO--DL.git"], check=True) + # Change to repository directory os.chdir("SAMO--DL") return True except subprocess.CalledProcessError: return False + def install_dependencies() -> bool: """Install all dependencies.""" # Install SAMO package @@ -55,44 +58,41 @@ def install_dependencies() -> bool: subprocess.run(["pip", "install", "-e", "."], check=True) except subprocess.CalledProcessError: return False - + # Install voice processing libraries - voice_packages = [ - "pyaudio", - "soundfile", - "librosa", - "openai-whisper", - "speechrecognition" - ] - + voice_packages = ["pyaudio", "soundfile", "librosa", "openai-whisper", "speechrecognition"] + for package in voice_packages: try: subprocess.run(["pip", "install", package], check=True) except subprocess.CalledProcessError: return False - + return True + def test_audio_libraries() -> bool: """Test audio processing libraries.""" try: - import soundfile as sf + pass except ImportError: return False - + try: - import librosa + pass except ImportError: return False - + try: import whisper + whisper.load_model("base") except ImportError: return False - + return True + def create_voice_demo() -> bool: """Create voice processing demo.""" demo_code = ''' @@ -108,27 +108,27 @@ def record_audio(duration=5, sample_rate=16000): chunk = 1024 format = pyaudio.paInt16 channels = 1 - + p = pyaudio.PyAudio() stream = p.open(format=format, channels=channels, rate=sample_rate, input=True, frames_per_buffer=chunk) - + print("๐ŸŽค Recording... Speak now!") frames = [] - + for i in range(0, int(sample_rate / chunk * duration)): data = stream.read(chunk) frames.append(data) - + print("โœ… Recording complete!") - + stream.stop_stream() stream.close() p.terminate() - + return frames def voice_to_text(audio_frames, sample_rate=16000): @@ -139,11 +139,11 @@ def voice_to_text(audio_frames, sample_rate=16000): wf.setsampwidth(2) wf.setframerate(sample_rate) wf.writeframes(b''.join(audio_frames)) - + # Transcribe with Whisper model = whisper.load_model("base") result = model.transcribe("temp_audio.wav") - + return result["text"] def detect_emotion_from_voice(audio_frames, sample_rate=16000): @@ -151,12 +151,12 @@ def detect_emotion_from_voice(audio_frames, sample_rate=16000): # Convert audio frames to numpy array audio_data = np.frombuffer(b''.join(audio_frames), dtype=np.int16) audio_data = audio_data.astype(np.float32) / 32768.0 - + # Extract audio features mfccs = librosa.feature.mfcc(y=audio_data, sr=sample_rate, n_mfcc=13) spectral_centroids = librosa.feature.spectral_centroid(y=audio_data, sr=sample_rate) zero_crossing_rate = librosa.feature.zero_crossing_rate(audio_data) - + # Calculate statistics features = { 'mfcc_mean': np.mean(mfccs), @@ -164,7 +164,7 @@ def detect_emotion_from_voice(audio_frames, sample_rate=16000): 'spectral_centroid_mean': np.mean(spectral_centroids), 'zero_crossing_rate_mean': np.mean(zero_crossing_rate) } - + # Simple emotion mapping if features['spectral_centroid_mean'] > 2000: emotion = "excited" @@ -172,7 +172,7 @@ def detect_emotion_from_voice(audio_frames, sample_rate=16000): emotion = "sad" else: emotion = "neutral" - + return emotion, features # Test voice processing @@ -185,12 +185,13 @@ def detect_emotion_from_voice(audio_frames, sample_rate=16000): print(f"๐Ÿ˜Š Detected emotion: {emotion}") print(f"๐Ÿ“Š Audio features: {features}") ''' - + with open("voice_demo.py", "w") as f: f.write(demo_code) - + return True + def create_f1_optimization_script() -> bool: """Create F1 optimization script.""" f1_code = ''' @@ -209,18 +210,18 @@ def create_f1_optimization_script() -> bool: class FocalLoss(nn.Module): """Focal Loss for handling class imbalance.""" - + def __init__(self, alpha=0.25, gamma=2.0, reduction="mean"): super().__init__() self.alpha = alpha self.gamma = gamma self.reduction = reduction - + def forward(self, inputs, targets): bce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction='none') pt = torch.exp(-bce_loss) focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss - + if self.reduction == "mean": return focal_loss.mean() elif self.reduction == "sum": @@ -231,72 +232,75 @@ def forward(self, inputs, targets): def optimize_f1_score(): """Optimize F1 score using focal loss and other techniques.""" print("๐Ÿš€ Starting F1 optimization...") - + # Setup device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") - + # Load dataset data_loader = GoEmotionsDataLoader() datasets = data_loader.prepare_datasets() - + # Create model model = BERTEmotionClassifier() model.to(device) - + # Create focal loss focal_loss = FocalLoss(alpha=0.25, gamma=2.0) - + # Setup optimizer optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5) - + print("โœ… F1 optimization setup complete!") print("๐ŸŽฏ Expected improvement: 13.2% โ†’ 50%+ F1 score") - + return model, focal_loss, optimizer # Run optimization if __name__ == "__main__": model, focal_loss, optimizer = optimize_f1_score() ''' - + with open("f1_optimization.py", "w") as f: f.write(f1_code) - + return True + def print_next_steps() -> None: """Print next steps for the user.""" + def main() -> bool: """Main setup function.""" print_header() - + # Check GPU if not check_gpu(): return False - + # Clone repository if not clone_repository(): return False - + # Install dependencies if not install_dependencies(): return False - + # Test audio libraries if not test_audio_libraries(): return 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b/scripts/training/__pycache__/validate_improved_notebook.cpython-38.pyc deleted file mode 100644 index bdc8aaf70..000000000 Binary files a/scripts/training/__pycache__/validate_improved_notebook.cpython-38.pyc and /dev/null differ diff --git a/scripts/training/__pycache__/vertex_automl_training.cpython-38.pyc b/scripts/training/__pycache__/vertex_automl_training.cpython-38.pyc deleted file mode 100644 index 38058ad64..000000000 Binary files a/scripts/training/__pycache__/vertex_automl_training.cpython-38.pyc and /dev/null differ diff --git a/scripts/training/add_advanced_features_to_notebook.py b/scripts/training/add_advanced_features_to_notebook.py index 3f063dc9e..7bbdae59c 100644 --- a/scripts/training/add_advanced_features_to_notebook.py +++ b/scripts/training/add_advanced_features_to_notebook.py @@ -11,22 +11,21 @@ import json + def add_advanced_features(): """Add advanced features to the ultimate notebook.""" - + # Read the existing notebook - with open('notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb', 'r') as f: + with open("notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb", "r") as f: notebook = json.load(f) - + # Add focal loss implementation focal_loss_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐ŸŽฏ IMPLEMENTING FOCAL LOSS" - ] + "source": ["## ๐ŸŽฏ IMPLEMENTING FOCAL LOSS"], } - + focal_loss_code = { "cell_type": "code", "execution_count": None, @@ -35,7 +34,7 @@ def add_advanced_features(): "source": [ "# Focal Loss Implementation\n", "class FocalLoss(torch.nn.Module):\n", - " \"\"\"Focal Loss for handling class imbalance.\"\"\"\n", + ' """Focal Loss for handling class imbalance."""\n', " \n", " def __init__(self, alpha=1, gamma=2, reduction='mean'):\n", " super(FocalLoss, self).__init__()\n", @@ -55,19 +54,17 @@ def add_advanced_features(): " else:\n", " return focal_loss\n", "\n", - "print('โœ… Focal Loss implementation ready')" - ] + "print('โœ… Focal Loss implementation ready')", + ], } - + # Add class weighting implementation class_weighting_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## โš–๏ธ IMPLEMENTING CLASS WEIGHTING" - ] + "source": ["## โš–๏ธ IMPLEMENTING CLASS WEIGHTING"], } - + class_weighting_code = { "cell_type": "code", "execution_count": None, @@ -93,19 +90,17 @@ def add_advanced_features(): "class_weights_tensor = torch.tensor(class_weights, dtype=torch.float32).to(device)\n", "\n", "print(f'โœ… Class weights calculated: {class_weights}')\n", - "print(f'โœ… Device: {device}')" - ] + "print(f'โœ… Device: {device}')", + ], } - + # Add WeightedLossTrainer weighted_trainer_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿš€ CREATING WEIGHTED LOSS TRAINER" - ] + "source": ["## ๐Ÿš€ CREATING WEIGHTED LOSS TRAINER"], } - + weighted_trainer_code = { "cell_type": "code", "execution_count": None, @@ -114,7 +109,7 @@ def add_advanced_features(): "source": [ "# Custom trainer with focal loss and class weighting\n", "class WeightedLossTrainer(Trainer):\n", - " \"\"\"Custom trainer with focal loss and class weighting.\"\"\"\n", + ' """Custom trainer with focal loss and class weighting."""\n', " \n", " def __init__(self, *args, focal_alpha=1, focal_gamma=2, class_weights=None, **kwargs):\n", " super().__init__(*args, **kwargs)\n", @@ -123,7 +118,7 @@ def add_advanced_features(): " self.class_weights = class_weights\n", " \n", " def compute_loss(self, model, inputs, return_outputs=False):\n", - " labels = inputs.pop(\"labels\")\n", + ' labels = inputs.pop("labels")\n', " outputs = model(**inputs)\n", " logits = outputs.logits\n", " \n", @@ -142,19 +137,17 @@ def add_advanced_features(): " \n", " return (loss, outputs) if return_outputs else loss\n", "\n", - "print('โœ… WeightedLossTrainer with focal loss ready')" - ] + "print('โœ… WeightedLossTrainer with focal loss ready')", + ], } - + # Add model loading and configuration model_loading_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ”ง LOADING MODEL WITH PROPER CONFIGURATION" - ] + "source": ["## ๐Ÿ”ง LOADING MODEL WITH PROPER CONFIGURATION"], } - + model_loading_code = { "cell_type": "code", "execution_count": None, @@ -180,19 +173,17 @@ def add_advanced_features(): "print(f'โœ… Model loaded: {specialized_model_name}')\n", "print(f'โœ… Number of labels: {model.config.num_labels}')\n", "print(f'โœ… id2label: {model.config.id2label}')\n", - "print(f'โœ… label2id: {model.config.label2id}')" - ] + "print(f'โœ… label2id: {model.config.label2id}')", + ], } - + # Add data preprocessing preprocessing_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ“ DATA PREPROCESSING" - ] + "source": ["## ๐Ÿ“ DATA PREPROCESSING"], } - + preprocessing_code = { "cell_type": "code", "execution_count": None, @@ -218,19 +209,17 @@ def add_advanced_features(): "val_dataset = train_val_dataset['test']\n", "\n", "print(f'โœ… Training samples: {len(train_dataset)}')\n", - "print(f'โœ… Validation samples: {len(val_dataset)}')" - ] + "print(f'โœ… Validation samples: {len(val_dataset)}')", + ], } - + # Add training arguments training_args_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## โš™๏ธ TRAINING ARGUMENTS" - ] + "source": ["## โš™๏ธ TRAINING ARGUMENTS"], } - + training_args_code = { "cell_type": "code", "execution_count": None, @@ -258,19 +247,17 @@ def add_advanced_features(): " remove_unused_columns=False\n", ")\n", "\n", - "print('โœ… Training arguments configured')" - ] + "print('โœ… Training arguments configured')", + ], } - + # Add compute metrics compute_metrics_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ“Š COMPUTE METRICS" - ] + "source": ["## ๐Ÿ“Š COMPUTE METRICS"], } - + compute_metrics_code = { "cell_type": "code", "execution_count": None, @@ -295,19 +282,17 @@ def add_advanced_features(): " 'recall': recall\n", " }\n", "\n", - "print('โœ… Compute metrics function ready')" - ] + "print('โœ… Compute metrics function ready')", + ], } - + # Add trainer initialization trainer_init_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿš€ INITIALIZING TRAINER" - ] + "source": ["## ๐Ÿš€ INITIALIZING TRAINER"], } - + trainer_init_code = { "cell_type": "code", "execution_count": None, @@ -327,19 +312,13 @@ def add_advanced_features(): " class_weights=class_weights_tensor\n", ")\n", "\n", - "print('โœ… Trainer initialized with focal loss and class weighting')" - ] + "print('โœ… Trainer initialized with focal loss and class weighting')", + ], } - + # Add training - training_cell = { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿš€ STARTING TRAINING" - ] - } - + training_cell = {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿš€ STARTING TRAINING"]} + training_code = { "cell_type": "code", "execution_count": None, @@ -358,19 +337,17 @@ def add_advanced_features(): "# Train the model\n", "trainer.train()\n", "\n", - "print('โœ… Training completed successfully!')" - ] + "print('โœ… Training completed successfully!')", + ], } - + # Add evaluation evaluation_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ“Š EVALUATING MODEL" - ] + "source": ["## ๐Ÿ“Š EVALUATING MODEL"], } - + evaluation_code = { "cell_type": "code", "execution_count": None, @@ -391,19 +368,17 @@ def add_advanced_features(): "if results['eval_f1'] >= 0.75:\n", " print('๐ŸŽ‰ TARGET ACHIEVED! F1 Score >= 75%')\n", "else:\n", - " print(f'โš ๏ธ Target not achieved. Need {0.75 - results[\"eval_f1\"]:.3f} more F1 points')" - ] + " print(f'โš ๏ธ Target not achieved. Need {0.75 - results[\"eval_f1\"]:.3f} more F1 points')", + ], } - + # Add advanced validation advanced_validation_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿงช ADVANCED VALIDATION" - ] + "source": ["## ๐Ÿงช ADVANCED VALIDATION"], } - + advanced_validation_code = { "cell_type": "code", "execution_count": None, @@ -479,19 +454,17 @@ def add_advanced_features(): " if accuracy < 0.8:\n", " print(f'โŒ Accuracy too low: {accuracy:.1%} (need >80%)')\n", " if max_bias > 0.3:\n", - " print(f'โŒ Too much bias: {max_bias:.1%} (need <30%)')" - ] + " print(f'โŒ Too much bias: {max_bias:.1%} (need <30%)')", + ], } - + # Add model saving with verification model_saving_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ’พ SAVING MODEL WITH VERIFICATION" - ] + "source": ["## ๐Ÿ’พ SAVING MODEL WITH VERIFICATION"], } - + model_saving_code = { "cell_type": "code", "execution_count": None, @@ -525,9 +498,9 @@ def add_advanced_features(): " with open(f'{output_dir}/config.json', 'r') as f:\n", " saved_config = json.load(f)\n", " \n", - " print(f'Saved model type: {saved_config.get(\"model_type\", \"NOT FOUND\")}')\n", - " print(f'Saved id2label: {saved_config.get(\"id2label\", \"NOT FOUND\")}')\n", - " print(f'Saved label2id: {saved_config.get(\"label2id\", \"NOT FOUND\")}')\n", + ' print(f\'Saved model type: {saved_config.get("model_type", "NOT FOUND")}\')\n', + ' print(f\'Saved id2label: {saved_config.get("id2label", "NOT FOUND")}\')\n', + ' print(f\'Saved label2id: {saved_config.get("label2id", "NOT FOUND")}\')\n', " \n", " # Verify the emotion labels are saved correctly\n", " expected_id2label = {str(i): emotion for i, emotion in enumerate(emotions)}\n", @@ -577,54 +550,55 @@ def add_advanced_features(): "print('\\n๐Ÿ“‹ Next steps:')\n", "print('1. Download the model files')\n", "print('2. Test locally with validation script')\n", - "print('3. Deploy if all tests pass')" - ] + "print('3. Deploy if all tests pass')", + ], } - + # Add all cells to the notebook new_cells = [ - focal_loss_cell, - focal_loss_code, - class_weighting_cell, - class_weighting_code, - weighted_trainer_cell, - weighted_trainer_code, - model_loading_cell, - model_loading_code, - preprocessing_cell, - preprocessing_code, - training_args_cell, - training_args_code, - compute_metrics_cell, - compute_metrics_code, - trainer_init_cell, - trainer_init_code, - training_cell, - training_code, - evaluation_cell, - evaluation_code, - advanced_validation_cell, - advanced_validation_code, - model_saving_cell, - model_saving_code - ] - - notebook['cells'].extend(new_cells) - + focal_loss_cell, + focal_loss_code, + class_weighting_cell, + class_weighting_code, + weighted_trainer_cell, + weighted_trainer_code, + model_loading_cell, + model_loading_code, + preprocessing_cell, + preprocessing_code, + training_args_cell, + training_args_code, + compute_metrics_cell, + compute_metrics_code, + trainer_init_cell, + trainer_init_code, + training_cell, + training_code, + evaluation_cell, + evaluation_code, + advanced_validation_cell, + advanced_validation_code, + model_saving_cell, + model_saving_code, + ] + + notebook["cells"].extend(new_cells) + # Save the enhanced notebook - with open('notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb', 'w') as f: + with open("notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook, f, indent=2) - - print('โœ… Enhanced notebook with all advanced features created!') - print('๐Ÿ“‹ All features included:') - print(' โœ… Configuration preservation') - print(' โœ… Focal loss implementation') - print(' โœ… Class weighting with WeightedLossTrainer') - print(' โœ… Data augmentation') - print(' โœ… Advanced validation') - print(' โœ… Model saving with verification') - - return 'notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb' + + print("โœ… Enhanced notebook with all advanced features created!") + print("๐Ÿ“‹ All features included:") + print(" โœ… Configuration preservation") + print(" โœ… Focal loss implementation") + print(" โœ… Class weighting with WeightedLossTrainer") + print(" โœ… Data augmentation") + print(" โœ… Advanced validation") + print(" โœ… Model saving with verification") + + return "notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb" + if __name__ == "__main__": - add_advanced_features() \ No newline at end of file + add_advanced_features() diff --git a/scripts/training/bulletproof_training.py b/scripts/training/bulletproof_training.py deleted file mode 100644 index 70695c761..000000000 --- a/scripts/training/bulletproof_training.py +++ /dev/null @@ -1,449 +0,0 @@ -#!/usr/bin/env python3 -""" -Bulletproof training script for REQ-DL-012 that handles notebook state corruption. -This script can be run in a fresh kernel and will validate everything step by step. -""" -import sys -import json -import pickle -import torch -import torch.nn as nn -import pandas as pd -from datasets import load_dataset -from torch.utils.data import Dataset, DataLoader -from sklearn.model_selection import train_test_split -from sklearn.metrics import f1_score, accuracy_score -from sklearn.preprocessing import LabelEncoder -from transformers import AutoModel, AutoTokenizer -import logging - -# Setup logging -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') -logger = logging.getLogger(__name__) - -def validate_environment(): - """Validate the environment and clear any corrupted state.""" - logger.info("๐Ÿ” Validating environment...") - - # Clear GPU memory - if torch.cuda.is_available(): - torch.cuda.empty_cache() - logger.info("โœ… GPU memory cleared") - - # Check CUDA - if torch.cuda.is_available(): - logger.info(f"โœ… CUDA available: {torch.cuda.get_device_name()}") - logger.info(f"โœ… CUDA memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB") - else: - logger.warning("โš ๏ธ CUDA not available, using CPU") - - # Test basic operations - try: - test_tensor = torch.randn(2, 3) - test_tensor.to('cuda' if torch.cuda.is_available() else 'cpu') - logger.info("โœ… Basic tensor operations work") - except Exception as e: - logger.error(f"โŒ Basic tensor operations failed: {e}") - return False - - return True - -def create_unified_label_encoder(): - """Create a unified label encoder for both datasets.""" - logger.info("๐Ÿ”ง Creating unified label encoder...") - - # Load datasets - go_emotions = load_dataset("go_emotions", "simplified") - with open('data/journal_test_dataset.json', 'r') as f: - journal_entries = json.load(f) - journal_df = pd.DataFrame(journal_entries) - - # Extract labels - go_labels = set() - for example in go_emotions['train']: - if example['labels']: - go_labels.update(example['labels']) - - journal_labels = set(journal_df['emotion'].unique()) - - # Find common labels - common_labels = sorted(list(go_labels.intersection(journal_labels))) - if not common_labels: - logger.warning("โš ๏ธ No common labels found! Using all labels...") - common_labels = sorted(list(go_labels.union(journal_labels))) - - logger.info(f"๐Ÿ“Š Using {len(common_labels)} labels: {common_labels}") - - # Create encoder - label_encoder = LabelEncoder() - label_encoder.fit(common_labels) - - # Save encoder - with open('unified_label_encoder.pkl', 'wb') as f: - pickle.dump(label_encoder, f) - - # Save mappings - label_to_id = {label: idx for idx, label in enumerate(label_encoder.classes_)} - id_to_label = {idx: label for label, idx in label_to_id.items()} - - with open('label_mappings.json', 'w') as f: - json.dump({ - 'label_to_id': label_to_id, - 'id_to_label': id_to_label, - 'num_labels': len(label_encoder.classes_), - 'classes': label_encoder.classes_.tolist() - }, f, indent=2) - - logger.info(f"โœ… Label encoder created with {len(label_encoder.classes_)} classes") - return label_encoder, label_to_id, id_to_label - -def prepare_filtered_data(label_encoder, label_to_id): - """Prepare filtered data using only common labels.""" - logger.info("๐Ÿ“Š Preparing filtered data...") - - # Load datasets - go_emotions = load_dataset("go_emotions", "simplified") - with open('data/journal_test_dataset.json', 'r') as f: - journal_entries = json.load(f) - journal_df = pd.DataFrame(journal_entries) - - valid_labels = set(label_encoder.classes_) - - # Filter GoEmotions data - go_texts = [] - go_labels = [] - for example in go_emotions['train']: - if example['labels']: - for label in example['labels']: - if label in valid_labels: - go_texts.append(example['text']) - go_labels.append(label_to_id[label]) - break - - # Filter journal data - journal_texts = [] - journal_labels = [] - for _, row in journal_df.iterrows(): - if row['emotion'] in valid_labels: - journal_texts.append(row['content']) - journal_labels.append(label_to_id[row['emotion']]) - - logger.info(f"๐Ÿ“Š Filtered GoEmotions: {len(go_texts)} samples") - logger.info(f"๐Ÿ“Š Filtered Journal: {len(journal_texts)} samples") - - # Validate label ranges - FIX: Convert to integers for comparison - if go_labels: - go_label_range = (min(go_labels), max(go_labels)) - else: - go_label_range = (0, 0) - - if journal_labels: - journal_label_range = (min(journal_labels), max(journal_labels)) - else: - journal_label_range = (0, 0) - - expected_range = (0, len(label_encoder.classes_) - 1) - - logger.info(f"๐Ÿ“Š GoEmotions label range: {go_label_range}") - logger.info(f"๐Ÿ“Š Journal label range: {journal_label_range}") - logger.info(f"๐Ÿ“Š Expected range: {expected_range}") - - if go_label_range[0] < expected_range[0] or go_label_range[1] > expected_range[1]: - logger.error(f"โŒ GoEmotions labels out of range!") - return None, None, None, None - - if journal_label_range[0] < expected_range[0] or journal_label_range[1] > expected_range[1]: - logger.error(f"โŒ Journal labels out of range!") - return None, None, None, None - - logger.info("โœ… All labels within expected range") - return go_texts, go_labels, journal_texts, journal_labels - -class SimpleEmotionDataset(Dataset): - """Simple dataset class with validation.""" - def __init__(self, texts, labels, tokenizer, max_length=128): - self.texts = texts - self.labels = labels - self.tokenizer = tokenizer - self.max_length = max_length - - # Validate data - if len(texts) != len(labels): - raise ValueError(f"Texts and labels have different lengths: {len(texts)} vs {len(labels)}") - - # Validate labels - for i, label in enumerate(labels): - if not isinstance(label, int) or label < 0: - raise ValueError(f"Invalid label at index {i}: {label}") - - def __len__(self): - return len(self.texts) - - def __getitem__(self, idx): - text = self.texts[idx] - label = self.labels[idx] - - # Validate inputs - if not isinstance(text, str) or not text.strip(): - raise ValueError(f"Invalid text at index {idx}") - - if not isinstance(label, int) or label < 0: - raise ValueError(f"Invalid label at index {idx}: {label}") - - encoding = self.tokenizer( - text, - truncation=True, - padding='max_length', - max_length=self.max_length, - return_tensors='pt' - ) - - return { - 'input_ids': encoding['input_ids'].flatten(), - 'attention_mask': encoding['attention_mask'].flatten(), - 'labels': torch.tensor(label, dtype=torch.long) - } - -class SimpleEmotionClassifier(nn.Module): - """Simple emotion classifier with validation.""" - def __init__(self, model_name="bert-base-uncased", num_labels=None): - super().__init__() - - if num_labels is None or num_labels <= 0: - raise ValueError(f"Invalid num_labels: {num_labels}") - - self.num_labels = num_labels - self.bert = AutoModel.from_pretrained(model_name) - self.dropout = nn.Dropout(0.3) - self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels) - - logger.info(f"โœ… Model initialized with {num_labels} labels") - - def forward(self, input_ids, attention_mask): - # Validate inputs - if input_ids.dim() != 2: - raise ValueError(f"Expected input_ids to be 2D, got {input_ids.dim()}D") - - if attention_mask.dim() != 2: - raise ValueError(f"Expected attention_mask to be 2D, got {attention_mask.dim()}D") - - outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) - pooled_output = outputs.pooler_output - logits = self.classifier(self.dropout(pooled_output)) - - # Validate outputs - if logits.shape[-1] != self.num_labels: - raise ValueError(f"Expected {self.num_labels} output classes, got {logits.shape[-1]}") - - return logits - -def train_model_simple(go_texts, go_labels, journal_texts, journal_labels, num_labels): - """Simple training function with comprehensive validation.""" - logger.info("๐Ÿš€ Starting simple training...") - - # Setup device - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - logger.info(f"โœ… Using device: {device}") - - # Initialize tokenizer and model - tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") - model = SimpleEmotionClassifier(model_name="bert-base-uncased", num_labels=num_labels) - model = model.to(device) - - # Create datasets - go_dataset = SimpleEmotionDataset(go_texts, go_labels, tokenizer) - journal_dataset = SimpleEmotionDataset(journal_texts, journal_labels, tokenizer) - - # Split journal data - journal_train_texts, journal_val_texts, journal_train_labels, journal_val_labels = train_test_split( - journal_texts, journal_labels, test_size=0.3, random_state=42, stratify=journal_labels - ) - - journal_train_dataset = SimpleEmotionDataset(journal_train_texts, journal_train_labels, tokenizer) - journal_val_dataset = SimpleEmotionDataset(journal_val_texts, journal_val_labels, tokenizer) - - # Create dataloaders - go_loader = DataLoader(go_dataset, batch_size=8, shuffle=True) - journal_train_loader = DataLoader(journal_train_dataset, batch_size=8, shuffle=True) - journal_val_loader = DataLoader(journal_val_dataset, batch_size=8, shuffle=False) - - logger.info(f"โœ… Training samples: {len(go_dataset)} GoEmotions + {len(journal_train_dataset)} Journal") - logger.info(f"โœ… Validation samples: {len(journal_val_dataset)} Journal") - - # Training setup - optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5) - criterion = nn.CrossEntropyLoss() - - # Training loop - num_epochs = 3 # Reduced for testing - best_f1 = 0.0 - - for epoch in range(num_epochs): - logger.info(f"๐Ÿ”„ Epoch {epoch + 1}/{num_epochs}") - - # Training - model.train() - total_loss = 0 - num_batches = 0 - - # Train on GoEmotions - logger.info(" ๐Ÿ“š Training on GoEmotions...") - for i, batch in enumerate(go_loader): - try: - # Validate batch - if 'input_ids' not in batch or 'attention_mask' not in batch or 'labels' not in batch: - logger.warning(f"โš ๏ธ Invalid batch structure at batch {i}") - continue - - # Move to device with validation - input_ids = batch['input_ids'].to(device) - attention_mask = batch['attention_mask'].to(device) - labels = batch['labels'].to(device) - - # Validate labels - if torch.any(labels >= num_labels) or torch.any(labels < 0): - logger.warning(f"โš ๏ธ Invalid labels in batch {i}: {labels}") - continue - - # Forward pass - optimizer.zero_grad() - outputs = model(input_ids=input_ids, attention_mask=attention_mask) - loss = criterion(outputs, labels) - loss.backward() - optimizer.step() - - total_loss += loss.item() - num_batches += 1 - - if i % 50 == 0: - logger.info(f" Batch {i}/{len(go_loader)}, Loss: {loss.item():.4f}") - - except Exception as e: - logger.error(f"โŒ Error in batch {i}: {e}") - continue - - # Train on journal data - logger.info(" ๐Ÿ“ Training on journal data...") - for i, batch in enumerate(journal_train_loader): - try: - input_ids = batch['input_ids'].to(device) - attention_mask = batch['attention_mask'].to(device) - labels = batch['labels'].to(device) - - if torch.any(labels >= num_labels) or torch.any(labels < 0): - continue - - optimizer.zero_grad() - outputs = model(input_ids=input_ids, attention_mask=attention_mask) - loss = criterion(outputs, labels) - loss.backward() - optimizer.step() - - total_loss += loss.item() - num_batches += 1 - - if i % 10 == 0: - logger.info(f" Batch {i}/{len(journal_train_loader)}, Loss: {loss.item():.4f}") - - except Exception as e: - logger.error(f"โŒ Error in journal batch {i}: {e}") - continue - - # Validation - logger.info(" ๐ŸŽฏ Validating...") - model.eval() - all_preds = [] - all_labels = [] - - with torch.no_grad(): - for batch in journal_val_loader: - try: - input_ids = batch['input_ids'].to(device) - attention_mask = batch['attention_mask'].to(device) - labels = batch['labels'].to(device) - - outputs = model(input_ids=input_ids, attention_mask=attention_mask) - preds = torch.argmax(outputs, dim=1) - - all_preds.extend(preds.cpu().numpy()) - all_labels.extend(labels.cpu().numpy()) - - except Exception as e: - logger.error(f"โŒ Error in validation batch: {e}") - continue - - # Calculate metrics - if all_preds and all_labels: - f1_macro = f1_score(all_labels, all_preds, average='macro') - accuracy = accuracy_score(all_labels, all_preds) - - avg_loss = total_loss / num_batches if num_batches > 0 else 0 - - logger.info(f" ๐Ÿ“Š Epoch {epoch + 1} Results:") - logger.info(f" Average Loss: {avg_loss:.4f}") - logger.info(f" Validation F1 (Macro): {f1_macro:.4f}") - logger.info(f" Validation Accuracy: {accuracy:.4f}") - - # Save best model - if f1_macro > best_f1: - best_f1 = f1_macro - torch.save(model.state_dict(), 'best_simple_model.pth') - logger.info(f" ๐Ÿ’พ New best model saved! F1: {best_f1:.4f}") - - # Clear GPU cache - if torch.cuda.is_available(): - torch.cuda.empty_cache() - - logger.info(f"๐Ÿ† Training completed! Best F1 Score: {best_f1:.4f}") - return best_f1 - -def main(): - """Main function with comprehensive error handling.""" - logger.info("๐Ÿš€ Starting bulletproof training for REQ-DL-012...") - - try: - # Step 1: Validate environment - if not validate_environment(): - logger.error("โŒ Environment validation failed") - return False - - # Step 2: Create unified label encoder - label_encoder, label_to_id, id_to_label = create_unified_label_encoder() - - # Step 3: Prepare filtered data - go_texts, go_labels, journal_texts, journal_labels = prepare_filtered_data(label_encoder, label_to_id) - - if go_texts is None: - logger.error("โŒ Data preparation failed") - return False - - # Step 4: Train model - num_labels = len(label_encoder.classes_) - best_f1 = train_model_simple(go_texts, go_labels, journal_texts, journal_labels, num_labels) - - # Step 5: Save results - results = { - 'best_f1': best_f1, - 'num_labels': num_labels, - 'target_achieved': best_f1 >= 0.7, - 'go_samples': len(go_texts), - 'journal_samples': len(journal_texts) - } - - with open('simple_training_results.json', 'w') as f: - json.dump(results, f, indent=2) - - logger.info("โœ… Training completed successfully!") - logger.info(f"๐Ÿ“Š Final F1 Score: {best_f1:.4f}") - logger.info(f"๐ŸŽฏ Target Met: {'โœ…' if best_f1 >= 0.7 else 'โŒ'}") - - return True - - except Exception as e: - logger.error(f"โŒ Training failed: {e}") - return False - -if __name__ == "__main__": - success = main() - if not success: - sys.exit(1) \ No newline at end of file diff --git a/scripts/training/bulletproof_training_cell.py b/scripts/training/bulletproof_training_cell.py index 20ab2d7f8..68bb5d4ea 100644 --- a/scripts/training/bulletproof_training_cell.py +++ b/scripts/training/bulletproof_training_cell.py @@ -5,38 +5,42 @@ print("๐Ÿš€ BULLETPROOF TRAINING FOR REQ-DL-012") print("=" * 50) +import json # Step 1: Clear everything and validate environment import os -import sys -import json import pickle -import torch -import torch.nn as nn +import sys + import numpy as np import pandas as pd -from datasets import load_dataset -from torch.utils.data import Dataset, DataLoader +import torch +import torch.nn as nn +# Download results +from google.colab import files +from sklearn.metrics import accuracy_score, f1_score from sklearn.model_selection import train_test_split -from sklearn.metrics import f1_score, accuracy_score from sklearn.preprocessing import LabelEncoder +from torch.utils.data import DataLoader, Dataset from transformers import AutoModel, AutoTokenizer +from datasets import load_dataset + print("โœ… Imports successful") # Clear GPU memory -if torch.cuda.is_available(): + if torch.cuda.is_available(): torch.cuda.empty_cache() print(f"โœ… GPU memory cleared: {torch.cuda.get_device_name()}") -else: + else: print("โš ๏ธ CUDA not available, using CPU") # Test basic operations -try: + try: test_tensor = torch.randn(2, 3) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") test_tensor.to(device) print("โœ… Basic tensor operations work") -except Exception as e: + except Exception as e: print(f"โŒ Basic tensor operations failed: {e}") raise @@ -54,7 +58,7 @@ # Extract labels go_labels = set() -for example in go_emotions['train']: + for example in go_emotions['train']: if example['labels']: go_labels.update(example['labels']) @@ -62,7 +66,7 @@ # Find common labels common_labels = sorted(list(go_labels.intersection(journal_labels))) -if not common_labels: + if not common_labels: print("โš ๏ธ No common labels found! Using all labels...") # FIX: Convert to strings before union to avoid type comparison issues all_go_labels = [str(label) for label in go_labels] @@ -87,7 +91,7 @@ # Filter GoEmotions data go_texts = [] go_labels = [] -for example in go_emotions['train']: + for example in go_emotions['train']: if example['labels']: for label in example['labels']: if label in valid_labels: @@ -98,7 +102,7 @@ # Filter journal data journal_texts = [] journal_labels = [] -for _, row in journal_df.iterrows(): + for _, row in journal_df.iterrows(): if row['emotion'] in valid_labels: journal_texts.append(row['content']) journal_labels.append(label_to_id[row['emotion']]) @@ -115,10 +119,10 @@ print(f"๐Ÿ“Š Journal label range: {journal_label_range}") print(f"๐Ÿ“Š Expected range: {expected_range}") -if go_label_range[0] < expected_range[0] or go_label_range[1] > expected_range[1]: + if go_label_range[0] < expected_range[0] or go_label_range[1] > expected_range[1]: raise ValueError("โŒ GoEmotions labels out of range!") -if journal_label_range[0] < expected_range[0] or journal_label_range[1] > expected_range[1]: + if journal_label_range[0] < expected_range[0] or journal_label_range[1] > expected_range[1]: raise ValueError("โŒ Journal labels out of range!") print("โœ… All labels within expected range") @@ -130,30 +134,30 @@ def __init__(self, texts, labels, tokenizer, max_length=128): self.labels = labels self.tokenizer = tokenizer self.max_length = max_length - + # Validate data if len(texts) != len(labels): raise ValueError(f"Texts and labels have different lengths: {len(texts)} vs {len(labels)}") - + # Validate labels for i, label in enumerate(labels): if not isinstance(label, int) or label < 0: raise ValueError(f"Invalid label at index {i}: {label}") - + def __len__(self): return len(self.texts) - + def __getitem__(self, idx): text = self.texts[idx] label = self.labels[idx] - + # Validate inputs if not isinstance(text, str) or not text.strip(): raise ValueError(f"Invalid text at index {idx}") - + if not isinstance(label, int) or label < 0: raise ValueError(f"Invalid label at index {idx}: {label}") - + encoding = self.tokenizer( text, truncation=True, @@ -161,7 +165,7 @@ def __getitem__(self, idx): max_length=self.max_length, return_tensors='pt' ) - + return { 'input_ids': encoding['input_ids'].flatten(), 'attention_mask': encoding['attention_mask'].flatten(), @@ -172,33 +176,33 @@ def __getitem__(self, idx): class SimpleEmotionClassifier(nn.Module): def __init__(self, model_name="bert-base-uncased", num_labels=None): super().__init__() - + if num_labels is None or num_labels <= 0: raise ValueError(f"Invalid num_labels: {num_labels}") - + self.num_labels = num_labels self.bert = AutoModel.from_pretrained(model_name) self.dropout = nn.Dropout(0.3) self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels) - + print(f"โœ… Model initialized with {num_labels} labels") - + def forward(self, input_ids, attention_mask): # Validate inputs if input_ids.dim() != 2: raise ValueError(f"Expected input_ids to be 2D, got {input_ids.dim()}D") - + if attention_mask.dim() != 2: raise ValueError(f"Expected attention_mask to be 2D, got {attention_mask.dim()}D") - + outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) pooled_output = outputs.pooler_output logits = self.classifier(self.dropout(pooled_output)) - + # Validate outputs if logits.shape[-1] != self.num_labels: raise ValueError(f"Expected {self.num_labels} output classes, got {logits.shape[-1]}") - + return logits # Step 7: Setup training @@ -242,14 +246,14 @@ def forward(self, input_ids, attention_mask): num_epochs = 3 # Reduced for testing best_f1 = 0.0 -for epoch in range(num_epochs): + for epoch in range(num_epochs): print(f"\n๐Ÿ”„ Epoch {epoch + 1}/{num_epochs}") - + # Training model.train() total_loss = 0 num_batches = 0 - + # Train on GoEmotions print(" ๐Ÿ“š Training on GoEmotions...") for i, batch in enumerate(go_loader): @@ -258,34 +262,34 @@ def forward(self, input_ids, attention_mask): if 'input_ids' not in batch or 'attention_mask' not in batch or 'labels' not in batch: print(f"โš ๏ธ Invalid batch structure at batch {i}") continue - + # Move to device with validation input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + # Validate labels if torch.any(labels >= num_labels) or torch.any(labels < 0): print(f"โš ๏ธ Invalid labels in batch {i}: {labels}") continue - + # Forward pass optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() num_batches += 1 - + if i % 50 == 0: print(f" Batch {i}/{len(go_loader)}, Loss: {loss.item():.4f}") - + except Exception as e: print(f"โŒ Error in batch {i}: {e}") continue - + # Train on journal data print(" ๐Ÿ“ Training on journal data...") for i, batch in enumerate(journal_train_loader): @@ -293,67 +297,67 @@ def forward(self, input_ids, attention_mask): input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + if torch.any(labels >= num_labels) or torch.any(labels < 0): continue - + optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() num_batches += 1 - + if i % 10 == 0: print(f" Batch {i}/{len(journal_train_loader)}, Loss: {loss.item():.4f}") - + except Exception as e: print(f"โŒ Error in journal batch {i}: {e}") continue - + # Validation print(" ๐ŸŽฏ Validating...") model.eval() all_preds = [] all_labels = [] - + with torch.no_grad(): for batch in journal_val_loader: try: input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + outputs = model(input_ids=input_ids, attention_mask=attention_mask) preds = torch.argmax(outputs, dim=1) - + all_preds.extend(preds.cpu().numpy()) all_labels.extend(labels.cpu().numpy()) - + except Exception as e: print(f"โŒ Error in validation batch: {e}") continue - + # Calculate metrics if all_preds and all_labels: f1_macro = f1_score(all_labels, all_preds, average='macro') accuracy = accuracy_score(all_labels, all_preds) - + avg_loss = total_loss / num_batches if num_batches > 0 else 0 - + print(f" ๐Ÿ“Š Epoch {epoch + 1} Results:") print(f" Average Loss: {avg_loss:.4f}") print(f" Validation F1 (Macro): {f1_macro:.4f}") print(f" Validation Accuracy: {accuracy:.4f}") - + # Save best model if f1_macro > best_f1: best_f1 = f1_macro torch.save(model.state_dict(), 'best_simple_model.pth') print(f" ๐Ÿ’พ New best model saved! F1: {best_f1:.4f}") - + # Clear GPU cache if torch.cuda.is_available(): torch.cuda.empty_cache() @@ -376,10 +380,8 @@ def forward(self, input_ids, attention_mask): print(f"๐Ÿ“Š Final F1 Score: {best_f1:.4f}") print(f"๐ŸŽฏ Target Met: {'โœ…' if best_f1 >= 0.7 else 'โŒ'}") -# Download results -from google.colab import files files.download('best_simple_model.pth') files.download('simple_training_results.json') print("\n๐ŸŽ‰ BULLETPROOF TRAINING COMPLETED!") -print("๐Ÿ“ Files downloaded: best_simple_model.pth, simple_training_results.json") \ No newline at end of file +print("๐Ÿ“ Files downloaded: best_simple_model.pth, simple_training_results.json") diff --git a/scripts/training/bulletproof_training_cell_fixed.py b/scripts/training/bulletproof_training_cell_fixed.py index 491742fe0..bf835519e 100644 --- a/scripts/training/bulletproof_training_cell_fixed.py +++ b/scripts/training/bulletproof_training_cell_fixed.py @@ -5,38 +5,42 @@ print("๐Ÿš€ BULLETPROOF TRAINING FOR REQ-DL-012 - FIXED LABEL MAPPING") print("=" * 60) +import json # Step 1: Clear everything and validate environment import os -import sys -import json import pickle -import torch -import torch.nn as nn +import sys + import numpy as np import pandas as pd -from datasets import load_dataset -from torch.utils.data import Dataset, DataLoader +import torch +import torch.nn as nn +# Download results +from google.colab import files +from sklearn.metrics import accuracy_score, f1_score from sklearn.model_selection import train_test_split -from sklearn.metrics import f1_score, accuracy_score from sklearn.preprocessing import LabelEncoder +from torch.utils.data import DataLoader, Dataset from transformers import AutoModel, AutoTokenizer +from datasets import load_dataset + print("โœ… Imports successful") # Clear GPU memory -if torch.cuda.is_available(): + if torch.cuda.is_available(): torch.cuda.empty_cache() print(f"โœ… GPU memory cleared: {torch.cuda.get_device_name()}") -else: + else: print("โš ๏ธ CUDA not available, using CPU") # Test basic operations -try: + try: test_tensor = torch.randn(2, 3) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") test_tensor.to(device) print("โœ… Basic tensor operations work") -except Exception as e: + except Exception as e: print(f"โŒ Basic tensor operations failed: {e}") raise @@ -96,7 +100,7 @@ # Filter GoEmotions data using mapping go_texts = [] go_labels = [] -for example in go_emotions['train']: + for example in go_emotions['train']: if example['labels']: for label in example['labels']: if label in emotion_mapping: @@ -138,30 +142,30 @@ def __init__(self, texts, labels, tokenizer, max_length=128): self.labels = labels self.tokenizer = tokenizer self.max_length = max_length - + # Validate data if len(texts) != len(labels): raise ValueError(f"Texts and labels have different lengths: {len(texts)} vs {len(labels)}") - + # Validate labels for i, label in enumerate(labels): if not isinstance(label, int) or label < 0: raise ValueError(f"Invalid label at index {i}: {label}") - + def __len__(self): return len(self.texts) - + def __getitem__(self, idx): text = self.texts[idx] label = self.labels[idx] - + # Validate inputs if not isinstance(text, str) or not text.strip(): raise ValueError(f"Invalid text at index {idx}") - + if not isinstance(label, int) or label < 0: raise ValueError(f"Invalid label at index {idx}: {label}") - + encoding = self.tokenizer( text, truncation=True, @@ -169,7 +173,7 @@ def __getitem__(self, idx): max_length=self.max_length, return_tensors='pt' ) - + return { 'input_ids': encoding['input_ids'].flatten(), 'attention_mask': encoding['attention_mask'].flatten(), @@ -180,33 +184,33 @@ def __getitem__(self, idx): class SimpleEmotionClassifier(nn.Module): def __init__(self, model_name="bert-base-uncased", num_labels=None): super().__init__() - + if num_labels is None or num_labels <= 0: raise ValueError(f"Invalid num_labels: {num_labels}") - + self.num_labels = num_labels self.bert = AutoModel.from_pretrained(model_name) self.dropout = nn.Dropout(0.3) self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels) - + print(f"โœ… Model initialized with {num_labels} labels") - + def forward(self, input_ids, attention_mask): # Validate inputs if input_ids.dim() != 2: raise ValueError(f"Expected input_ids to be 2D, got {input_ids.dim()}D") - + if attention_mask.dim() != 2: raise ValueError(f"Expected attention_mask to be 2D, got {attention_mask.dim()}D") - + outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) pooled_output = outputs.pooler_output logits = self.classifier(self.dropout(pooled_output)) - + # Validate outputs if logits.shape[-1] != self.num_labels: raise ValueError(f"Expected {self.num_labels} output classes, got {logits.shape[-1]}") - + return logits # Step 7: Setup training @@ -250,14 +254,14 @@ def forward(self, input_ids, attention_mask): num_epochs = 3 # Reduced for testing best_f1 = 0.0 -for epoch in range(num_epochs): + for epoch in range(num_epochs): print(f"\n๐Ÿ”„ Epoch {epoch + 1}/{num_epochs}") - + # Training model.train() total_loss = 0 num_batches = 0 - + # Train on GoEmotions print(" ๐Ÿ“š Training on GoEmotions...") for i, batch in enumerate(go_loader): @@ -266,34 +270,34 @@ def forward(self, input_ids, attention_mask): if 'input_ids' not in batch or 'attention_mask' not in batch or 'labels' not in batch: print(f"โš ๏ธ Invalid batch structure at batch {i}") continue - + # Move to device with validation input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + # Validate labels if torch.any(labels >= num_labels) or torch.any(labels < 0): print(f"โš ๏ธ Invalid labels in batch {i}: {labels}") continue - + # Forward pass optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() num_batches += 1 - + if i % 50 == 0: print(f" Batch {i}/{len(go_loader)}, Loss: {loss.item():.4f}") - + except Exception as e: print(f"โŒ Error in batch {i}: {e}") continue - + # Train on journal data print(" ๐Ÿ“ Training on journal data...") for i, batch in enumerate(journal_train_loader): @@ -301,67 +305,67 @@ def forward(self, input_ids, attention_mask): input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + if torch.any(labels >= num_labels) or torch.any(labels < 0): continue - + optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() num_batches += 1 - + if i % 10 == 0: print(f" Batch {i}/{len(journal_train_loader)}, Loss: {loss.item():.4f}") - + except Exception as e: print(f"โŒ Error in journal batch {i}: {e}") continue - + # Validation print(" ๐ŸŽฏ Validating...") model.eval() all_preds = [] all_labels = [] - + with torch.no_grad(): for batch in journal_val_loader: try: input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + outputs = model(input_ids=input_ids, attention_mask=attention_mask) preds = torch.argmax(outputs, dim=1) - + all_preds.extend(preds.cpu().numpy()) all_labels.extend(labels.cpu().numpy()) - + except Exception as e: print(f"โŒ Error in validation batch: {e}") continue - + # Calculate metrics if all_preds and all_labels: f1_macro = f1_score(all_labels, all_preds, average='macro') accuracy = accuracy_score(all_labels, all_preds) - + avg_loss = total_loss / num_batches if num_batches > 0 else 0 - + print(f" ๐Ÿ“Š Epoch {epoch + 1} Results:") print(f" Average Loss: {avg_loss:.4f}") print(f" Validation F1 (Macro): {f1_macro:.4f}") print(f" Validation Accuracy: {accuracy:.4f}") - + # Save best model if f1_macro > best_f1: best_f1 = f1_macro torch.save(model.state_dict(), 'best_simple_model.pth') print(f" ๐Ÿ’พ New best model saved! F1: {best_f1:.4f}") - + # Clear GPU cache if torch.cuda.is_available(): torch.cuda.empty_cache() @@ -385,10 +389,8 @@ def forward(self, input_ids, attention_mask): print(f"๐Ÿ“Š Final F1 Score: {best_f1:.4f}") print(f"๐ŸŽฏ Target Met: {'โœ…' if best_f1 >= 0.7 else 'โŒ'}") -# Download results -from google.colab import files files.download('best_simple_model.pth') files.download('simple_training_results.json') print("\n๐ŸŽ‰ BULLETPROOF TRAINING COMPLETED!") -print("๐Ÿ“ Files downloaded: best_simple_model.pth, simple_training_results.json") \ No newline at end of file +print("๐Ÿ“ Files downloaded: best_simple_model.pth, simple_training_results.json") diff --git a/scripts/training/complete_simple_notebook.py b/scripts/training/complete_simple_notebook.py index 752ebcb4e..fe9b12692 100644 --- a/scripts/training/complete_simple_notebook.py +++ b/scripts/training/complete_simple_notebook.py @@ -9,22 +9,17 @@ import json + def complete_simple_notebook(): """Add all missing components to the simple notebook.""" - + # Read the existing notebook - with open('notebooks/SIMPLE_ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb', 'r') as f: + with open("notebooks/SIMPLE_ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb", "r") as f: notebook = json.load(f) - + # Add all the missing cells new_cells = [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐ŸŽฏ FOCAL LOSS IMPLEMENTATION" - ] - }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐ŸŽฏ FOCAL LOSS IMPLEMENTATION"]}, { "cell_type": "code", "execution_count": None, @@ -33,7 +28,7 @@ def complete_simple_notebook(): "source": [ "# Focal Loss Implementation\n", "class FocalLoss(torch.nn.Module):\n", - " \"\"\"Focal Loss for handling class imbalance.\"\"\"\n", + ' """Focal Loss for handling class imbalance."""\n', " \n", " def __init__(self, alpha=1, gamma=2, reduction='mean'):\n", " super(FocalLoss, self).__init__()\n", @@ -53,15 +48,13 @@ def complete_simple_notebook(): " else:\n", " return focal_loss\n", "\n", - "print('โœ… Focal Loss implementation ready')" - ] + "print('โœ… Focal Loss implementation ready')", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## โš–๏ธ CLASS WEIGHTING & WEIGHTED LOSS TRAINER" - ] + "source": ["## โš–๏ธ CLASS WEIGHTING & WEIGHTED LOSS TRAINER"], }, { "cell_type": "code", @@ -89,7 +82,7 @@ def complete_simple_notebook(): "\n", "# Weighted Loss Trainer\n", "class WeightedLossTrainer(Trainer):\n", - " \"\"\"Custom trainer with focal loss and class weighting.\"\"\"\n", + ' """Custom trainer with focal loss and class weighting."""\n', " \n", " def __init__(self, focal_alpha=1, focal_gamma=2, class_weights=None, *args, **kwargs):\n", " super().__init__(*args, **kwargs)\n", @@ -97,7 +90,7 @@ def complete_simple_notebook(): " self.class_weights = class_weights\n", " \n", " def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):\n", - " labels = inputs.pop(\"labels\")\n", + ' labels = inputs.pop("labels")\n', " outputs = model(**inputs)\n", " logits = outputs.logits\n", " \n", @@ -119,16 +112,10 @@ def complete_simple_notebook(): " \n", " return (loss, outputs) if return_outputs else loss\n", "\n", - "print('โœ… WeightedLossTrainer ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ”ง LOADING & CONFIGURING MODEL" - ] + "print('โœ… WeightedLossTrainer ready')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ”ง LOADING & CONFIGURING MODEL"]}, { "cell_type": "code", "execution_count": None, @@ -155,16 +142,10 @@ def complete_simple_notebook(): "# Move to GPU if available\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "model = model.to(device)\n", - "print(f'โœ… Model moved to: {device}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ“ DATA PREPROCESSING" - ] + "print(f'โœ… Model moved to: {device}')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ“ DATA PREPROCESSING"]}, { "cell_type": "code", "execution_count": None, @@ -219,16 +200,10 @@ def complete_simple_notebook(): "train_dataset = SimpleDataset(train_encodings, train_labels)\n", "val_dataset = SimpleDataset(val_encodings, val_labels)\n", "\n", - "print('โœ… Data preprocessing completed')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## โš™๏ธ TRAINING ARGUMENTS" - ] + "print('โœ… Data preprocessing completed')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## โš™๏ธ TRAINING ARGUMENTS"]}, { "cell_type": "code", "execution_count": None, @@ -256,16 +231,10 @@ def complete_simple_notebook(): " run_name='ultimate_emotion_model'\n", ")\n", "\n", - "print('โœ… Training arguments configured')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ“Š COMPUTE METRICS" - ] + "print('โœ… Training arguments configured')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ“Š COMPUTE METRICS"]}, { "cell_type": "code", "execution_count": None, @@ -274,7 +243,7 @@ def complete_simple_notebook(): "source": [ "# Compute metrics function\n", "def compute_metrics(eval_pred):\n", - " \"\"\"Compute evaluation metrics.\"\"\"\n", + ' """Compute evaluation metrics."""\n', " predictions, labels = eval_pred\n", " predictions = np.argmax(predictions, axis=1)\n", " \n", @@ -285,16 +254,10 @@ def complete_simple_notebook(): " 'recall': recall_score(labels, predictions, average='weighted')\n", " }\n", "\n", - "print('โœ… Compute metrics function ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿš€ TRAINING" - ] + "print('โœ… Compute metrics function ready')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿš€ TRAINING"]}, { "cell_type": "code", "execution_count": None, @@ -328,16 +291,10 @@ def complete_simple_notebook(): "# Train the model\n", "trainer.train()\n", "\n", - "print('โœ… Training completed successfully!')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ“ˆ EVALUATION" - ] + "print('โœ… Training completed successfully!')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ“ˆ EVALUATION"]}, { "cell_type": "code", "execution_count": None, @@ -355,16 +312,10 @@ def complete_simple_notebook(): "print(f'Precision: {results[\"eval_precision\"]:.4f}')\n", "print(f'Recall: {results[\"eval_recall\"]:.4f}')\n", "\n", - "print('โœ… Evaluation completed!')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿงช ADVANCED VALIDATION" - ] + "print('โœ… Evaluation completed!')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿงช ADVANCED VALIDATION"]}, { "cell_type": "code", "execution_count": None, @@ -404,15 +355,13 @@ def complete_simple_notebook(): " \n", " print(f'{i+1:2d}. \"{example}\" โ†’ {emotions[predicted_class]} ({confidence:.3f})')\n", "\n", - "print('โœ… Advanced validation completed!')" - ] + "print('โœ… Advanced validation completed!')", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ’พ MODEL SAVING WITH VERIFICATION" - ] + "source": ["## ๐Ÿ’พ MODEL SAVING WITH VERIFICATION"], }, { "cell_type": "code", @@ -437,10 +386,10 @@ def complete_simple_notebook(): "with open(config_path, 'r') as f:\n", " config = json.load(f)\n", "\n", - "print(f'Model type: {config.get(\"model_type\", \"NOT SET\")}')\n", - "print(f'Number of labels: {config.get(\"num_labels\", \"NOT SET\")}')\n", - "print(f'id2label: {config.get(\"id2label\", \"NOT SET\")}')\n", - "print(f'label2id: {config.get(\"label2id\", \"NOT SET\")}')\n", + 'print(f\'Model type: {config.get("model_type", "NOT SET")}\')\n', + 'print(f\'Number of labels: {config.get("num_labels", "NOT SET")}\')\n', + 'print(f\'id2label: {config.get("id2label", "NOT SET")}\')\n', + 'print(f\'label2id: {config.get("label2id", "NOT SET")}\')\n', "\n", "# Test loading the saved model\n", "print('\\n๐Ÿงช TESTING SAVED MODEL:')\n", @@ -461,31 +410,32 @@ def complete_simple_notebook(): "print(f'Predicted emotion: {test_model.config.id2label[test_predicted_class]}')\n", "print(f'Confidence: {test_confidence:.3f}')\n", "\n", - "print('\\nโœ… Model saving and verification completed!')" - ] - } + "print('\\nโœ… Model saving and verification completed!')", + ], + }, ] - + # Add all new cells - notebook['cells'].extend(new_cells) - + notebook["cells"].extend(new_cells) + # Save the completed notebook - with open('notebooks/SIMPLE_ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb', 'w') as f: + with open("notebooks/SIMPLE_ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook, f, indent=2) - - print('โœ… Completed simple notebook with ALL components!') - print('๐Ÿ“‹ Added components:') - print(' โœ… Focal Loss implementation') - print(' โœ… Class weighting & WeightedLossTrainer') - print(' โœ… Model loading & configuration') - print(' โœ… Data preprocessing (simple approach)') - print(' โœ… Training arguments') - print(' โœ… Compute metrics') - print(' โœ… Training execution') - print(' โœ… Evaluation') - print(' โœ… Advanced validation') - print(' โœ… Model saving with verification') - print('\\n๐Ÿš€ The notebook is now COMPLETE and ready to use!') + + print("โœ… Completed simple notebook with ALL components!") + print("๐Ÿ“‹ Added components:") + print(" โœ… Focal Loss implementation") + print(" โœ… Class weighting & WeightedLossTrainer") + print(" โœ… Model loading & configuration") + print(" โœ… Data preprocessing (simple approach)") + print(" โœ… Training arguments") + print(" โœ… Compute metrics") + print(" โœ… Training execution") + print(" โœ… Evaluation") + print(" โœ… Advanced validation") + print(" โœ… Model saving with verification") + print("\\n๐Ÿš€ The notebook is now COMPLETE and ready to use!") + if __name__ == "__main__": - complete_simple_notebook() \ No newline at end of file + complete_simple_notebook() diff --git a/scripts/training/comprehensive_domain_adaptation_training.py b/scripts/training/comprehensive_domain_adaptation_training.py deleted file mode 100644 index 2abaa2fc5..000000000 --- a/scripts/training/comprehensive_domain_adaptation_training.py +++ /dev/null @@ -1,709 +0,0 @@ -#!/usr/bin/env python3 -""" -SAMO Deep Learning - Comprehensive Domain Adaptation Training Script - -SENIOR-LEVEL IMPLEMENTATION for REQ-DL-012: Domain-Adapted Emotion Detection -that completely avoids dependency hell and provides production-ready code. - -Target: Achieve 70% F1 score on journal entries through domain adaptation from GoEmotions - -Features: -- Comprehensive error handling and validation -- Modular, production-ready design -- Robust dependency management -- GPU optimization and memory management -- Domain adaptation with focal loss -- Comprehensive logging and monitoring -- Model checkpointing and recovery -- Performance optimization -""" - -import os -import sys -import json -import warnings -import subprocess -import logging -from pathlib import Path -from typing import Dict, List, Optional, Tuple, Any, Union -from dataclasses import dataclass - -# Suppress warnings for cleaner output -warnings.filterwarnings('ignore') - -# Set environment variables for stability -os.environ['CUDA_LAUNCH_BLOCKING'] = "1" -os.environ['TOKENIZERS_PARALLELISM'] = "false" - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', - handlers=[ - logging.FileHandler('domain_adaptation_training.log'), - logging.StreamHandler(sys.stdout) - ] -) -logger = logging.getLogger(__name__) - -@dataclass -class TrainingConfig: - """Configuration class for training parameters.""" - model_name: str = "bert-base-uncased" - num_epochs: int = 5 - batch_size: int = 16 - learning_rate: float = 2e-5 - weight_decay: float = 0.01 - max_length: int = 128 - dropout: float = 0.3 - focal_alpha: float = 1.0 - focal_gamma: float = 2.0 - domain_lambda: float = 0.1 - warmup_steps: int = 100 - save_steps: int = 500 - eval_steps: int = 250 - target_f1: float = 0.7 - patience: int = 3 - -class EnvironmentManager: - """Manages environment setup and dependency installation.""" - - def __init__(self): - self.is_colab = self._detect_colab() - self.installation_success = False - - def _detect_colab(self) -> bool: - """Detect if running in Google Colab.""" - try: - import google.colab - logger.info("โœ… Running in Google Colab") - return True - except ImportError: - logger.info("โ„น๏ธ Running in local environment") - return False - - def install_dependencies(self) -> bool: - """Install dependencies with comprehensive error handling.""" - logger.info("๐Ÿ“ฆ Installing dependencies with compatibility fixes...") - - # Define compatible versions - more conservative approach - dependencies = { - 'torch': '2.0.1', - 'torchvision': '0.15.2', - 'torchaudio': '2.0.2', - 'transformers': '4.28.0', - 'datasets': '2.12.0', - 'evaluate': '0.4.0', - 'scikit-learn': '1.3.0', - 'pandas': '2.0.3', - 'numpy': '1.23.5', # Conservative version - 'matplotlib': '3.7.2', - 'seaborn': '0.12.2', - 'accelerate': '0.20.3', - 'wandb': '0.15.8' - } - - try: - # Step 1: Clean slate - remove conflicting packages - logger.info("๐Ÿงน Cleaning existing packages...") - subprocess.run([ - "pip", "uninstall", "torch", "torchvision", "torchaudio", - "transformers", "datasets", "-y" - ], capture_output=True) - - # Step 2: Install PyTorch with compatible CUDA version - logger.info("๐Ÿ”ฅ Installing PyTorch with CUDA support...") - result = subprocess.run([ - "pip", "install", f"torch=={dependencies['torch']}", - f"torchvision=={dependencies['torchvision']}", - f"torchaudio=={dependencies['torchaudio']}", - "--index-url", "https://download.pytorch.org/whl/cu118", - "--no-cache-dir" - ], capture_output=True, text=True, timeout=600) - - if result.returncode != 0: - logger.error(f"โŒ PyTorch installation failed: {result.stderr}") - return False - - # Step 3: Install Transformers with compatible version - logger.info("๐Ÿค— Installing Transformers...") - result = subprocess.run([ - "pip", "install", f"transformers=={dependencies['transformers']}", - f"datasets=={dependencies['datasets']}", "--no-cache-dir" - ], capture_output=True, text=True, timeout=300) - - if result.returncode != 0: - logger.error(f"โŒ Transformers installation failed: {result.stderr}") - return False - - # Step 4: Install additional dependencies - logger.info("๐Ÿ“š Installing additional dependencies...") - result = subprocess.run([ - "pip", "install", - f"evaluate=={dependencies['evaluate']}", - f"scikit-learn=={dependencies['scikit-learn']}", - f"pandas=={dependencies['pandas']}", - f"numpy=={dependencies['numpy']}", - f"matplotlib=={dependencies['matplotlib']}", - f"seaborn=={dependencies['seaborn']}", - f"accelerate=={dependencies['accelerate']}", - f"wandb=={dependencies['wandb']}", - "--no-cache-dir" - ], capture_output=True, text=True, timeout=300) - - if result.returncode != 0: - logger.error(f"โŒ Additional dependencies installation failed: {result.stderr}") - return False - - # Step 5: Apply numpy compatibility fix proactively - logger.info("๐Ÿ”ง Applying numpy compatibility fix...") - try: - import numpy as np - if not hasattr(np.lib.stride_tricks, 'broadcast_to'): - def broadcast_to(array, shape): - return np.broadcast_arrays(array, np.empty(shape))[0] - np.lib.stride_tricks.broadcast_to = broadcast_to - logger.info(" โœ… Numpy compatibility fix applied proactively") - except Exception as e: - logger.warning(f"โš ๏ธ Could not apply numpy fix proactively: {e}") - - logger.info("โœ… Dependencies installed successfully") - self.installation_success = True - return True - - except subprocess.TimeoutExpired: - logger.error("โŒ Installation timed out") - return False - except Exception as e: - logger.error(f"โŒ Installation failed: {e}") - return False - - def verify_installation(self) -> bool: - """Verify that all critical packages are installed correctly.""" - logger.info("๐Ÿ” Verifying installation...") - - try: - import torch - import transformers - import datasets - - logger.info(f" PyTorch: {torch.__version__}") - logger.info(f" Transformers: {transformers.__version__}") - logger.info(f" Datasets: {datasets.__version__}") - logger.info(f" CUDA Available: {torch.cuda.is_available()}") - - if torch.cuda.is_available(): - logger.info(f" GPU: {torch.cuda.get_device_name(0)}") - logger.info(f" Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB") - torch.backends.cudnn.benchmark = True - logger.info(" โœ… GPU optimized for training") - else: - logger.warning("โš ๏ธ No GPU available. Training will be slow on CPU.") - - # Test critical imports with numpy compatibility fix - try: - from transformers import AutoModel, AutoTokenizer - logger.info(" โœ… Transformers imports successful") - except ImportError as e: - if "broadcast_to" in str(e): - logger.warning("โš ๏ธ Numpy compatibility issue detected. Applying workaround...") - # Apply numpy compatibility fix - import numpy as np - if not hasattr(np.lib.stride_tricks, 'broadcast_to'): - # Add broadcast_to to numpy if missing - def broadcast_to(array, shape): - return np.broadcast_arrays(array, np.empty(shape))[0] - np.lib.stride_tricks.broadcast_to = broadcast_to - logger.info(" โœ… Numpy compatibility fix applied") - - # Try imports again - from transformers import AutoModel, AutoTokenizer - logger.info(" โœ… Transformers imports successful after fix") - else: - raise e - - return True - - except Exception as e: - logger.error(f" โŒ Installation verification failed: {e}") - - # Try to fix numpy compatibility issue - if "broadcast_to" in str(e): - logger.info("๐Ÿ”„ Attempting to fix numpy compatibility issue...") - try: - import numpy as np - if not hasattr(np.lib.stride_tricks, 'broadcast_to'): - def broadcast_to(array, shape): - return np.broadcast_arrays(array, np.empty(shape))[0] - np.lib.stride_tricks.broadcast_to = broadcast_to - logger.info("โœ… Numpy compatibility fix applied") - - # Try verification again - from transformers import AutoModel, AutoTokenizer - logger.info("โœ… Transformers imports successful after fix") - return True - except Exception as fix_error: - logger.error(f"โŒ Could not fix numpy issue: {fix_error}") - - return False - -class RepositoryManager: - """Manages repository setup and file validation.""" - - def __init__(self): - self.project_root = None - - def setup_repository(self) -> bool: - """Setup the SAMO-DL repository with comprehensive error handling.""" - logger.info("๐Ÿ“ Setting up repository...") - - def run_command_safe(command: str, description: str) -> bool: - """Execute command with comprehensive error handling.""" - logger.info(f"๐Ÿ”„ {description}...") - try: - result = subprocess.run(command, shell=True, capture_output=True, text=True, timeout=300) - if result.returncode == 0: - logger.info(f" โœ… {description} completed") - return True - else: - logger.error(f" โŒ {description} failed: {result.stderr}") - return False - except subprocess.TimeoutExpired: - logger.error(f" โŒ {description} timed out") - return False - except Exception as e: - logger.error(f" โŒ {description} failed: {e}") - return False - - # Clone repository if not exists - if not Path('SAMO--DL').exists(): - if not run_command_safe('git clone https://github.com/uelkerd/SAMO--DL.git', 'Cloning repository'): - return False - - # Change to project directory - try: - os.chdir('SAMO--DL') - self.project_root = Path.cwd() - logger.info(f"๐Ÿ“ Working directory: {self.project_root}") - except Exception as e: - logger.error(f"โŒ Failed to change directory: {e}") - return False - - # Pull latest changes - run_command_safe('git pull origin main', 'Pulling latest changes') - - # Verify essential files exist - essential_files = [ - 'data/journal_test_dataset.json', - 'scripts/robust_domain_adaptation_training.py', - 'README.md' - ] - - missing_files = [] - for file_path in essential_files: - if not Path(file_path).exists(): - missing_files.append(file_path) - - if missing_files: - logger.error(f"โš ๏ธ Missing essential files: {missing_files}") - return False - - logger.info("โœ… Repository setup completed successfully") - return True - -class DataManager: - """Manages data loading and preprocessing with comprehensive error handling.""" - - def __init__(self): - self.go_emotions = None - self.journal_df = None - self.label_encoder = None - self.num_labels = 0 - - def load_datasets(self) -> bool: - """Load datasets with comprehensive error handling.""" - logger.info("๐Ÿ“Š Loading datasets...") - - try: - # Load GoEmotions dataset - from datasets import load_dataset - self.go_emotions = load_dataset("go_emotions", "simplified") - logger.info("โœ… GoEmotions dataset loaded") - - # Load journal dataset - with open('data/journal_test_dataset.json', 'r', encoding='utf-8') as f: - journal_entries = json.load(f) - - import pandas as pd - self.journal_df = pd.DataFrame(journal_entries) - logger.info(f"โœ… Journal dataset loaded ({len(journal_entries)} entries)") - - return True - - except Exception as e: - logger.error(f"โŒ Failed to load datasets: {e}") - return False - - def prepare_label_encoder(self) -> bool: - """Prepare label encoder for unified emotion classification.""" - logger.info("๐Ÿงฌ Preparing label encoder...") - - try: - from sklearn.preprocessing import LabelEncoder - - # Get GoEmotions labels - go_train = self.go_emotions['train'] - go_label_names = go_train.features['labels'].feature.names - go_single_labels_int = [label[0] if label else 0 for label in go_train['labels'][:1000]] - go_single_labels_str = [go_label_names[i] for i in go_single_labels_int] - - # Get journal labels - journal_emotions = self.journal_df['emotion'].tolist() - - # Create unified label encoder - self.label_encoder = LabelEncoder() - all_emotions = list(set(go_single_labels_str) | set(journal_emotions)) - self.label_encoder.fit(all_emotions) - - self.num_labels = len(self.label_encoder.classes_) - logger.info(f"๐Ÿ“Š Total emotion classes: {self.num_labels}") - logger.info(f"๐Ÿ“Š Classes: {list(self.label_encoder.classes_)}") - - return True - - except Exception as e: - logger.error(f"โŒ Failed to prepare label encoder: {e}") - return False - - def analyze_domain_gap(self) -> bool: - """Analyze domain gap between GoEmotions and journal entries.""" - logger.info("๐Ÿ” Analyzing domain gap...") - - try: - import numpy as np - - # Get sample texts - go_texts = self.go_emotions['train']['text'][:1000] - journal_texts = self.journal_df['content'].tolist() - - # Analyze writing styles - def analyze_style(texts, domain_name): - valid_texts = [text for text in texts if text and isinstance(text, str) and len(text.strip()) > 0] - - if not valid_texts: - logger.warning(f"โš ๏ธ No valid texts for {domain_name}") - return None - - avg_length = np.mean([len(text.split()) for text in valid_texts]) - personal_pronouns = sum(['I ' in text or 'my ' in text or 'me ' in text for text in valid_texts]) / len(valid_texts) - reflection_words = sum(['think' in text.lower() or 'feel' in text.lower() or 'believe' in text.lower() - for text in valid_texts]) / len(valid_texts) - - logger.info(f"{domain_name} Style Analysis:") - logger.info(f" Average length: {avg_length:.1f} words") - logger.info(f" Personal pronouns: {personal_pronouns:.1%}") - logger.info(f" Reflection words: {reflection_words:.1%}") - logger.info(f" Sample size: {len(valid_texts)} texts") - - return { - 'avg_length': avg_length, - 'personal_pronouns': personal_pronouns, - 'reflection_words': reflection_words, - 'sample_size': len(valid_texts) - } - - go_analysis = analyze_style(go_texts, "GoEmotions (Reddit)") - journal_analysis = analyze_style(journal_texts, "Journal Entries") - - if go_analysis and journal_analysis: - logger.info("๐ŸŽฏ Key Insights:") - logger.info(f"- Journal entries are {journal_analysis['avg_length']/go_analysis['avg_length']:.1f}x longer") - logger.info(f"- Journal entries use {journal_analysis['personal_pronouns']/go_analysis['personal_pronouns']:.1f}x more personal pronouns") - logger.info(f"- Journal entries contain {journal_analysis['reflection_words']/go_analysis['reflection_words']:.1f}x more reflection words") - - return True - else: - logger.error("โŒ Domain analysis failed") - return False - - except Exception as e: - logger.error(f"โŒ Domain analysis failed: {e}") - return False - -class ModelManager: - """Manages model architecture and initialization.""" - - def __init__(self, config: TrainingConfig): - self.config = config - self.model = None - self.tokenizer = None - self.device = None - - def setup_device(self) -> bool: - """Setup device (GPU/CPU) with optimization.""" - try: - import torch - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - if torch.cuda.is_available(): - logger.info(f"๐Ÿš€ Using GPU: {torch.cuda.get_device_name(0)}") - logger.info(f"๐Ÿ’พ GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB") - torch.backends.cudnn.benchmark = True - torch.backends.cudnn.deterministic = False - else: - logger.warning("โš ๏ธ Using CPU - training will be slow") - - return True - - except Exception as e: - logger.error(f"โŒ Device setup failed: {e}") - return False - - def initialize_model(self, num_labels: int) -> bool: - """Initialize model with comprehensive error handling.""" - logger.info(f"๐Ÿ—๏ธ Initializing model with {num_labels} labels...") - - try: - import torch - import torch.nn as nn - from transformers import AutoModel, AutoTokenizer - - # Initialize tokenizer - self.tokenizer = AutoTokenizer.from_pretrained(self.config.model_name) - logger.info(f"โœ… Tokenizer loaded: {self.config.model_name}") - - # Initialize model - self.model = DomainAdaptedEmotionClassifier( - model_name=self.config.model_name, - num_labels=num_labels, - dropout=self.config.dropout - ) - - # Move to device - self.model = self.model.to(self.device) - logger.info(f"โœ… Model moved to {self.device}") - - # Verify model parameters - total_params = sum(p.numel() for p in self.model.parameters()) - trainable_params = sum(p.numel() for p in self.model.parameters() if p.requires_grad) - logger.info(f"๐Ÿ“Š Model parameters: {total_params:,} (trainable: {trainable_params:,})") - - return True - - except Exception as e: - logger.error(f"โŒ Model initialization failed: {e}") - return False - -class FocalLoss: - """Focal Loss for addressing class imbalance in emotion detection.""" - - def __init__(self, alpha=1, gamma=2, reduction='mean'): - import torch.nn as nn - self.alpha = alpha - self.gamma = gamma - self.reduction = reduction - - def __call__(self, inputs, targets): - import torch - import torch.nn.functional as F - ce_loss = F.cross_entropy(inputs, targets, reduction='none') - pt = torch.exp(-ce_loss) - focal_loss = self.alpha * (1 - pt) ** self.gamma * ce_loss - - if self.reduction == 'mean': - return focal_loss.mean() - elif self.reduction == 'sum': - return focal_loss.sum() - else: - return focal_loss - -class DomainAdaptedEmotionClassifier: - """BERT-based emotion classifier with domain adaptation capabilities.""" - - def __init__(self, model_name="bert-base-uncased", num_labels=None, dropout=0.3): - # Validate num_labels - if num_labels is None: - logger.warning("โš ๏ธ num_labels not provided, using default value of 12") - num_labels = 12 - elif num_labels <= 0: - raise ValueError(f"num_labels must be positive, got {num_labels}") - - logger.info(f"๐Ÿ—๏ธ Initializing DomainAdaptedEmotionClassifier with num_labels = {num_labels}") - - try: - import torch.nn as nn - from transformers import AutoModel - - self.bert = AutoModel.from_pretrained(model_name) - self.dropout = nn.Dropout(dropout) - self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels) - - # Domain adaptation layer - self.domain_classifier = nn.Sequential( - nn.Linear(self.bert.config.hidden_size, 512), - nn.ReLU(), - nn.Dropout(0.3), - nn.Linear(512, 2) # 2 domains: GoEmotions vs Journal - ) - - logger.info(f"โœ… Model initialized successfully with {num_labels} labels") - - except Exception as e: - logger.error(f"โŒ Failed to initialize model: {e}") - raise - - def forward(self, input_ids, attention_mask, domain_labels=None): - try: - outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) - pooled_output = outputs.pooler_output - - # Emotion classification - emotion_logits = self.classifier(self.dropout(pooled_output)) - - # Domain classification (for domain adaptation) - domain_logits = self.domain_classifier(pooled_output) - - if domain_labels is not None: - return emotion_logits, domain_logits - return emotion_logits - - except Exception as e: - logger.error(f"โŒ Forward pass failed: {e}") - raise - -class TrainingManager: - """Manages the complete training pipeline.""" - - def __init__(self, config: TrainingConfig, model_manager: ModelManager, data_manager: DataManager): - self.config = config - self.model_manager = model_manager - self.data_manager = data_manager - self.optimizer = None - self.scheduler = None - self.criterion = None - self.best_f1 = 0.0 - self.patience_counter = 0 - - def setup_training(self) -> bool: - """Setup training components.""" - logger.info("๐ŸŽฏ Setting up training components...") - - try: - import torch - from torch.optim import AdamW - from transformers import get_linear_schedule_with_warmup - - # Setup optimizer - self.optimizer = AdamW( - self.model_manager.model.parameters(), - lr=self.config.learning_rate, - weight_decay=self.config.weight_decay - ) - - # Setup scheduler - total_steps = len(self.data_manager.go_emotions['train']) // self.config.batch_size * self.config.num_epochs - self.scheduler = get_linear_schedule_with_warmup( - self.optimizer, - num_warmup_steps=self.config.warmup_steps, - num_training_steps=total_steps - ) - - # Setup loss function - self.criterion = FocalLoss( - alpha=self.config.focal_alpha, - gamma=self.config.focal_gamma - ) - - logger.info("โœ… Training components setup completed") - return True - - except Exception as e: - logger.error(f"โŒ Training setup failed: {e}") - return False - - def train(self) -> bool: - """Execute the complete training pipeline.""" - logger.info("๐Ÿš€ Starting training pipeline...") - - try: - # Training loop implementation would go here - # This is a placeholder for the actual training implementation - logger.info("โœ… Training pipeline ready") - return True - - except Exception as e: - logger.error(f"โŒ Training failed: {e}") - return False - -def main(): - """Main execution function with comprehensive error handling.""" - logger.info("๐Ÿš€ Starting SAMO Deep Learning - Comprehensive Domain Adaptation Training") - logger.info("=" * 80) - - # Initialize configuration - config = TrainingConfig() - - # Step 1: Environment setup - env_manager = EnvironmentManager() - if not env_manager.install_dependencies(): - logger.error("โŒ Environment setup failed") - return False - - if not env_manager.verify_installation(): - logger.error("โŒ Installation verification failed") - return False - - # Step 2: Repository setup - repo_manager = RepositoryManager() - if not repo_manager.setup_repository(): - logger.error("โŒ Repository setup failed") - return False - - # Step 3: Data management - data_manager = DataManager() - if not data_manager.load_datasets(): - logger.error("โŒ Data loading failed") - return False - - if not data_manager.prepare_label_encoder(): - logger.error("โŒ Label encoder preparation failed") - return False - - if not data_manager.analyze_domain_gap(): - logger.error("โŒ Domain analysis failed") - return False - - # Step 4: Model management - model_manager = ModelManager(config) - if not model_manager.setup_device(): - logger.error("โŒ Device setup failed") - return False - - if not model_manager.initialize_model(data_manager.num_labels): - logger.error("โŒ Model initialization failed") - return False - - # Step 5: Training setup - training_manager = TrainingManager(config, model_manager, data_manager) - if not training_manager.setup_training(): - logger.error("โŒ Training setup failed") - return False - - # Step 6: Execute training - if not training_manager.train(): - logger.error("โŒ Training execution failed") - return False - - logger.info("๐ŸŽ‰ Training pipeline completed successfully!") - logger.info("๐Ÿ“‹ Next steps:") - logger.info(" 1. Evaluate model performance") - logger.info(" 2. Save best model") - logger.info(" 3. Generate performance report") - logger.info(" 4. Update PRD with results") - - return True - -if __name__ == "__main__": - success = main() - if not success: - sys.exit(1) \ No newline at end of file diff --git a/scripts/training/create_bulletproof_colab_notebook.py b/scripts/training/create_bulletproof_colab_notebook.py deleted file mode 100644 index 66f7d214a..000000000 --- a/scripts/training/create_bulletproof_colab_notebook.py +++ /dev/null @@ -1,717 +0,0 @@ -#!/usr/bin/env python3 -""" -๐Ÿš€ CREATE BULLETPROOF COLAB NOTEBOOK -==================================== - -This script creates a bulletproof Colab notebook that automatically detects -file paths and handles all edge cases for reliable training. -""" - -import json - -def create_bulletproof_colab_notebook(): - """Create the bulletproof Colab notebook content""" - - notebook_content = { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ๐Ÿš€ BULLETPROOF COMBINED TRAINING - JOURNAL + CMU-MOSEI\n", - "\n", - "**Target: 75-85% F1 Score** \n", - "**Current: 67% F1 Score** \n", - "**Strategy: Combine high-quality datasets**\n", - "\n", - "This notebook combines:\n", - "- Original 150 high-quality journal samples\n", - "- CMU-MOSEI samples for diversity\n", - "- Optimized hyperparameters for 75-85% F1\n", - "\n", - "**BULLETPROOF**: Automatic path detection and error handling" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Install dependencies\n", - "!pip install transformers torch scikit-learn pandas numpy\n", - "print(\"โœ… All dependencies installed!\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Clone repository\n", - "!git clone https://github.com/uelkerd/SAMO--DL.git\n", - "print(\"๐Ÿ“‚ Repository cloned successfully!\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Import libraries\n", - "import json\n", - "import pandas as pd\n", - "import numpy as np\n", - "import torch\n", - "import os\n", - "import glob\n", - "from torch.utils.data import Dataset, DataLoader\n", - "from transformers import (\n", - " AutoTokenizer,\n", - " AutoModelForSequenceClassification,\n", - " TrainingArguments,\n", - " Trainer,\n", - " EarlyStoppingCallback\n", - ")\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from sklearn.metrics import f1_score, accuracy_score, classification_report\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "print(\"โœ… All libraries imported!\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# BULLETPROOF: Auto-detect repository path and data files\n", - "print(\"๐Ÿ” Auto-detecting repository structure...\")\n", - "\n", - "# Find the repository directory\n", - "possible_paths = [\n", - " '/content/SAMO--DL',\n", - " '/content/SAMO--DL/SAMO--DL',\n", - " '/content/SAMO--DL-main',\n", - " '/content/SAMO--DL-main/SAMO--DL',\n", - " '/content/SAMO--DL-main/SAMO--DL-main'\n", - "]\n", - "\n", - "repo_path = None\n", - "for path in possible_paths:\n", - " if os.path.exists(path):\n", - " repo_path = path\n", - " print(f\"โœ… Found repository at: {repo_path}\")\n", - " break\n", - "\n", - "if repo_path is None:\n", - " print(\"โŒ Could not find repository! Listing /content:\")\n", - " !ls -la /content/\n", - " raise Exception(\"Repository not found!\")\n", - "\n", - "# List contents to verify structure\n", - "print(f\"๐Ÿ“‚ Repository contents:\")\n", - "!ls -la {repo_path}/\n", - "\n", - "# Check if data directory exists\n", - "data_path = os.path.join(repo_path, 'data')\n", - "if os.path.exists(data_path):\n", - " print(f\"โœ… Data directory found at: {data_path}\")\n", - " print(f\"๐Ÿ“‚ Data directory contents:\")\n", - " !ls -la {data_path}/\n", - "else:\n", - " print(f\"โŒ Data directory not found at: {data_path}\")\n", - " raise Exception(\"Data directory not found!\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# BULLETPROOF: Load combined dataset with automatic path detection\n", - "print(\"๐Ÿ“Š Loading combined dataset...\")\n", - "\n", - "combined_samples = []\n", - "\n", - "# Load journal data with multiple fallback paths\n", - "journal_paths = [\n", - " os.path.join(repo_path, 'data', 'journal_test_dataset.json'),\n", - " os.path.join(repo_path, 'data', 'journal_dataset.json'),\n", - " os.path.join(repo_path, 'data', 'expanded_journal_dataset.json')\n", - "]\n", - "\n", - "journal_loaded = False\n", - "for journal_path in journal_paths:\n", - " try:\n", - " if os.path.exists(journal_path):\n", - " with open(journal_path, 'r') as f:\n", - " journal_data = json.load(f)\n", - " \n", - " # Handle different data structures\n", - " for item in journal_data:\n", - " if 'content' in item and 'emotion' in item:\n", - " combined_samples.append({\n", - " 'text': item['content'],\n", - " 'emotion': item['emotion']\n", - " })\n", - " elif 'text' in item and 'emotion' in item:\n", - " combined_samples.append({\n", - " 'text': item['text'],\n", - " 'emotion': item['emotion']\n", - " })\n", - " \n", - " print(f\"โœ… Loaded {len(journal_data)} journal samples from {journal_path}\")\n", - " journal_loaded = True\n", - " break\n", - " except Exception as e:\n", - " print(f\"โš ๏ธ Could not load from {journal_path}: {e}\")\n", - " continue\n", - "\n", - "if not journal_loaded:\n", - " print(\"โŒ Could not load any journal data!\")\n", - "\n", - "# Load CMU-MOSEI data\n", - "cmu_paths = [\n", - " os.path.join(repo_path, 'data', 'cmu_mosei_balanced_dataset.json'),\n", - " os.path.join(repo_path, 'data', 'cmu_mosei_emotion_dataset.json')\n", - "]\n", - "\n", - "cmu_loaded = False\n", - "for cmu_path in cmu_paths:\n", - " try:\n", - " if os.path.exists(cmu_path):\n", - " with open(cmu_path, 'r') as f:\n", - " cmu_data = json.load(f)\n", - " \n", - " for item in cmu_data:\n", - " if 'text' in item and 'emotion' in item:\n", - " combined_samples.append({\n", - " 'text': item['text'],\n", - " 'emotion': item['emotion']\n", - " })\n", - " \n", - " print(f\"โœ… Loaded {len(cmu_data)} CMU-MOSEI samples from {cmu_path}\")\n", - " cmu_loaded = True\n", - " break\n", - " except Exception as e:\n", - " print(f\"โš ๏ธ Could not load from {cmu_path}: {e}\")\n", - " continue\n", - "\n", - "if not cmu_loaded:\n", - " print(\"โŒ Could not load any CMU-MOSEI data!\")\n", - "\n", - "print(f\"๐Ÿ“Š Total combined samples: {len(combined_samples)}\")\n", - "\n", - "# Show emotion distribution\n", - "if combined_samples:\n", - " emotion_counts = {}\n", - " for sample in combined_samples:\n", - " emotion = sample['emotion']\n", - " emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1\n", - " \n", - " print(\"๐Ÿ“Š Emotion distribution:\")\n", - " for emotion, count in sorted(emotion_counts.items()):\n", - " print(f\" {emotion}: {count} samples\")\n", - "else:\n", - " print(\"โŒ No data loaded! Check file paths.\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# BULLETPROOF: Create comprehensive fallback dataset if needed\n", - "if len(combined_samples) < 50:\n", - " print(f\"โš ๏ธ Only {len(combined_samples)} samples loaded! Creating comprehensive fallback dataset...\")\n", - " \n", - " # Create comprehensive fallback dataset with 12 samples per emotion\n", - " fallback_samples = [\n", - " # Happy samples\n", - " {\"text\": \"I'm feeling really happy today! Everything is going well.\", \"emotion\": \"happy\"},\n", - " {\"text\": \"I'm so excited about this amazing news!\", \"emotion\": \"happy\"},\n", - " {\"text\": \"Today has been absolutely wonderful!\", \"emotion\": \"happy\"},\n", - " {\"text\": \"I'm thrilled with how things are working out!\", \"emotion\": \"happy\"},\n", - " {\"text\": \"This is the best day ever!\", \"emotion\": \"happy\"},\n", - " {\"text\": \"I'm overjoyed with the results!\", \"emotion\": \"happy\"},\n", - " {\"text\": \"I'm feeling fantastic today!\", \"emotion\": \"happy\"},\n", - " {\"text\": \"Everything is perfect right now!\", \"emotion\": \"happy\"},\n", - " {\"text\": \"I'm so grateful for this happiness!\", \"emotion\": \"happy\"},\n", - " {\"text\": \"I'm beaming with joy!\", \"emotion\": \"happy\"},\n", - " {\"text\": \"This makes me incredibly happy!\", \"emotion\": \"happy\"},\n", - " {\"text\": \"I'm feeling pure joy right now!\", \"emotion\": \"happy\"},\n", - " \n", - " # Frustrated samples\n", - " {\"text\": \"I'm so frustrated with this project. Nothing is working.\", \"emotion\": \"frustrated\"},\n", - " {\"text\": \"This is driving me crazy!\", \"emotion\": \"frustrated\"},\n", - " {\"text\": \"I'm getting really annoyed with this situation.\", \"emotion\": \"frustrated\"},\n", - " {\"text\": \"This is so irritating!\", \"emotion\": \"frustrated\"},\n", - " {\"text\": \"I'm fed up with all these problems.\", \"emotion\": \"frustrated\"},\n", - " {\"text\": \"This is really getting on my nerves.\", \"emotion\": \"frustrated\"},\n", - " {\"text\": \"I'm so tired of dealing with this.\", \"emotion\": \"frustrated\"},\n", - " {\"text\": \"This is absolutely maddening!\", \"emotion\": \"frustrated\"},\n", - " {\"text\": \"I'm really frustrated with the lack of progress.\", \"emotion\": \"frustrated\"},\n", - " {\"text\": \"This is so aggravating!\", \"emotion\": \"frustrated\"},\n", - " {\"text\": \"I'm getting really frustrated here.\", \"emotion\": \"frustrated\"},\n", - " {\"text\": \"This is beyond frustrating!\", \"emotion\": \"frustrated\"},\n", - " \n", - " # Anxious samples\n", - " {\"text\": \"I feel anxious about the upcoming presentation.\", \"emotion\": \"anxious\"},\n", - " {\"text\": \"I'm worried about what might happen.\", \"emotion\": \"anxious\"},\n", - " {\"text\": \"I'm feeling nervous about this situation.\", \"emotion\": \"anxious\"},\n", - " {\"text\": \"I'm anxious about the future.\", \"emotion\": \"anxious\"},\n", - " {\"text\": \"I'm feeling uneasy about this.\", \"emotion\": \"anxious\"},\n", - " {\"text\": \"I'm worried about making the right decision.\", \"emotion\": \"anxious\"},\n", - " {\"text\": \"I'm feeling tense about this.\", \"emotion\": \"anxious\"},\n", - " {\"text\": \"I'm anxious about the outcome.\", \"emotion\": \"anxious\"},\n", - " {\"text\": \"I'm feeling stressed about this.\", \"emotion\": \"anxious\"},\n", - " {\"text\": \"I'm worried about what others think.\", \"emotion\": \"anxious\"},\n", - " {\"text\": \"I'm feeling apprehensive about this.\", \"emotion\": \"anxious\"},\n", - " {\"text\": \"I'm anxious about the unknown.\", \"emotion\": \"anxious\"},\n", - " \n", - " # Grateful samples\n", - " {\"text\": \"I'm grateful for all the support I've received.\", \"emotion\": \"grateful\"},\n", - " {\"text\": \"I'm thankful for this opportunity.\", \"emotion\": \"grateful\"},\n", - " {\"text\": \"I'm so grateful for my friends and family.\", \"emotion\": \"grateful\"},\n", - " {\"text\": \"I'm thankful for all the blessings in my life.\", \"emotion\": \"grateful\"},\n", - " {\"text\": \"I'm grateful for this amazing experience.\", \"emotion\": \"grateful\"},\n", - " {\"text\": \"I'm thankful for the lessons I've learned.\", \"emotion\": \"grateful\"},\n", - " {\"text\": \"I'm grateful for the people who believe in me.\", \"emotion\": \"grateful\"},\n", - " {\"text\": \"I'm thankful for this moment.\", \"emotion\": \"grateful\"},\n", - " {\"text\": \"I'm grateful for the challenges that made me stronger.\", \"emotion\": \"grateful\"},\n", - " {\"text\": \"I'm thankful for the beauty in everyday life.\", \"emotion\": \"grateful\"},\n", - " {\"text\": \"I'm grateful for the love I receive.\", \"emotion\": \"grateful\"},\n", - " {\"text\": \"I'm thankful for this journey.\", \"emotion\": \"grateful\"},\n", - " \n", - " # Overwhelmed samples\n", - " {\"text\": \"I'm feeling overwhelmed with all these tasks.\", \"emotion\": \"overwhelmed\"},\n", - " {\"text\": \"This is too much to handle right now.\", \"emotion\": \"overwhelmed\"},\n", - " {\"text\": \"I'm feeling swamped with responsibilities.\", \"emotion\": \"overwhelmed\"},\n", - " {\"text\": \"I'm drowning in all this work.\", \"emotion\": \"overwhelmed\"},\n", - " {\"text\": \"I'm feeling buried under all these tasks.\", \"emotion\": \"overwhelmed\"},\n", - " {\"text\": \"This is overwhelming me completely.\", \"emotion\": \"overwhelmed\"},\n", - " {\"text\": \"I'm feeling crushed by all this pressure.\", \"emotion\": \"overwhelmed\"},\n", - " {\"text\": \"I'm feeling suffocated by all these demands.\", \"emotion\": \"overwhelmed\"},\n", - " {\"text\": \"This is too overwhelming to process.\", \"emotion\": \"overwhelmed\"},\n", - " {\"text\": \"I'm feeling buried alive by all this work.\", \"emotion\": \"overwhelmed\"},\n", - " {\"text\": \"I'm feeling completely overwhelmed.\", \"emotion\": \"overwhelmed\"},\n", - " {\"text\": \"This is just too much for me.\", \"emotion\": \"overwhelmed\"},\n", - " \n", - " # Proud samples\n", - " {\"text\": \"I'm proud of what I've accomplished so far.\", \"emotion\": \"proud\"},\n", - " {\"text\": \"I'm proud of how far I've come.\", \"emotion\": \"proud\"},\n", - " {\"text\": \"I'm proud of my achievements.\", \"emotion\": \"proud\"},\n", - " {\"text\": \"I'm proud of the person I've become.\", \"emotion\": \"proud\"},\n", - " {\"text\": \"I'm proud of my hard work.\", \"emotion\": \"proud\"},\n", - " {\"text\": \"I'm proud of my determination.\", \"emotion\": \"proud\"},\n", - " {\"text\": \"I'm proud of my resilience.\", \"emotion\": \"proud\"},\n", - " {\"text\": \"I'm proud of my growth.\", \"emotion\": \"proud\"},\n", - " {\"text\": \"I'm proud of my progress.\", \"emotion\": \"proud\"},\n", - " {\"text\": \"I'm proud of my strength.\", \"emotion\": \"proud\"},\n", - " {\"text\": \"I'm proud of my courage.\", \"emotion\": \"proud\"},\n", - " {\"text\": \"I'm proud of my journey.\", \"emotion\": \"proud\"},\n", - " \n", - " # Sad samples\n", - " {\"text\": \"I'm feeling sad and lonely today.\", \"emotion\": \"sad\"},\n", - " {\"text\": \"I'm feeling down and depressed.\", \"emotion\": \"sad\"},\n", - " {\"text\": \"I'm feeling blue today.\", \"emotion\": \"sad\"},\n", - " {\"text\": \"I'm feeling heartbroken.\", \"emotion\": \"sad\"},\n", - " {\"text\": \"I'm feeling miserable.\", \"emotion\": \"sad\"},\n", - " {\"text\": \"I'm feeling dejected.\", \"emotion\": \"sad\"},\n", - " {\"text\": \"I'm feeling sorrowful.\", \"emotion\": \"sad\"},\n", - " {\"text\": \"I'm feeling melancholic.\", \"emotion\": \"sad\"},\n", - " {\"text\": \"I'm feeling despondent.\", \"emotion\": \"sad\"},\n", - " {\"text\": \"I'm feeling crestfallen.\", \"emotion\": \"sad\"},\n", - " {\"text\": \"I'm feeling disheartened.\", \"emotion\": \"sad\"},\n", - " {\"text\": \"I'm feeling forlorn.\", \"emotion\": \"sad\"},\n", - " \n", - " # Excited samples\n", - " {\"text\": \"I'm excited about the new opportunities ahead.\", \"emotion\": \"excited\"},\n", - " {\"text\": \"I'm thrilled about this new adventure!\", \"emotion\": \"excited\"},\n", - " {\"text\": \"I'm pumped about what's coming next!\", \"emotion\": \"excited\"},\n", - " {\"text\": \"I'm stoked about this opportunity!\", \"emotion\": \"excited\"},\n", - " {\"text\": \"I'm jazzed about this new project!\", \"emotion\": \"excited\"},\n", - " {\"text\": \"I'm hyped about this new challenge!\", \"emotion\": \"excited\"},\n", - " {\"text\": \"I'm elated about this new beginning!\", \"emotion\": \"excited\"},\n", - " {\"text\": \"I'm ecstatic about this new chapter!\", \"emotion\": \"excited\"},\n", - " {\"text\": \"I'm overjoyed about this new direction!\", \"emotion\": \"excited\"},\n", - " {\"text\": \"I'm exhilarated about this new journey!\", \"emotion\": \"excited\"},\n", - " {\"text\": \"I'm euphoric about this new opportunity!\", \"emotion\": \"excited\"},\n", - " {\"text\": \"I'm rapturous about this new adventure!\", \"emotion\": \"excited\"},\n", - " \n", - " # Calm samples\n", - " {\"text\": \"I feel calm and peaceful right now.\", \"emotion\": \"calm\"},\n", - " {\"text\": \"I'm feeling serene and tranquil.\", \"emotion\": \"calm\"},\n", - " {\"text\": \"I'm feeling relaxed and at ease.\", \"emotion\": \"calm\"},\n", - " {\"text\": \"I'm feeling composed and collected.\", \"emotion\": \"calm\"},\n", - " {\"text\": \"I'm feeling centered and balanced.\", \"emotion\": \"calm\"},\n", - " {\"text\": \"I'm feeling grounded and stable.\", \"emotion\": \"calm\"},\n", - " {\"text\": \"I'm feeling mellow and laid-back.\", \"emotion\": \"calm\"},\n", - " {\"text\": \"I'm feeling placid and undisturbed.\", \"emotion\": \"calm\"},\n", - " {\"text\": \"I'm feeling unruffled and untroubled.\", \"emotion\": \"calm\"},\n", - " {\"text\": \"I'm feeling cool and collected.\", \"emotion\": \"calm\"},\n", - " {\"text\": \"I'm feeling steady and secure.\", \"emotion\": \"calm\"},\n", - " {\"text\": \"I'm feeling peaceful and content.\", \"emotion\": \"calm\"},\n", - " \n", - " # Hopeful samples\n", - " {\"text\": \"I'm hopeful that things will get better.\", \"emotion\": \"hopeful\"},\n", - " {\"text\": \"I'm optimistic about the future.\", \"emotion\": \"hopeful\"},\n", - " {\"text\": \"I'm hopeful for positive changes.\", \"emotion\": \"hopeful\"},\n", - " {\"text\": \"I'm optimistic about what's ahead.\", \"emotion\": \"hopeful\"},\n", - " {\"text\": \"I'm hopeful for better days.\", \"emotion\": \"hopeful\"},\n", - " {\"text\": \"I'm optimistic about the possibilities.\", \"emotion\": \"hopeful\"},\n", - " {\"text\": \"I'm hopeful for a brighter future.\", \"emotion\": \"hopeful\"},\n", - " {\"text\": \"I'm optimistic about the outcome.\", \"emotion\": \"hopeful\"},\n", - " {\"text\": \"I'm hopeful for positive results.\", \"emotion\": \"hopeful\"},\n", - " {\"text\": \"I'm optimistic about the journey.\", \"emotion\": \"hopeful\"},\n", - " {\"text\": \"I'm hopeful for success.\", \"emotion\": \"hopeful\"},\n", - " {\"text\": \"I'm optimistic about the path forward.\", \"emotion\": \"hopeful\"},\n", - " \n", - " # Tired samples\n", - " {\"text\": \"I'm tired and need some rest.\", \"emotion\": \"tired\"},\n", - " {\"text\": \"I'm exhausted from all this work.\", \"emotion\": \"tired\"},\n", - " {\"text\": \"I'm feeling worn out.\", \"emotion\": \"tired\"},\n", - " {\"text\": \"I'm feeling fatigued.\", \"emotion\": \"tired\"},\n", - " {\"text\": \"I'm feeling drained.\", \"emotion\": \"tired\"},\n", - " {\"text\": \"I'm feeling weary.\", \"emotion\": \"tired\"},\n", - " {\"text\": \"I'm feeling depleted.\", \"emotion\": \"tired\"},\n", - " {\"text\": \"I'm feeling spent.\", \"emotion\": \"tired\"},\n", - " {\"text\": \"I'm feeling run down.\", \"emotion\": \"tired\"},\n", - " {\"text\": \"I'm feeling beat.\", \"emotion\": \"tired\"},\n", - " {\"text\": \"I'm feeling pooped.\", \"emotion\": \"tired\"},\n", - " {\"text\": \"I'm feeling knackered.\", \"emotion\": \"tired\"},\n", - " \n", - " # Content samples\n", - " {\"text\": \"I'm content with how things are going.\", \"emotion\": \"content\"},\n", - " {\"text\": \"I'm satisfied with the current situation.\", \"emotion\": \"content\"},\n", - " {\"text\": \"I'm pleased with how things are.\", \"emotion\": \"content\"},\n", - " {\"text\": \"I'm comfortable with the way things are.\", \"emotion\": \"content\"},\n", - " {\"text\": \"I'm at peace with the current state.\", \"emotion\": \"content\"},\n", - " {\"text\": \"I'm satisfied with the progress.\", \"emotion\": \"content\"},\n", - " {\"text\": \"I'm comfortable with this situation.\", \"emotion\": \"content\"},\n", - " {\"text\": \"I'm pleased with the outcome.\", \"emotion\": \"content\"},\n", - " {\"text\": \"I'm satisfied with the results.\", \"emotion\": \"content\"},\n", - " {\"text\": \"I'm comfortable with the arrangement.\", \"emotion\": \"content\"},\n", - " {\"text\": \"I'm pleased with the current state.\", \"emotion\": \"content\"},\n", - " {\"text\": \"I'm satisfied with how things turned out.\", \"emotion\": \"content\"}\n", - " ]\n", - " \n", - " combined_samples = fallback_samples\n", - " print(f\"โœ… Created {len(combined_samples)} comprehensive fallback samples\")\n", - "\n", - "print(f\"๐Ÿ“Š Final dataset size: {len(combined_samples)} samples\")\n", - "\n", - "# Verify we have enough data\n", - "if len(combined_samples) < 50:\n", - " raise Exception(f\"Insufficient data! Only {len(combined_samples)} samples. Need at least 50.\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Custom dataset class\n", - "class EmotionDataset(Dataset):\n", - " def __init__(self, texts, labels, tokenizer, max_length=128):\n", - " self.texts = texts\n", - " self.labels = labels\n", - " self.tokenizer = tokenizer\n", - " self.max_length = max_length\n", - " \n", - " def __len__(self):\n", - " return len(self.texts)\n", - " \n", - " def __getitem__(self, idx):\n", - " text = str(self.texts[idx])\n", - " label = self.labels[idx]\n", - " \n", - " encoding = self.tokenizer(\n", - " text,\n", - " truncation=True,\n", - " padding='max_length',\n", - " max_length=self.max_length,\n", - " return_tensors='pt'\n", - " )\n", - " \n", - " return {\n", - " 'input_ids': encoding['input_ids'].flatten(),\n", - " 'attention_mask': encoding['attention_mask'].flatten(),\n", - " 'labels': torch.tensor(label, dtype=torch.long)\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Prepare data\n", - "texts = [sample['text'] for sample in combined_samples]\n", - "emotions = [sample['emotion'] for sample in combined_samples]\n", - "\n", - "# Encode labels\n", - "label_encoder = LabelEncoder()\n", - "labels = label_encoder.fit_transform(emotions)\n", - "\n", - "print(f\"๐ŸŽฏ Number of labels: {len(label_encoder.classes_)}\")\n", - "print(f\"๐Ÿ“Š Labels: {list(label_encoder.classes_)}\")\n", - "\n", - "# Split data\n", - "train_texts, test_texts, train_labels, test_labels = train_test_split(\n", - " texts, labels, test_size=0.2, random_state=42, stratify=labels\n", - ")\n", - "\n", - "print(f\"๐Ÿ“ˆ Training samples: {len(train_texts)}\")\n", - "print(f\"๐Ÿงช Test samples: {len(test_labels)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Load model and tokenizer\n", - "model_name = \"bert-base-uncased\"\n", - "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", - "model = AutoModelForSequenceClassification.from_pretrained(\n", - " model_name, \n", - " num_labels=len(label_encoder.classes_),\n", - " problem_type=\"single_label_classification\"\n", - ")\n", - "\n", - "print(f\"โœ… Model loaded: {model_name}\")\n", - "print(f\"๐Ÿ“Š Number of classes: {len(label_encoder.classes_)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Create datasets\n", - "train_dataset = EmotionDataset(train_texts, train_labels, tokenizer)\n", - "test_dataset = EmotionDataset(test_texts, test_labels, tokenizer)\n", - "\n", - "print(f\"โœ… Datasets created\")\n", - "print(f\"๐Ÿ“ˆ Train dataset: {len(train_dataset)} samples\")\n", - "print(f\"๐Ÿงช Test dataset: {len(test_dataset)} samples\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Define metrics function\n", - "def compute_metrics(eval_pred):\n", - " predictions, labels = eval_pred\n", - " predictions = np.argmax(predictions, axis=1)\n", - " \n", - " f1 = f1_score(labels, predictions, average='weighted')\n", - " accuracy = accuracy_score(labels, predictions)\n", - " \n", - " return {'f1': f1, 'accuracy': accuracy}" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Training arguments with optimized hyperparameters\n", - "training_args = TrainingArguments(\n", - " output_dir=\"./emotion_model_bulletproof\",\n", - " num_train_epochs=5, # Reduced to prevent overfitting\n", - " per_device_train_batch_size=8, # Smaller batch size\n", - " per_device_eval_batch_size=8,\n", - " warmup_steps=100, # Reduced warmup\n", - " weight_decay=0.01,\n", - " logging_dir=\"./logs\",\n", - " logging_steps=10, # More frequent logging\n", - " eval_strategy=\"steps\",\n", - " eval_steps=50, # More frequent evaluation\n", - " save_strategy=\"steps\",\n", - " save_steps=50,\n", - " load_best_model_at_end=True,\n", - " metric_for_best_model=\"f1\",\n", - " greater_is_better=True,\n", - " dataloader_num_workers=2,\n", - " remove_unused_columns=False,\n", - " report_to=None,\n", - " learning_rate=1e-5, # Lower learning rate\n", - " gradient_accumulation_steps=4, # Increased for stability\n", - ")\n", - "\n", - "print(\"โœ… Training arguments configured\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Create trainer\n", - "trainer = Trainer(\n", - " model=model,\n", - " args=training_args,\n", - " train_dataset=train_dataset,\n", - " eval_dataset=test_dataset,\n", - " compute_metrics=compute_metrics,\n", - " callbacks=[EarlyStoppingCallback(early_stopping_patience=2)] # Shorter patience\n", - ")\n", - "\n", - "print(\"โœ… Trainer created with early stopping\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Start training\n", - "print(\"๐Ÿš€ Starting BULLETPROOF training...\")\n", - "print(\"๐ŸŽฏ Target F1 Score: 75-85%\")\n", - "print(\"๐Ÿ“Š Current Best: 67%\")\n", - "print(\"๐Ÿ“ˆ Expected Improvement: 8-18%\")\n", - "print(f\"๐Ÿ“Š Training on {len(train_dataset)} samples\")\n", - "print(f\"๐Ÿงช Evaluating on {len(test_dataset)} samples\")\n", - "\n", - "trainer.train()\n", - "\n", - "print(\"โœ… Training completed!\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Evaluate final model\n", - "print(\"๐Ÿ“Š Evaluating final model...\")\n", - "results = trainer.evaluate()\n", - "\n", - "print(f\"๐Ÿ† Final F1 Score: {results['eval_f1']:.4f} ({results['eval_f1']*100:.2f}%)\")\n", - "print(f\"๐ŸŽฏ Target achieved: {'โœ… YES!' if results['eval_f1'] >= 0.75 else 'โŒ Not yet'}\")\n", - "\n", - "# Save model\n", - "trainer.save_model(\"./emotion_model_bulletproof_final\")\n", - "print(\"๐Ÿ’พ Model saved to ./emotion_model_bulletproof_final\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Test on sample texts\n", - "print(\"๐Ÿงช Testing on sample texts...\")\n", - "\n", - "test_texts = [\n", - " \"I'm feeling really happy today!\",\n", - " \"I'm so frustrated with this project.\",\n", - " \"I feel anxious about the presentation.\",\n", - " \"I'm grateful for all the support.\",\n", - " \"I'm feeling overwhelmed with tasks.\"\n", - "]\n", - "\n", - "model.eval()\n", - "with torch.no_grad():\n", - " for i, text in enumerate(test_texts, 1):\n", - " inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True)\n", - " outputs = model(**inputs)\n", - " probabilities = torch.softmax(outputs.logits, dim=1)\n", - " predicted_class = torch.argmax(probabilities, dim=1).item()\n", - " confidence = probabilities[0][predicted_class].item()\n", - " predicted_emotion = label_encoder.classes_[predicted_class]\n", - " \n", - " print(f\"{i}. Text: {text}\")\n", - " print(f\" Predicted: {predicted_emotion} (confidence: {confidence:.3f})\")\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐ŸŽ‰ BULLETPROOF Training Complete!\n", - "\n", - "**Results Summary:**\n", - "- Final F1 Score: [See output above]\n", - "- Target: 75-85%\n", - "- Improvement: [Calculated above]\n", - "\n", - "**Key Features:**\n", - "- โœ… Automatic path detection\n", - "- โœ… Comprehensive fallback dataset\n", - "- โœ… Optimized hyperparameters\n", - "- โœ… Robust error handling\n", - "- โœ… Detailed logging\n", - "\n", - "**Next Steps:**\n", - "1. If F1 < 75%: The fallback dataset should still achieve decent results\n", - "2. If F1 >= 75%: Model is ready for production!\n", - "3. Download the saved model from `./emotion_model_bulletproof_final`" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 - } - - # Write notebook to file - with open('notebooks/BULLETPROOF_COMBINED_TRAINING_COLAB.ipynb', 'w') as f: - json.dump(notebook_content, f, indent=2) - - print("โœ… Bulletproof notebook created: notebooks/BULLETPROOF_COMBINED_TRAINING_COLAB.ipynb") - print("๐Ÿ“‹ Instructions:") - print(" 1. Download the notebook file") - print(" 2. Upload to Google Colab") - print(" 3. Set Runtime โ†’ GPU") - print(" 4. Run all cells") - print(" 5. Expect 75-85% F1 score!") - print("\n๐Ÿ”ง Key Features:") - print(" - Automatic path detection") - print(" - Comprehensive fallback dataset (144 samples)") - print(" - Optimized hyperparameters") - print(" - Robust error handling") - -if __name__ == "__main__": - create_bulletproof_colab_notebook() \ No newline at end of file diff --git a/scripts/training/create_colab_expanded_training.py b/scripts/training/create_colab_expanded_training.py index 59cf5ad5e..848fdf146 100644 --- a/scripts/training/create_colab_expanded_training.py +++ b/scripts/training/create_colab_expanded_training.py @@ -3,10 +3,11 @@ Create a Colab notebook for expanded dataset training. """ + def create_colab_notebook(): """Create a complete Colab notebook for expanded training.""" - - notebook_content = '''{ + + notebook_content = """{ "cells": [ { "cell_type": "markdown", @@ -719,12 +720,12 @@ def create_colab_notebook(): }, "nbformat": 4, "nbformat_minor": 4 -}''' - +}""" + # Save the notebook - with open('notebooks/expanded_dataset_training.ipynb', 'w') as f: + with open("notebooks/expanded_dataset_training.ipynb", "w") as f: f.write(notebook_content) - + print("โœ… Created Colab notebook: notebooks/expanded_dataset_training.ipynb") print("๐Ÿ“‹ Instructions:") print(" 1. Download the notebook file") @@ -733,5 +734,6 @@ def create_colab_notebook(): print(" 4. Run all cells") print(" 5. Expect 75-85% F1 score!") + if __name__ == "__main__": - create_colab_notebook() \ No newline at end of file + create_colab_notebook() diff --git a/scripts/training/create_colab_notebook.py b/scripts/training/create_colab_notebook.py index 44888870b..8aa46ffc8 100644 --- a/scripts/training/create_colab_notebook.py +++ b/scripts/training/create_colab_notebook.py @@ -5,9 +5,10 @@ import json + def create_colab_notebook(): """Create the domain adaptation GPU training notebook.""" - + notebook = { "cells": [ { @@ -24,15 +25,13 @@ def create_colab_notebook(): "- Bridge domain gap between Reddit comments and journal entries\n", "- Implement focal loss for class imbalance\n", "- Use domain adaptation techniques for better transfer learning\n", - "- Optimize for GPU training on Colab" - ] + "- Optimize for GPU training on Colab", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿš€ Environment Setup & GPU Configuration" - ] + "source": ["## ๐Ÿš€ Environment Setup & GPU Configuration"], }, { "cell_type": "code", @@ -44,28 +43,22 @@ def create_colab_notebook(): "import torch\n", "import gc\n", "\n", - "print(f\"CUDA Available: {torch.cuda.is_available()}\")\n", + 'print(f"CUDA Available: {torch.cuda.is_available()}")\n', "if torch.cuda.is_available():\n", - " print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n", - " print(f\"Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB\")\n", + ' print(f"GPU: {torch.cuda.get_device_name(0)}")\n', + ' print(f"Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")\n', " \n", " # Clear GPU cache\n", " torch.cuda.empty_cache()\n", " gc.collect()\n", "else:\n", - " print(\"โš ๏ธ No GPU available. Training will be slow on CPU.\")\n", + ' print("โš ๏ธ No GPU available. Training will be slow on CPU.")\n', "\n", "# Enable cudnn benchmarking for faster training\n", - "torch.backends.cudnn.benchmark = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ“ฆ Install Dependencies" - ] + "torch.backends.cudnn.benchmark = True", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ“ฆ Install Dependencies"]}, { "cell_type": "code", "execution_count": None, @@ -79,16 +72,10 @@ def create_colab_notebook(): "\n", "# Clone repository if not already done\n", "!git clone https://github.com/uelkerd/SAMO--DL.git\n", - "%cd SAMO--DL" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ” Domain Gap Analysis" - ] + "%cd SAMO--DL", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ” Domain Gap Analysis"]}, { "cell_type": "code", "execution_count": None, @@ -103,16 +90,16 @@ def create_colab_notebook(): "import seaborn as sns\n", "\n", "def analyze_writing_style(texts, domain_name):\n", - " \"\"\"Analyze writing style characteristics of a domain.\"\"\"\n", + ' """Analyze writing style characteristics of a domain."""\n', " avg_length = np.mean([len(text.split()) for text in texts])\n", " personal_pronouns = sum(['I ' in text or 'my ' in text or 'me ' in text for text in texts]) / len(texts)\n", " reflection_words = sum(['think' in text.lower() or 'feel' in text.lower() or 'believe' in text.lower() \n", " for text in texts]) / len(texts)\n", " \n", - " print(f\"{domain_name} Style Analysis:\")\n", - " print(f\" Average length: {avg_length:.1f} words\")\n", - " print(f\" Personal pronouns: {personal_pronouns:.1%}\")\n", - " print(f\" Reflection words: {reflection_words:.1%}\")\n", + ' print(f"{domain_name} Style Analysis:")\n', + ' print(f" Average length: {avg_length:.1f} words")\n', + ' print(f" Personal pronouns: {personal_pronouns:.1%}")\n', + ' print(f" Reflection words: {reflection_words:.1%}")\n', " \n", " return {\n", " 'avg_length': avg_length,\n", @@ -121,10 +108,10 @@ def create_colab_notebook(): " }\n", "\n", "# Load datasets\n", - "print(\"๐Ÿ“Š Loading datasets...\")\n", + 'print("๐Ÿ“Š Loading datasets...")\n', "\n", "# Load GoEmotions dataset\n", - "go_emotions = load_dataset(\"go_emotions\", \"simplified\")\n", + 'go_emotions = load_dataset("go_emotions", "simplified")\n', "go_texts = go_emotions['train']['text'][:1000] # Sample for analysis\n", "\n", "# Load journal dataset\n", @@ -135,9 +122,9 @@ def create_colab_notebook(): "journal_texts = journal_df['content'].tolist()\n", "\n", "# Analyze domains\n", - "print(\"\\n๐Ÿ” Domain Gap Analysis:\")\n", - "go_analysis = analyze_writing_style(go_texts, \"GoEmotions (Reddit)\")\n", - "journal_analysis = analyze_writing_style(journal_texts, \"Journal Entries\")\n", + 'print("\\n๐Ÿ” Domain Gap Analysis:")\n', + 'go_analysis = analyze_writing_style(go_texts, "GoEmotions (Reddit)")\n', + 'journal_analysis = analyze_writing_style(journal_texts, "Journal Entries")\n', "\n", "# Visualize differences\n", "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", @@ -154,19 +141,13 @@ def create_colab_notebook(): "plt.tight_layout()\n", "plt.show()\n", "\n", - "print(\"\\n๐ŸŽฏ Key Insights:\")\n", + 'print("\\n๐ŸŽฏ Key Insights:")\n', "print(f\"- Journal entries are {journal_analysis['avg_length']/go_analysis['avg_length']:.1f}x longer\")\n", "print(f\"- Journal entries use {journal_analysis['personal_pronouns']/go_analysis['personal_pronouns']:.1f}x more personal pronouns\")\n", - "print(f\"- Journal entries contain {journal_analysis['reflection_words']/go_analysis['reflection_words']:.1f}x more reflection words\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ—๏ธ Model Architecture" - ] + "print(f\"- Journal entries contain {journal_analysis['reflection_words']/go_analysis['reflection_words']:.1f}x more reflection words\")", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ—๏ธ Model Architecture"]}, { "cell_type": "code", "execution_count": None, @@ -179,7 +160,7 @@ def create_colab_notebook(): "from transformers import AutoModel, AutoTokenizer\n", "\n", "class FocalLoss(nn.Module):\n", - " \"\"\"Focal Loss for addressing class imbalance in emotion detection.\"\"\"\n", + ' """Focal Loss for addressing class imbalance in emotion detection."""\n', " \n", " def __init__(self, alpha=1, gamma=2, reduction='mean'):\n", " super(FocalLoss, self).__init__()\n", @@ -200,9 +181,9 @@ def create_colab_notebook(): " return focal_loss\n", "\n", "class DomainAdaptedEmotionClassifier(nn.Module):\n", - " \"\"\"BERT-based emotion classifier with domain adaptation capabilities.\"\"\"\n", + ' """BERT-based emotion classifier with domain adaptation capabilities."""\n', " \n", - " def __init__(self, model_name=\"bert-base-uncased\", num_labels=12, dropout=0.3):\n", + ' def __init__(self, model_name="bert-base-uncased", num_labels=12, dropout=0.3):\n', " super().__init__()\n", " self.bert = AutoModel.from_pretrained(model_name)\n", " self.dropout = nn.Dropout(dropout)\n", @@ -231,25 +212,19 @@ def create_colab_notebook(): " return emotion_logits\n", "\n", "# Initialize model and tokenizer\n", - "print(\"๐Ÿ—๏ธ Initializing model...\")\n", - "model_name = \"bert-base-uncased\"\n", + 'print("๐Ÿ—๏ธ Initializing model...")\n', + 'model_name = "bert-base-uncased"\n', "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "model = DomainAdaptedEmotionClassifier(model_name=model_name, num_labels=12)\n", "\n", - "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + 'device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n', "model = model.to(device)\n", "\n", - "print(f\"โœ… Model loaded on {device}\")\n", - "print(f\"๐Ÿ“Š Model parameters: {sum(p.numel() for p in model.parameters()):,}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ“Š Data Preparation" - ] + 'print(f"โœ… Model loaded on {device}")\n', + 'print(f"๐Ÿ“Š Model parameters: {sum(p.numel() for p in model.parameters()):,}")', + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ“Š Data Preparation"]}, { "cell_type": "code", "execution_count": None, @@ -261,7 +236,7 @@ def create_colab_notebook(): "from sklearn.preprocessing import LabelEncoder\n", "\n", "class EmotionDataset(Dataset):\n", - " \"\"\"Custom dataset for emotion classification.\"\"\"\n", + ' """Custom dataset for emotion classification."""\n', " \n", " def __init__(self, texts, labels, tokenizer, max_length=128):\n", " self.texts = texts\n", @@ -291,7 +266,7 @@ def create_colab_notebook(): " }\n", "\n", "# Prepare GoEmotions data\n", - "print(\"๐Ÿ“Š Preparing GoEmotions data...\")\n", + 'print("๐Ÿ“Š Preparing GoEmotions data...")\n', "go_train = go_emotions['train']\n", "go_texts = go_train['text'][:10000] # Use subset for faster training\n", "go_labels = go_train['labels'][:10000]\n", @@ -300,7 +275,7 @@ def create_colab_notebook(): "go_single_labels = [label[0] if label else 0 for label in go_labels]\n", "\n", "# Prepare journal data\n", - "print(\"๐Ÿ“Š Preparing journal data...\")\n", + 'print("๐Ÿ“Š Preparing journal data...")\n', "journal_texts = journal_df['content'].tolist()\n", "journal_emotions = journal_df['emotion'].tolist()\n", "\n", @@ -329,20 +304,14 @@ def create_colab_notebook(): "journal_train_loader = DataLoader(journal_train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n", "journal_val_loader = DataLoader(journal_val_dataset, batch_size=batch_size, shuffle=False, num_workers=2)\n", "\n", - "print(f\"โœ… Data prepared:\")\n", - "print(f\" GoEmotions: {len(go_dataset)} samples\")\n", - "print(f\" Journal Train: {len(journal_train_dataset)} samples\")\n", - "print(f\" Journal Val: {len(journal_val_dataset)} samples\")\n", - "print(f\" Total classes: {len(label_encoder.classes_)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐ŸŽฏ Training Pipeline" - ] + 'print(f"โœ… Data prepared:")\n', + 'print(f" GoEmotions: {len(go_dataset)} samples")\n', + 'print(f" Journal Train: {len(journal_train_dataset)} samples")\n', + 'print(f" Journal Val: {len(journal_val_dataset)} samples")\n', + 'print(f" Total classes: {len(label_encoder.classes_)}")', + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐ŸŽฏ Training Pipeline"]}, { "cell_type": "code", "execution_count": None, @@ -353,7 +322,7 @@ def create_colab_notebook(): "import wandb\n", "\n", "class DomainAdaptationTrainer:\n", - " \"\"\"Trainer for domain adaptation training.\"\"\"\n", + ' """Trainer for domain adaptation training."""\n', " \n", " def __init__(self, model, tokenizer, device):\n", " self.model = model\n", @@ -363,7 +332,7 @@ def create_colab_notebook(): " self.domain_criterion = nn.CrossEntropyLoss()\n", " \n", " def train_step(self, batch, domain_labels, lambda_domain=0.1):\n", - " \"\"\"Single training step with domain adaptation.\"\"\"\n", + ' """Single training step with domain adaptation."""\n', " self.model.train()\n", " \n", " input_ids = batch['input_ids'].to(self.device)\n", @@ -388,7 +357,7 @@ def create_colab_notebook(): " }\n", " \n", " def evaluate(self, dataloader):\n", - " \"\"\"Evaluate model on validation set.\"\"\"\n", + ' """Evaluate model on validation set."""\n', " self.model.eval()\n", " total_loss = 0\n", " all_predictions = []\n", @@ -425,14 +394,14 @@ def create_colab_notebook(): "\n", "# Initialize wandb (optional)\n", "try:\n", - " wandb.init(project=\"samo-domain-adaptation\", name=\"journal-emotion-detection\")\n", + ' wandb.init(project="samo-domain-adaptation", name="journal-emotion-detection")\n', " use_wandb = True\n", "except:\n", - " print(\"โš ๏ธ Wandb not available, continuing without logging\")\n", + ' print("โš ๏ธ Wandb not available, continuing without logging")\n', " use_wandb = False\n", "\n", - "print(\"๐ŸŽฏ Starting domain adaptation training...\")" - ] + 'print("๐ŸŽฏ Starting domain adaptation training...")', + ], }, { "cell_type": "code", @@ -446,14 +415,14 @@ def create_colab_notebook(): "training_history = []\n", "\n", "for epoch in range(num_epochs):\n", - " print(f\"\\n๐Ÿ”„ Epoch {epoch + 1}/{num_epochs}\")\n", + ' print(f"\\n๐Ÿ”„ Epoch {epoch + 1}/{num_epochs}")\n', " \n", " # Training phase\n", " model.train()\n", " total_loss = 0\n", " \n", " # Train on GoEmotions data\n", - " print(\" ๐Ÿ“š Training on GoEmotions data...\")\n", + ' print(" ๐Ÿ“š Training on GoEmotions data...")\n', " for i, batch in enumerate(go_loader):\n", " domain_labels = torch.zeros(batch['input_ids'].size(0), dtype=torch.long)\n", " losses = trainer.train_step(batch, domain_labels, lambda_domain=0.1)\n", @@ -468,7 +437,7 @@ def create_colab_notebook(): " print(f\" Batch {i}/{len(go_loader)}, Loss: {losses['total_loss'].item():.4f}\")\n", " \n", " # Train on journal data\n", - " print(\" ๐Ÿ“ Training on journal data...\")\n", + ' print(" ๐Ÿ“ Training on journal data...")\n', " for i, batch in enumerate(journal_train_loader):\n", " domain_labels = torch.ones(batch['input_ids'].size(0), dtype=torch.long)\n", " losses = trainer.train_step(batch, domain_labels, lambda_domain=0.1)\n", @@ -483,13 +452,13 @@ def create_colab_notebook(): " print(f\" Batch {i}/{len(journal_train_loader)}, Loss: {losses['total_loss'].item():.4f}\")\n", " \n", " # Validation\n", - " print(\" ๐ŸŽฏ Validating on journal test set...\")\n", + ' print(" ๐ŸŽฏ Validating on journal test set...")\n', " val_results = trainer.evaluate(journal_val_loader)\n", " \n", " avg_loss = total_loss / (len(go_loader) + len(journal_train_loader))\n", " \n", - " print(f\" ๐Ÿ“Š Epoch {epoch + 1} Results:\")\n", - " print(f\" Average Loss: {avg_loss:.4f}\")\n", + ' print(f" ๐Ÿ“Š Epoch {epoch + 1} Results:")\n', + ' print(f" Average Loss: {avg_loss:.4f}")\n', " print(f\" Validation F1 (Macro): {val_results['f1_macro']:.4f}\")\n", " print(f\" Validation F1 (Weighted): {val_results['f1_weighted']:.4f}\")\n", " \n", @@ -507,7 +476,7 @@ def create_colab_notebook(): " if val_results['f1_macro'] > best_f1:\n", " best_f1 = val_results['f1_macro']\n", " torch.save(model.state_dict(), 'best_domain_adapted_model.pth')\n", - " print(f\" ๐Ÿ’พ New best model saved! F1: {best_f1:.4f}\")\n", + ' print(f" ๐Ÿ’พ New best model saved! F1: {best_f1:.4f}")\n', " \n", " training_history.append({\n", " 'epoch': epoch,\n", @@ -520,15 +489,13 @@ def create_colab_notebook(): " if torch.cuda.is_available():\n", " torch.cuda.empty_cache()\n", "\n", - "print(f\"\\n๐ŸŽ‰ Training completed! Best F1 Score: {best_f1:.4f}\")" - ] + 'print(f"\\n๐ŸŽ‰ Training completed! Best F1 Score: {best_f1:.4f}")', + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ“ˆ Results Analysis & Visualization" - ] + "source": ["## ๐Ÿ“ˆ Results Analysis & Visualization"], }, { "cell_type": "code", @@ -563,28 +530,26 @@ def create_colab_notebook(): "plt.show()\n", "\n", "# Final evaluation\n", - "print(\"\\n๐ŸŽฏ Final Model Evaluation:\")\n", + 'print("\\n๐ŸŽฏ Final Model Evaluation:")\n', "model.load_state_dict(torch.load('best_domain_adapted_model.pth'))\n", "final_results = trainer.evaluate(journal_val_loader)\n", "\n", - "print(f\"๐Ÿ“Š Final Results:\")\n", + 'print(f"๐Ÿ“Š Final Results:")\n', "print(f\" F1 Score (Macro): {final_results['f1_macro']:.4f}\")\n", "print(f\" F1 Score (Weighted): {final_results['f1_weighted']:.4f}\")\n", "print(f\" Target Met (70%): {'โœ…' if final_results['f1_macro'] >= 0.7 else 'โŒ'}\")\n", "\n", "# REQ-DL-012 Validation\n", - "print(f\"\\n๐ŸŽฏ REQ-DL-012 Validation:\")\n", - "print(f\" Target: 70% F1 score on journal entries\")\n", + 'print(f"\\n๐ŸŽฏ REQ-DL-012 Validation:")\n', + 'print(f" Target: 70% F1 score on journal entries")\n', "print(f\" Achieved: {final_results['f1_macro']:.1%} F1 score\")\n", - "print(f\" Status: {'โœ… SUCCESS' if final_results['f1_macro'] >= 0.7 else 'โŒ NEEDS IMPROVEMENT'}\")" - ] + "print(f\" Status: {'โœ… SUCCESS' if final_results['f1_macro'] >= 0.7 else 'โŒ NEEDS IMPROVEMENT'}\")", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ’พ Model Export & Deployment" - ] + "source": ["## ๐Ÿ’พ Model Export & Deployment"], }, { "cell_type": "code", @@ -614,11 +579,11 @@ def create_colab_notebook(): "with open('model_config.json', 'w') as f:\n", " json.dump(model_config, f, indent=2)\n", "\n", - "print(\"๐Ÿ’พ Model artifacts saved:\")\n", - "print(\" - best_domain_adapted_model.pth (model weights)\")\n", - "print(\" - label_encoder.pkl (label encoder)\")\n", - "print(\" - domain_adapted_model/ (tokenizer)\")\n", - "print(\" - model_config.json (configuration)\")\n", + 'print("๐Ÿ’พ Model artifacts saved:")\n', + 'print(" - best_domain_adapted_model.pth (model weights)")\n', + 'print(" - label_encoder.pkl (label encoder)")\n', + 'print(" - domain_adapted_model/ (tokenizer)")\n', + 'print(" - model_config.json (configuration)")\n', "\n", "# Download files (for Colab)\n", "from google.colab import files\n", @@ -626,43 +591,36 @@ def create_colab_notebook(): "files.download('label_encoder.pkl')\n", "files.download('model_config.json')\n", "\n", - "print(\"\\n๐Ÿš€ Model ready for deployment!\")\n", - "print(\"๐Ÿ“‹ Next steps:\")\n", - "print(\" 1. Integrate model into SAMO-DL pipeline\")\n", - "print(\" 2. Update emotion detection API\")\n", - "print(\" 3. Deploy to production environment\")\n", - "print(\" 4. Update PRD with achieved metrics\")" - ] - } + 'print("\\n๐Ÿš€ Model ready for deployment!")\n', + 'print("๐Ÿ“‹ Next steps:")\n', + 'print(" 1. Integrate model into SAMO-DL pipeline")\n', + 'print(" 2. Update emotion detection API")\n', + 'print(" 3. Deploy to production environment")\n', + 'print(" 4. Update PRD with achieved metrics")', + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - + # Write the notebook to file notebook_path = "notebooks/domain_adaptation_gpu_training.ipynb" - with open(notebook_path, 'w') as f: + with open(notebook_path, "w") as f: json.dump(notebook, f, indent=1) - + print(f"โœ… Created Colab notebook: {notebook_path}") print("๐Ÿ“‹ Notebook includes:") print(" - GPU environment setup") @@ -672,5 +630,6 @@ def create_colab_notebook(): print(" - REQ-DL-012 validation") print(" - Model export for deployment") + if __name__ == "__main__": - create_colab_notebook() \ No newline at end of file + create_colab_notebook() diff --git a/scripts/training/create_comprehensive_notebook.py b/scripts/training/create_comprehensive_notebook.py index 53aeac663..48925e4cf 100644 --- a/scripts/training/create_comprehensive_notebook.py +++ b/scripts/training/create_comprehensive_notebook.py @@ -9,9 +9,10 @@ import json + def create_comprehensive_notebook(): """Create a comprehensive notebook with all advanced features.""" - + notebook_content = { "cells": [ { @@ -31,8 +32,8 @@ def create_comprehensive_notebook(): "โœ… Model architecture fixes\n", "โœ… Comprehensive dataset\n", "\n", - "**Target**: Reliable 75-85% F1 score with consistent performance" - ] + "**Target**: Reliable 75-85% F1 score with consistent performance", + ], }, { "cell_type": "code", @@ -41,8 +42,8 @@ def create_comprehensive_notebook(): "outputs": [], "source": [ "# Install required packages\n", - "!pip install transformers torch scikit-learn numpy pandas huggingface_hub wandb" - ] + "!pip install transformers torch scikit-learn numpy pandas huggingface_hub wandb", + ], }, { "cell_type": "code", @@ -63,16 +64,10 @@ def create_comprehensive_notebook(): "\n", "print('โœ… All packages imported successfully')\n", "print(f'PyTorch version: {torch.__version__}')\n", - "print(f'CUDA available: {torch.cuda.is_available()}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ”‘ WANDB API KEY SETUP" - ] + "print(f'CUDA available: {torch.cuda.is_available()}')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ”‘ WANDB API KEY SETUP"]}, { "cell_type": "code", "execution_count": None, @@ -141,15 +136,13 @@ def create_comprehensive_notebook(): " print('3. Enter your API key when prompted')\n", " print('\\nโš ๏ธ Continuing without WandB logging...')\n", "\n", - "print('\\nโœ… WandB setup completed')" - ] + "print('\\nโœ… WandB setup completed')", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ” VERIFYING SPECIALIZED MODEL ACCESS" - ] + "source": ["## ๐Ÿ” VERIFYING SPECIALIZED MODEL ACCESS"], }, { "cell_type": "code", @@ -187,16 +180,10 @@ def create_comprehensive_notebook(): " specialized_model_name = 'roberta-base'\n", " test_tokenizer = AutoTokenizer.from_pretrained(specialized_model_name)\n", " test_model = AutoModelForSequenceClassification.from_pretrained(specialized_model_name, num_labels=12)\n", - " print(f'โœ… Fallback model loaded: {specialized_model_name}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐ŸŽฏ DEFINING EMOTION CLASSES" - ] + " print(f'โœ… Fallback model loaded: {specialized_model_name}')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐ŸŽฏ DEFINING EMOTION CLASSES"]}, { "cell_type": "code", "execution_count": None, @@ -206,15 +193,13 @@ def create_comprehensive_notebook(): "# Define our emotion classes\n", "emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired']\n", "print(f'๐ŸŽฏ Our emotion classes: {emotions}')\n", - "print(f'๐Ÿ“Š Number of emotions: {len(emotions)}')" - ] + "print(f'๐Ÿ“Š Number of emotions: {len(emotions)}')", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ“Š CREATING COMPREHENSIVE ENHANCED DATASET" - ] + "source": ["## ๐Ÿ“Š CREATING COMPREHENSIVE ENHANCED DATASET"], }, { "cell_type": "code", @@ -496,7 +481,7 @@ def create_comprehensive_notebook(): "\n", "# Advanced data augmentation function\n", "def augment_text(text, emotion):\n", - " \"\"\"Create augmented versions of the text with sophisticated techniques.\"\"\"\n", + ' """Create augmented versions of the text with sophisticated techniques."""\n', " augmented = []\n", " \n", " # Synonym replacement with emotion-specific synonyms\n", @@ -556,38 +541,31 @@ def create_comprehensive_notebook(): "texts = [item['text'] for item in enhanced_data]\n", "labels = [item['label'] for item in enhanced_data]\n", "\n", - "print(f'โœ… Comprehensive dataset prepared with {len(texts)} samples')" - ] - } + "print(f'โœ… Comprehensive dataset prepared with {len(texts)} samples')", + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - + # Save the notebook output_path = "notebooks/COMPREHENSIVE_ULTIMATE_TRAINING_COLAB.ipynb" - with open(output_path, 'w') as f: + with open(output_path, "w") as f: json.dump(notebook_content, f, indent=2) - + print(f"โœ… Created comprehensive notebook: {output_path}") print("๐Ÿ“‹ Features included:") print(" โœ… Comprehensive dataset (240 base + augmentation)") @@ -596,8 +574,9 @@ def create_comprehensive_notebook(): print(" โœ… Model architecture fixes") print(" โœ… All advanced features (to be added)") print("\\n๐Ÿš€ This will be a full-featured notebook!") - + return output_path + if __name__ == "__main__": - create_comprehensive_notebook() \ No newline at end of file + create_comprehensive_notebook() diff --git a/scripts/training/create_corrected_specialized_notebook.py b/scripts/training/create_corrected_specialized_notebook.py index b3be8ffb6..694b1969a 100644 --- a/scripts/training/create_corrected_specialized_notebook.py +++ b/scripts/training/create_corrected_specialized_notebook.py @@ -7,10 +7,11 @@ """ from pathlib import Path + def create_corrected_notebook(): """Create a corrected notebook with proper specialized model usage""" - - notebook_content = '''{ + + notebook_content = """{ "cells": [ { "cell_type": "markdown", @@ -618,13 +619,15 @@ def create_corrected_notebook(): }, "nbformat": 4, "nbformat_minor": 4 -}''' - +}""" + # Save the notebook - notebook_path = Path(__file__).parent.parent / 'notebooks' / 'CORRECTED_SPECIALIZED_TRAINING.ipynb' - with open(notebook_path, 'w') as f: + notebook_path = ( + Path(__file__).parent.parent / "notebooks" / "CORRECTED_SPECIALIZED_TRAINING.ipynb" + ) + with open(notebook_path, "w") as f: f.write(notebook_content) - + print(f"โœ… Created corrected specialized notebook: {notebook_path}") print(f"๐Ÿ“‹ Key improvements:") print(f" 1. Verifies access to j-hartmann/emotion-english-distilroberta-base") @@ -640,6 +643,7 @@ def create_corrected_notebook(): print(f" 5. Verify the model is actually using the specialized architecture") print(f" 6. Only deploy if reliability tests pass") + if __name__ == "__main__": create_corrected_notebook() - print("โœ… Corrected specialized notebook created successfully!") \ No newline at end of file + print("โœ… Corrected specialized notebook created successfully!") diff --git a/scripts/training/create_emotion_specialized_notebook.py b/scripts/training/create_emotion_specialized_notebook.py index 031cb1c7e..e1da899fb 100644 --- a/scripts/training/create_emotion_specialized_notebook.py +++ b/scripts/training/create_emotion_specialized_notebook.py @@ -8,9 +8,10 @@ import json + def create_emotion_specialized_notebook(): """Create the emotion specialized notebook content""" - + notebook_content = { "cells": [ { @@ -27,8 +28,8 @@ def create_emotion_specialized_notebook(): "- **finiteautomata/bertweet-base-emotion-analysis** (specialized for emotions)\n", "- **j-hartmann/emotion-english-distilroberta-base** (emotion-specific)\n", "- **SamLowe/roberta-base-go_emotions** (GoEmotions trained)\n", - "- Optimized hyperparameters for emotion classification" - ] + "- Optimized hyperparameters for emotion classification", + ], }, { "cell_type": "code", @@ -37,8 +38,8 @@ def create_emotion_specialized_notebook(): "outputs": [], "source": [ "# Install required packages\n", - "!pip install transformers torch scikit-learn numpy pandas" - ] + "!pip install transformers torch scikit-learn numpy pandas", + ], }, { "cell_type": "code", @@ -66,8 +67,8 @@ def create_emotion_specialized_notebook(): "warnings.filterwarnings('ignore')\n", "\n", "print('๐Ÿš€ EMOTION SPECIALIZED TRAINING - BETTER MODELS')\n", - "print('=' * 60)" - ] + "print('=' * 60)", + ], }, { "cell_type": "code", @@ -108,8 +109,8 @@ def create_emotion_specialized_notebook(): "\n", "print(f'โœ… Data directory found: {data_path}')\n", "print('๐Ÿ“‚ Listing data files:')\n", - "!ls -la {data_path}/" - ] + "!ls -la {data_path}/", + ], }, { "cell_type": "code", @@ -174,8 +175,8 @@ def create_emotion_specialized_notebook(): "if len(texts) != len(unique_texts):\n", " print('โŒ WARNING: DUPLICATES FOUND! This will cause model collapse!')\n", "else:\n", - " print('โœ… All samples are unique - no model collapse risk!')" - ] + " print('โœ… All samples are unique - no model collapse risk!')", + ], }, { "cell_type": "code", @@ -211,8 +212,8 @@ def create_emotion_specialized_notebook(): "\n", "print('\\n๐Ÿ“Š Emotion Distribution:')\n", "for emotion, count in sorted(emotion_counts.items()):\n", - " print(f' {emotion}: {count} samples')" - ] + " print(f' {emotion}: {count} samples')", + ], }, { "cell_type": "code", @@ -247,8 +248,8 @@ def create_emotion_specialized_notebook(): " 'input_ids': encoding['input_ids'].flatten(),\n", " 'attention_mask': encoding['attention_mask'].flatten(),\n", " 'labels': torch.tensor(label, dtype=torch.long)\n", - " }" - ] + " }", + ], }, { "cell_type": "code", @@ -302,8 +303,8 @@ def create_emotion_specialized_notebook(): "train_dataset = EmotionDataset(train_texts, train_labels, tokenizer)\n", "test_dataset = EmotionDataset(test_texts, test_labels, tokenizer)\n", "\n", - "print('โœ… Datasets created successfully')" - ] + "print('โœ… Datasets created successfully')", + ], }, { "cell_type": "code", @@ -319,8 +320,8 @@ def create_emotion_specialized_notebook(): " f1 = f1_score(labels, predictions, average='weighted')\n", " accuracy = accuracy_score(labels, predictions)\n", " \n", - " return {'f1': f1, 'accuracy': accuracy}" - ] + " return {'f1': f1, 'accuracy': accuracy}", + ], }, { "cell_type": "code", @@ -374,8 +375,8 @@ def create_emotion_specialized_notebook(): "print(f'๐ŸŽฏ Using specialized model: {model_name}')\n", "\n", "# Start training\n", - "trainer.train()" - ] + "trainer.train()", + ], }, { "cell_type": "code", @@ -387,14 +388,14 @@ def create_emotion_specialized_notebook(): "print('๐Ÿ“Š Evaluating final model...')\n", "results = trainer.evaluate()\n", "\n", - "print(f'๐Ÿ† Final F1 Score: {results[\"eval_f1\"]:.4f} ({results[\"eval_f1\"]*100:.2f}%)')\n", - "print(f'๐ŸŽฏ Target achieved: {\"โœ… YES!\" if results[\"eval_f1\"] >= 0.75 else \"โŒ Not yet\"}')\n", + 'print(f\'๐Ÿ† Final F1 Score: {results["eval_f1"]:.4f} ({results["eval_f1"]*100:.2f}%)\')\n', + 'print(f\'๐ŸŽฏ Target achieved: {"โœ… YES!" if results["eval_f1"] >= 0.75 else "โŒ Not yet"}\')\n', "print(f'๐Ÿ“ˆ Improvement from baseline: {((results[\"eval_f1\"] - 0.052) / 0.052 * 100):.1f}%')\n", "\n", "# Save model\n", "trainer.save_model('./emotion_model_specialized_final')\n", - "print('๐Ÿ’พ Model saved to ./emotion_model_specialized_final')" - ] + "print('๐Ÿ’พ Model saved to ./emotion_model_specialized_final')", + ], }, { "cell_type": "code", @@ -406,11 +407,11 @@ def create_emotion_specialized_notebook(): "print('๐Ÿงช Testing on sample texts...')\n", "\n", "test_texts = [\n", - " \"I'm feeling really happy today!\",\n", - " \"I'm so frustrated with this project.\",\n", - " \"I feel anxious about the presentation.\",\n", - " \"I'm grateful for all the support.\",\n", - " \"I'm feeling overwhelmed with tasks.\"\n", + ' "I\'m feeling really happy today!",\n', + ' "I\'m so frustrated with this project.",\n', + ' "I feel anxious about the presentation.",\n', + ' "I\'m grateful for all the support.",\n', + ' "I\'m feeling overwhelmed with tasks."\n', "]\n", "\n", "model.eval()\n", @@ -431,8 +432,8 @@ def create_emotion_specialized_notebook(): " predicted_emotion = label_encoder.inverse_transform([predicted_class])[0]\n", " \n", " print(f'{i}. Text: {text}')\n", - " print(f' Predicted: {predicted_emotion} (confidence: {confidence:.3f})\\n')" - ] + " print(f' Predicted: {predicted_emotion} (confidence: {confidence:.3f})\\n')", + ], }, { "cell_type": "markdown", @@ -455,37 +456,32 @@ def create_emotion_specialized_notebook(): "**Next Steps:**\n", "1. Review the F1 score achieved\n", "2. If still low, try other specialized models\n", - "3. Consider data augmentation techniques" - ] - } + "3. Consider data augmentation techniques", + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - - with open('notebooks/EMOTION_SPECIALIZED_TRAINING_COLAB.ipynb', 'w') as f: + + with open("notebooks/EMOTION_SPECIALIZED_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook_content, f, indent=2) - - print("โœ… Emotion specialized notebook created: notebooks/EMOTION_SPECIALIZED_TRAINING_COLAB.ipynb") + + print( + "โœ… Emotion specialized notebook created: notebooks/EMOTION_SPECIALIZED_TRAINING_COLAB.ipynb" + ) print("๐Ÿ“‹ Instructions:") print(" 1. Download the notebook file") print(" 2. Upload to Google Colab") @@ -498,5 +494,6 @@ def create_emotion_specialized_notebook(): print(" - Optimized for small datasets") print(" - Better hyperparameters") + if __name__ == "__main__": - create_emotion_specialized_notebook() \ No newline at end of file + create_emotion_specialized_notebook() diff --git a/scripts/training/create_final_bulletproof_notebook.py b/scripts/training/create_final_bulletproof_notebook.py index d0359a26d..8420f076e 100644 --- a/scripts/training/create_final_bulletproof_notebook.py +++ b/scripts/training/create_final_bulletproof_notebook.py @@ -5,9 +5,10 @@ import json + def create_final_bulletproof_notebook(): """Create a Colab notebook that handles all dependency and path issues""" - + notebook = { "cells": [ { @@ -23,8 +24,8 @@ def create_final_bulletproof_notebook(): "**Target**: 75-85% F1 Score with expanded dataset\n", "**Expected Time**: 10-15 minutes\n", "**GPU Required**: T4 or V100\n", - "**No Restarts**: Everything works in one go!" - ] + "**No Restarts**: Everything works in one go!", + ], }, { "cell_type": "markdown", @@ -32,8 +33,8 @@ def create_final_bulletproof_notebook(): "source": [ "## **Step 1: Smart Environment Setup (All Issues Fixed)**\n", "\n", - "This cell handles NumPy conflicts and installs all required dependencies." - ] + "This cell handles NumPy conflicts and installs all required dependencies.", + ], }, { "cell_type": "code", @@ -42,7 +43,7 @@ def create_final_bulletproof_notebook(): "outputs": [], "source": [ "# ๐Ÿ”ง FINAL SMART ENVIRONMENT SETUP\n", - "print(\"๐Ÿš€ Setting up environment intelligently...\")\n", + 'print("๐Ÿš€ Setting up environment intelligently...")\n', "\n", "# Check what's already installed\n", "import sys\n", @@ -64,7 +65,7 @@ def create_final_bulletproof_notebook(): " return 'not installed'\n", "\n", "# Check current state\n", - "print(\"๐Ÿ“Š Current environment status:\")\n", + 'print("๐Ÿ“Š Current environment status:")\n', "print(f\" NumPy: {get_package_version('numpy')}\")\n", "print(f\" PyTorch: {get_package_version('torch')}\")\n", "print(f\" Transformers: {get_package_version('transformers')}\")\n", @@ -76,11 +77,11 @@ def create_final_bulletproof_notebook(): "# Check NumPy version - only downgrade if it's 2.x\n", "numpy_version = get_package_version('numpy')\n", "if numpy_version.startswith('2.'):\n", - " print(\"โš ๏ธ NumPy 2.x detected - will downgrade to 1.x\")\n", + ' print("โš ๏ธ NumPy 2.x detected - will downgrade to 1.x")\n', " # Fix: Use proper pip command without extra quotes\n", " install_commands.append('pip install numpy==1.24.3 --force-reinstall --quiet')\n", "else:\n", - " print(\"โœ… NumPy version is compatible\")\n", + ' print("โœ… NumPy version is compatible")\n', "\n", "# Check other dependencies\n", "dependencies = [\n", @@ -93,48 +94,48 @@ def create_final_bulletproof_notebook(): "\n", "for package, install_name in dependencies:\n", " if not check_package(package):\n", - " print(f\"๐Ÿ“ฆ {package} not found - installing...\")\n", + ' print(f"๐Ÿ“ฆ {package} not found - installing...")\n', " install_commands.append(f'pip install {install_name} --quiet')\n", " else:\n", - " print(f\"โœ… {package} already installed\")\n", + ' print(f"โœ… {package} already installed")\n', "\n", "# Execute installation commands if needed\n", "if install_commands:\n", - " print(\"\\n๐Ÿ”ง Installing missing dependencies...\")\n", + ' print("\\n๐Ÿ”ง Installing missing dependencies...")\n', " for cmd in install_commands:\n", - " print(f\"Running: {cmd}\")\n", + ' print(f"Running: {cmd}")\n', " result = subprocess.run(cmd.split(), capture_output=True, text=True)\n", " if result.returncode != 0:\n", - " print(f\"โš ๏ธ Warning: {result.stderr}\")\n", + ' print(f"โš ๏ธ Warning: {result.stderr}")\n', " else:\n", - " print(f\"โœ… Success\")\n", + ' print(f"โœ… Success")\n', "else:\n", - " print(\"\\n๐ŸŽ‰ All dependencies already installed!\")\n", + ' print("\\n๐ŸŽ‰ All dependencies already installed!")\n', "\n", "# Final verification\n", - "print(\"\\n๐Ÿ” Final verification...\")\n", + 'print("\\n๐Ÿ” Final verification...")\n', "try:\n", " import numpy as np\n", " import torch\n", " import transformers\n", " import sklearn\n", " \n", - " print(f\"โœ… NumPy: {np.__version__}\")\n", - " print(f\"โœ… PyTorch: {torch.__version__}\")\n", - " print(f\"โœ… Transformers: {transformers.__version__}\")\n", - " print(f\"โœ… CUDA Available: {torch.cuda.is_available()}\")\n", + ' print(f"โœ… NumPy: {np.__version__}")\n', + ' print(f"โœ… PyTorch: {torch.__version__}")\n', + ' print(f"โœ… Transformers: {transformers.__version__}")\n', + ' print(f"โœ… CUDA Available: {torch.cuda.is_available()}")\n', " \n", " if torch.cuda.is_available():\n", - " print(f\"โœ… GPU: {torch.cuda.get_device_name(0)}\")\n", - " print(f\"โœ… GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB\")\n", + ' print(f"โœ… GPU: {torch.cuda.get_device_name(0)}")\n', + ' print(f"โœ… GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")\n', " \n", - " print(\"\\n๐ŸŽ‰ Environment ready! No restart required!\")\n", + ' print("\\n๐ŸŽ‰ Environment ready! No restart required!")\n', " \n", "except Exception as e:\n", - " print(f\"โŒ Error during verification: {e}\")\n", - " print(\"๐Ÿ’ก If you see errors above, you may need to restart the runtime once.\")\n", - " print(\" This is normal for the first run only.\")" - ] + ' print(f"โŒ Error during verification: {e}")\n', + ' print("๐Ÿ’ก If you see errors above, you may need to restart the runtime once.")\n', + ' print(" This is normal for the first run only.")', + ], }, { "cell_type": "markdown", @@ -142,8 +143,8 @@ def create_final_bulletproof_notebook(): "source": [ "## **Step 2: Clone Repository & Fix Path Issues**\n", "\n", - "Clone the repository and handle the directory structure properly." - ] + "Clone the repository and handle the directory structure properly.", + ], }, { "cell_type": "code", @@ -152,23 +153,23 @@ def create_final_bulletproof_notebook(): "outputs": [], "source": [ "# ๐Ÿ“ฅ CLONE REPOSITORY & FIX PATHS\n", - "print(\"๐Ÿ“ฅ Cloning repository...\")\n", + 'print("๐Ÿ“ฅ Cloning repository...")\n', "!git clone https://github.com/uelkerd/SAMO--DL.git\n", "\n", "# Fix: Handle the nested directory structure\n", "import os\n", "if os.path.exists('SAMO--DL/SAMO--DL'):\n", - " print(\"๐Ÿ“ Found nested directory structure - navigating correctly...\")\n", + ' print("๐Ÿ“ Found nested directory structure - navigating correctly...")\n', " %cd SAMO--DL/SAMO--DL\n", "else:\n", - " print(\"๐Ÿ“ Using standard directory structure...\")\n", + ' print("๐Ÿ“ Using standard directory structure...")\n', " %cd SAMO--DL\n", "\n", - "print(f\"๐Ÿ“‚ Current directory: {os.getcwd()}\")\n", + 'print(f"๐Ÿ“‚ Current directory: {os.getcwd()}")\n', "print(f\"๐Ÿ“ Contents: {os.listdir('.')}\")\n", "\n", "# ๐Ÿ”ง LOAD EXPANDED DATASET\n", - "print(\"\\n๐Ÿ“Š Loading expanded dataset...\")\n", + 'print("\\n๐Ÿ“Š Loading expanded dataset...")\n', "import json\n", "import pandas as pd\n", "from sklearn.model_selection import train_test_split\n", @@ -184,14 +185,14 @@ def create_final_bulletproof_notebook(): "# Check if expanded dataset exists\n", "dataset_path = 'data/expanded_journal_dataset.json'\n", "if os.path.exists(dataset_path):\n", - " print(f\"โœ… Found expanded dataset at {dataset_path}\")\n", + ' print(f"โœ… Found expanded dataset at {dataset_path}")\n', " with open(dataset_path, 'r') as f:\n", " expanded_data = json.load(f)\n", - " print(f\"โœ… Loaded {len(expanded_data)} expanded samples\")\n", + ' print(f"โœ… Loaded {len(expanded_data)} expanded samples")\n', " print(f\"๐Ÿ“Š Emotions: {list(set([item['emotion'] for item in expanded_data]))}\")\n", "else:\n", - " print(f\"โŒ Expanded dataset not found at {dataset_path}\")\n", - " print(\"๐Ÿ”ง Creating expanded dataset on the fly...\")\n", + ' print(f"โŒ Expanded dataset not found at {dataset_path}")\n', + ' print("๐Ÿ”ง Creating expanded dataset on the fly...")\n', " \n", " # Create a simple expanded dataset\n", " base_emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', \n", @@ -202,38 +203,38 @@ def create_final_bulletproof_notebook(): " # Create 83 samples per emotion\n", " for i in range(83):\n", " if emotion == 'happy':\n", - " text = f\"I'm feeling really happy today! Everything is going well. Sample {i+1}\"\n", + ' text = f"I\'m feeling really happy today! Everything is going well. Sample {i+1}"\n', " elif emotion == 'sad':\n", - " text = f\"I'm feeling sad and lonely today. Sample {i+1}\"\n", + ' text = f"I\'m feeling sad and lonely today. Sample {i+1}"\n', " elif emotion == 'anxious':\n", - " text = f\"I feel anxious about the upcoming presentation. Sample {i+1}\"\n", + ' text = f"I feel anxious about the upcoming presentation. Sample {i+1}"\n', " elif emotion == 'excited':\n", - " text = f\"I'm excited about the new opportunities ahead! Sample {i+1}\"\n", + ' text = f"I\'m excited about the new opportunities ahead! Sample {i+1}"\n', " elif emotion == 'frustrated':\n", - " text = f\"I'm so frustrated with this project. Nothing is working. Sample {i+1}\"\n", + ' text = f"I\'m so frustrated with this project. Nothing is working. Sample {i+1}"\n', " elif emotion == 'grateful':\n", " text = f\"I'm grateful for all the support I've received. Sample {i+1}\"\n", " elif emotion == 'proud':\n", " text = f\"I'm proud of what I've accomplished so far. Sample {i+1}\"\n", " elif emotion == 'calm':\n", - " text = f\"I feel calm and peaceful right now. Sample {i+1}\"\n", + ' text = f"I feel calm and peaceful right now. Sample {i+1}"\n', " elif emotion == 'hopeful':\n", - " text = f\"I'm hopeful that things will get better. Sample {i+1}\"\n", + ' text = f"I\'m hopeful that things will get better. Sample {i+1}"\n', " elif emotion == 'tired':\n", - " text = f\"I'm tired and need some rest. Sample {i+1}\"\n", + ' text = f"I\'m tired and need some rest. Sample {i+1}"\n', " elif emotion == 'content':\n", - " text = f\"I'm content with how things are going. Sample {i+1}\"\n", + ' text = f"I\'m content with how things are going. Sample {i+1}"\n', " elif emotion == 'overwhelmed':\n", - " text = f\"I'm feeling overwhelmed with all these tasks. Sample {i+1}\"\n", + ' text = f"I\'m feeling overwhelmed with all these tasks. Sample {i+1}"\n', " \n", " expanded_data.append({\n", " 'text': text,\n", " 'emotion': emotion\n", " })\n", " \n", - " print(f\"โœ… Created {len(expanded_data)} expanded samples\")\n", - " print(f\"๐Ÿ“Š Emotions: {list(set([item['emotion'] for item in expanded_data]))}\")" - ] + ' print(f"โœ… Created {len(expanded_data)} expanded samples")\n', + " print(f\"๐Ÿ“Š Emotions: {list(set([item['emotion'] for item in expanded_data]))}\")", + ], }, { "cell_type": "markdown", @@ -241,8 +242,8 @@ def create_final_bulletproof_notebook(): "source": [ "## **Step 3: Load GoEmotions Dataset**\n", "\n", - "Load and prepare the GoEmotions dataset for domain adaptation." - ] + "Load and prepare the GoEmotions dataset for domain adaptation.", + ], }, { "cell_type": "code", @@ -251,7 +252,7 @@ def create_final_bulletproof_notebook(): "outputs": [], "source": [ "# ๐Ÿ“Š LOAD GOEMOTIONS DATASET\n", - "print(\"๐Ÿ“Š Loading GoEmotions dataset...\")\n", + 'print("๐Ÿ“Š Loading GoEmotions dataset...")\n', "from datasets import load_dataset\n", "\n", "# Load GoEmotions dataset\n", @@ -259,7 +260,7 @@ def create_final_bulletproof_notebook(): "\n", "# Get emotion names\n", "emotion_names = go_emotions['train'].features['labels'].feature.names\n", - "print(f\"โœ… Loaded GoEmotions with {len(emotion_names)} emotions\")\n", + 'print(f"โœ… Loaded GoEmotions with {len(emotion_names)} emotions")\n', "print(f\"๐Ÿ“Š Total samples: {len(go_emotions['train'])}\")\n", "\n", "# Define emotion mapping (GoEmotions โ†’ Journal emotions)\n", @@ -294,8 +295,8 @@ def create_final_bulletproof_notebook(): " 'neutral': 'calm'\n", "}\n", "\n", - "print(f\"โœ… Emotion mapping defined with {len(emotion_mapping)} mappings\")" - ] + 'print(f"โœ… Emotion mapping defined with {len(emotion_mapping)} mappings")', + ], }, { "cell_type": "markdown", @@ -303,8 +304,8 @@ def create_final_bulletproof_notebook(): "source": [ "## **Step 4: Prepare Combined Dataset**\n", "\n", - "Combine GoEmotions and expanded journal data for training." - ] + "Combine GoEmotions and expanded journal data for training.", + ], }, { "cell_type": "code", @@ -313,7 +314,7 @@ def create_final_bulletproof_notebook(): "outputs": [], "source": [ "# ๐Ÿ”„ PREPARE COMBINED DATASET\n", - "print(\"๐Ÿ”„ Preparing combined dataset...\")\n", + 'print("๐Ÿ”„ Preparing combined dataset...")\n', "\n", "# Process GoEmotions data\n", "go_emotions_processed = []\n", @@ -333,22 +334,22 @@ def create_final_bulletproof_notebook(): "# Combine datasets\n", "combined_data = go_emotions_processed + expanded_data\n", "\n", - "print(f\"๐Ÿ“Š GoEmotions samples: {len(go_emotions_processed)}\")\n", - "print(f\"๐Ÿ“Š Journal samples: {len(expanded_data)}\")\n", - "print(f\"๐Ÿ“Š Combined samples: {len(combined_data)}\")\n", + 'print(f"๐Ÿ“Š GoEmotions samples: {len(go_emotions_processed)}")\n', + 'print(f"๐Ÿ“Š Journal samples: {len(expanded_data)}")\n', + 'print(f"๐Ÿ“Š Combined samples: {len(combined_data)}")\n', "\n", "# Create DataFrame\n", "df = pd.DataFrame(combined_data)\n", - "print(f\"\\n๐Ÿ“ˆ Emotion distribution:\")\n", + 'print(f"\\n๐Ÿ“ˆ Emotion distribution:")\n', "print(df['emotion'].value_counts())\n", "\n", "# Encode labels\n", "label_encoder = LabelEncoder()\n", "df['label'] = label_encoder.fit_transform(df['emotion'])\n", "\n", - "print(f\"\\nโœ… Labels encoded: {list(label_encoder.classes_)}\")\n", - "print(f\"๐Ÿ“Š Total unique emotions: {len(label_encoder.classes_)}\")" - ] + 'print(f"\\nโœ… Labels encoded: {list(label_encoder.classes_)}")\n', + 'print(f"๐Ÿ“Š Total unique emotions: {len(label_encoder.classes_)}")', + ], }, { "cell_type": "markdown", @@ -356,8 +357,8 @@ def create_final_bulletproof_notebook(): "source": [ "## **Step 5: Create PyTorch Dataset**\n", "\n", - "Create custom PyTorch dataset with GPU optimizations." - ] + "Create custom PyTorch dataset with GPU optimizations.", + ], }, { "cell_type": "code", @@ -366,7 +367,7 @@ def create_final_bulletproof_notebook(): "outputs": [], "source": [ "# ๐Ÿ—๏ธ CREATE PYTORCH DATASET\n", - "print(\"๐Ÿ—๏ธ Creating PyTorch dataset...\")\n", + 'print("๐Ÿ—๏ธ Creating PyTorch dataset...")\n', "\n", "# Initialize tokenizer\n", "model_name = 'bert-base-uncased'\n", @@ -415,12 +416,12 @@ def create_final_bulletproof_notebook(): "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)\n", "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)\n", "\n", - "print(f\"โœ… Created datasets:\")\n", - "print(f\" Training: {len(train_dataset)} samples\")\n", - "print(f\" Validation: {len(val_dataset)} samples\")\n", - "print(f\" Batch size: {batch_size}\")\n", - "print(f\" GPU optimizations: num_workers=2, pin_memory=True\")" - ] + 'print(f"โœ… Created datasets:")\n', + 'print(f" Training: {len(train_dataset)} samples")\n', + 'print(f" Validation: {len(val_dataset)} samples")\n', + 'print(f" Batch size: {batch_size}")\n', + 'print(f" GPU optimizations: num_workers=2, pin_memory=True")', + ], }, { "cell_type": "markdown", @@ -428,8 +429,8 @@ def create_final_bulletproof_notebook(): "source": [ "## **Step 6: Train Model with GPU Optimizations**\n", "\n", - "Train the model with all optimizations: mixed precision, early stopping, and learning rate scheduling." - ] + "Train the model with all optimizations: mixed precision, early stopping, and learning rate scheduling.", + ], }, { "cell_type": "code", @@ -438,15 +439,15 @@ def create_final_bulletproof_notebook(): "outputs": [], "source": [ "# ๐Ÿš€ TRAIN MODEL WITH GPU OPTIMIZATIONS\n", - "print(\"๐Ÿš€ Starting model training with GPU optimizations...\")\n", + 'print("๐Ÿš€ Starting model training with GPU optimizations...")\n', "\n", "# GPU optimizations\n", "if torch.cuda.is_available():\n", - " print(\"๐Ÿ”ง Applying GPU optimizations...\")\n", + ' print("๐Ÿ”ง Applying GPU optimizations...")\n', " torch.backends.cudnn.benchmark = True\n", " torch.backends.cudnn.deterministic = False\n", - " print(f\"๐Ÿ“Š GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB\")\n", - " print(f\"๐Ÿ“Š Available Memory: {torch.cuda.memory_allocated(0) / 1e9:.1f} GB\")\n", + ' print(f"๐Ÿ“Š GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")\n', + ' print(f"๐Ÿ“Š Available Memory: {torch.cuda.memory_allocated(0) / 1e9:.1f} GB")\n', "\n", "# Clear GPU cache\n", "if torch.cuda.is_available():\n", @@ -480,8 +481,8 @@ def create_final_bulletproof_notebook(): "patience_counter = 0\n", "patience = 3\n", "\n", - "print(f\"๐ŸŽฏ Training for {num_epochs} epochs with early stopping (patience={patience})\")\n", - "print(f\"๐Ÿ“Š Target F1 Score: 75-85%\")\n", + 'print(f"๐ŸŽฏ Training for {num_epochs} epochs with early stopping (patience={patience})")\n', + 'print(f"๐Ÿ“Š Target F1 Score: 75-85%")\n', "\n", "for epoch in range(num_epochs):\n", " # Training phase\n", @@ -538,9 +539,9 @@ def create_final_bulletproof_notebook(): " # Learning rate scheduling\n", " scheduler.step(f1_macro)\n", " \n", - " print(f\"Epoch {epoch+1}/{num_epochs}:\")\n", - " print(f\" Train Loss: {train_loss/len(train_loader):.4f}, Train Acc: {train_acc:.4f}\")\n", - " print(f\" Val Loss: {val_loss/len(val_loader):.4f}, Val Acc: {val_acc:.4f}, F1: {f1_macro:.4f}\")\n", + ' print(f"Epoch {epoch+1}/{num_epochs}:")\n', + ' print(f" Train Loss: {train_loss/len(train_loader):.4f}, Train Acc: {train_acc:.4f}")\n', + ' print(f" Val Loss: {val_loss/len(val_loader):.4f}, Val Acc: {val_acc:.4f}, F1: {f1_macro:.4f}")\n', " \n", " # Early stopping check\n", " if f1_macro > best_f1:\n", @@ -548,24 +549,24 @@ def create_final_bulletproof_notebook(): " patience_counter = 0\n", " # Save best model\n", " torch.save(model.state_dict(), 'best_emotion_model.pth')\n", - " print(f\" ๐ŸŽ‰ New best F1: {best_f1:.4f} - Model saved!\")\n", + ' print(f" ๐ŸŽ‰ New best F1: {best_f1:.4f} - Model saved!")\n', " else:\n", " patience_counter += 1\n", - " print(f\" โณ No improvement for {patience_counter} epochs\")\n", + ' print(f" โณ No improvement for {patience_counter} epochs")\n', " \n", " # Early stopping\n", " if patience_counter >= patience:\n", - " print(f\"๐Ÿ›‘ Early stopping triggered after {epoch+1} epochs\")\n", + ' print(f"๐Ÿ›‘ Early stopping triggered after {epoch+1} epochs")\n', " break\n", " \n", " # Clear GPU cache periodically\n", " if torch.cuda.is_available():\n", " torch.cuda.empty_cache()\n", "\n", - "print(f\"\\n๐ŸŽ‰ Training completed!\")\n", - "print(f\"๐Ÿ† Best F1 Score: {best_f1:.4f} ({best_f1*100:.1f}%)\")\n", - "print(f\"๐ŸŽฏ Target achieved: {'โœ… YES!' if best_f1 >= 0.75 else 'โŒ Not yet'}\")" - ] + 'print(f"\\n๐ŸŽ‰ Training completed!")\n', + 'print(f"๐Ÿ† Best F1 Score: {best_f1:.4f} ({best_f1*100:.1f}%)")\n', + "print(f\"๐ŸŽฏ Target achieved: {'โœ… YES!' if best_f1 >= 0.75 else 'โŒ Not yet'}\")", + ], }, { "cell_type": "markdown", @@ -573,8 +574,8 @@ def create_final_bulletproof_notebook(): "source": [ "## **Step 7: Model Evaluation & Testing**\n", "\n", - "Load the best model and test it on sample journal entries." - ] + "Load the best model and test it on sample journal entries.", + ], }, { "cell_type": "code", @@ -583,7 +584,7 @@ def create_final_bulletproof_notebook(): "outputs": [], "source": [ "# ๐Ÿงช MODEL EVALUATION & TESTING\n", - "print(\"๐Ÿงช Evaluating best model...\")\n", + 'print("๐Ÿงช Evaluating best model...")\n', "\n", "# Load best model\n", "model.load_state_dict(torch.load('best_emotion_model.pth'))\n", @@ -591,22 +592,22 @@ def create_final_bulletproof_notebook(): "\n", "# Test samples\n", "test_samples = [\n", - " \"I'm feeling really happy today! Everything is going well.\",\n", - " \"I'm so frustrated with this project. Nothing is working.\",\n", - " \"I feel anxious about the upcoming presentation.\",\n", + ' "I\'m feeling really happy today! Everything is going well.",\n', + ' "I\'m so frustrated with this project. Nothing is working.",\n', + ' "I feel anxious about the upcoming presentation.",\n', " \"I'm grateful for all the support I've received.\",\n", - " \"I'm feeling overwhelmed with all these tasks.\",\n", + ' "I\'m feeling overwhelmed with all these tasks.",\n', " \"I'm proud of what I've accomplished so far.\",\n", - " \"I'm feeling sad and lonely today.\",\n", - " \"I'm excited about the new opportunities ahead.\",\n", - " \"I feel calm and peaceful right now.\",\n", - " \"I'm hopeful that things will get better.\",\n", - " \"I'm tired and need some rest.\",\n", - " \"I'm content with how things are going.\"\n", + ' "I\'m feeling sad and lonely today.",\n', + ' "I\'m excited about the new opportunities ahead.",\n', + ' "I feel calm and peaceful right now.",\n', + ' "I\'m hopeful that things will get better.",\n', + ' "I\'m tired and need some rest.",\n', + ' "I\'m content with how things are going."\n', "]\n", "\n", - "print(\"๐Ÿ“Š Testing Results:\")\n", - "print(\"=\" * 80)\n", + 'print("๐Ÿ“Š Testing Results:")\n', + 'print("=" * 80)\n', "\n", "correct_predictions = 0\n", "expected_emotions = ['happy', 'frustrated', 'anxious', 'grateful', 'overwhelmed', \n", @@ -636,28 +637,28 @@ def create_final_bulletproof_notebook(): " if is_correct:\n", " correct_predictions += 1\n", " \n", - " print(f\"{i}. Text: {text}\")\n", - " print(f\" Predicted: {predicted_emotion} (confidence: {confidence:.3f})\")\n", - " print(f\" Expected: {expected}\")\n", + ' print(f"{i}. Text: {text}")\n', + ' print(f" Predicted: {predicted_emotion} (confidence: {confidence:.3f})")\n', + ' print(f" Expected: {expected}")\n', " print(f\" {'โœ… CORRECT' if is_correct else 'โŒ WRONG'}\")\n", - " print(f\" Top 3 predictions:\")\n", + ' print(f" Top 3 predictions:")\n', " for emotion, prob in zip(top_3_emotions, top_3_probs):\n", - " print(f\" - {emotion}: {prob:.3f}\")\n", + ' print(f" - {emotion}: {prob:.3f}")\n', " print()\n", "\n", "accuracy = correct_predictions / len(test_samples)\n", - "print(f\"\\n๐Ÿ“ˆ Final Results:\")\n", - "print(f\" Test Accuracy: {accuracy:.2%} ({correct_predictions}/{len(test_samples)})\")\n", - "print(f\" Best F1 Score: {best_f1:.4f} ({best_f1*100:.1f}%)\")\n", + 'print(f"\\n๐Ÿ“ˆ Final Results:")\n', + 'print(f" Test Accuracy: {accuracy:.2%} ({correct_predictions}/{len(test_samples)})")\n', + 'print(f" Best F1 Score: {best_f1:.4f} ({best_f1*100:.1f}%)")\n', "print(f\" Target Achieved: {'โœ… YES!' if best_f1 >= 0.75 else 'โŒ Not yet'}\")\n", "\n", "if best_f1 >= 0.75:\n", - " print(f\"\\n๐ŸŽ‰ SUCCESS! Model achieved {best_f1*100:.1f}% F1 score!\")\n", - " print(f\"๐Ÿš€ Ready for production deployment!\")\n", + ' print(f"\\n๐ŸŽ‰ SUCCESS! Model achieved {best_f1*100:.1f}% F1 score!")\n', + ' print(f"๐Ÿš€ Ready for production deployment!")\n', "else:\n", - " print(f\"\\n๐Ÿ“ˆ Good progress! Current F1: {best_f1*100:.1f}%\")\n", - " print(f\"๐Ÿ’ก Consider: more data, hyperparameter tuning, or different model architecture\")" - ] + ' print(f"\\n๐Ÿ“ˆ Good progress! Current F1: {best_f1*100:.1f}%")\n', + ' print(f"๐Ÿ’ก Consider: more data, hyperparameter tuning, or different model architecture")', + ], }, { "cell_type": "markdown", @@ -686,38 +687,31 @@ def create_final_bulletproof_notebook(): "\n", "**Model saved as:** `best_emotion_model.pth`\n", "\n", - "**๐ŸŽฏ All Issues: SOLVED!** ๐Ÿš€" - ] - } + "**๐ŸŽฏ All Issues: SOLVED!** ๐Ÿš€", + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - + # Save notebook - output_path = 'notebooks/expanded_dataset_training_final.ipynb' - with open(output_path, 'w') as f: + output_path = "notebooks/expanded_dataset_training_final.ipynb" + with open(output_path, "w") as f: json.dump(notebook, f, indent=2) - + print(f"โœ… Created final bulletproof notebook: {output_path}") print("๐Ÿ”ง All issues fixed:") print(" - Fixed NumPy installation command (removed extra quotes)") @@ -732,5 +726,6 @@ def create_final_bulletproof_notebook(): print(" 4. Get 75-85% F1 score!") print("\n๐ŸŽฏ This should work perfectly now!") + if __name__ == "__main__": - create_final_bulletproof_notebook() \ No newline at end of file + create_final_bulletproof_notebook() diff --git a/scripts/training/create_final_colab_notebook.py b/scripts/training/create_final_colab_notebook.py index a400b0c09..0127ce163 100644 --- a/scripts/training/create_final_colab_notebook.py +++ b/scripts/training/create_final_colab_notebook.py @@ -8,9 +8,10 @@ import json + def create_colab_notebook(): """Create the final Colab notebook content""" - + notebook_content = { "cells": [ { @@ -27,16 +28,10 @@ def create_colab_notebook(): "1. โœ… Original journal dataset (150 high-quality samples)\n", "2. โœ… CMU-MOSEI dataset (diverse, real-world samples)\n", "3. โœ… Optimized hyperparameters\n", - "4. โœ… GPU training for maximum performance" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ“ฅ Setup and Dependencies" - ] + "4. โœ… GPU training for maximum performance", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ“ฅ Setup and Dependencies"]}, { "cell_type": "code", "execution_count": None, @@ -65,15 +60,13 @@ def create_colab_notebook(): "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", - "print(\"โœ… All dependencies installed and imported!\")" - ] + 'print("โœ… All dependencies installed and imported!")', + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ”ง Clone Repository and Load Data" - ] + "source": ["## ๐Ÿ”ง Clone Repository and Load Data"], }, { "cell_type": "code", @@ -85,8 +78,8 @@ def create_colab_notebook(): "!git clone https://github.com/uelkerd/SAMO--DL.git\n", "!cd SAMO--DL\n", "\n", - "print(\"๐Ÿ“‚ Repository cloned successfully!\")" - ] + 'print("๐Ÿ“‚ Repository cloned successfully!")', + ], }, { "cell_type": "code", @@ -95,7 +88,7 @@ def create_colab_notebook(): "outputs": [], "source": [ "# Load combined dataset\n", - "print(\"๐Ÿ“Š Loading combined dataset...\")\n", + 'print("๐Ÿ“Š Loading combined dataset...")\n', "\n", "combined_samples = []\n", "\n", @@ -110,9 +103,9 @@ def create_colab_notebook(): " 'emotion': item['emotion'],\n", " 'source': 'journal'\n", " })\n", - " print(f\"โœ… Loaded {len(journal_data)} journal samples\")\n", + ' print(f"โœ… Loaded {len(journal_data)} journal samples")\n', "except Exception as e:\n", - " print(f\"โš ๏ธ Could not load journal data: {e}\")\n", + ' print(f"โš ๏ธ Could not load journal data: {e}")\n', "\n", "# Load expanded journal dataset (subset to avoid synthetic issues)\n", "try:\n", @@ -129,11 +122,11 @@ def create_colab_notebook(): " 'emotion': item['emotion'],\n", " 'source': 'expanded_journal'\n", " })\n", - " print(f\"โœ… Loaded {subset_size} expanded journal samples\")\n", + ' print(f"โœ… Loaded {subset_size} expanded journal samples")\n', "except Exception as e:\n", - " print(f\"โš ๏ธ Could not load expanded journal data: {e}\")\n", + ' print(f"โš ๏ธ Could not load expanded journal data: {e}")\n', "\n", - "print(f\"๐Ÿ“Š Total combined samples: {len(combined_samples)}\")\n", + 'print(f"๐Ÿ“Š Total combined samples: {len(combined_samples)}")\n', "\n", "# Show emotion distribution\n", "emotion_counts = {}\n", @@ -141,18 +134,12 @@ def create_colab_notebook(): " emotion = sample['emotion']\n", " emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1\n", "\n", - "print(\"๐Ÿ“Š Emotion distribution:\")\n", + 'print("๐Ÿ“Š Emotion distribution:")\n', "for emotion, count in sorted(emotion_counts.items()):\n", - " print(f\" {emotion}: {count} samples\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ—‚๏ธ Data Preparation" - ] + ' print(f" {emotion}: {count} samples")', + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ—‚๏ธ Data Preparation"]}, { "cell_type": "code", "execution_count": None, @@ -167,17 +154,17 @@ def create_colab_notebook(): "label_encoder = LabelEncoder()\n", "labels = label_encoder.fit_transform(emotions)\n", "\n", - "print(f\"๐ŸŽฏ Number of labels: {len(label_encoder.classes_)}\")\n", - "print(f\"๐Ÿ“Š Labels: {list(label_encoder.classes_)}\")\n", + 'print(f"๐ŸŽฏ Number of labels: {len(label_encoder.classes_)}")\n', + 'print(f"๐Ÿ“Š Labels: {list(label_encoder.classes_)}")\n', "\n", "# Split data\n", "train_texts, test_texts, train_labels, test_labels = train_test_split(\n", " texts, labels, test_size=0.2, random_state=42, stratify=labels\n", ")\n", "\n", - "print(f\"๐Ÿ“ˆ Training samples: {len(train_texts)}\")\n", - "print(f\"๐Ÿงช Test samples: {len(test_labels)}\")" - ] + 'print(f"๐Ÿ“ˆ Training samples: {len(train_texts)}")\n', + 'print(f"๐Ÿงช Test samples: {len(test_labels)}")', + ], }, { "cell_type": "code", @@ -187,7 +174,7 @@ def create_colab_notebook(): "source": [ "# Custom dataset class\n", "class EmotionDataset(Dataset):\n", - " \"\"\"Custom dataset for emotion classification\"\"\"\n", + ' """Custom dataset for emotion classification"""\n', " \n", " def __init__(self, texts, labels, tokenizer, max_length=128):\n", " self.texts = texts\n", @@ -217,7 +204,7 @@ def create_colab_notebook(): " }\n", "\n", "def compute_metrics(eval_pred):\n", - " \"\"\"Compute F1 score and accuracy\"\"\"\n", + ' """Compute F1 score and accuracy"""\n', " predictions, labels = eval_pred\n", " predictions = np.argmax(predictions, axis=1)\n", " \n", @@ -229,16 +216,10 @@ def create_colab_notebook(): " 'accuracy': accuracy\n", " }\n", "\n", - "print(\"โœ… Dataset class and metrics function defined!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿš€ Model Training" - ] + 'print("โœ… Dataset class and metrics function defined!")', + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿš€ Model Training"]}, { "cell_type": "code", "execution_count": None, @@ -246,22 +227,22 @@ def create_colab_notebook(): "outputs": [], "source": [ "# Initialize tokenizer and model\n", - "print(\"๐Ÿ”ง Initializing model...\")\n", - "model_name = \"bert-base-uncased\"\n", + 'print("๐Ÿ”ง Initializing model...")\n', + 'model_name = "bert-base-uncased"\n', "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "\n", "model = AutoModelForSequenceClassification.from_pretrained(\n", " model_name,\n", " num_labels=len(label_encoder.classes_),\n", - " problem_type=\"single_label_classification\"\n", + ' problem_type="single_label_classification"\n', ")\n", "\n", "# Create datasets\n", "train_dataset = EmotionDataset(train_texts, train_labels, tokenizer)\n", "test_dataset = EmotionDataset(test_texts, test_labels, tokenizer)\n", "\n", - "print(\"โœ… Model and datasets initialized!\")" - ] + 'print("โœ… Model and datasets initialized!")', + ], }, { "cell_type": "code", @@ -271,20 +252,20 @@ def create_colab_notebook(): "source": [ "# Training arguments optimized for performance\n", "training_args = TrainingArguments(\n", - " output_dir=\"./emotion_model_combined\",\n", + ' output_dir="./emotion_model_combined",\n', " num_train_epochs=8, # More epochs for better performance\n", " per_device_train_batch_size=16,\n", " per_device_eval_batch_size=16,\n", " warmup_steps=500,\n", " weight_decay=0.01,\n", - " logging_dir=\"./logs\",\n", + ' logging_dir="./logs",\n', " logging_steps=50,\n", - " eval_strategy=\"steps\",\n", + ' eval_strategy="steps",\n', " eval_steps=100,\n", - " save_strategy=\"steps\",\n", + ' save_strategy="steps",\n', " save_steps=100,\n", " load_best_model_at_end=True,\n", - " metric_for_best_model=\"f1\",\n", + ' metric_for_best_model="f1",\n', " greater_is_better=True,\n", " dataloader_num_workers=2,\n", " remove_unused_columns=False,\n", @@ -304,8 +285,8 @@ def create_colab_notebook(): " callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]\n", ")\n", "\n", - "print(\"โœ… Trainer initialized with optimized settings!\")" - ] + 'print("โœ… Trainer initialized with optimized settings!")', + ], }, { "cell_type": "code", @@ -314,24 +295,18 @@ def create_colab_notebook(): "outputs": [], "source": [ "# Train model\n", - "print(\"๐Ÿš€ Starting training...\")\n", - "print(\"๐ŸŽฏ Target F1 Score: 75-85%\")\n", - "print(\"๐Ÿ”ง Current Best: 67%\")\n", - "print(\"๐Ÿ“ˆ Expected Improvement: 8-18%\")\n", + 'print("๐Ÿš€ Starting training...")\n', + 'print("๐ŸŽฏ Target F1 Score: 75-85%")\n', + 'print("๐Ÿ”ง Current Best: 67%")\n', + 'print("๐Ÿ“ˆ Expected Improvement: 8-18%")\n', "print()\n", "\n", "trainer.train()\n", "\n", - "print(\"โœ… Training completed!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ“Š Results and Evaluation" - ] + 'print("โœ… Training completed!")', + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ“Š Results and Evaluation"]}, { "cell_type": "code", "execution_count": None, @@ -339,7 +314,7 @@ def create_colab_notebook(): "outputs": [], "source": [ "# Evaluate final model\n", - "print(\"๐Ÿ“Š Evaluating final model...\")\n", + 'print("๐Ÿ“Š Evaluating final model...")\n', "results = trainer.evaluate()\n", "\n", "print(f\"๐Ÿ† Final F1 Score: {results['eval_f1']:.4f} ({results['eval_f1']*100:.2f}%)\")\n", @@ -349,8 +324,8 @@ def create_colab_notebook(): "# Calculate improvement\n", "baseline_f1 = 0.67\n", "improvement = ((results['eval_f1'] - baseline_f1) / baseline_f1) * 100\n", - "print(f\"๐Ÿ“ˆ Improvement from baseline: {improvement:.1f}%\")" - ] + 'print(f"๐Ÿ“ˆ Improvement from baseline: {improvement:.1f}%")', + ], }, { "cell_type": "code", @@ -359,40 +334,34 @@ def create_colab_notebook(): "outputs": [], "source": [ "# Test on sample texts\n", - "print(\"\\n๐Ÿงช Testing on sample texts...\")\n", + 'print("\\n๐Ÿงช Testing on sample texts...")\n', "test_texts = [\n", - " \"I'm feeling really happy today!\",\n", - " \"This is so frustrating, nothing works.\",\n", - " \"I'm anxious about the presentation.\",\n", - " \"I'm grateful for all the support.\",\n", - " \"I'm tired and need some rest.\",\n", - " \"I'm proud of what we accomplished.\",\n", - " \"I'm hopeful about the future.\",\n", - " \"I'm content with how things are going.\"\n", + ' "I\'m feeling really happy today!",\n', + ' "This is so frustrating, nothing works.",\n', + ' "I\'m anxious about the presentation.",\n', + ' "I\'m grateful for all the support.",\n', + ' "I\'m tired and need some rest.",\n', + ' "I\'m proud of what we accomplished.",\n', + ' "I\'m hopeful about the future.",\n', + ' "I\'m content with how things are going."\n', "]\n", "\n", "model.eval()\n", "with torch.no_grad():\n", " for text in test_texts:\n", - " inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, max_length=128)\n", + ' inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)\n', " outputs = model(**inputs)\n", " probs = torch.softmax(outputs.logits, dim=1)\n", " predicted_label = torch.argmax(probs, dim=1).item()\n", " confidence = torch.max(probs).item()\n", " \n", " predicted_emotion = label_encoder.inverse_transform([predicted_label])[0]\n", - " print(f\"Text: {text}\")\n", - " print(f\"Predicted: {predicted_emotion} (confidence: {confidence:.3f})\")\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ’พ Save Model" - ] + ' print(f"Text: {text}")\n', + ' print(f"Predicted: {predicted_emotion} (confidence: {confidence:.3f})")\n', + " print()", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ’พ Save Model"]}, { "cell_type": "code", "execution_count": None, @@ -400,79 +369,67 @@ def create_colab_notebook(): "outputs": [], "source": [ "# Save model\n", - "trainer.save_model(\"./emotion_model_final_combined\")\n", - "print(\"๐Ÿ’พ Model saved to ./emotion_model_final_combined\")\n", + 'trainer.save_model("./emotion_model_final_combined")\n', + 'print("๐Ÿ’พ Model saved to ./emotion_model_final_combined")\n', "\n", "# Save label encoder\n", "import pickle\n", "with open('./emotion_model_final_combined/label_encoder.pkl', 'wb') as f:\n", " pickle.dump(label_encoder, f)\n", - "print(\"๐Ÿ’พ Label encoder saved!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐ŸŽ‰ Final Summary" - ] + 'print("๐Ÿ’พ Label encoder saved!")', + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐ŸŽ‰ Final Summary"]}, { "cell_type": "code", "execution_count": None, "metadata": {}, "outputs": [], "source": [ - "print(\"๐ŸŽ‰ TRAINING COMPLETED!\")\n", - "print(\"=\" * 50)\n", + 'print("๐ŸŽ‰ TRAINING COMPLETED!")\n', + 'print("=" * 50)\n', "print(f\"๐Ÿ“ˆ Final F1 Score: {results['eval_f1']*100:.2f}%\")\n", - "print(f\"๐ŸŽฏ Target: 75-85%\")\n", - "print(f\"๐Ÿ“Š Improvement: {improvement:.1f}% from baseline\")\n", - "print(f\"๐Ÿ“ˆ Training samples: {len(train_texts)}\")\n", - "print(f\"๐Ÿงช Test samples: {len(test_labels)}\")\n", - "print(f\"๐ŸŽฏ Emotions: {len(label_encoder.classes_)}\")\n", + 'print(f"๐ŸŽฏ Target: 75-85%")\n', + 'print(f"๐Ÿ“Š Improvement: {improvement:.1f}% from baseline")\n', + 'print(f"๐Ÿ“ˆ Training samples: {len(train_texts)}")\n', + 'print(f"๐Ÿงช Test samples: {len(test_labels)}")\n', + 'print(f"๐ŸŽฏ Emotions: {len(label_encoder.classes_)}")\n', "print()\n", - "print(\"โœ… Model saved and ready for deployment!\")\n", - "print(\"โœ… Target achieved: {'YES!' if results['eval_f1'] >= 0.75 else 'Not yet, but close!'}\")" - ] - } + 'print("โœ… Model saved and ready for deployment!")\n', + "print(\"โœ… Target achieved: {'YES!' if results['eval_f1'] >= 0.75 else 'Not yet, but close!'}\")", + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - + return notebook_content + def main(): """Create the notebook file""" print("๐Ÿš€ Creating final Colab notebook...") - + notebook_content = create_colab_notebook() - + # Save to file output_file = "notebooks/FINAL_COMBINED_TRAINING_COLAB.ipynb" - with open(output_file, 'w') as f: + with open(output_file, "w") as f: json.dump(notebook_content, f, indent=2) - + print(f"โœ… Notebook created: {output_file}") print("๐Ÿ“‹ Instructions:") print(" 1. Download the notebook file") @@ -481,5 +438,6 @@ def main(): print(" 4. Run all cells") print(" 5. Expect 75-85% F1 score!") + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/training/create_fixed_bulletproof_notebook.py b/scripts/training/create_fixed_bulletproof_notebook.py index 219cd8c78..699188f55 100644 --- a/scripts/training/create_fixed_bulletproof_notebook.py +++ b/scripts/training/create_fixed_bulletproof_notebook.py @@ -8,9 +8,10 @@ import json + def create_fixed_bulletproof_notebook(): """Create the fixed bulletproof notebook content""" - + notebook_content = { "cells": [ { @@ -27,8 +28,8 @@ def create_fixed_bulletproof_notebook(): "- Original 150 high-quality journal samples\n", "- CMU-MOSEI samples for diversity\n", "- **UNIQUE** fallback dataset (144 samples, no duplicates)\n", - "- Optimized hyperparameters for 75-85% F1" - ] + "- Optimized hyperparameters for 75-85% F1", + ], }, { "cell_type": "code", @@ -37,37 +38,37 @@ def create_fixed_bulletproof_notebook(): "outputs": [], "source": [ "# Install required packages\n", - "!pip install transformers torch scikit-learn numpy pandas" - ] + "!pip install transformers torch scikit-learn numpy pandas", + ], }, { "cell_type": "code", - "execution_count": None, - "metadata": {}, - "outputs": [], - "source": [ - "# Import libraries\n", - "import json\n", - "import pandas as pd\n", - "import numpy as np\n", - "import torch\n", - "from torch.utils.data import Dataset, DataLoader\n", - "from transformers import (\n", - " AutoTokenizer,\n", - " AutoModelForSequenceClassification,\n", - " TrainingArguments,\n", - " Trainer,\n", - " EarlyStoppingCallback\n", - ")\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from sklearn.metrics import f1_score, accuracy_score, classification_report\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "print('๐Ÿš€ FIXED BULLETPROOF TRAINING - UNIQUE DATASET')\n", - "print('=' * 60)" - ] + "execution_count": None, + "metadata": {}, + "outputs": [], + "source": [ + "# Import libraries\n", + "import json\n", + "import pandas as pd\n", + "import numpy as np\n", + "import torch\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from transformers import (\n", + " AutoTokenizer,\n", + " AutoModelForSequenceClassification,\n", + " TrainingArguments,\n", + " Trainer,\n", + " EarlyStoppingCallback\n", + ")\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.metrics import f1_score, accuracy_score, classification_report\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "print('๐Ÿš€ FIXED BULLETPROOF TRAINING - UNIQUE DATASET')\n", + "print('=' * 60)", + ], }, { "cell_type": "code", @@ -108,8 +109,8 @@ def create_fixed_bulletproof_notebook(): "\n", "print(f'โœ… Data directory found: {data_path}')\n", "print('๐Ÿ“‚ Listing data files:')\n", - "!ls -la {data_path}/" - ] + "!ls -la {data_path}/", + ], }, { "cell_type": "code", @@ -175,8 +176,8 @@ def create_fixed_bulletproof_notebook(): "if len(texts) != len(unique_texts):\n", " print('โŒ WARNING: DUPLICATES FOUND! This will cause model collapse!')\n", "else:\n", - " print('โœ… All samples are unique - no model collapse risk!')" - ] + " print('โœ… All samples are unique - no model collapse risk!')", + ], }, { "cell_type": "code", @@ -212,8 +213,8 @@ def create_fixed_bulletproof_notebook(): "\n", "print('\\n๐Ÿ“Š Emotion Distribution:')\n", "for emotion, count in sorted(emotion_counts.items()):\n", - " print(f' {emotion}: {count} samples')" - ] + " print(f' {emotion}: {count} samples')", + ], }, { "cell_type": "code", @@ -248,8 +249,8 @@ def create_fixed_bulletproof_notebook(): " 'input_ids': encoding['input_ids'].flatten(),\n", " 'attention_mask': encoding['attention_mask'].flatten(),\n", " 'labels': torch.tensor(label, dtype=torch.long)\n", - " }" - ] + " }", + ], }, { "cell_type": "code", @@ -274,8 +275,8 @@ def create_fixed_bulletproof_notebook(): "train_dataset = EmotionDataset(train_texts, train_labels, tokenizer)\n", "test_dataset = EmotionDataset(test_texts, test_labels, tokenizer)\n", "\n", - "print('โœ… Datasets created successfully')" - ] + "print('โœ… Datasets created successfully')", + ], }, { "cell_type": "code", @@ -291,8 +292,8 @@ def create_fixed_bulletproof_notebook(): " f1 = f1_score(labels, predictions, average='weighted')\n", " accuracy = accuracy_score(labels, predictions)\n", " \n", - " return {'f1': f1, 'accuracy': accuracy}" - ] + " return {'f1': f1, 'accuracy': accuracy}", + ], }, { "cell_type": "code", @@ -344,8 +345,8 @@ def create_fixed_bulletproof_notebook(): "print(f'๐Ÿงช Evaluating on {len(test_labels)} samples')\n", "\n", "# Start training\n", - "trainer.train()" - ] + "trainer.train()", + ], }, { "cell_type": "code", @@ -357,13 +358,13 @@ def create_fixed_bulletproof_notebook(): "print('๐Ÿ“Š Evaluating final model...')\n", "results = trainer.evaluate()\n", "\n", - "print(f'๐Ÿ† Final F1 Score: {results[\"eval_f1\"]:.4f} ({results[\"eval_f1\"]*100:.2f}%)')\n", - "print(f'๐ŸŽฏ Target achieved: {\"โœ… YES!\" if results[\"eval_f1\"] >= 0.75 else \"โŒ Not yet\"}')\n", + 'print(f\'๐Ÿ† Final F1 Score: {results["eval_f1"]:.4f} ({results["eval_f1"]*100:.2f}%)\')\n', + 'print(f\'๐ŸŽฏ Target achieved: {"โœ… YES!" if results["eval_f1"] >= 0.75 else "โŒ Not yet"}\')\n', "\n", "# Save model\n", "trainer.save_model('./emotion_model_fixed_bulletproof_final')\n", - "print('๐Ÿ’พ Model saved to ./emotion_model_fixed_bulletproof_final')" - ] + "print('๐Ÿ’พ Model saved to ./emotion_model_fixed_bulletproof_final')", + ], }, { "cell_type": "code", @@ -375,11 +376,11 @@ def create_fixed_bulletproof_notebook(): "print('๐Ÿงช Testing on sample texts...')\n", "\n", "test_texts = [\n", - " \"I'm feeling really happy today!\",\n", - " \"I'm so frustrated with this project.\",\n", - " \"I feel anxious about the presentation.\",\n", - " \"I'm grateful for all the support.\",\n", - " \"I'm feeling overwhelmed with tasks.\"\n", + ' "I\'m feeling really happy today!",\n', + ' "I\'m so frustrated with this project.",\n', + ' "I feel anxious about the presentation.",\n', + ' "I\'m grateful for all the support.",\n', + ' "I\'m feeling overwhelmed with tasks."\n', "]\n", "\n", "model.eval()\n", @@ -400,8 +401,8 @@ def create_fixed_bulletproof_notebook(): " predicted_emotion = label_encoder.inverse_transform([predicted_class])[0]\n", " \n", " print(f'{i}. Text: {text}')\n", - " print(f' Predicted: {predicted_emotion} (confidence: {confidence:.3f})\\n')" - ] + " print(f' Predicted: {predicted_emotion} (confidence: {confidence:.3f})\\n')", + ], }, { "cell_type": "markdown", @@ -424,37 +425,32 @@ def create_fixed_bulletproof_notebook(): "**Next Steps:**\n", "1. Review the F1 score achieved\n", "2. If below 75%, consider adding more real data\n", - "3. Fine-tune hyperparameters if needed" - ] - } + "3. Fine-tune hyperparameters if needed", + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - - with open('notebooks/FIXED_BULLETPROOF_COMBINED_TRAINING_COLAB.ipynb', 'w') as f: + + with open("notebooks/FIXED_BULLETPROOF_COMBINED_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook_content, f, indent=2) - - print("โœ… Fixed bulletproof notebook created: notebooks/FIXED_BULLETPROOF_COMBINED_TRAINING_COLAB.ipynb") + + print( + "โœ… Fixed bulletproof notebook created: notebooks/FIXED_BULLETPROOF_COMBINED_TRAINING_COLAB.ipynb" + ) print("๐Ÿ“‹ Instructions:") print(" 1. Download the notebook file") print(" 2. Upload to Google Colab") @@ -467,5 +463,6 @@ def create_fixed_bulletproof_notebook(): print(" - Optimized hyperparameters") print(" - Robust error handling") + if __name__ == "__main__": - create_fixed_bulletproof_notebook() \ No newline at end of file + create_fixed_bulletproof_notebook() diff --git a/scripts/training/create_fixed_colab_notebook.py b/scripts/training/create_fixed_colab_notebook.py index f30f8ddca..d8ca9826c 100644 --- a/scripts/training/create_fixed_colab_notebook.py +++ b/scripts/training/create_fixed_colab_notebook.py @@ -8,9 +8,10 @@ import json + def create_fixed_colab_notebook(): """Create the fixed Colab notebook content""" - + notebook_content = { "cells": [ { @@ -28,8 +29,8 @@ def create_fixed_colab_notebook(): "- CMU-MOSEI samples for diversity\n", "- Optimized hyperparameters for 75-85% F1\n", "\n", - "**FIXED**: Correct data loading for journal content field" - ] + "**FIXED**: Correct data loading for journal content field", + ], }, { "cell_type": "code", @@ -39,8 +40,8 @@ def create_fixed_colab_notebook(): "source": [ "# Install dependencies\n", "!pip install transformers torch scikit-learn pandas numpy\n", - "print(\"โœ… All dependencies installed!\")" - ] + 'print("โœ… All dependencies installed!")', + ], }, { "cell_type": "code", @@ -50,8 +51,8 @@ def create_fixed_colab_notebook(): "source": [ "# Clone repository\n", "!git clone https://github.com/uelkerd/SAMO--DL.git\n", - "print(\"๐Ÿ“‚ Repository cloned successfully!\")" - ] + 'print("๐Ÿ“‚ Repository cloned successfully!")', + ], }, { "cell_type": "code", @@ -78,8 +79,8 @@ def create_fixed_colab_notebook(): "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", - "print(\"โœ… All libraries imported!\")" - ] + 'print("โœ… All libraries imported!")', + ], }, { "cell_type": "code", @@ -88,7 +89,7 @@ def create_fixed_colab_notebook(): "outputs": [], "source": [ "# FIXED: Load combined dataset with correct field names\n", - "print(\"๐Ÿ“Š Loading combined dataset...\")\n", + 'print("๐Ÿ“Š Loading combined dataset...")\n', "\n", "combined_samples = []\n", "\n", @@ -102,9 +103,9 @@ def create_fixed_colab_notebook(): " 'text': item['content'], # FIXED: use 'content' not 'text'\n", " 'emotion': item['emotion']\n", " })\n", - " print(f\"โœ… Loaded {len(journal_data)} journal samples\")\n", + ' print(f"โœ… Loaded {len(journal_data)} journal samples")\n', "except Exception as e:\n", - " print(f\"โš ๏ธ Could not load journal data: {e}\")\n", + ' print(f"โš ๏ธ Could not load journal data: {e}")\n', "\n", "# Load CMU-MOSEI data (uses 'text' field)\n", "try:\n", @@ -116,11 +117,11 @@ def create_fixed_colab_notebook(): " 'text': item['text'], # CMU-MOSEI uses 'text' field\n", " 'emotion': item['emotion']\n", " })\n", - " print(f\"โœ… Loaded {len(cmu_data)} CMU-MOSEI samples\")\n", + ' print(f"โœ… Loaded {len(cmu_data)} CMU-MOSEI samples")\n', "except Exception as e:\n", - " print(f\"โš ๏ธ Could not load CMU-MOSEI data: {e}\")\n", + ' print(f"โš ๏ธ Could not load CMU-MOSEI data: {e}")\n', "\n", - "print(f\"๐Ÿ“Š Total combined samples: {len(combined_samples)}\")\n", + 'print(f"๐Ÿ“Š Total combined samples: {len(combined_samples)}")\n', "\n", "# Show emotion distribution\n", "if combined_samples:\n", @@ -129,12 +130,12 @@ def create_fixed_colab_notebook(): " emotion = sample['emotion']\n", " emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1\n", " \n", - " print(\"๐Ÿ“Š Emotion distribution:\")\n", + ' print("๐Ÿ“Š Emotion distribution:")\n', " for emotion, count in sorted(emotion_counts.items()):\n", - " print(f\" {emotion}: {count} samples\")\n", + ' print(f" {emotion}: {count} samples")\n', "else:\n", - " print(\"โŒ No data loaded! Check file paths.\")" - ] + ' print("โŒ No data loaded! Check file paths.")', + ], }, { "cell_type": "code", @@ -144,28 +145,28 @@ def create_fixed_colab_notebook(): "source": [ "# Check if we have data\n", "if len(combined_samples) == 0:\n", - " print(\"โŒ No data loaded! Creating fallback dataset...\")\n", + ' print("โŒ No data loaded! Creating fallback dataset...")\n', " \n", " # Create minimal fallback dataset\n", " fallback_samples = [\n", - " {\"text\": \"I'm feeling happy today!\", \"emotion\": \"happy\"},\n", - " {\"text\": \"I'm so frustrated with this project.\", \"emotion\": \"frustrated\"},\n", - " {\"text\": \"I feel anxious about the presentation.\", \"emotion\": \"anxious\"},\n", - " {\"text\": \"I'm grateful for all the support.\", \"emotion\": \"grateful\"},\n", - " {\"text\": \"I'm feeling overwhelmed with tasks.\", \"emotion\": \"overwhelmed\"},\n", - " {\"text\": \"I'm proud of what I accomplished.\", \"emotion\": \"proud\"},\n", - " {\"text\": \"I'm feeling sad and lonely.\", \"emotion\": \"sad\"},\n", - " {\"text\": \"I'm excited about new opportunities.\", \"emotion\": \"excited\"},\n", - " {\"text\": \"I feel calm and peaceful.\", \"emotion\": \"calm\"},\n", - " {\"text\": \"I'm hopeful things will get better.\", \"emotion\": \"hopeful\"},\n", - " {\"text\": \"I'm tired and need rest.\", \"emotion\": \"tired\"},\n", - " {\"text\": \"I'm content with how things are.\", \"emotion\": \"content\"}\n", + ' {"text": "I\'m feeling happy today!", "emotion": "happy"},\n', + ' {"text": "I\'m so frustrated with this project.", "emotion": "frustrated"},\n', + ' {"text": "I feel anxious about the presentation.", "emotion": "anxious"},\n', + ' {"text": "I\'m grateful for all the support.", "emotion": "grateful"},\n', + ' {"text": "I\'m feeling overwhelmed with tasks.", "emotion": "overwhelmed"},\n', + ' {"text": "I\'m proud of what I accomplished.", "emotion": "proud"},\n', + ' {"text": "I\'m feeling sad and lonely.", "emotion": "sad"},\n', + ' {"text": "I\'m excited about new opportunities.", "emotion": "excited"},\n', + ' {"text": "I feel calm and peaceful.", "emotion": "calm"},\n', + ' {"text": "I\'m hopeful things will get better.", "emotion": "hopeful"},\n', + ' {"text": "I\'m tired and need rest.", "emotion": "tired"},\n', + ' {"text": "I\'m content with how things are.", "emotion": "content"}\n', " ]\n", " combined_samples = fallback_samples\n", - " print(f\"โœ… Created {len(combined_samples)} fallback samples\")\n", + ' print(f"โœ… Created {len(combined_samples)} fallback samples")\n', "\n", - "print(f\"๐Ÿ“Š Final dataset size: {len(combined_samples)} samples\")" - ] + 'print(f"๐Ÿ“Š Final dataset size: {len(combined_samples)} samples")', + ], }, { "cell_type": "code", @@ -200,8 +201,8 @@ def create_fixed_colab_notebook(): " 'input_ids': encoding['input_ids'].flatten(),\n", " 'attention_mask': encoding['attention_mask'].flatten(),\n", " 'labels': torch.tensor(label, dtype=torch.long)\n", - " }" - ] + " }", + ], }, { "cell_type": "code", @@ -217,17 +218,17 @@ def create_fixed_colab_notebook(): "label_encoder = LabelEncoder()\n", "labels = label_encoder.fit_transform(emotions)\n", "\n", - "print(f\"๐ŸŽฏ Number of labels: {len(label_encoder.classes_)}\")\n", - "print(f\"๐Ÿ“Š Labels: {list(label_encoder.classes_)}\")\n", + 'print(f"๐ŸŽฏ Number of labels: {len(label_encoder.classes_)}")\n', + 'print(f"๐Ÿ“Š Labels: {list(label_encoder.classes_)}")\n', "\n", "# Split data\n", "train_texts, test_texts, train_labels, test_labels = train_test_split(\n", " texts, labels, test_size=0.2, random_state=42, stratify=labels\n", ")\n", "\n", - "print(f\"๐Ÿ“ˆ Training samples: {len(train_texts)}\")\n", - "print(f\"๐Ÿงช Test samples: {len(test_labels)}\")" - ] + 'print(f"๐Ÿ“ˆ Training samples: {len(train_texts)}")\n', + 'print(f"๐Ÿงช Test samples: {len(test_labels)}")', + ], }, { "cell_type": "code", @@ -236,17 +237,17 @@ def create_fixed_colab_notebook(): "outputs": [], "source": [ "# Load model and tokenizer\n", - "model_name = \"bert-base-uncased\"\n", + 'model_name = "bert-base-uncased"\n', "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "model = AutoModelForSequenceClassification.from_pretrained(\n", " model_name, \n", " num_labels=len(label_encoder.classes_),\n", - " problem_type=\"single_label_classification\"\n", + ' problem_type="single_label_classification"\n', ")\n", "\n", - "print(f\"โœ… Model loaded: {model_name}\")\n", - "print(f\"๐Ÿ“Š Number of classes: {len(label_encoder.classes_)}\")" - ] + 'print(f"โœ… Model loaded: {model_name}")\n', + 'print(f"๐Ÿ“Š Number of classes: {len(label_encoder.classes_)}")', + ], }, { "cell_type": "code", @@ -258,10 +259,10 @@ def create_fixed_colab_notebook(): "train_dataset = EmotionDataset(train_texts, train_labels, tokenizer)\n", "test_dataset = EmotionDataset(test_texts, test_labels, tokenizer)\n", "\n", - "print(f\"โœ… Datasets created\")\n", - "print(f\"๐Ÿ“ˆ Train dataset: {len(train_dataset)} samples\")\n", - "print(f\"๐Ÿงช Test dataset: {len(test_dataset)} samples\")" - ] + 'print(f"โœ… Datasets created")\n', + 'print(f"๐Ÿ“ˆ Train dataset: {len(train_dataset)} samples")\n', + 'print(f"๐Ÿงช Test dataset: {len(test_dataset)} samples")', + ], }, { "cell_type": "code", @@ -277,8 +278,8 @@ def create_fixed_colab_notebook(): " f1 = f1_score(labels, predictions, average='weighted')\n", " accuracy = accuracy_score(labels, predictions)\n", " \n", - " return {'f1': f1, 'accuracy': accuracy}" - ] + " return {'f1': f1, 'accuracy': accuracy}", + ], }, { "cell_type": "code", @@ -288,20 +289,20 @@ def create_fixed_colab_notebook(): "source": [ "# Training arguments\n", "training_args = TrainingArguments(\n", - " output_dir=\"./emotion_model_combined\",\n", + ' output_dir="./emotion_model_combined",\n', " num_train_epochs=8,\n", " per_device_train_batch_size=16,\n", " per_device_eval_batch_size=16,\n", " warmup_steps=500,\n", " weight_decay=0.01,\n", - " logging_dir=\"./logs\",\n", + ' logging_dir="./logs",\n', " logging_steps=50,\n", - " eval_strategy=\"steps\",\n", + ' eval_strategy="steps",\n', " eval_steps=100,\n", - " save_strategy=\"steps\",\n", + ' save_strategy="steps",\n', " save_steps=100,\n", " load_best_model_at_end=True,\n", - " metric_for_best_model=\"f1\",\n", + ' metric_for_best_model="f1",\n', " greater_is_better=True,\n", " dataloader_num_workers=2,\n", " remove_unused_columns=False,\n", @@ -310,8 +311,8 @@ def create_fixed_colab_notebook(): " gradient_accumulation_steps=2,\n", ")\n", "\n", - "print(\"โœ… Training arguments configured\")" - ] + 'print("โœ… Training arguments configured")', + ], }, { "cell_type": "code", @@ -329,8 +330,8 @@ def create_fixed_colab_notebook(): " callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]\n", ")\n", "\n", - "print(\"โœ… Trainer created with early stopping\")" - ] + 'print("โœ… Trainer created with early stopping")', + ], }, { "cell_type": "code", @@ -339,15 +340,15 @@ def create_fixed_colab_notebook(): "outputs": [], "source": [ "# Start training\n", - "print(\"๐Ÿš€ Starting training...\")\n", - "print(\"๐ŸŽฏ Target F1 Score: 75-85%\")\n", - "print(\"๐Ÿ“Š Current Best: 67%\")\n", - "print(\"๐Ÿ“ˆ Expected Improvement: 8-18%\")\n", + 'print("๐Ÿš€ Starting training...")\n', + 'print("๐ŸŽฏ Target F1 Score: 75-85%")\n', + 'print("๐Ÿ“Š Current Best: 67%")\n', + 'print("๐Ÿ“ˆ Expected Improvement: 8-18%")\n', "\n", "trainer.train()\n", "\n", - "print(\"โœ… Training completed!\")" - ] + 'print("โœ… Training completed!")', + ], }, { "cell_type": "code", @@ -356,16 +357,16 @@ def create_fixed_colab_notebook(): "outputs": [], "source": [ "# Evaluate final model\n", - "print(\"๐Ÿ“Š Evaluating final model...\")\n", + 'print("๐Ÿ“Š Evaluating final model...")\n', "results = trainer.evaluate()\n", "\n", "print(f\"๐Ÿ† Final F1 Score: {results['eval_f1']:.4f} ({results['eval_f1']*100:.2f}%)\")\n", "print(f\"๐ŸŽฏ Target achieved: {'โœ… YES!' if results['eval_f1'] >= 0.75 else 'โŒ Not yet'}\")\n", "\n", "# Save model\n", - "trainer.save_model(\"./emotion_model_final_combined\")\n", - "print(\"๐Ÿ’พ Model saved to ./emotion_model_final_combined\")" - ] + 'trainer.save_model("./emotion_model_final_combined")\n', + 'print("๐Ÿ’พ Model saved to ./emotion_model_final_combined")', + ], }, { "cell_type": "code", @@ -374,30 +375,30 @@ def create_fixed_colab_notebook(): "outputs": [], "source": [ "# Test on sample texts\n", - "print(\"๐Ÿงช Testing on sample texts...\")\n", + 'print("๐Ÿงช Testing on sample texts...")\n', "\n", "test_texts = [\n", - " \"I'm feeling really happy today!\",\n", - " \"I'm so frustrated with this project.\",\n", - " \"I feel anxious about the presentation.\",\n", - " \"I'm grateful for all the support.\",\n", - " \"I'm feeling overwhelmed with tasks.\"\n", + ' "I\'m feeling really happy today!",\n', + ' "I\'m so frustrated with this project.",\n', + ' "I feel anxious about the presentation.",\n', + ' "I\'m grateful for all the support.",\n', + ' "I\'m feeling overwhelmed with tasks."\n', "]\n", "\n", "model.eval()\n", "with torch.no_grad():\n", " for i, text in enumerate(test_texts, 1):\n", - " inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True)\n", + ' inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)\n', " outputs = model(**inputs)\n", " probabilities = torch.softmax(outputs.logits, dim=1)\n", " predicted_class = torch.argmax(probabilities, dim=1).item()\n", " confidence = probabilities[0][predicted_class].item()\n", " predicted_emotion = label_encoder.classes_[predicted_class]\n", " \n", - " print(f\"{i}. Text: {text}\")\n", - " print(f\" Predicted: {predicted_emotion} (confidence: {confidence:.3f})\")\n", - " print()" - ] + ' print(f"{i}. Text: {text}")\n', + ' print(f" Predicted: {predicted_emotion} (confidence: {confidence:.3f})")\n', + " print()", + ], }, { "cell_type": "markdown", @@ -413,37 +414,30 @@ def create_fixed_colab_notebook(): "**Next Steps:**\n", "1. If F1 < 75%: Try different hyperparameters or more data\n", "2. If F1 >= 75%: Model is ready for production!\n", - "3. Download the saved model from `./emotion_model_final_combined`" - ] - } + "3. Download the saved model from `./emotion_model_final_combined`", + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - + # Write notebook to file - with open('notebooks/FIXED_COMBINED_TRAINING_COLAB.ipynb', 'w') as f: + with open("notebooks/FIXED_COMBINED_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook_content, f, indent=2) - + print("โœ… Fixed notebook created: notebooks/FIXED_COMBINED_TRAINING_COLAB.ipynb") print("๐Ÿ“‹ Instructions:") print(" 1. Download the notebook file") @@ -452,5 +446,6 @@ def create_fixed_colab_notebook(): print(" 4. Run all cells") print(" 5. Expect 75-85% F1 score!") + if __name__ == "__main__": - create_fixed_colab_notebook() \ No newline at end of file + create_fixed_colab_notebook() diff --git a/scripts/training/create_fixed_notebook.py b/scripts/training/create_fixed_notebook.py index db7a1502e..130433c8a 100644 --- a/scripts/training/create_fixed_notebook.py +++ b/scripts/training/create_fixed_notebook.py @@ -9,9 +9,10 @@ import json from pathlib import Path + def create_fixed_notebook(): """Create a fixed notebook with proper JSON escaping""" - + # Create the notebook structure notebook = { "cells": [ @@ -25,8 +26,8 @@ def create_fixed_notebook(): "**CRITICAL**: This notebook ensures we use the correct specialized emotion model\n", "and verifies it's working properly before training.\n", "\n", - "**Target**: Reliable 75-85% F1 score with proper emotion-specialized model" - ] + "**Target**: Reliable 75-85% F1 score with proper emotion-specialized model", + ], }, { "cell_type": "code", @@ -35,8 +36,8 @@ def create_fixed_notebook(): "outputs": [], "source": [ "# Install required packages\n", - "!pip install transformers datasets torch scikit-learn numpy pandas huggingface_hub" - ] + "!pip install transformers datasets torch scikit-learn numpy pandas huggingface_hub", + ], }, { "cell_type": "code", @@ -55,8 +56,8 @@ def create_fixed_notebook(): "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", - "print('โœ… Packages imported successfully')" - ] + "print('โœ… Packages imported successfully')", + ], }, { "cell_type": "code", @@ -95,8 +96,8 @@ def create_fixed_notebook(): " specialized_model_name = 'roberta-base'\n", " test_tokenizer = AutoTokenizer.from_pretrained(specialized_model_name)\n", " test_model = AutoModelForSequenceClassification.from_pretrained(specialized_model_name, num_labels=12)\n", - " print(f'โœ… Fallback model loaded: {specialized_model_name}')" - ] + " print(f'โœ… Fallback model loaded: {specialized_model_name}')", + ], }, { "cell_type": "code", @@ -107,8 +108,8 @@ def create_fixed_notebook(): "# Define our emotion classes\n", "emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired']\n", "print(f'๐ŸŽฏ Our emotion classes: {emotions}')\n", - "print(f'๐Ÿ“Š Number of emotions: {len(emotions)}')" - ] + "print(f'๐Ÿ“Š Number of emotions: {len(emotions)}')", + ], }, { "cell_type": "code", @@ -301,8 +302,8 @@ def create_fixed_notebook(): "\n", "print('\\n๐Ÿ“ˆ Emotion distribution:')\n", "for emotion, count in emotion_counts.items():\n", - " print(f' {emotion}: {count} samples')" - ] + " print(f' {emotion}: {count} samples')", + ], }, { "cell_type": "code", @@ -323,8 +324,8 @@ def create_fixed_notebook(): "train_dataset = Dataset.from_list(train_data)\n", "val_dataset = Dataset.from_list(val_data)\n", "\n", - "print('โœ… Datasets created successfully')" - ] + "print('โœ… Datasets created successfully')", + ], }, { "cell_type": "code", @@ -355,8 +356,8 @@ def create_fixed_notebook(): "print(f'Hidden layers: {model.config.num_hidden_layers}')\n", "print(f'Hidden size: {model.config.hidden_size}')\n", "print(f'Number of labels: {model.config.num_labels}')\n", - "print(f'Our labels: {model.config.id2label}')" - ] + "print(f'Our labels: {model.config.id2label}')", + ], }, { "cell_type": "code", @@ -371,8 +372,8 @@ def create_fixed_notebook(): "train_dataset = train_dataset.map(tokenize_function, batched=True)\n", "val_dataset = val_dataset.map(tokenize_function, batched=True)\n", "\n", - "print('โœ… Data tokenized successfully')" - ] + "print('โœ… Data tokenized successfully')", + ], }, { "cell_type": "code", @@ -405,8 +406,8 @@ def create_fixed_notebook(): " save_total_limit=3 # Keep only best 3 checkpoints\n", ")\n", "\n", - "print('โœ… Training arguments configured')" - ] + "print('โœ… Training arguments configured')", + ], }, { "cell_type": "code", @@ -427,8 +428,8 @@ def create_fixed_notebook(): " 'accuracy': report['accuracy'],\n", " 'precision': report['weighted avg']['precision'],\n", " 'recall': report['weighted avg']['recall']\n", - " }" - ] + " }", + ], }, { "cell_type": "code", @@ -445,8 +446,8 @@ def create_fixed_notebook(): " compute_metrics=compute_metrics\n", ")\n", "\n", - "print('โœ… Trainer initialized successfully')" - ] + "print('โœ… Trainer initialized successfully')", + ], }, { "cell_type": "code", @@ -464,8 +465,8 @@ def create_fixed_notebook(): "\n", "trainer.train()\n", "\n", - "print('โœ… Training completed successfully')" - ] + "print('โœ… Training completed successfully')", + ], }, { "cell_type": "code", @@ -481,8 +482,8 @@ def create_fixed_notebook(): "print(f'Final F1 Score: {results[\"eval_f1\"]:.3f}')\n", "print(f'Final Accuracy: {results[\"eval_accuracy\"]:.3f}')\n", "print(f'Final Precision: {results[\"eval_precision\"]:.3f}')\n", - "print(f'Final Recall: {results[\"eval_recall\"]:.3f}')" - ] + "print(f'Final Recall: {results[\"eval_recall\"]:.3f}')", + ], }, { "cell_type": "code", @@ -558,8 +559,8 @@ def create_fixed_notebook(): " if accuracy < 0.8:\n", " print(f'โŒ Accuracy too low: {accuracy:.1%} (need >80%)')\n", " if max_bias > 0.3:\n", - " print(f'โŒ Too much bias: {max_bias:.1%} (need <30%)')" - ] + " print(f'โŒ Too much bias: {max_bias:.1%} (need <30%)')", + ], }, { "cell_type": "code", @@ -597,38 +598,31 @@ def create_fixed_notebook(): "print('\\n๐Ÿ“‹ Next steps:')\n", "print('1. Download the model files')\n", "print('2. Test locally with validation script')\n", - "print('3. Deploy if all tests pass')" - ] - } + "print('3. Deploy if all tests pass')", + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - + # Save the notebook with proper JSON formatting - notebook_path = Path(__file__).parent.parent / 'notebooks' / 'FIXED_SPECIALIZED_TRAINING.ipynb' - with open(notebook_path, 'w') as f: + notebook_path = Path(__file__).parent.parent / "notebooks" / "FIXED_SPECIALIZED_TRAINING.ipynb" + with open(notebook_path, "w") as f: json.dump(notebook, f, indent=1) - + print(f"โœ… Created fixed specialized notebook: {notebook_path}") print(f"๐Ÿ“‹ Key improvements:") print(f" 1. Proper JSON formatting (no syntax errors)") @@ -644,6 +638,7 @@ def create_fixed_notebook(): print(f" 5. Verify the model is actually using the specialized architecture") print(f" 6. Only deploy if reliability tests pass") + if __name__ == "__main__": create_fixed_notebook() - print("โœ… Fixed specialized notebook created successfully!") \ No newline at end of file + print("โœ… Fixed specialized notebook created successfully!") diff --git a/scripts/training/create_fixed_specialized_training_notebook.py b/scripts/training/create_fixed_specialized_training_notebook.py index 874bdccfe..83ba39777 100644 --- a/scripts/training/create_fixed_specialized_training_notebook.py +++ b/scripts/training/create_fixed_specialized_training_notebook.py @@ -12,9 +12,10 @@ import json + def create_fixed_notebook(): """Create a corrected training notebook with proper configuration preservation.""" - + notebook_content = { "cells": [ { @@ -27,8 +28,8 @@ def create_fixed_notebook(): "**CRITICAL FIX**: This notebook ensures emotion label mappings are properly preserved\n", "in the saved model configuration to prevent the 8.3% vs 75% performance discrepancy.\n", "\n", - "**Target**: Reliable 75-85% F1 score with consistent performance between Colab and local deployment" - ] + "**Target**: Reliable 75-85% F1 score with consistent performance between Colab and local deployment", + ], }, { "cell_type": "code", @@ -37,8 +38,8 @@ def create_fixed_notebook(): "outputs": [], "source": [ "# Install required packages\n", - "!pip install transformers datasets torch scikit-learn numpy pandas huggingface_hub" - ] + "!pip install transformers datasets torch scikit-learn numpy pandas huggingface_hub", + ], }, { "cell_type": "code", @@ -57,8 +58,8 @@ def create_fixed_notebook(): "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", - "print('โœ… Packages imported successfully')" - ] + "print('โœ… Packages imported successfully')", + ], }, { "cell_type": "code", @@ -97,8 +98,8 @@ def create_fixed_notebook(): " specialized_model_name = 'roberta-base'\n", " test_tokenizer = AutoTokenizer.from_pretrained(specialized_model_name)\n", " test_model = AutoModelForSequenceClassification.from_pretrained(specialized_model_name, num_labels=12)\n", - " print(f'โœ… Fallback model loaded: {specialized_model_name}')" - ] + " print(f'โœ… Fallback model loaded: {specialized_model_name}')", + ], }, { "cell_type": "code", @@ -109,8 +110,8 @@ def create_fixed_notebook(): "# Define our emotion classes\n", "emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired']\n", "print(f'๐ŸŽฏ Our emotion classes: {emotions}')\n", - "print(f'๐Ÿ“Š Number of emotions: {len(emotions)}')" - ] + "print(f'๐Ÿ“Š Number of emotions: {len(emotions)}')", + ], }, { "cell_type": "code", @@ -303,8 +304,8 @@ def create_fixed_notebook(): "val_dataset = Dataset.from_pandas(val_data)\n", "\n", "print(f'โœ… Training samples: {len(train_data)}')\n", - "print(f'โœ… Validation samples: {len(val_data)}')" - ] + "print(f'โœ… Validation samples: {len(val_data)}')", + ], }, { "cell_type": "code", @@ -348,8 +349,8 @@ def create_fixed_notebook(): " print('โœ… CONFIRMED: Emotion label mappings set correctly')\n", "else:\n", " print('โŒ ERROR: Emotion label mappings not set correctly')\n", - " raise ValueError('Emotion label mappings not set correctly')" - ] + " raise ValueError('Emotion label mappings not set correctly')", + ], }, { "cell_type": "code", @@ -364,8 +365,8 @@ def create_fixed_notebook(): "train_dataset = train_dataset.map(tokenize_function, batched=True)\n", "val_dataset = val_dataset.map(tokenize_function, batched=True)\n", "\n", - "print('โœ… Data tokenized successfully')" - ] + "print('โœ… Data tokenized successfully')", + ], }, { "cell_type": "code", @@ -398,8 +399,8 @@ def create_fixed_notebook(): " save_total_limit=3\n", ")\n", "\n", - "print('โœ… Training arguments configured')" - ] + "print('โœ… Training arguments configured')", + ], }, { "cell_type": "code", @@ -420,8 +421,8 @@ def create_fixed_notebook(): " 'accuracy': report['accuracy'],\n", " 'precision': report['weighted avg']['precision'],\n", " 'recall': report['weighted avg']['recall']\n", - " }" - ] + " }", + ], }, { "cell_type": "code", @@ -438,8 +439,8 @@ def create_fixed_notebook(): " compute_metrics=compute_metrics\n", ")\n", "\n", - "print('โœ… Trainer initialized successfully')" - ] + "print('โœ… Trainer initialized successfully')", + ], }, { "cell_type": "code", @@ -457,8 +458,8 @@ def create_fixed_notebook(): "\n", "trainer.train()\n", "\n", - "print('โœ… Training completed successfully')" - ] + "print('โœ… Training completed successfully')", + ], }, { "cell_type": "code", @@ -474,8 +475,8 @@ def create_fixed_notebook(): "print(f'Final F1 Score: {results[\"eval_f1\"]:.3f}')\n", "print(f'Final Accuracy: {results[\"eval_accuracy\"]:.3f}')\n", "print(f'Final Precision: {results[\"eval_precision\"]:.3f}')\n", - "print(f'Final Recall: {results[\"eval_recall\"]:.3f}')" - ] + "print(f'Final Recall: {results[\"eval_recall\"]:.3f}')", + ], }, { "cell_type": "code", @@ -551,8 +552,8 @@ def create_fixed_notebook(): " if accuracy < 0.8:\n", " print(f'โŒ Accuracy too low: {accuracy:.1%} (need >80%)')\n", " if max_bias > 0.3:\n", - " print(f'โŒ Too much bias: {max_bias:.1%} (need <30%)')" - ] + " print(f'โŒ Too much bias: {max_bias:.1%} (need <30%)')", + ], }, { "cell_type": "code", @@ -588,9 +589,9 @@ def create_fixed_notebook(): " with open(f'{output_dir}/config.json', 'r') as f:\n", " saved_config = json.load(f)\n", " \n", - " print(f'Saved model type: {saved_config.get(\"model_type\", \"NOT FOUND\")}')\n", - " print(f'Saved id2label: {saved_config.get(\"id2label\", \"NOT FOUND\")}')\n", - " print(f'Saved label2id: {saved_config.get(\"label2id\", \"NOT FOUND\")}')\n", + ' print(f\'Saved model type: {saved_config.get("model_type", "NOT FOUND")}\')\n', + ' print(f\'Saved id2label: {saved_config.get("id2label", "NOT FOUND")}\')\n', + ' print(f\'Saved label2id: {saved_config.get("label2id", "NOT FOUND")}\')\n', " \n", " # Verify the emotion labels are saved correctly\n", " expected_id2label = {str(i): emotion for i, emotion in enumerate(emotions)}\n", @@ -637,38 +638,31 @@ def create_fixed_notebook(): "print('\\n๐Ÿ“‹ Next steps:')\n", "print('1. Download the model files')\n", "print('2. Test locally with validation script')\n", - "print('3. Deploy if all tests pass')" - ] - } + "print('3. Deploy if all tests pass')", + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - + # Save the notebook output_path = "notebooks/FIXED_SPECIALIZED_TRAINING_CONFIG_PRESERVATION.ipynb" - with open(output_path, 'w') as f: + with open(output_path, "w") as f: json.dump(notebook_content, f, indent=2) - + print(f"โœ… Created fixed training notebook: {output_path}") print("\n๐Ÿ”ง Key fixes implemented:") print("1. โœ… Explicit emotion label mapping before training") @@ -676,8 +670,9 @@ def create_fixed_notebook(): print("3. โœ… Configuration re-setting before saving") print("4. โœ… Saved configuration verification") print("5. โœ… Comprehensive error checking") - + return output_path + if __name__ == "__main__": - create_fixed_notebook() \ No newline at end of file + create_fixed_notebook() diff --git a/scripts/training/create_improved_expanded_notebook.py b/scripts/training/create_improved_expanded_notebook.py deleted file mode 100644 index 84bb4fa86..000000000 --- a/scripts/training/create_improved_expanded_notebook.py +++ /dev/null @@ -1,767 +0,0 @@ -#!/usr/bin/env python3 -""" -Create Improved Expanded Training Notebook -Generates a new notebook with proper JSON escaping and GPU optimizations -""" - -import json - -def create_improved_notebook(): - """Create an improved version of the expanded training notebook.""" - - notebook = { - "cells": [ - { - "cell_type": "markdown", - "metadata": {"id": "header"}, - "source": [ - "# ๐Ÿš€ REQ-DL-012: Expanded Dataset Retraining\n", - "## Domain-Adapted Emotion Detection with 1000+ Samples\n", - "\n", - "**Target**: Achieve 75-85% F1 Score\n", - "**Current**: 67% F1 Score\n", - "**Expected Improvement**: 8-18% F1 Score\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {"id": "setup"}, - "source": [ - "## ๐Ÿ”ง Setup and Dependencies" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {"id": "clone_repo"}, - "outputs": [], - "source": [ - "# Clone repository\n", - "!git clone https://github.com/uelkerd/SAMO--DL.git\n", - "%cd SAMO--DL\n", - "print(\"โœ… Repository cloned and ready!\")" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {"id": "install_deps"}, - "outputs": [], - "source": [ - "# Install dependencies with compatibility fixes\n", - "print(\"๐Ÿ“ฆ Installing dependencies with compatibility fixes...\")\n", - "\n", - "# Step 1: Uninstall existing PyTorch to avoid conflicts\n", - "!pip uninstall torch torchvision torchaudio -y\n", - "\n", - "# Step 2: Install PyTorch with compatible CUDA version\n", - "!pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu118\n", - "\n", - "# Step 3: Install Transformers with compatible version\n", - "!pip install transformers==4.30.0 datasets==2.13.0 evaluate scikit-learn pandas numpy matplotlib seaborn\n", - "\n", - "# Step 4: Verify installation\n", - "print(\"๐Ÿ” Verifying installation...\")\n", - "import torch\n", - "import transformers\n", - "print(f\"PyTorch: {torch.__version__}\")\n", - "print(f\"Transformers: {transformers.__version__}\")\n", - "print(f\"CUDA Available: {torch.cuda.is_available()}\")\n", - "\n", - "# Step 5: Test critical imports\n", - "try:\n", - " from transformers import AutoModel, AutoTokenizer\n", - " print(\"โœ… Transformers imports successful\")\n", - "except Exception as e:\n", - " print(f\"โŒ Transformers import failed: {e}\")\n", - " print(\"๐Ÿ”„ Restarting runtime and trying again...\")\n", - " import os\n", - " os._exit(0) # Force restart\n", - "\n", - "print(\"โœ… Dependencies installed and verified!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {"id": "expand_dataset"}, - "source": [ - "## ๐Ÿ“Š Create Expanded Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {"id": "create_expanded_dataset"}, - "outputs": [], - "source": [ - "# Create expanded dataset directly in Colab\n", - "import json\n", - "import random\n", - "from typing import List, Dict\n", - "\n", - "def load_current_dataset():\n", - " \"\"\"Load the current journal dataset.\"\"\"\n", - " with open('data/journal_test_dataset.json', 'r') as f:\n", - " return json.load(f)\n", - "\n", - "def create_variation(base_sample: Dict, emotion: str) -> Dict:\n", - " \"\"\"Create a variation of a base sample.\"\"\"\n", - " \n", - " # Templates for different emotions\n", - " emotion_templates = {\n", - " 'happy': [\n", - " \"I'm feeling really happy today!\",\n", - " \"I'm so happy about this!\",\n", - " \"This makes me incredibly happy!\",\n", - " \"I'm feeling joyful and happy!\",\n", - " \"I'm really happy with how things are going!\",\n", - " \"This brings me so much happiness!\",\n", - " \"I'm feeling happy and content!\",\n", - " \"I'm really happy about this outcome!\",\n", - " \"This makes me feel so happy!\",\n", - " \"I'm feeling happy and grateful!\"\n", - " ],\n", - " 'sad': [\n", - " \"I'm feeling really sad today.\",\n", - " \"This makes me so sad.\",\n", - " \"I'm feeling down and sad.\",\n", - " \"I'm really sad about this situation.\",\n", - " \"This brings me sadness.\",\n", - " \"I'm feeling sad and lonely.\",\n", - " \"I'm really sad about what happened.\",\n", - " \"This makes me feel so sad.\",\n", - " \"I'm feeling sad and disappointed.\",\n", - " \"I'm really sad about this outcome.\"\n", - " ],\n", - " 'frustrated': [\n", - " \"I'm so frustrated with this!\",\n", - " \"This is really frustrating me.\",\n", - " \"I'm feeling frustrated and annoyed.\",\n", - " \"I'm really frustrated about this situation.\",\n", - " \"This is so frustrating!\",\n", - " \"I'm feeling frustrated and angry.\",\n", - " \"I'm really frustrated with how this is going.\",\n", - " \"This makes me so frustrated.\",\n", - " \"I'm feeling frustrated and upset.\",\n", - " \"I'm really frustrated about this outcome.\"\n", - " ],\n", - " 'anxious': [\n", - " \"I'm feeling really anxious about this.\",\n", - " \"This is making me anxious.\",\n", - " \"I'm feeling anxious and worried.\",\n", - " \"I'm really anxious about what might happen.\",\n", - " \"This gives me anxiety.\",\n", - " \"I'm feeling anxious and nervous.\",\n", - " \"I'm really anxious about this situation.\",\n", - " \"This makes me feel so anxious.\",\n", - " \"I'm feeling anxious and stressed.\",\n", - " \"I'm really anxious about the outcome.\"\n", - " ],\n", - " 'excited': [\n", - " \"I'm so excited about this!\",\n", - " \"This makes me really excited!\",\n", - " \"I'm feeling excited and enthusiastic!\",\n", - " \"I'm really excited about what's coming!\",\n", - " \"This is so exciting!\",\n", - " \"I'm feeling excited and eager!\",\n", - " \"I'm really excited about this opportunity!\",\n", - " \"This makes me feel so excited!\",\n", - " \"I'm feeling excited and thrilled!\",\n", - " \"I'm really excited about this outcome!\"\n", - " ],\n", - " 'calm': [\n", - " \"I'm feeling really calm right now.\",\n", - " \"This brings me a sense of calm.\",\n", - " \"I'm feeling calm and peaceful.\",\n", - " \"I'm really calm about this situation.\",\n", - " \"This makes me feel calm.\",\n", - " \"I'm feeling calm and relaxed.\",\n", - " \"I'm really calm about what's happening.\",\n", - " \"This gives me a calm feeling.\",\n", - " \"I'm feeling calm and content.\",\n", - " \"I'm really calm about this outcome.\"\n", - " ],\n", - " 'content': [\n", - " \"I'm feeling really content with this.\",\n", - " \"This makes me feel content.\",\n", - " \"I'm feeling content and satisfied.\",\n", - " \"I'm really content with how things are.\",\n", - " \"This brings me contentment.\",\n", - " \"I'm feeling content and happy.\",\n", - " \"I'm really content with this situation.\",\n", - " \"This makes me feel so content.\",\n", - " \"I'm feeling content and peaceful.\",\n", - " \"I'm really content with this outcome.\"\n", - " ],\n", - " 'grateful': [\n", - " \"I'm feeling really grateful for this.\",\n", - " \"This makes me so grateful.\",\n", - " \"I'm feeling grateful and thankful.\",\n", - " \"I'm really grateful for this opportunity.\",\n", - " \"This fills me with gratitude.\",\n", - " \"I'm feeling grateful and blessed.\",\n", - " \"I'm really grateful for this situation.\",\n", - " \"This makes me feel so grateful.\",\n", - " \"I'm feeling grateful and appreciative.\",\n", - " \"I'm really grateful for this outcome.\"\n", - " ],\n", - " 'hopeful': [\n", - " \"I'm feeling really hopeful about this.\",\n", - " \"This gives me hope.\",\n", - " \"I'm feeling hopeful and optimistic.\",\n", - " \"I'm really hopeful about what's coming.\",\n", - " \"This brings me hope.\",\n", - " \"I'm feeling hopeful and positive.\",\n", - " \"I'm really hopeful about this situation.\",\n", - " \"This makes me feel so hopeful.\",\n", - " \"I'm feeling hopeful and confident.\",\n", - " \"I'm really hopeful about this outcome.\"\n", - " ],\n", - " 'overwhelmed': [\n", - " \"I'm feeling really overwhelmed by this.\",\n", - " \"This is overwhelming me.\",\n", - " \"I'm feeling overwhelmed and stressed.\",\n", - " \"I'm really overwhelmed by this situation.\",\n", - " \"This is so overwhelming.\",\n", - " \"I'm feeling overwhelmed and anxious.\",\n", - " \"I'm really overwhelmed by what's happening.\",\n", - " \"This makes me feel so overwhelmed.\",\n", - " \"I'm feeling overwhelmed and exhausted.\",\n", - " \"I'm really overwhelmed by this outcome.\"\n", - " ],\n", - " 'proud': [\n", - " \"I'm feeling really proud of this.\",\n", - " \"This makes me so proud.\",\n", - " \"I'm feeling proud and accomplished.\",\n", - " \"I'm really proud of what I've done.\",\n", - " \"This fills me with pride.\",\n", - " \"I'm feeling proud and satisfied.\",\n", - " \"I'm really proud of this achievement.\",\n", - " \"This makes me feel so proud.\",\n", - " \"I'm feeling proud and confident.\",\n", - " \"I'm really proud of this outcome.\"\n", - " ],\n", - " 'tired': [\n", - " \"I'm feeling really tired today.\",\n", - " \"This is making me tired.\",\n", - " \"I'm feeling tired and exhausted.\",\n", - " \"I'm really tired from all this work.\",\n", - " \"This is so tiring.\",\n", - " \"I'm feeling tired and worn out.\",\n", - " \"I'm really tired of this situation.\",\n", - " \"This makes me feel so tired.\",\n", - " \"I'm feeling tired and drained.\",\n", - " \"I'm really tired of dealing with this.\"\n", - " ]\n", - " }\n", - " \n", - " # Get templates for this emotion\n", - " templates = emotion_templates.get(emotion, [f\"I'm feeling {emotion}.\"])\n", - " \n", - " # Create variation\n", - " template = random.choice(templates)\n", - " \n", - " # Add some variety to the content\n", - " variations = [\n", - " f\"{template} {random.choice(['It\\'s been a long day.', 'Things are going well.', 'I need to process this.', 'This is important to me.'])}\",\n", - " f\"{template} {random.choice(['I hope this continues.', 'I wonder what\\'s next.', 'This feels right.', 'I\\'m processing this.'])}\",\n", - " f\"{template} {random.choice(['I should reflect on this.', 'This is meaningful.', 'I appreciate this moment.', 'I\\'m learning from this.'])}\"\n", - " ]\n", - " \n", - " content = random.choice(variations)\n", - " \n", - " return {\n", - " 'content': content,\n", - " 'emotion': emotion,\n", - " 'id': f\"expanded_{emotion}_{random.randint(1000, 9999)}\"\n", - " }\n", - "\n", - "def create_balanced_dataset(target_size=1000):\n", - " \"\"\"Create a balanced expanded dataset.\"\"\"\n", - " print(\"๐Ÿ”ง Creating balanced expanded dataset...\")\n", - " \n", - " # Load current data\n", - " current_data = load_current_dataset()\n", - " \n", - " # Analyze current distribution\n", - " emotion_counts = {}\n", - " for entry in current_data:\n", - " emotion = entry['emotion']\n", - " emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1\n", - " \n", - " print(f\"๐Ÿ“Š Current emotion distribution:\")\n", - " for emotion, count in sorted(emotion_counts.items()):\n", - " print(f\" {emotion}: {count} samples\")\n", - " \n", - " # Calculate target per emotion\n", - " target_per_emotion = target_size // len(emotion_counts)\n", - " print(f\"\\n๐ŸŽฏ Target: {target_per_emotion} samples per emotion\")\n", - " \n", - " # Create expanded dataset\n", - " expanded_data = []\n", - " \n", - " for emotion in emotion_counts.keys():\n", - " # Get existing samples for this emotion\n", - " existing_samples = [entry for entry in current_data if entry['emotion'] == emotion]\n", - " current_count = len(existing_samples)\n", - " \n", - " print(f\"\\n๐Ÿ“ Expanding '{emotion}' from {current_count} to {target_per_emotion} samples...\")\n", - " \n", - " # Add existing samples\n", - " expanded_data.extend(existing_samples)\n", - " \n", - " # Generate additional samples\n", - " needed_samples = target_per_emotion - current_count\n", - " \n", - " if needed_samples > 0:\n", - " # Create variations of existing samples\n", - " for i in range(needed_samples):\n", - " # Pick a random existing sample to base variation on\n", - " base_sample = random.choice(existing_samples)\n", - " \n", - " # Create variation\n", - " variation = create_variation(base_sample, emotion)\n", - " expanded_data.append(variation)\n", - " \n", - " print(f\"\\nโœ… Expanded dataset created:\")\n", - " print(f\" Original samples: {len(current_data)}\")\n", - " print(f\" Expanded samples: {len(expanded_data)}\")\n", - " print(f\" Target size: {target_size}\")\n", - " \n", - " return expanded_data\n", - "\n", - "# Create expanded dataset\n", - "expanded_data = create_balanced_dataset(target_size=1000)\n", - "\n", - "# Save expanded dataset\n", - "with open('data/expanded_journal_dataset.json', 'w') as f:\n", - " json.dump(expanded_data, f, indent=2)\n", - "\n", - "print(\"โœ… Expanded dataset saved to data/expanded_journal_dataset.json\")\n", - "\n", - "# Analyze expanded dataset\n", - "emotion_counts = {}\n", - "for entry in expanded_data:\n", - " emotion = entry['emotion']\n", - " emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1\n", - "\n", - "print(\"\\n๐Ÿ“Š Expanded Dataset Analysis:\")\n", - "print(\"=\" * 40)\n", - "print(\"Emotion distribution:\")\n", - "for emotion, count in sorted(emotion_counts.items()):\n", - " print(f\" {emotion}: {count} samples\")\n", - "\n", - "print(f\"\\nTotal samples: {len(expanded_data)}\")\n", - "print(f\"Unique emotions: {len(emotion_counts)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {"id": "training"}, - "source": [ - "## ๐Ÿš€ Training with Expanded Dataset (GPU Optimized)" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {"id": "expanded_training"}, - "outputs": [], - "source": [ - "# Complete training script with expanded dataset and GPU optimizations\n", - "import torch\n", - "import torch.nn as nn\n", - "from torch.utils.data import Dataset, DataLoader\n", - "from transformers import AutoModel, AutoTokenizer\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import f1_score, accuracy_score\n", - "import numpy as np\n", - "from torch.cuda.amp import autocast, GradScaler\n", - "\n", - "class ExpandedEmotionDataset(Dataset):\n", - " def __init__(self, texts, labels, tokenizer, max_length=128):\n", - " self.texts = texts\n", - " self.labels = labels\n", - " self.tokenizer = tokenizer\n", - " self.max_length = max_length\n", - " \n", - " def __len__(self):\n", - " return len(self.texts)\n", - " \n", - " def __getitem__(self, idx):\n", - " text = self.texts[idx]\n", - " label = self.labels[idx]\n", - " \n", - " encoding = self.tokenizer(\n", - " text,\n", - " truncation=True,\n", - " padding='max_length',\n", - " max_length=self.max_length,\n", - " return_tensors='pt'\n", - " )\n", - " \n", - " return {\n", - " 'input_ids': encoding['input_ids'].flatten(),\n", - " 'attention_mask': encoding['attention_mask'].flatten(),\n", - " 'labels': torch.tensor(label, dtype=torch.long)\n", - " }\n", - "\n", - "class ExpandedEmotionClassifier(nn.Module):\n", - " def __init__(self, model_name=\"bert-base-uncased\", num_labels=12):\n", - " super().__init__()\n", - " self.num_labels = num_labels\n", - " self.bert = AutoModel.from_pretrained(model_name)\n", - " self.dropout = nn.Dropout(0.3)\n", - " self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels)\n", - " \n", - " def forward(self, input_ids, attention_mask):\n", - " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n", - " pooled_output = outputs.pooler_output\n", - " logits = self.classifier(self.dropout(pooled_output))\n", - " return logits\n", - "\n", - "def prepare_expanded_data(data, test_size=0.2, val_size=0.1):\n", - " \"\"\"Prepare data for training with expanded dataset.\"\"\"\n", - " print(\"๐Ÿ”ง Preparing expanded data...\")\n", - " \n", - " # Extract texts and emotions\n", - " texts = [entry['content'] for entry in data]\n", - " emotions = [entry['emotion'] for entry in data]\n", - " \n", - " # Create label encoder\n", - " label_encoder = LabelEncoder()\n", - " labels = label_encoder.fit_transform(emotions)\n", - " \n", - " print(f\"โœ… Label encoder created with {len(label_encoder.classes_)} classes\")\n", - " print(f\"๐Ÿ“Š Classes: {list(label_encoder.classes_)}\")\n", - " \n", - " # Split data\n", - " X_temp, X_test, y_temp, y_test = train_test_split(\n", - " texts, labels, test_size=test_size, random_state=42, stratify=labels\n", - " )\n", - " \n", - " X_train, X_val, y_train, y_val = train_test_split(\n", - " X_temp, y_temp, test_size=val_size/(1-test_size), random_state=42, stratify=y_temp\n", - " )\n", - " \n", - " print(f\"๐Ÿ“Š Data split:\")\n", - " print(f\" Training: {len(X_train)} samples\")\n", - " print(f\" Validation: {len(X_val)} samples\")\n", - " print(f\" Test: {len(X_test)} samples\")\n", - " \n", - " return (X_train, y_train), (X_val, y_val), (X_test, y_test), label_encoder\n", - "\n", - "def train_expanded_model(train_data, val_data, label_encoder, epochs=5, batch_size=16):\n", - " \"\"\"Train the model with expanded dataset and GPU optimizations.\"\"\"\n", - " print(\"๐Ÿš€ Training with expanded dataset...\")\n", - " \n", - " # Setup\n", - " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - " print(f\"โœ… Using device: {device}\")\n", - " \n", - " # GPU optimizations\n", - " if torch.cuda.is_available():\n", - " print(\"๐Ÿ”ง Applying GPU optimizations...\")\n", - " torch.backends.cudnn.benchmark = True\n", - " torch.backends.cudnn.deterministic = False\n", - " print(f\"๐Ÿ“Š GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB\")\n", - " print(f\"๐Ÿ“Š Available Memory: {torch.cuda.memory_allocated(0) / 1e9:.1f} GB\")\n", - " \n", - " # Clear GPU cache\n", - " if torch.cuda.is_available():\n", - " torch.cuda.empty_cache()\n", - " \n", - " # Load tokenizer\n", - " tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n", - " \n", - " # Create datasets\n", - " X_train, y_train = train_data\n", - " X_val, y_val = val_data\n", - " \n", - " train_dataset = ExpandedEmotionDataset(X_train, y_train, tokenizer)\n", - " val_dataset = ExpandedEmotionDataset(X_val, y_val, tokenizer)\n", - " \n", - " train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)\n", - " val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)\n", - " \n", - " # Initialize model\n", - " model = ExpandedEmotionClassifier(num_labels=len(label_encoder.classes_))\n", - " model.to(device)\n", - " \n", - " # Setup training with optimizations\n", - " optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)\n", - " scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=2, verbose=True)\n", - " criterion = nn.CrossEntropyLoss()\n", - " scaler = GradScaler()\n", - " \n", - " # Training loop\n", - " best_f1 = 0\n", - " training_history = []\n", - " \n", - " for epoch in range(epochs):\n", - " print(f\"\\n๐Ÿ”„ Epoch {epoch + 1}/{epochs}\")\n", - " \n", - " # Training\n", - " model.train()\n", - " total_loss = 0\n", - " \n", - " for i, batch in enumerate(train_loader):\n", - " input_ids = batch['input_ids'].to(device, non_blocking=True)\n", - " attention_mask = batch['attention_mask'].to(device, non_blocking=True)\n", - " labels = batch['labels'].to(device, non_blocking=True)\n", - " \n", - " optimizer.zero_grad()\n", - " \n", - " # Mixed precision training\n", - " with autocast():\n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", - " loss = criterion(outputs, labels)\n", - " \n", - " scaler.scale(loss).backward()\n", - " scaler.step(optimizer)\n", - " scaler.update()\n", - " \n", - " total_loss += loss.item()\n", - " \n", - " if i % 50 == 0:\n", - " print(f\" Batch {i}/{len(train_loader)}, Loss: {loss.item():.4f}\")\n", - " \n", - " # Validation\n", - " model.eval()\n", - " val_loss = 0\n", - " all_preds = []\n", - " all_labels = []\n", - " \n", - " with torch.no_grad():\n", - " for batch in val_loader:\n", - " input_ids = batch['input_ids'].to(device, non_blocking=True)\n", - " attention_mask = batch['attention_mask'].to(device, non_blocking=True)\n", - " labels = batch['labels'].to(device, non_blocking=True)\n", - " \n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", - " loss = criterion(outputs, labels)\n", - " val_loss += loss.item()\n", - " \n", - " preds = torch.argmax(outputs, dim=1)\n", - " all_preds.extend(preds.cpu().numpy())\n", - " all_labels.extend(labels.cpu().numpy())\n", - " \n", - " # Calculate metrics\n", - " avg_train_loss = total_loss / len(train_loader)\n", - " avg_val_loss = val_loss / len(val_loader)\n", - " f1_macro = f1_score(all_labels, all_preds, average='macro')\n", - " accuracy = accuracy_score(all_labels, all_preds)\n", - " \n", - " print(f\"๐Ÿ“Š Epoch {epoch + 1} Results:\")\n", - " print(f\" Train Loss: {avg_train_loss:.4f}\")\n", - " print(f\" Val Loss: {avg_val_loss:.4f}\")\n", - " print(f\" Val F1 (Macro): {f1_macro:.4f}\")\n", - " print(f\" Val Accuracy: {accuracy:.4f}\")\n", - " \n", - " # Early stopping check\n", - " if epoch > 2 and f1_macro < best_f1 * 0.95:\n", - " print(f\"๐Ÿ›‘ Early stopping triggered. F1 dropped below 95% of best.\")\n", - " break\n", - " \n", - " # Save best model\n", - " if f1_macro > best_f1:\n", - " best_f1 = f1_macro\n", - " torch.save(model.state_dict(), 'best_expanded_model.pth')\n", - " print(f\"๐Ÿ’พ New best model saved! F1: {best_f1:.4f}\")\n", - " scheduler.step(f1_macro)\n", - " \n", - " training_history.append({\n", - " 'epoch': epoch,\n", - " 'train_loss': avg_train_loss,\n", - " 'val_loss': avg_val_loss,\n", - " 'val_f1_macro': f1_macro,\n", - " 'val_accuracy': accuracy\n", - " })\n", - " \n", - " return model, training_history, best_f1\n", - "\n", - "# Load expanded dataset\n", - "with open('data/expanded_journal_dataset.json', 'r') as f:\n", - " expanded_data = json.load(f)\n", - "\n", - "print(f\"๐Ÿ“Š Loaded {len(expanded_data)} expanded samples\")\n", - "\n", - "# Prepare data\n", - "train_data, val_data, test_data, label_encoder = prepare_expanded_data(expanded_data)\n", - "\n", - "# Train model\n", - "model, training_history, best_f1 = train_expanded_model(train_data, val_data, label_encoder)\n", - "\n", - "print(f\"\\n๐ŸŽ‰ Training completed!\")\n", - "print(f\"๐Ÿ“Š Best F1 Score: {best_f1:.4f}\")\n", - "print(f\"๐ŸŽฏ Target Achieved: {best_f1 >= 0.70}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {"id": "testing"}, - "source": [ - "## ๐Ÿงช Test the New Model" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {"id": "test_new_model"}, - "outputs": [], - "source": [ - "# Test the new model with sample entries\n", - "def test_new_model():\n", - " \"\"\"Test the new model with sample journal entries.\"\"\"\n", - " print(\"๐Ÿงช Testing new expanded model...\")\n", - " \n", - " # Load best model\n", - " device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - " model = ExpandedEmotionClassifier(num_labels=len(label_encoder.classes_))\n", - " model.load_state_dict(torch.load('best_expanded_model.pth'))\n", - " model.to(device)\n", - " model.eval()\n", - " \n", - " # Load tokenizer\n", - " tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n", - " \n", - " # Sample test entries\n", - " test_entries = [\n", - " \"I'm feeling really happy today! Everything is going well.\",\n", - " \"I'm so frustrated with this project. Nothing is working.\",\n", - " \"I feel anxious about the upcoming presentation.\",\n", - " \"I'm grateful for all the support I've received.\",\n", - " \"I'm feeling overwhelmed with all these tasks.\",\n", - " \"I'm proud of what I've accomplished so far.\",\n", - " \"I'm feeling sad and lonely today.\",\n", - " \"I'm excited about the new opportunities ahead.\",\n", - " \"I feel calm and peaceful right now.\",\n", - " \"I'm hopeful that things will get better.\",\n", - " \"I'm tired and need some rest.\",\n", - " \"I'm content with how things are going.\"\n", - " ]\n", - " \n", - " print(\"\\n๐Ÿ“Š Testing Results:\")\n", - " print(\"=\" * 80)\n", - " \n", - " for i, text in enumerate(test_entries, 1):\n", - " # Tokenize\n", - " encoding = tokenizer(\n", - " text,\n", - " truncation=True,\n", - " padding='max_length',\n", - " max_length=128,\n", - " return_tensors='pt'\n", - " )\n", - " \n", - " # Predict\n", - " with torch.no_grad():\n", - " input_ids = encoding['input_ids'].to(device)\n", - " attention_mask = encoding['attention_mask'].to(device)\n", - " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", - " probabilities = torch.softmax(outputs, dim=1)\n", - " predicted_class = torch.argmax(probabilities, dim=1).item()\n", - " confidence = probabilities[0][predicted_class].item()\n", - " \n", - " # Get emotion label\n", - " emotion = label_encoder.inverse_transform([predicted_class])[0]\n", - " \n", - " print(f\"\\n{i}. Text: {text}\")\n", - " print(f\" Predicted: {emotion} (confidence: {confidence:.3f})\")\n", - " \n", - " # Show top 3 predictions\n", - " all_probs = probabilities[0].cpu().numpy()\n", - " top_indices = np.argsort(all_probs)[-3:][::-1]\n", - " print(\" Top 3 predictions:\")\n", - " for idx in top_indices:\n", - " prob = all_probs[idx]\n", - " emotion_name = label_encoder.inverse_transform([idx])[0]\n", - " print(f\" - {emotion_name}: {prob:.3f}\")\n", - " \n", - " print(\"\\nโœ… Model testing completed!\")\n", - "\n", - "# Test the new model\n", - "test_new_model()" - ] - }, - { - "cell_type": "markdown", - "metadata": {"id": "download"}, - "source": [ - "## ๐Ÿ’พ Download Results" - ] - }, - { - "cell_type": "code", - "execution_count": None, - "metadata": {"id": "download_results"}, - "outputs": [], - "source": [ - "# Download the trained model and results\n", - "from google.colab import files\n", - "\n", - "print(\"๐Ÿ“ฅ Downloading results...\")\n", - "\n", - "# Download model\n", - "files.download('best_expanded_model.pth')\n", - "\n", - "# Save and download results\n", - "results = {\n", - " 'best_f1': best_f1,\n", - " 'target_achieved': best_f1 >= 0.70,\n", - " 'num_labels': len(label_encoder.classes_),\n", - " 'all_emotions': list(label_encoder.classes_),\n", - " 'training_history': training_history,\n", - " 'expanded_samples': len(expanded_data)\n", - "}\n", - "\n", - "with open('expanded_training_results.json', 'w') as f:\n", - " json.dump(results, f, indent=2)\n", - "\n", - "files.download('expanded_training_results.json')\n", - "\n", - "print(\"โœ… Downloads completed!\")\n", - "print(f\"๐Ÿ“Š Final F1 Score: {best_f1:.4f}\")\n", - "print(f\"๐ŸŽฏ Target Achieved: {best_f1 >= 0.70}\")" - ] - } - ], - "metadata": { - "colab": {"provenance": []}, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": {"name": "ipython", "version": 3}, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 - } - - # Save the improved notebook - with open('notebooks/expanded_dataset_training_improved.ipynb', 'w') as f: - json.dump(notebook, f, indent=2) - - print("โœ… Improved notebook created: 'notebooks/expanded_dataset_training_improved.ipynb'") - print("๐Ÿ“‹ Key improvements:") - print(" - Fixed JSON syntax errors") - print(" - Added GPU optimizations (cudnn benchmark, memory management)") - print(" - Mixed precision training for faster training") - print(" - Early stopping to prevent overfitting") - print(" - Learning rate scheduling with ReduceLROnPlateau") - print(" - Better memory management with non_blocking transfers") - print(" - DataLoader optimizations (num_workers, pin_memory)") - -if __name__ == "__main__": - create_improved_notebook() \ No newline at end of file diff --git a/scripts/training/create_minimal_working_notebook.py b/scripts/training/create_minimal_working_notebook.py index 215da793b..69f20e503 100644 --- a/scripts/training/create_minimal_working_notebook.py +++ b/scripts/training/create_minimal_working_notebook.py @@ -9,9 +9,10 @@ import json + def create_minimal_notebook(): """Create a minimal working notebook.""" - + notebook_content = { "cells": [ { @@ -27,8 +28,8 @@ def create_minimal_notebook(): "โœ… Simple data processing\n", "โœ… Model saving with verification\n", "\n", - "**Target**: Get training working first, then optimize" - ] + "**Target**: Get training working first, then optimize", + ], }, { "cell_type": "code", @@ -37,8 +38,8 @@ def create_minimal_notebook(): "outputs": [], "source": [ "# Install required packages\n", - "!pip install transformers torch scikit-learn numpy pandas" - ] + "!pip install transformers torch scikit-learn numpy pandas", + ], }, { "cell_type": "code", @@ -58,16 +59,10 @@ def create_minimal_notebook(): "\n", "print('โœ… All packages imported successfully')\n", "print(f'PyTorch version: {torch.__version__}')\n", - "print(f'CUDA available: {torch.cuda.is_available()}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐ŸŽฏ SETUP" - ] + "print(f'CUDA available: {torch.cuda.is_available()}')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐ŸŽฏ SETUP"]}, { "cell_type": "code", "execution_count": None, @@ -107,16 +102,10 @@ def create_minimal_notebook(): " {'text': 'I feel exhausted from the work.', 'label': 11}\n", "]\n", "\n", - "print(f'๐Ÿ“Š Dataset size: {len(data)} samples')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ”ง MODEL SETUP" - ] + "print(f'๐Ÿ“Š Dataset size: {len(data)} samples')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ”ง MODEL SETUP"]}, { "cell_type": "code", "execution_count": None, @@ -136,16 +125,10 @@ def create_minimal_notebook(): "model.config.label2id = {emotion: i for i, emotion in enumerate(emotions)}\n", "\n", "print(f'โœ… Model configured for {len(emotions)} emotions')\n", - "print(f'โœ… id2label: {model.config.id2label}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ“ DATA PREPROCESSING" - ] + "print(f'โœ… id2label: {model.config.id2label}')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ“ DATA PREPROCESSING"]}, { "cell_type": "code", "execution_count": None, @@ -185,15 +168,13 @@ def create_minimal_notebook(): "train_dataset = SimpleDataset(train_encodings, train_labels)\n", "val_dataset = SimpleDataset(val_encodings, val_labels)\n", "\n", - "print('โœ… Data preprocessing completed')" - ] + "print('โœ… Data preprocessing completed')", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## โš™๏ธ MINIMAL TRAINING ARGUMENTS" - ] + "source": ["## โš™๏ธ MINIMAL TRAINING ARGUMENTS"], }, { "cell_type": "code", @@ -212,16 +193,10 @@ def create_minimal_notebook(): " eval_steps=50\n", ")\n", "\n", - "print('โœ… Minimal training arguments configured')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ“Š COMPUTE METRICS" - ] + "print('โœ… Minimal training arguments configured')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ“Š COMPUTE METRICS"]}, { "cell_type": "code", "execution_count": None, @@ -238,16 +213,10 @@ def create_minimal_notebook(): " 'accuracy': accuracy_score(labels, predictions)\n", " }\n", "\n", - "print('โœ… Compute metrics function ready')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿš€ TRAINING" - ] + "print('โœ… Compute metrics function ready')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿš€ TRAINING"]}, { "cell_type": "code", "execution_count": None, @@ -275,16 +244,10 @@ def create_minimal_notebook(): "# Train the model\n", "trainer.train()\n", "\n", - "print('โœ… Training completed successfully!')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ“ˆ EVALUATION" - ] + "print('โœ… Training completed successfully!')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ“ˆ EVALUATION"]}, { "cell_type": "code", "execution_count": None, @@ -300,16 +263,10 @@ def create_minimal_notebook(): "print(f'F1 Score: {results[\"eval_f1\"]:.4f}')\n", "print(f'Accuracy: {results[\"eval_accuracy\"]:.4f}')\n", "\n", - "print('โœ… Evaluation completed!')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ’พ MODEL SAVING" - ] + "print('โœ… Evaluation completed!')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐Ÿ’พ MODEL SAVING"]}, { "cell_type": "code", "execution_count": None, @@ -332,42 +289,35 @@ def create_minimal_notebook(): " config = json.load(f)\n", "\n", "print(f'\\n๐Ÿ” SAVED CONFIGURATION:')\n", - "print(f'Model type: {config.get(\"model_type\", \"NOT SET\")}')\n", - "print(f'Number of labels: {config.get(\"num_labels\", \"NOT SET\")}')\n", - "print(f'id2label: {config.get(\"id2label\", \"NOT SET\")}')\n", + 'print(f\'Model type: {config.get("model_type", "NOT SET")}\')\n', + 'print(f\'Number of labels: {config.get("num_labels", "NOT SET")}\')\n', + 'print(f\'id2label: {config.get("id2label", "NOT SET")}\')\n', "\n", - "print('\\nโœ… Model saving completed!')" - ] - } + "print('\\nโœ… Model saving completed!')", + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - + # Save the notebook output_path = "notebooks/MINIMAL_WORKING_TRAINING_COLAB.ipynb" - with open(output_path, 'w') as f: + with open(output_path, "w") as f: json.dump(notebook_content, f, indent=2) - + print(f"โœ… Created minimal working notebook: {output_path}") print("๐Ÿ“‹ Features:") print(" โœ… Ultra-minimal training arguments") @@ -375,8 +325,9 @@ def create_minimal_notebook(): print(" โœ… Basic training and evaluation") print(" โœ… Model saving with verification") print("\\n๐Ÿš€ This should work in ANY transformers version!") - + return output_path + if __name__ == "__main__": - create_minimal_notebook() \ No newline at end of file + create_minimal_notebook() diff --git a/scripts/training/create_model_ensemble_notebook.py b/scripts/training/create_model_ensemble_notebook.py index a5ee53d59..fa01c3af7 100644 --- a/scripts/training/create_model_ensemble_notebook.py +++ b/scripts/training/create_model_ensemble_notebook.py @@ -8,9 +8,10 @@ import json + def create_model_ensemble_notebook(): """Create the model ensemble notebook content""" - + notebook_content = { "cells": [ { @@ -28,8 +29,8 @@ def create_model_ensemble_notebook(): "- Uses **data augmentation** techniques\n", "- Implements **hyperparameter optimization**\n", "- **Ensembles** the best models\n", - "- **Augments** the small dataset" - ] + "- **Augments** the small dataset", + ], }, { "cell_type": "code", @@ -38,8 +39,8 @@ def create_model_ensemble_notebook(): "outputs": [], "source": [ "# Install required packages\n", - "!pip install transformers torch scikit-learn numpy pandas nltk nlpaug" - ] + "!pip install transformers torch scikit-learn numpy pandas nltk nlpaug", + ], }, { "cell_type": "code", @@ -77,8 +78,8 @@ def create_model_ensemble_notebook(): " print('NLTK data already downloaded')\n", "\n", "print('๐Ÿš€ MODEL ENSEMBLE TRAINING - TEST ALL SPECIALIZED MODELS')\n", - "print('=' * 70)" - ] + "print('=' * 70)", + ], }, { "cell_type": "code", @@ -119,8 +120,8 @@ def create_model_ensemble_notebook(): "\n", "print(f'โœ… Data directory found: {data_path}')\n", "print('๐Ÿ“‚ Listing data files:')\n", - "!ls -la {data_path}/" - ] + "!ls -la {data_path}/", + ], }, { "cell_type": "code", @@ -185,8 +186,8 @@ def create_model_ensemble_notebook(): "if len(texts) != len(unique_texts):\n", " print('โŒ WARNING: DUPLICATES FOUND! This will cause model collapse!')\n", "else:\n", - " print('โœ… All samples are unique - no model collapse risk!')" - ] + " print('โœ… All samples are unique - no model collapse risk!')", + ], }, { "cell_type": "code", @@ -199,7 +200,7 @@ def create_model_ensemble_notebook(): "print('=' * 50)\n", "\n", "def get_synonyms(word):\n", - " \"\"\"Get synonyms for a word using WordNet\"\"\"\n", + ' """Get synonyms for a word using WordNet"""\n', " synonyms = []\n", " for syn in wordnet.synsets(word):\n", " for lemma in syn.lemmas():\n", @@ -208,7 +209,7 @@ def create_model_ensemble_notebook(): " return list(set(synonyms))\n", "\n", "def augment_text(text, emotion):\n", - " \"\"\"Create augmented versions of text\"\"\"\n", + ' """Create augmented versions of text"""\n', " augmented_samples = []\n", " \n", " # Original sample\n", @@ -271,8 +272,8 @@ def create_model_ensemble_notebook(): "\n", "# Use augmented dataset\n", "combined_samples = unique_augmented\n", - "print(f'โœ… Final augmented dataset size: {len(combined_samples)} samples')" - ] + "print(f'โœ… Final augmented dataset size: {len(combined_samples)} samples')", + ], }, { "cell_type": "code", @@ -308,8 +309,8 @@ def create_model_ensemble_notebook(): "\n", "print('\\n๐Ÿ“Š Emotion Distribution:')\n", "for emotion, count in sorted(emotion_counts.items()):\n", - " print(f' {emotion}: {count} samples')" - ] + " print(f' {emotion}: {count} samples')", + ], }, { "cell_type": "code", @@ -344,8 +345,8 @@ def create_model_ensemble_notebook(): " 'input_ids': encoding['input_ids'].flatten(),\n", " 'attention_mask': encoding['attention_mask'].flatten(),\n", " 'labels': torch.tensor(label, dtype=torch.long)\n", - " }" - ] + " }", + ], }, { "cell_type": "code", @@ -454,8 +455,8 @@ def create_model_ensemble_notebook(): "print(f'๐Ÿ† BEST F1 SCORE: {best_f1:.4f} ({best_f1*100:.2f}%)')\n", "print('\\n๐Ÿ“Š All Model Results:')\n", "for model_name, f1 in sorted(model_results.items(), key=lambda x: x[1], reverse=True):\n", - " print(f' {model_name}: {f1:.4f} ({f1*100:.2f}%)')" - ] + " print(f' {model_name}: {f1:.4f} ({f1*100:.2f}%)')", + ], }, { "cell_type": "code", @@ -491,8 +492,8 @@ def create_model_ensemble_notebook(): "\n", "print(f'โœ… Best model loaded: {best_model}')\n", "print(f'โœ… Model initialized with {len(label_encoder.classes_)} labels')\n", - "print(f'โœ… Datasets created successfully')" - ] + "print(f'โœ… Datasets created successfully')", + ], }, { "cell_type": "code", @@ -546,8 +547,8 @@ def create_model_ensemble_notebook(): "print(f'๐ŸŽฏ Using best model: {best_model}')\n", "\n", "# Start training\n", - "trainer.train()" - ] + "trainer.train()", + ], }, { "cell_type": "code", @@ -559,15 +560,15 @@ def create_model_ensemble_notebook(): "print('๐Ÿ“Š Evaluating final model...')\n", "results = trainer.evaluate()\n", "\n", - "print(f'๐Ÿ† Final F1 Score: {results[\"eval_f1\"]:.4f} ({results[\"eval_f1\"]*100:.2f}%)')\n", - "print(f'๐ŸŽฏ Target achieved: {\"โœ… YES!\" if results[\"eval_f1\"] >= 0.75 else \"โŒ Not yet\"}')\n", + 'print(f\'๐Ÿ† Final F1 Score: {results["eval_f1"]:.4f} ({results["eval_f1"]*100:.2f}%)\')\n', + 'print(f\'๐ŸŽฏ Target achieved: {"โœ… YES!" if results["eval_f1"] >= 0.75 else "โŒ Not yet"}\')\n', "print(f'๐Ÿ“ˆ Improvement from baseline: {((results[\"eval_f1\"] - 0.052) / 0.052 * 100):.1f}%')\n", "print(f'๐Ÿ“ˆ Improvement from specialized: {((results[\"eval_f1\"] - 0.3273) / 0.3273 * 100):.1f}%')\n", "\n", "# Save model\n", "trainer.save_model('./emotion_model_ensemble_final')\n", - "print('๐Ÿ’พ Model saved to ./emotion_model_ensemble_final')" - ] + "print('๐Ÿ’พ Model saved to ./emotion_model_ensemble_final')", + ], }, { "cell_type": "code", @@ -579,11 +580,11 @@ def create_model_ensemble_notebook(): "print('๐Ÿงช Testing on sample texts...')\n", "\n", "test_texts = [\n", - " \"I'm feeling really happy today!\",\n", - " \"I'm so frustrated with this project.\",\n", - " \"I feel anxious about the presentation.\",\n", - " \"I'm grateful for all the support.\",\n", - " \"I'm feeling overwhelmed with tasks.\"\n", + ' "I\'m feeling really happy today!",\n', + ' "I\'m so frustrated with this project.",\n', + ' "I feel anxious about the presentation.",\n', + ' "I\'m grateful for all the support.",\n', + ' "I\'m feeling overwhelmed with tasks."\n', "]\n", "\n", "model.eval()\n", @@ -604,8 +605,8 @@ def create_model_ensemble_notebook(): " predicted_emotion = label_encoder.inverse_transform([predicted_class])[0]\n", " \n", " print(f'{i}. Text: {text}')\n", - " print(f' Predicted: {predicted_emotion} (confidence: {confidence:.3f})\\n')" - ] + " print(f' Predicted: {predicted_emotion} (confidence: {confidence:.3f})\\n')", + ], }, { "cell_type": "markdown", @@ -630,36 +631,29 @@ def create_model_ensemble_notebook(): "**Next Steps:**\n", "1. Review the F1 score achieved\n", "2. If still low, consider more aggressive augmentation\n", - "3. Try ensemble voting of multiple models" - ] - } + "3. Try ensemble voting of multiple models", + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - - with open('notebooks/MODEL_ENSEMBLE_TRAINING_COLAB.ipynb', 'w') as f: + + with open("notebooks/MODEL_ENSEMBLE_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook_content, f, indent=2) - + print("โœ… Model ensemble notebook created: notebooks/MODEL_ENSEMBLE_TRAINING_COLAB.ipynb") print("๐Ÿ“‹ Instructions:") print(" 1. Download the notebook file") @@ -673,5 +667,6 @@ def create_model_ensemble_notebook(): print(" - Automatic best model selection") print(" - Optimized hyperparameters") + if __name__ == "__main__": - create_model_ensemble_notebook() \ No newline at end of file + create_model_ensemble_notebook() diff --git a/scripts/training/create_simple_ultimate_notebook.py b/scripts/training/create_simple_ultimate_notebook.py index 91af37aa3..c8892eb2d 100644 --- a/scripts/training/create_simple_ultimate_notebook.py +++ b/scripts/training/create_simple_ultimate_notebook.py @@ -9,9 +9,10 @@ import json + def create_simple_notebook(): """Create a simplified ultimate notebook.""" - + notebook_content = { "cells": [ { @@ -29,8 +30,8 @@ def create_simple_notebook(): "โœ… Advanced validation (proper testing)\n", "โœ… Simple, direct approach (no datasets library issues)\n", "\n", - "**Target**: Reliable 75-85% F1 score with consistent performance" - ] + "**Target**: Reliable 75-85% F1 score with consistent performance", + ], }, { "cell_type": "code", @@ -39,8 +40,8 @@ def create_simple_notebook(): "outputs": [], "source": [ "# Install required packages\n", - "!pip install transformers torch scikit-learn numpy pandas huggingface_hub" - ] + "!pip install transformers torch scikit-learn numpy pandas huggingface_hub", + ], }, { "cell_type": "code", @@ -61,15 +62,13 @@ def create_simple_notebook(): "\n", "print('โœ… All packages imported successfully')\n", "print(f'PyTorch version: {torch.__version__}')\n", - "print(f'CUDA available: {torch.cuda.is_available()}')" - ] + "print(f'CUDA available: {torch.cuda.is_available()}')", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ” VERIFYING SPECIALIZED MODEL ACCESS" - ] + "source": ["## ๐Ÿ” VERIFYING SPECIALIZED MODEL ACCESS"], }, { "cell_type": "code", @@ -107,16 +106,10 @@ def create_simple_notebook(): " specialized_model_name = 'roberta-base'\n", " test_tokenizer = AutoTokenizer.from_pretrained(specialized_model_name)\n", " test_model = AutoModelForSequenceClassification.from_pretrained(specialized_model_name, num_labels=12)\n", - " print(f'โœ… Fallback model loaded: {specialized_model_name}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐ŸŽฏ DEFINING EMOTION CLASSES" - ] + " print(f'โœ… Fallback model loaded: {specialized_model_name}')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐ŸŽฏ DEFINING EMOTION CLASSES"]}, { "cell_type": "code", "execution_count": None, @@ -126,15 +119,13 @@ def create_simple_notebook(): "# Define our emotion classes\n", "emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired']\n", "print(f'๐ŸŽฏ Our emotion classes: {emotions}')\n", - "print(f'๐Ÿ“Š Number of emotions: {len(emotions)}')" - ] + "print(f'๐Ÿ“Š Number of emotions: {len(emotions)}')", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ“Š CREATING ENHANCED DATASET WITH AUGMENTATION" - ] + "source": ["## ๐Ÿ“Š CREATING ENHANCED DATASET WITH AUGMENTATION"], }, { "cell_type": "code", @@ -320,7 +311,7 @@ def create_simple_notebook(): "\n", "# Data augmentation function\n", "def augment_text(text, emotion):\n", - " \"\"\"Create augmented versions of the text.\"\"\"\n", + ' """Create augmented versions of the text."""\n', " augmented = []\n", " \n", " # Synonym replacement\n", @@ -370,38 +361,31 @@ def create_simple_notebook(): "texts = [item['text'] for item in enhanced_data]\n", "labels = [item['label'] for item in enhanced_data]\n", "\n", - "print(f'โœ… Dataset prepared with {len(texts)} samples')" - ] - } + "print(f'โœ… Dataset prepared with {len(texts)} samples')", + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - + # Save the notebook output_path = "notebooks/SIMPLE_ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb" - with open(output_path, 'w') as f: + with open(output_path, "w") as f: json.dump(notebook_content, f, indent=2) - + print(f"โœ… Created simple ultimate notebook: {output_path}") print("๐Ÿ“‹ Features included:") print(" โœ… Configuration preservation") @@ -410,8 +394,9 @@ def create_simple_notebook(): print(" โœ… Data augmentation") print(" โœ… Simple approach (no datasets library)") print(" โœ… Advanced validation (to be added)") - + return output_path + if __name__ == "__main__": - create_simple_notebook() \ No newline at end of file + create_simple_notebook() diff --git a/scripts/training/create_ultimate_bulletproof_notebook.py b/scripts/training/create_ultimate_bulletproof_notebook.py index ccba22de0..1d465889f 100644 --- a/scripts/training/create_ultimate_bulletproof_notebook.py +++ b/scripts/training/create_ultimate_bulletproof_notebook.py @@ -7,7 +7,7 @@ previous iterations: โœ… Configuration preservation (from current notebook) -โœ… Focal loss (from previous iterations) +โœ… Focal loss (from previous iterations) โœ… Class weighting (from previous iterations) โœ… Data augmentation (from previous iterations) โœ… Advanced validation (from previous iterations) @@ -17,9 +17,10 @@ import json + def create_ultimate_notebook(): """Create the ultimate bulletproof training notebook.""" - + notebook_content = { "cells": [ { @@ -36,8 +37,8 @@ def create_ultimate_notebook(): "โœ… Data augmentation (sophisticated techniques)\n", "โœ… Advanced validation (proper testing)\n", "\n", - "**Target**: Reliable 75-85% F1 score with consistent performance" - ] + "**Target**: Reliable 75-85% F1 score with consistent performance", + ], }, { "cell_type": "code", @@ -46,8 +47,8 @@ def create_ultimate_notebook(): "outputs": [], "source": [ "# Install required packages\n", - "!pip install transformers datasets torch scikit-learn numpy pandas huggingface_hub" - ] + "!pip install transformers datasets torch scikit-learn numpy pandas huggingface_hub", + ], }, { "cell_type": "code", @@ -67,15 +68,13 @@ def create_ultimate_notebook(): "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", - "print('โœ… Packages imported successfully')" - ] + "print('โœ… Packages imported successfully')", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ” VERIFYING SPECIALIZED MODEL ACCESS" - ] + "source": ["## ๐Ÿ” VERIFYING SPECIALIZED MODEL ACCESS"], }, { "cell_type": "code", @@ -113,16 +112,10 @@ def create_ultimate_notebook(): " specialized_model_name = 'roberta-base'\n", " test_tokenizer = AutoTokenizer.from_pretrained(specialized_model_name)\n", " test_model = AutoModelForSequenceClassification.from_pretrained(specialized_model_name, num_labels=12)\n", - " print(f'โœ… Fallback model loaded: {specialized_model_name}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐ŸŽฏ DEFINING EMOTION CLASSES" - ] + " print(f'โœ… Fallback model loaded: {specialized_model_name}')", + ], }, + {"cell_type": "markdown", "metadata": {}, "source": ["## ๐ŸŽฏ DEFINING EMOTION CLASSES"]}, { "cell_type": "code", "execution_count": None, @@ -132,15 +125,13 @@ def create_ultimate_notebook(): "# Define our emotion classes\n", "emotions = ['anxious', 'calm', 'content', 'excited', 'frustrated', 'grateful', 'happy', 'hopeful', 'overwhelmed', 'proud', 'sad', 'tired']\n", "print(f'๐ŸŽฏ Our emotion classes: {emotions}')\n", - "print(f'๐Ÿ“Š Number of emotions: {len(emotions)}')" - ] + "print(f'๐Ÿ“Š Number of emotions: {len(emotions)}')", + ], }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ“Š CREATING ENHANCED DATASET WITH AUGMENTATION" - ] + "source": ["## ๐Ÿ“Š CREATING ENHANCED DATASET WITH AUGMENTATION"], }, { "cell_type": "code", @@ -326,7 +317,7 @@ def create_ultimate_notebook(): "\n", "# Data augmentation function\n", "def augment_text(text, emotion):\n", - " \"\"\"Create augmented versions of the text.\"\"\"\n", + ' """Create augmented versions of the text."""\n', " augmented = []\n", " \n", " # Synonym replacement\n", @@ -374,38 +365,31 @@ def create_ultimate_notebook(): "\n", "# Create dataset\n", "dataset = Dataset.from_list(enhanced_data)\n", - "print(f'โœ… Dataset created with {len(dataset)} samples')" - ] - } + "print(f'โœ… Dataset created with {len(dataset)} samples')", + ], + }, ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, + "codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - } + "version": "3.8.5", + }, }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 4, } - + # Save the notebook output_path = "notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb" - with open(output_path, 'w') as f: + with open(output_path, "w") as f: json.dump(notebook_content, f, indent=2) - + print(f"โœ… Created ultimate bulletproof notebook: {output_path}") print("๐Ÿ“‹ Features included:") print(" โœ… Configuration preservation") @@ -413,8 +397,9 @@ def create_ultimate_notebook(): print(" โœ… Class weighting (to be added)") print(" โœ… Data augmentation") print(" โœ… Advanced validation (to be added)") - + return output_path + if __name__ == "__main__": - create_ultimate_notebook() \ No newline at end of file + create_ultimate_notebook() diff --git a/scripts/training/debug_colab_compatibility.py b/scripts/training/debug_colab_compatibility.py index 5f3b9b784..2146633db 100644 --- a/scripts/training/debug_colab_compatibility.py +++ b/scripts/training/debug_colab_compatibility.py @@ -9,17 +9,31 @@ python scripts/debug_colab_compatibility.py """ -import sys +import shlex import subprocess +import sys import warnings -warnings.filterwarnings('ignore') +warnings.filterwarnings("ignore") + def run_command(command, description): """Run a command and return success status.""" print(f"๐Ÿ”ง {description}...") try: - result = subprocess.run(command, shell=True, capture_output=True, text=True) + # Convert string command to argv safely + if isinstance(command, str): + command = shlex.split(command) + elif isinstance(command, tuple): + command = list(command) + result = subprocess.run( + command, + capture_output=True, + text=True, + check=False, + shell=False, + timeout=300, + ) if result.returncode == 0: print(f"โœ… {description} successful") return True, result.stdout @@ -30,12 +44,13 @@ def run_command(command, description): print(f"โŒ {description} failed: {e}") return False, str(e) + def check_python_version(): """Check Python version compatibility.""" print("๐Ÿ Checking Python version...") version = sys.version_info print(f"Python {version.major}.{version.minor}.{version.micro}") - + if version.major == 3 and version.minor >= 8: print("โœ… Python version is compatible") return True @@ -43,14 +58,16 @@ def check_python_version(): print("โŒ Python version may be incompatible (recommend 3.8+)") return False + def check_gpu_availability(): """Check GPU availability and CUDA compatibility.""" print("๐Ÿ–ฅ๏ธ Checking GPU availability...") - + try: import torch + print(f"PyTorch version: {torch.__version__}") - + if torch.cuda.is_available(): print(f"โœ… CUDA available") print(f"GPU: {torch.cuda.get_device_name(0)}") @@ -64,71 +81,78 @@ def check_gpu_availability(): print("โŒ PyTorch not installed") return False + def check_pytorch_installation(): """Check PyTorch installation and compatibility.""" print("๐Ÿ” Checking PyTorch installation...") - + try: import torch + print(f"PyTorch: {torch.__version__}") - + # Test basic operations x = torch.randn(2, 2) y = torch.randn(2, 2) - z = torch.mm(x, y) + _z = torch.mm(x, y) print("โœ… Basic PyTorch operations work") - + # Test CUDA operations if available if torch.cuda.is_available(): x_cuda = x.cuda() y_cuda = y.cuda() z_cuda = torch.mm(x_cuda, y_cuda) print("โœ… CUDA operations work") - + return True except Exception as e: print(f"โŒ PyTorch test failed: {e}") return False + def check_transformers_installation(): """Check Transformers installation and compatibility.""" print("๐Ÿค— Checking Transformers installation...") - + try: import transformers + print(f"Transformers: {transformers.__version__}") - + # Test basic imports from transformers import AutoModel, AutoTokenizer + print("โœ… Transformers imports successful") - + # Test model loading tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") model = AutoModel.from_pretrained("bert-base-uncased") print("โœ… Model loading successful") - + return True except Exception as e: print(f"โŒ Transformers test failed: {e}") return False + def check_triton_compatibility(): """Check Triton compatibility (common source of errors).""" print("๐Ÿ”ง Checking Triton compatibility...") - + try: import torch - + # Check if Triton is available - if hasattr(torch, 'sparse') and hasattr(torch.sparse, '_triton_ops_meta'): + if hasattr(torch, "sparse") and hasattr(torch.sparse, "_triton_ops_meta"): print("โœ… Triton ops available") return True else: print("โš ๏ธ Triton ops not available - this may cause issues") - + # Try to import triton directly try: import triton + print(f"Triton version: {triton.__version__}") return True except ImportError: @@ -138,25 +162,36 @@ def check_triton_compatibility(): print(f"โŒ Triton check failed: {e}") return False + def fix_pytorch_installation(): """Fix PyTorch installation issues.""" print("๐Ÿ”ง Fixing PyTorch installation...") - + # Uninstall existing PyTorch success, _ = run_command( - "pip uninstall torch torchvision torchaudio -y", - "Uninstalling existing PyTorch" + [sys.executable, "-m", "pip", "uninstall", "-y", "torch", "torchvision", "torchaudio"], + "Uninstalling existing PyTorch", ) - + if not success: print("โš ๏ธ Failed to uninstall PyTorch") - + # Install compatible PyTorch success, _ = run_command( - "pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu118", - "Installing compatible PyTorch" + [ + sys.executable, + "-m", + "pip", + "install", + "--index-url", + "https://download.pytorch.org/whl/cu118", + "torch==2.1.0", + "torchvision==0.16.0", + "torchaudio==2.1.0", + ], + "Installing compatible PyTorch", ) - + if success: print("โœ… PyTorch installation fixed") return True @@ -164,25 +199,26 @@ def fix_pytorch_installation(): print("โŒ PyTorch installation failed") return False + def fix_transformers_installation(): """Fix Transformers installation issues.""" print("๐Ÿ”ง Fixing Transformers installation...") - + # Uninstall existing Transformers success, _ = run_command( - "pip uninstall transformers -y", - "Uninstalling existing Transformers" + [sys.executable, "-m", "pip", "uninstall", "-y", "transformers"], + "Uninstalling existing Transformers", ) - + if not success: print("โš ๏ธ Failed to uninstall Transformers") - + # Install compatible Transformers success, _ = run_command( - "pip install transformers==4.30.0", - "Installing compatible Transformers" + [sys.executable, "-m", "pip", "install", "transformers==4.30.0"], + "Installing compatible Transformers", ) - + if success: print("โœ… Transformers installation fixed") return True @@ -190,67 +226,72 @@ def fix_transformers_installation(): print("โŒ Transformers installation failed") return False + def test_model_initialization(): """Test model initialization to catch common errors.""" print("๐Ÿงช Testing model initialization...") - + try: import torch from transformers import AutoModel, AutoTokenizer - + # Test tokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") print("โœ… Tokenizer loaded") - + # Test model model = AutoModel.from_pretrained("bert-base-uncased") print("โœ… Model loaded") - + # Test forward pass inputs = tokenizer("Hello world", return_tensors="pt") - outputs = model(**inputs) + _outputs = model(**inputs) print("โœ… Forward pass successful") - + # Test GPU if available if torch.cuda.is_available(): model = model.cuda() inputs = {k: v.cuda() for k, v in inputs.items()} - outputs = model(**inputs) + _outputs = model(**inputs) print("โœ… GPU forward pass successful") - + return True except Exception as e: print(f"โŒ Model initialization failed: {e}") import traceback + traceback.print_exc() return False + def check_dataset_loading(): """Check dataset loading capabilities.""" print("๐Ÿ“Š Checking dataset loading...") - + try: from datasets import load_dataset - + # Test loading GoEmotions dataset = load_dataset("go_emotions", "simplified") print(f"โœ… GoEmotions dataset loaded: {len(dataset['train'])} samples") - + # Test journal dataset import json - with open('data/journal_test_dataset.json', 'r') as f: + + with open("data/journal_test_dataset.json", "r") as f: journal_data = json.load(f) print(f"โœ… Journal dataset loaded: {len(journal_data)} samples") - + return True except Exception as e: print(f"โŒ Dataset loading failed: {e}") return False + def generate_compatibility_report(): """Generate a comprehensive compatibility report.""" print("๐Ÿ“‹ Generating compatibility report...") - + report = { "python_version": check_python_version(), "gpu_available": check_gpu_availability(), @@ -258,20 +299,20 @@ def generate_compatibility_report(): "transformers_working": check_transformers_installation(), "triton_compatible": check_triton_compatibility(), "model_initialization": test_model_initialization(), - "dataset_loading": check_dataset_loading() + "dataset_loading": check_dataset_loading(), } - - print("\n" + "="*50) + + print("\n" + "=" * 50) print("COMPATIBILITY REPORT") - print("="*50) - + print("=" * 50) + for test, result in report.items(): status = "โœ… PASS" if result else "โŒ FAIL" print(f"{test.replace('_', ' ').title()}: {status}") - + all_passed = all(report.values()) print(f"\nOverall Status: {'โœ… READY' if all_passed else 'โŒ NEEDS FIXES'}") - + if not all_passed: print("\n๐Ÿ”ง Recommended fixes:") if not report["pytorch_working"]: @@ -280,42 +321,45 @@ def generate_compatibility_report(): print("- Run: fix_transformers_installation()") if not report["triton_compatible"]: print("- Consider reinstalling PyTorch with Triton support") - + return report + def main(): """Main debugging function.""" print("๐Ÿš€ SAMO Deep Learning - Colab Compatibility Debug") - print("="*50) - + print("=" * 50) + # Check if we're in Colab try: - import google.colab + pass + print("โœ… Running in Google Colab") except ImportError: print("โš ๏ธ Not running in Google Colab") - + # Generate report report = generate_compatibility_report() - + # Offer fixes if not report["pytorch_working"]: print("\n๐Ÿ”ง Would you like to fix PyTorch installation? (y/n)") response = input().lower() - if response == 'y': + if response == "y": fix_pytorch_installation() - + if not report["transformers_working"]: print("\n๐Ÿ”ง Would you like to fix Transformers installation? (y/n)") response = input().lower() - if response == 'y': + if response == "y": fix_transformers_installation() - + print("\n๐ŸŽฏ Debug complete!") print("๐Ÿ“‹ If issues persist, try:") print(" 1. Restart Colab runtime") print(" 2. Use the fixed notebook: domain_adaptation_gpu_training_fixed.ipynb") print(" 3. Check the Colab GPU development guide") + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/training/debug_training_loss.py b/scripts/training/debug_training_loss.py index 7ff4f581f..4c84a28c8 100644 --- a/scripts/training/debug_training_loss.py +++ b/scripts/training/debug_training_loss.py @@ -16,13 +16,14 @@ import torch from torch import nn -# Add src to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) - from src.models.emotion_detection.bert_classifier import WeightedBCELoss from src.models.emotion_detection.dataset_loader import create_goemotions_loader from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer +# Add src to path +sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) + + # Configure logging logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) @@ -37,7 +38,7 @@ def debug_data_loading(): model_name="bert-base-uncased", batch_size=4, # Small batch for debugging num_epochs=1, - dev_mode=True + dev_mode=True, ) datasets = trainer.prepare_data(dev_mode=True) @@ -73,14 +74,18 @@ def debug_data_loading(): # Check for extreme values if labels.max() > 1.0 or labels.min() < 0.0: - logger.warning(f"โš ๏ธ Labels outside [0,1] range: min={labels.min().item()}, max={labels.max().item()}") + logger.warning( + f"โš ๏ธ Labels outside [0,1] range: min={labels.min().item()}, max={labels.max().item()}" + ) # Check label distribution per class for class_idx in range(labels.shape[1]): class_labels = labels[:, class_idx] positive_count = (class_labels > 0).sum().item() total_count = class_labels.numel() - logger.info(f" Class {class_idx}: {positive_count}/{total_count} positive ({positive_count/total_count:.2%})") + logger.info( + f" Class {class_idx}: {positive_count}/{total_count} positive ({positive_count/total_count:.2%})" + ) return True @@ -95,10 +100,7 @@ def debug_model_outputs(datasets): try: trainer = EmotionDetectionTrainer( - model_name="bert-base-uncased", - batch_size=4, - num_epochs=1, - dev_mode=True + model_name="bert-base-uncased", batch_size=4, num_epochs=1, dev_mode=True ) # Initialize trainer and model @@ -131,7 +133,9 @@ def debug_model_outputs(datasets): # Check for extreme values if predictions.max() > 0.999 or predictions.min() < 0.001: - logger.warning(f"โš ๏ธ Predictions near extremes: min={predictions.min().item():.4f}, max={predictions.max().item():.4f}") + logger.warning( + f"โš ๏ธ Predictions near extremes: min={predictions.min().item():.4f}, max={predictions.max().item():.4f}" + ) # Examine first batch logger.info("๐Ÿ“‹ First batch details:") @@ -161,8 +165,8 @@ def debug_loss_calculation(logits, predictions, labels): epsilon = 1e-7 predictions_clipped = torch.clamp(predictions, epsilon, 1 - epsilon) manual_loss = -torch.mean( - labels * torch.log(predictions_clipped) + - (1 - labels) * torch.log(1 - predictions_clipped) + labels * torch.log(predictions_clipped) + + (1 - labels) * torch.log(1 - predictions_clipped) ) logger.info(f"๐Ÿ“Š Manual BCE Loss: {manual_loss.item():.6f}") @@ -186,8 +190,7 @@ def debug_loss_calculation(logits, predictions, labels): # 6. Test with small epsilon predictions_eps = torch.clamp(predictions, 1e-10, 1 - 1e-10) loss_eps = -torch.mean( - labels * torch.log(predictions_eps) + - (1 - labels) * torch.log(1 - predictions_eps) + labels * torch.log(predictions_eps) + (1 - labels) * torch.log(1 - predictions_eps) ) logger.info(f"๐Ÿ“Š Loss with epsilon: {loss_eps.item():.6f}") @@ -222,10 +225,14 @@ def debug_class_weights(): # Calculate weights total_samples = len(train_data) - class_weights = total_samples / (num_classes * class_counts + 1) # Add 1 to avoid division by zero + class_weights = total_samples / ( + num_classes * class_counts + 1 + ) # Add 1 to avoid division by zero logger.info(f"๐Ÿ“Š First 10 class weights: {class_weights[:10].tolist()}") - logger.info(f"๐Ÿ“Š Weight range: {class_weights.min().item():.2f} - {class_weights.max().item():.2f}") + logger.info( + f"๐Ÿ“Š Weight range: {class_weights.min().item():.2f} - {class_weights.max().item():.2f}" + ) return True @@ -248,13 +255,10 @@ def main(): # Debug model outputs trainer = EmotionDetectionTrainer( - model_name="bert-base-uncased", - batch_size=4, - num_epochs=1, - dev_mode=True + model_name="bert-base-uncased", batch_size=4, num_epochs=1, dev_mode=True ) datasets = trainer.prepare_data(dev_mode=True) - + logits, predictions, labels = debug_model_outputs(datasets) if logits is None: return False diff --git a/scripts/training/final_bulletproof_training_cell.py b/scripts/training/final_bulletproof_training_cell.py index 4a4cce5cb..f73f00288 100644 --- a/scripts/training/final_bulletproof_training_cell.py +++ b/scripts/training/final_bulletproof_training_cell.py @@ -5,38 +5,42 @@ print("๐Ÿš€ FINAL BULLETPROOF TRAINING FOR REQ-DL-012 - PROPER LABEL MAPPING") print("=" * 70) +import json # Step 1: Clear everything and validate environment import os -import sys -import json import pickle -import torch -import torch.nn as nn +import sys + import numpy as np import pandas as pd -from datasets import load_dataset -from torch.utils.data import Dataset, DataLoader +import torch +import torch.nn as nn +# Download results +from google.colab import files +from sklearn.metrics import accuracy_score, f1_score from sklearn.model_selection import train_test_split -from sklearn.metrics import f1_score, accuracy_score from sklearn.preprocessing import LabelEncoder +from torch.utils.data import DataLoader, Dataset from transformers import AutoModel, AutoTokenizer +from datasets import load_dataset + print("โœ… Imports successful") # Clear GPU memory -if torch.cuda.is_available(): + if torch.cuda.is_available(): torch.cuda.empty_cache() print(f"โœ… GPU memory cleared: {torch.cuda.get_device_name()}") -else: + else: print("โš ๏ธ CUDA not available, using CPU") # Test basic operations -try: + try: test_tensor = torch.randn(2, 3) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") test_tensor.to(device) print("โœ… Basic tensor operations work") -except Exception as e: + except Exception as e: print(f"โŒ Basic tensor operations failed: {e}") raise @@ -107,7 +111,7 @@ go_texts = [] go_labels = [] -for example in go_emotions['train']: + for example in go_emotions['train']: if example['labels']: # Convert integer labels to emotion names emotion_indices = example['labels'] @@ -152,10 +156,10 @@ expected_range = (0, len(label_encoder.classes_) - 1) print(f"๐Ÿ“Š Expected range: {expected_range}") -if min(go_label_ids) >= expected_range[0] and max(go_label_ids) <= expected_range[1] and \ + if min(go_label_ids) >= expected_range[0] and max(go_label_ids) <= expected_range[1] and \ min(journal_label_ids) >= expected_range[0] and max(journal_label_ids) <= expected_range[1]: print("โœ… All labels within expected range") -else: + else: print("โŒ Labels outside expected range!") raise ValueError("Label range validation failed") @@ -166,30 +170,30 @@ def __init__(self, texts, labels, tokenizer, max_length=128): self.labels = labels self.tokenizer = tokenizer self.max_length = max_length - + # Validate data if len(texts) != len(labels): raise ValueError(f"Texts and labels have different lengths: {len(texts)} vs {len(labels)}") - + # Validate labels for i, label in enumerate(labels): if not isinstance(label, int) or label < 0: raise ValueError(f"Invalid label at index {i}: {label}") - + def __len__(self): return len(self.texts) - + def __getitem__(self, idx): text = self.texts[idx] label = self.labels[idx] - + # Validate inputs if not isinstance(text, str) or not text.strip(): raise ValueError(f"Invalid text at index {idx}") - + if not isinstance(label, int) or label < 0: raise ValueError(f"Invalid label at index {idx}: {label}") - + encoding = self.tokenizer( text, truncation=True, @@ -197,7 +201,7 @@ def __getitem__(self, idx): max_length=self.max_length, return_tensors='pt' ) - + return { 'input_ids': encoding['input_ids'].flatten(), 'attention_mask': encoding['attention_mask'].flatten(), @@ -208,33 +212,33 @@ def __getitem__(self, idx): class SimpleEmotionClassifier(nn.Module): def __init__(self, model_name="bert-base-uncased", num_labels=None): super().__init__() - + if num_labels is None or num_labels <= 0: raise ValueError(f"Invalid num_labels: {num_labels}") - + self.num_labels = num_labels self.bert = AutoModel.from_pretrained(model_name) self.dropout = nn.Dropout(0.3) self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels) - + print(f"โœ… Model initialized with {num_labels} labels") - + def forward(self, input_ids, attention_mask): # Validate inputs if input_ids.dim() != 2: raise ValueError(f"Expected input_ids to be 2D, got {input_ids.dim()}D") - + if attention_mask.dim() != 2: raise ValueError(f"Expected attention_mask to be 2D, got {attention_mask.dim()}D") - + outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) pooled_output = outputs.pooler_output logits = self.classifier(self.dropout(pooled_output)) - + # Validate outputs if logits.shape[-1] != self.num_labels: raise ValueError(f"Expected {self.num_labels} output classes, got {logits.shape[-1]}") - + return logits # Step 9: Setup training @@ -278,14 +282,14 @@ def forward(self, input_ids, attention_mask): num_epochs = 3 # Reduced for testing best_f1 = 0.0 -for epoch in range(num_epochs): + for epoch in range(num_epochs): print(f"\n๐Ÿ”„ Epoch {epoch + 1}/{num_epochs}") - + # Training model.train() total_loss = 0 num_batches = 0 - + # Train on GoEmotions print(" ๐Ÿ“š Training on GoEmotions...") for i, batch in enumerate(go_loader): @@ -294,34 +298,34 @@ def forward(self, input_ids, attention_mask): if 'input_ids' not in batch or 'attention_mask' not in batch or 'labels' not in batch: print(f"โš ๏ธ Invalid batch structure at batch {i}") continue - + # Move to device with validation input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + # Validate labels if torch.any(labels >= num_labels) or torch.any(labels < 0): print(f"โš ๏ธ Invalid labels in batch {i}: {labels}") continue - + # Forward pass optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() num_batches += 1 - + if i % 50 == 0: print(f" Batch {i}/{len(go_loader)}, Loss: {loss.item():.4f}") - + except Exception as e: print(f"โŒ Error in batch {i}: {e}") continue - + # Train on journal data print(" ๐Ÿ“ Training on journal data...") for i, batch in enumerate(journal_train_loader): @@ -329,67 +333,67 @@ def forward(self, input_ids, attention_mask): input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + if torch.any(labels >= num_labels) or torch.any(labels < 0): continue - + optimizer.zero_grad() outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() num_batches += 1 - + if i % 10 == 0: print(f" Batch {i}/{len(journal_train_loader)}, Loss: {loss.item():.4f}") - + except Exception as e: print(f"โŒ Error in journal batch {i}: {e}") continue - + # Validation print(" ๐ŸŽฏ Validating...") model.eval() all_preds = [] all_labels = [] - + with torch.no_grad(): for batch in journal_val_loader: try: input_ids = batch['input_ids'].to(device) attention_mask = batch['attention_mask'].to(device) labels = batch['labels'].to(device) - + outputs = model(input_ids=input_ids, attention_mask=attention_mask) preds = torch.argmax(outputs, dim=1) - + all_preds.extend(preds.cpu().numpy()) all_labels.extend(labels.cpu().numpy()) - + except Exception as e: print(f"โŒ Error in validation batch: {e}") continue - + # Calculate metrics if all_preds and all_labels: f1_macro = f1_score(all_labels, all_preds, average='macro') accuracy = accuracy_score(all_labels, all_preds) - + avg_loss = total_loss / num_batches if num_batches > 0 else 0 - + print(f" ๐Ÿ“Š Epoch {epoch + 1} Results:") print(f" Average Loss: {avg_loss:.4f}") print(f" Validation F1 (Macro): {f1_macro:.4f}") print(f" Validation Accuracy: {accuracy:.4f}") - + # Save best model if f1_macro > best_f1: best_f1 = f1_macro torch.save(model.state_dict(), 'best_simple_model.pth') print(f" ๐Ÿ’พ New best model saved! F1: {best_f1:.4f}") - + # Clear GPU cache if torch.cuda.is_available(): torch.cuda.empty_cache() @@ -414,8 +418,6 @@ def forward(self, input_ids, attention_mask): print(f"๐Ÿ“Š Final F1 Score: {best_f1:.4f}") print(f"๐ŸŽฏ Target Met: {'โœ…' if best_f1 >= 0.7 else 'โŒ'}") -# Download results -from google.colab import files files.download('best_simple_model.pth') files.download('simple_training_results.json') @@ -423,4 +425,4 @@ def forward(self, input_ids, attention_mask): print("๐Ÿ“ Files downloaded: best_simple_model.pth, simple_training_results.json") print("\n๐Ÿ”ฅ THIS VERSION HAS PROPER INTEGER-TO-EMOTION MAPPING!") print("๐Ÿ”ฅ NO MORE ZERO SAMPLES ISSUE!") -print("๐Ÿ”ฅ READY TO ACHIEVE 70% F1 SCORE!") \ No newline at end of file +print("๐Ÿ”ฅ READY TO ACHIEVE 70% F1 SCORE!") diff --git a/scripts/training/final_combined_training.py b/scripts/training/final_combined_training.py index 0d278c1a2..5e2ead651 100644 --- a/scripts/training/final_combined_training.py +++ b/scripts/training/final_combined_training.py @@ -14,136 +14,133 @@ """ import json +import warnings + import numpy as np import torch +from sklearn.metrics import accuracy_score, f1_score +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import LabelEncoder from torch.utils.data import Dataset from transformers import ( - AutoTokenizer, - AutoModelForSequenceClassification, - TrainingArguments, + AutoModelForSequenceClassification, + AutoTokenizer, + EarlyStoppingCallback, Trainer, - EarlyStoppingCallback + TrainingArguments, ) -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import LabelEncoder -from sklearn.metrics import f1_score, accuracy_score -import warnings -warnings.filterwarnings('ignore') + +warnings.filterwarnings("ignore") print("๐Ÿš€ FINAL COMBINED TRAINING - JOURNAL + CMU-MOSEI") print("=" * 60) + def load_combined_dataset(): """Load and combine journal and CMU-MOSEI datasets""" print("๐Ÿ“Š Loading combined dataset...") - + combined_samples = [] - + # Load original journal dataset (150 high-quality samples) try: - with open('data/journal_test_dataset.json', 'r') as f: + with open("data/journal_test_dataset.json", "r") as f: journal_data = json.load(f) - + for item in journal_data: - combined_samples.append({ - 'text': item['text'], - 'emotion': item['emotion'], - 'source': 'journal' - }) + combined_samples.append( + {"text": item["text"], "emotion": item["emotion"], "source": "journal"} + ) print(f"โœ… Loaded {len(journal_data)} journal samples") except Exception as e: print(f"โš ๏ธ Could not load journal data: {e}") - + # Load CMU-MOSEI dataset try: - with open('data/cmu_mosei_balanced_dataset.json', 'r') as f: + with open("data/cmu_mosei_balanced_dataset.json", "r") as f: cmu_data = json.load(f) - + for item in cmu_data: - combined_samples.append({ - 'text': item['text'], - 'emotion': item['emotion'], - 'source': 'cmu_mosei' - }) + combined_samples.append( + {"text": item["text"], "emotion": item["emotion"], "source": "cmu_mosei"} + ) print(f"โœ… Loaded {len(cmu_data)} CMU-MOSEI samples") except Exception as e: print(f"โš ๏ธ Could not load CMU-MOSEI data: {e}") - + # Load expanded journal dataset as backup try: - with open('data/expanded_journal_dataset.json', 'r') as f: + with open("data/expanded_journal_dataset.json", "r") as f: expanded_data = json.load(f) - + # Only use a subset to avoid synthetic data issues subset_size = min(200, len(expanded_data)) selected_samples = np.random.choice(expanded_data, size=subset_size, replace=False) - + for item in selected_samples: - combined_samples.append({ - 'text': item['text'], - 'emotion': item['emotion'], - 'source': 'expanded_journal' - }) + combined_samples.append( + {"text": item["text"], "emotion": item["emotion"], "source": "expanded_journal"} + ) print(f"โœ… Loaded {subset_size} expanded journal samples") except Exception as e: print(f"โš ๏ธ Could not load expanded journal data: {e}") - + print(f"๐Ÿ“Š Total combined samples: {len(combined_samples)}") - + # Show emotion distribution emotion_counts = {} for sample in combined_samples: - emotion = sample['emotion'] + emotion = sample["emotion"] emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1 - + print("๐Ÿ“Š Emotion distribution:") for emotion, count in sorted(emotion_counts.items()): print(f" {emotion}: {count} samples") - + return combined_samples + class EmotionDataset(Dataset): """Custom dataset for emotion classification""" - + def __init__(self, texts, labels, tokenizer, max_length=128): self.texts = texts self.labels = labels self.tokenizer = tokenizer self.max_length = max_length - + def __len__(self): return len(self.texts) - + def __getitem__(self, idx): text = str(self.texts[idx]) label = self.labels[idx] - + encoding = self.tokenizer( text, truncation=True, - padding='max_length', + padding="max_length", max_length=self.max_length, - return_tensors='pt' + return_tensors="pt", ) - + return { - 'input_ids': encoding['input_ids'].flatten(), - 'attention_mask': encoding['attention_mask'].flatten(), - 'labels': torch.tensor(label, dtype=torch.long) + "input_ids": encoding["input_ids"].flatten(), + "attention_mask": encoding["attention_mask"].flatten(), + "labels": torch.tensor(label, dtype=torch.long), } + def compute_metrics(eval_pred): """Compute F1 score and accuracy""" predictions, labels = eval_pred predictions = np.argmax(predictions, axis=1) - - f1 = f1_score(labels, predictions, average='weighted') + + f1 = f1_score(labels, predictions, average="weighted") accuracy = accuracy_score(labels, predictions) - - return { - 'f1': f1, - 'accuracy': accuracy - } + + return {"f1": f1, "accuracy": accuracy} + def main(): """Main training function""" @@ -151,48 +148,48 @@ def main(): print("๐Ÿ”ง Current Best: 67%") print("๐Ÿ“ˆ Expected Improvement: 8-18%") print() - + # Load combined dataset samples = load_combined_dataset() - + if not samples: print("โŒ No samples loaded!") return - + # Prepare data - texts = [sample['text'] for sample in samples] - emotions = [sample['emotion'] for sample in samples] - + texts = [sample["text"] for sample in samples] + emotions = [sample["emotion"] for sample in samples] + # Encode labels label_encoder = LabelEncoder() labels = label_encoder.fit_transform(emotions) - + print(f"๐ŸŽฏ Number of labels: {len(label_encoder.classes_)}") print(f"๐Ÿ“Š Labels: {list(label_encoder.classes_)}") - + # Split data train_texts, test_texts, train_labels, test_labels = train_test_split( texts, labels, test_size=0.2, random_state=42, stratify=labels ) - + print(f"๐Ÿ“ˆ Training samples: {len(train_texts)}") print(f"๐Ÿงช Test samples: {len(test_labels)}") - + # Initialize tokenizer and model print("๐Ÿ”ง Initializing model...") model_name = "bert-base-uncased" tokenizer = AutoTokenizer.from_pretrained(model_name) - + model = AutoModelForSequenceClassification.from_pretrained( model_name, num_labels=len(label_encoder.classes_), - problem_type="single_label_classification" + problem_type="single_label_classification", ) - + # Create datasets train_dataset = EmotionDataset(train_texts, train_labels, tokenizer) test_dataset = EmotionDataset(test_texts, test_labels, tokenizer) - + # Training arguments optimized for performance training_args = TrainingArguments( output_dir="./emotion_model_combined", @@ -216,7 +213,7 @@ def main(): learning_rate=2e-5, # Optimal learning rate gradient_accumulation_steps=2, # Effective batch size = 32 ) - + # Initialize trainer trainer = Trainer( model=model, @@ -224,24 +221,24 @@ def main(): train_dataset=train_dataset, eval_dataset=test_dataset, compute_metrics=compute_metrics, - callbacks=[EarlyStoppingCallback(early_stopping_patience=3)] + callbacks=[EarlyStoppingCallback(early_stopping_patience=3)], ) - + # Train model print("๐Ÿš€ Starting training...") trainer.train() - + # Evaluate final model print("๐Ÿ“Š Evaluating final model...") results = trainer.evaluate() - + print(f"๐Ÿ† Final F1 Score: {results['eval_f1']:.4f} ({results['eval_f1']*100:.2f}%)") print(f"๐ŸŽฏ Target achieved: {'โœ… YES!' if results['eval_f1'] >= 0.75 else 'โŒ Not yet'}") - + # Save model trainer.save_model("./emotion_model_final_combined") print("๐Ÿ’พ Model saved to ./emotion_model_final_combined") - + # Test on sample texts print("\n๐Ÿงช Testing on sample texts...") test_texts = [ @@ -249,9 +246,9 @@ def main(): "This is so frustrating, nothing works.", "I'm anxious about the presentation.", "I'm grateful for all the support.", - "I'm tired and need some rest." + "I'm tired and need some rest.", ] - + model.eval() with torch.no_grad(): for text in test_texts: @@ -260,16 +257,17 @@ def main(): probs = torch.softmax(outputs.logits, dim=1) predicted_label = torch.argmax(probs, dim=1).item() confidence = torch.max(probs).item() - + predicted_emotion = label_encoder.inverse_transform([predicted_label])[0] print(f"Text: {text}") print(f"Predicted: {predicted_emotion} (confidence: {confidence:.3f})") print() - + print("๐ŸŽ‰ Training completed!") print(f"๐Ÿ“ˆ Final F1 Score: {results['eval_f1']*100:.2f}%") print(f"๐ŸŽฏ Target: 75-85%") print(f"๐Ÿ“Š Improvement: {((results['eval_f1'] - 0.67) / 0.67 * 100):.1f}% from baseline") + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/training/final_expanded_training.py b/scripts/training/final_expanded_training.py index 435792b4d..3e09776b9 100644 --- a/scripts/training/final_expanded_training.py +++ b/scripts/training/final_expanded_training.py @@ -7,40 +7,42 @@ to achieve the target 75-85% F1 score. Target: 75-85% F1 Score -Current: 67% F1 Score +Current: 67% F1 Score Expected: 8-18% improvement """ import json +import warnings + import numpy as np import torch +from sklearn.metrics import accuracy_score, f1_score +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import LabelEncoder from torch.utils.data import Dataset from transformers import ( - AutoTokenizer, - AutoModelForSequenceClassification, - TrainingArguments, + AutoModelForSequenceClassification, + AutoTokenizer, + EarlyStoppingCallback, Trainer, - EarlyStoppingCallback + TrainingArguments, ) -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import LabelEncoder -from sklearn.metrics import f1_score, accuracy_score -import warnings -warnings.filterwarnings('ignore') + +warnings.filterwarnings("ignore") print("๐Ÿš€ FINAL EXPANDED DATASET TRAINING") print("=" * 50) # Load expanded dataset print("๐Ÿ“Š Loading expanded dataset...") -with open('data/expanded_journal_dataset.json', 'r') as f: +with open("data/expanded_journal_dataset.json", "r") as f: expanded_data = json.load(f) print(f"โœ… Loaded {len(expanded_data)} expanded samples") # Prepare data -texts = [item['content'] for item in expanded_data] -emotions = [item['emotion'] for item in expanded_data] +texts = [item["content"] for item in expanded_data] +emotions = [item["emotion"] for item in expanded_data] # Encode labels label_encoder = LabelEncoder() @@ -58,6 +60,7 @@ print(f"๐Ÿ“ˆ Training samples: {len(X_train)}") print(f"๐Ÿงช Test samples: {len(X_test)}") + # Create dataset class class EmotionDataset(Dataset): def __init__(self, texts, labels, tokenizer, max_length=128): @@ -65,36 +68,35 @@ def __init__(self, texts, labels, tokenizer, max_length=128): self.labels = labels self.tokenizer = tokenizer self.max_length = max_length - + def __len__(self): return len(self.texts) - + def __getitem__(self, idx): text = str(self.texts[idx]) label = self.labels[idx] - + encoding = self.tokenizer( text, truncation=True, - padding='max_length', + padding="max_length", max_length=self.max_length, - return_tensors='pt' + return_tensors="pt", ) - + return { - 'input_ids': encoding['input_ids'].flatten(), - 'attention_mask': encoding['attention_mask'].flatten(), - 'labels': torch.tensor(label, dtype=torch.long) + "input_ids": encoding["input_ids"].flatten(), + "attention_mask": encoding["attention_mask"].flatten(), + "labels": torch.tensor(label, dtype=torch.long), } + # Initialize tokenizer and model print("๐Ÿ”ง Initializing model...") model_name = "bert-base-uncased" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained( - model_name, - num_labels=num_labels, - problem_type="single_label_classification" + model_name, num_labels=num_labels, problem_type="single_label_classification" ) # Create datasets @@ -124,18 +126,17 @@ def __getitem__(self, idx): report_to=None, # Disable wandb ) + # Custom compute_metrics function def compute_metrics(eval_pred): predictions, labels = eval_pred predictions = np.argmax(predictions, axis=1) - - f1 = f1_score(labels, predictions, average='weighted') + + f1 = f1_score(labels, predictions, average="weighted") accuracy = accuracy_score(labels, predictions) - - return { - 'f1': f1, - 'accuracy': accuracy - } + + return {"f1": f1, "accuracy": accuracy} + # Initialize trainer trainer = Trainer( @@ -144,7 +145,7 @@ def compute_metrics(eval_pred): train_dataset=train_dataset, eval_dataset=test_dataset, compute_metrics=compute_metrics, - callbacks=[EarlyStoppingCallback(early_stopping_patience=3)] + callbacks=[EarlyStoppingCallback(early_stopping_patience=3)], ) # Train the model @@ -154,7 +155,7 @@ def compute_metrics(eval_pred): # Evaluate on test set print("๐Ÿงช Evaluating model...") results = trainer.evaluate() -print(f"๐Ÿ“Š Final Results:") +print("๐Ÿ“Š Final Results:") print(f" F1 Score: {results['eval_f1']:.4f} ({results['eval_f1']*100:.1f}%)") print(f" Accuracy: {results['eval_accuracy']:.4f} ({results['eval_accuracy']*100:.1f}%)") @@ -177,11 +178,23 @@ def compute_metrics(eval_pred): "I feel calm and peaceful right now.", "I'm hopeful that things will get better.", "I'm tired and need some rest.", - "I'm content with how things are going." + "I'm content with how things are going.", ] -expected_emotions = ['happy', 'frustrated', 'anxious', 'grateful', 'overwhelmed', - 'proud', 'sad', 'excited', 'calm', 'hopeful', 'tired', 'content'] +expected_emotions = [ + "happy", + "frustrated", + "anxious", + "grateful", + "overwhelmed", + "proud", + "sad", + "excited", + "calm", + "hopeful", + "tired", + "content", +] print("๐Ÿ“Š Testing Results:") print("=" * 80) @@ -189,8 +202,8 @@ def compute_metrics(eval_pred): correct_predictions = 0 for i, (text, expected) in enumerate(zip(test_samples, expected_emotions), 1): # Tokenize - inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=128) - + inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128) + # Predict with torch.no_grad(): outputs = model(**inputs) @@ -198,40 +211,40 @@ def compute_metrics(eval_pred): predicted_idx = torch.argmax(probabilities, dim=1).item() confidence = probabilities[0][predicted_idx].item() predicted_emotion = label_encoder.inverse_transform([predicted_idx])[0] - + # Get top 3 predictions top_3_indices = torch.topk(probabilities[0], 3).indices top_3_emotions = label_encoder.inverse_transform(top_3_indices.cpu().numpy()) top_3_probs = torch.topk(probabilities[0], 3).values.cpu().numpy() - + # Check if correct is_correct = predicted_emotion == expected if is_correct: correct_predictions += 1 - + print(f"{i}. Text: {text}") print(f" Predicted: {predicted_emotion} (confidence: {confidence:.3f})") print(f" Expected: {expected}") print(f" {'โœ… CORRECT' if is_correct else 'โŒ WRONG'}") - print(f" Top 3 predictions:") + print(" Top 3 predictions:") for emotion, prob in zip(top_3_emotions, top_3_probs): print(f" - {emotion}: {prob:.3f}") print() test_accuracy = correct_predictions / len(test_samples) -final_f1 = results['eval_f1'] +final_f1 = results["eval_f1"] -print(f"\n๐Ÿ“ˆ FINAL RESULTS:") +print("\n๐Ÿ“ˆ FINAL RESULTS:") print(f" Test Accuracy: {test_accuracy:.2%} ({correct_predictions}/{len(test_samples)})") print(f" F1 Score: {final_f1:.4f} ({final_f1*100:.1f}%)") print(f" Target Achieved: {'โœ… YES!' if final_f1 >= 0.75 else 'โŒ Not yet'}") if final_f1 >= 0.75: print(f"\n๐ŸŽ‰ SUCCESS! Model achieved {final_f1*100:.1f}% F1 score!") - print(f"๐Ÿš€ Ready for production deployment!") + print("๐Ÿš€ Ready for production deployment!") else: print(f"\n๐Ÿ“ˆ Good progress! Current F1: {final_f1*100:.1f}%") - print(f"๐Ÿ’ก Consider: more data, hyperparameter tuning, or different model architecture") + print("๐Ÿ’ก Consider: more data, hyperparameter tuning, or different model architecture") -print(f"\n๐Ÿ’พ Model saved to: ./best_emotion_model_final") -print(f"๐Ÿ“Š Training completed successfully!") \ No newline at end of file +print("\n๐Ÿ’พ Model saved to: ./best_emotion_model_final") +print("๐Ÿ“Š Training completed successfully!") diff --git a/scripts/training/fix_imports_in_notebook.py b/scripts/training/fix_imports_in_notebook.py index b65d8d307..25a79c375 100644 --- a/scripts/training/fix_imports_in_notebook.py +++ b/scripts/training/fix_imports_in_notebook.py @@ -9,18 +9,19 @@ import json + def fix_imports(): """Add missing imports to the ultimate notebook.""" - + # Read the existing notebook - with open('notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb', 'r') as f: + with open("notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb", "r") as f: notebook = json.load(f) - + # Find the imports cell and update it - for cell in notebook['cells']: - if cell['cell_type'] == 'code' and 'import torch' in ''.join(cell['source']): + for cell in notebook["cells"]: + if cell["cell_type"] == "code" and "import torch" in "".join(cell["source"]): # Update the imports cell - cell['source'] = [ + cell["source"] = [ "import torch\n", "import numpy as np\n", "import pandas as pd\n", @@ -35,19 +36,20 @@ def fix_imports(): "\n", "print('โœ… All packages imported successfully')\n", "print(f'PyTorch version: {torch.__version__}')\n", - "print(f'CUDA available: {torch.cuda.is_available()}')" + "print(f'CUDA available: {torch.cuda.is_available()}')", ] break - + # Save the updated notebook - with open('notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb', 'w') as f: + with open("notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook, f, indent=2) - - print('โœ… Fixed imports in ultimate notebook!') - print('๐Ÿ“‹ Added missing imports:') - print(' โœ… f1_score, accuracy_score, precision_score, recall_score') - print(' โœ… compute_class_weight') - print(' โœ… CUDA availability check') + + print("โœ… Fixed imports in ultimate notebook!") + print("๐Ÿ“‹ Added missing imports:") + print(" โœ… f1_score, accuracy_score, precision_score, recall_score") + print(" โœ… compute_class_weight") + print(" โœ… CUDA availability check") + if __name__ == "__main__": - fix_imports() \ No newline at end of file + fix_imports() diff --git a/scripts/training/fix_notebook_json.py b/scripts/training/fix_notebook_json.py index c3ff9a2f0..0f5df9058 100644 --- a/scripts/training/fix_notebook_json.py +++ b/scripts/training/fix_notebook_json.py @@ -5,13 +5,14 @@ import re + def fix_notebook_json(): """Fix JSON syntax errors in the notebook.""" - + # Read the notebook as text - with open('notebooks/expanded_dataset_training.ipynb', 'r') as f: + with open("notebooks/expanded_dataset_training.ipynb", "r") as f: content = f.read() - + # Fix unescaped quotes in strings # Replace "I'm" with "I\\'m" and similar patterns content = re.sub(r'"I\'m', r'"I\\\'m', content) @@ -32,24 +33,26 @@ def fix_notebook_json(): content = re.sub(r'"shouldn\'t', r'"shouldn\\\'t', content) content = re.sub(r'"mightn\'t', r'"mightn\\\'t', content) content = re.sub(r'"mustn\'t', r'"mustn\\\'t', content) - + # Fix other common contractions content = re.sub(r'"(\w+)\'(\w+)"', r'"\\1\\\'\\2"', content) - + # Write the fixed content - with open('notebooks/expanded_dataset_training_fixed.ipynb', 'w') as f: + with open("notebooks/expanded_dataset_training_fixed.ipynb", "w") as f: f.write(content) - + print("โœ… Fixed notebook saved as 'notebooks/expanded_dataset_training_fixed.ipynb'") - + # Test if the JSON is valid try: import json - with open('notebooks/expanded_dataset_training_fixed.ipynb', 'r') as f: + + with open("notebooks/expanded_dataset_training_fixed.ipynb", "r") as f: json.load(f) print("โœ… JSON syntax is now valid") except Exception as e: print(f"โŒ JSON still has issues: {e}") + if __name__ == "__main__": - fix_notebook_json() \ No newline at end of file + fix_notebook_json() diff --git a/scripts/training/fix_preprocessing_in_notebook.py b/scripts/training/fix_preprocessing_in_notebook.py index 1bc9eae51..b24e47ba4 100644 --- a/scripts/training/fix_preprocessing_in_notebook.py +++ b/scripts/training/fix_preprocessing_in_notebook.py @@ -9,21 +9,22 @@ import json + def fix_preprocessing(): """Fix the preprocessing function in the ultimate notebook.""" - + # Read the existing notebook - with open('notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb', 'r') as f: + with open("notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb", "r") as f: notebook = json.load(f) - + # Find and replace the preprocessing cell - for i, cell in enumerate(notebook['cells']): - if cell['cell_type'] == 'code' and 'def preprocess_function' in ''.join(cell['source']): + for i, cell in enumerate(notebook["cells"]): + if cell["cell_type"] == "code" and "def preprocess_function" in "".join(cell["source"]): # Replace with fixed preprocessing - cell['source'] = [ + cell["source"] = [ "# Data preprocessing function\n", "def preprocess_function(examples):\n", - " \"\"\"Preprocess the data with proper tokenization.\"\"\"\n", + ' """Preprocess the data with proper tokenization."""\n', " # Tokenize the texts\n", " tokenized = tokenizer(\n", " examples['text'],\n", @@ -64,19 +65,17 @@ def fix_preprocessing(): "print(f'Input IDs shape: {len(sample[\"input_ids\"])}')\n", "print(f'Attention mask shape: {len(sample[\"attention_mask\"])}')\n", "print(f'Label: {sample[\"labels\"]}')\n", - "print('โœ… Data structure verified!')" + "print('โœ… Data structure verified!')", ] break - + # Also add a data collator cell after the training arguments data_collator_cell = { "cell_type": "markdown", "metadata": {}, - "source": [ - "## ๐Ÿ”ง DATA COLLATOR" - ] + "source": ["## ๐Ÿ”ง DATA COLLATOR"], } - + data_collator_code = { "cell_type": "code", "execution_count": None, @@ -92,23 +91,23 @@ def fix_preprocessing(): " return_tensors='pt'\n", ")\n", "\n", - "print('โœ… Data collator configured')" - ] + "print('โœ… Data collator configured')", + ], } - + # Find the training arguments cell and add the data collator after it - for i, cell in enumerate(notebook['cells']): - if cell['cell_type'] == 'code' and 'TrainingArguments(' in ''.join(cell['source']): + for i, cell in enumerate(notebook["cells"]): + if cell["cell_type"] == "code" and "TrainingArguments(" in "".join(cell["source"]): # Insert data collator after training arguments - notebook['cells'].insert(i + 2, data_collator_cell) - notebook['cells'].insert(i + 3, data_collator_code) + notebook["cells"].insert(i + 2, data_collator_cell) + notebook["cells"].insert(i + 3, data_collator_code) break - + # Update the trainer initialization to include the data collator - for cell in notebook['cells']: - if cell['cell_type'] == 'code' and 'WeightedLossTrainer(' in ''.join(cell['source']): + for cell in notebook["cells"]: + if cell["cell_type"] == "code" and "WeightedLossTrainer(" in "".join(cell["source"]): # Update the trainer initialization - cell['source'] = [ + cell["source"] = [ "# Initialize trainer with focal loss and class weighting\n", "trainer = WeightedLossTrainer(\n", " model=model,\n", @@ -123,20 +122,21 @@ def fix_preprocessing(): " class_weights=class_weights_tensor\n", ")\n", "\n", - "print('โœ… Trainer initialized with focal loss and class weighting')" + "print('โœ… Trainer initialized with focal loss and class weighting')", ] break - + # Save the updated notebook - with open('notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb', 'w') as f: + with open("notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook, f, indent=2) - - print('โœ… Fixed preprocessing in ultimate notebook!') - print('๐Ÿ“‹ Changes made:') - print(' โœ… Updated preprocessing function with proper tokenization') - print(' โœ… Added data collator for proper batching') - print(' โœ… Added data structure verification') - print(' โœ… Updated trainer initialization with data collator') + + print("โœ… Fixed preprocessing in ultimate notebook!") + print("๐Ÿ“‹ Changes made:") + print(" โœ… Updated preprocessing function with proper tokenization") + print(" โœ… Added data collator for proper batching") + print(" โœ… Added data structure verification") + print(" โœ… Updated trainer initialization with data collator") + if __name__ == "__main__": - fix_preprocessing() \ No newline at end of file + fix_preprocessing() diff --git a/scripts/training/fix_training_arguments.py b/scripts/training/fix_training_arguments.py index a9dcebb1b..e4a8cae4a 100644 --- a/scripts/training/fix_training_arguments.py +++ b/scripts/training/fix_training_arguments.py @@ -9,18 +9,19 @@ import json + def fix_training_arguments(): """Fix the training arguments in the simple notebook.""" - + # Read the existing notebook - with open('notebooks/SIMPLE_ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb', 'r') as f: + with open("notebooks/SIMPLE_ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb", "r") as f: notebook = json.load(f) - + # Find and replace the training arguments cell - for cell in notebook['cells']: - if cell['cell_type'] == 'code' and 'TrainingArguments(' in ''.join(cell['source']): + for cell in notebook["cells"]: + if cell["cell_type"] == "code" and "TrainingArguments(" in "".join(cell["source"]): # Replace with fixed training arguments - cell['source'] = [ + cell["source"] = [ "# Training arguments\n", "training_args = TrainingArguments(\n", " output_dir='./ultimate_emotion_model',\n", @@ -40,19 +41,20 @@ def fix_training_arguments(): " run_name='ultimate_emotion_model'\n", ")\n", "\n", - "print('โœ… Training arguments configured')" + "print('โœ… Training arguments configured')", ] break - + # Save the updated notebook - with open('notebooks/SIMPLE_ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb', 'w') as f: + with open("notebooks/SIMPLE_ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb", "w") as f: json.dump(notebook, f, indent=2) - - print('โœ… Fixed training arguments in simple notebook!') - print('๐Ÿ“‹ Changes made:') - print(' โœ… Removed evaluation_strategy parameter') - print(' โœ… Removed save_strategy parameter') - print(' โœ… Kept all other parameters intact') + + print("โœ… Fixed training arguments in simple notebook!") + print("๐Ÿ“‹ Changes made:") + print(" โœ… Removed evaluation_strategy parameter") + print(" โœ… Removed save_strategy parameter") + print(" โœ… Kept all other parameters intact") + if __name__ == "__main__": - fix_training_arguments() \ No newline at end of file + fix_training_arguments() diff --git a/scripts/training/fixed_focal_training.py b/scripts/training/fixed_focal_training.py index 2c5becf52..82b96f8d2 100644 --- a/scripts/training/fixed_focal_training.py +++ b/scripts/training/fixed_focal_training.py @@ -1,14 +1,15 @@ #!/usr/bin/env python3 -from pathlib import Path -from sklearn.metrics import f1_score, precision_score, recall_score -from torch import nn -from tqdm import tqdm import json import logging -import numpy as np import random +from pathlib import Path + +import numpy as np import torch import torch.nn.functional as F +from sklearn.metrics import f1_score, precision_score, recall_score +from torch import nn +from tqdm import tqdm from transformers import AutoModel, AutoTokenizer """ @@ -63,16 +64,39 @@ def create_proper_training_data(): logger.info("๐Ÿ“Š Creating proper training data with diverse emotion labels...") emotion_names = [ - "admiration", "amusement", "anger", "annoyance", "approval", "caring", - "confusion", "curiosity", "desire", "disappointment", "disapproval", - "disgust", "embarrassment", "excitement", "fear", "gratitude", "grief", - "joy", "love", "nervousness", "optimism", "pride", "realization", - "relief", "remorse", "sadness", "surprise", "neutral" + "admiration", + "amusement", + "anger", + "annoyance", + "approval", + "caring", + "confusion", + "curiosity", + "desire", + "disappointment", + "disapproval", + "disgust", + "embarrassment", + "excitement", + "fear", + "gratitude", + "grief", + "joy", + "love", + "nervousness", + "optimism", + "pride", + "realization", + "relief", + "remorse", + "sadness", + "surprise", + "neutral", ] # Create diverse training data with proper emotion labels training_data = [] - + # Joy examples joy_examples = [ "I'm so happy today! Everything is going great!", @@ -84,9 +108,9 @@ def create_proper_training_data(): "I'm feeling great and optimistic!", "What a fantastic experience!", "I'm delighted with how things turned out!", - "This brings me so much joy!" + "This brings me so much joy!", ] - + # Sadness examples sadness_examples = [ "I'm feeling really down today.", @@ -98,9 +122,9 @@ def create_proper_training_data(): "This is so disappointing and sad.", "I'm feeling really low today.", "Everything is going wrong.", - "I'm so upset about this situation." + "I'm so upset about this situation.", ] - + # Anger examples anger_examples = [ "I'm so angry about this!", @@ -112,9 +136,9 @@ def create_proper_training_data(): "This is unacceptable!", "I'm so frustrated and angry!", "This is driving me crazy!", - "I'm really annoyed and angry!" + "I'm really annoyed and angry!", ] - + # Fear examples fear_examples = [ "I'm really scared about what might happen.", @@ -126,9 +150,9 @@ def create_proper_training_data(): "I'm scared of what comes next.", "This is causing me a lot of fear.", "I'm terrified of the outcome.", - "This is making me really nervous." + "This is making me really nervous.", ] - + # Love examples love_examples = [ "I love you so much!", @@ -140,9 +164,9 @@ def create_proper_training_data(): "I'm so grateful for your love.", "You're my everything.", "I love spending time with you.", - "You're the love of my life." + "You're the love of my life.", ] - + # Disgust examples disgust_examples = [ "This is absolutely disgusting!", @@ -154,9 +178,9 @@ def create_proper_training_data(): "This is revolting!", "I'm appalled by this.", "This is really sickening.", - "I'm really grossed out." + "I'm really grossed out.", ] - + # Surprise examples surprise_examples = [ "Oh my God! I can't believe this!", @@ -168,9 +192,9 @@ def create_proper_training_data(): "I'm stunned by this revelation!", "This is unbelievable!", "I'm really surprised by this!", - "This is astonishing!" + "This is astonishing!", ] - + # Neutral examples neutral_examples = [ "The weather is cloudy today.", @@ -182,7 +206,7 @@ def create_proper_training_data(): "The car is parked outside.", "I have an appointment at 3 PM.", "The computer is working fine.", - "I'm reading a book." + "I'm reading a book.", ] # Create labeled data @@ -190,37 +214,37 @@ def create_proper_training_data(): labels = [0] * 28 labels[emotion_names.index("joy")] = 1 training_data.append({"text": text, "labels": labels}) - + for text in sadness_examples: labels = [0] * 28 labels[emotion_names.index("sadness")] = 1 training_data.append({"text": text, "labels": labels}) - + for text in anger_examples: labels = [0] * 28 labels[emotion_names.index("anger")] = 1 training_data.append({"text": text, "labels": labels}) - + for text in fear_examples: labels = [0] * 28 labels[emotion_names.index("fear")] = 1 training_data.append({"text": text, "labels": labels}) - + for text in love_examples: labels = [0] * 28 labels[emotion_names.index("love")] = 1 training_data.append({"text": text, "labels": labels}) - + for text in disgust_examples: labels = [0] * 28 labels[emotion_names.index("disgust")] = 1 training_data.append({"text": text, "labels": labels}) - + for text in surprise_examples: labels = [0] * 28 labels[emotion_names.index("surprise")] = 1 training_data.append({"text": text, "labels": labels}) - + for text in neutral_examples: labels = [0] * 28 labels[emotion_names.index("neutral")] = 1 @@ -228,76 +252,76 @@ def create_proper_training_data(): # Shuffle the data random.shuffle(training_data) - + # Split into train/val/test total_samples = len(training_data) train_size = int(0.7 * total_samples) val_size = int(0.15 * total_samples) - + train_data = training_data[:train_size] - val_data = training_data[train_size:train_size + val_size] - test_data = training_data[train_size + val_size:] - - logger.info(f"โœ… Created {len(train_data)} training, {len(val_data)} validation, {len(test_data)} test samples") - + val_data = training_data[train_size : train_size + val_size] + test_data = training_data[train_size + val_size :] + + logger.info( + f"โœ… Created {len(train_data)} training, {len(val_data)} validation, {len(test_data)} test samples" + ) + return train_data, val_data, test_data def create_dataloader(data, model, batch_size=8): """Create a simple dataloader for the data.""" dataloader = [] - + for i in range(0, len(data), batch_size): - batch = data[i:i + batch_size] - + batch = data[i : i + batch_size] + texts = [item["text"] for item in batch] labels = [item["labels"] for item in batch] - + # Tokenize tokenized = model.tokenizer( - texts, - padding=True, - truncation=True, - max_length=512, - return_tensors="pt" + texts, padding=True, truncation=True, max_length=512, return_tensors="pt" ) - - dataloader.append({ - "input_ids": tokenized["input_ids"], - "attention_mask": tokenized["attention_mask"], - "labels": torch.tensor(labels, dtype=torch.float32) - }) - + + dataloader.append( + { + "input_ids": tokenized["input_ids"], + "attention_mask": tokenized["attention_mask"], + "labels": torch.tensor(labels, dtype=torch.float32), + } + ) + return dataloader def train_model(model, train_data, val_data, device, epochs=10): """Train the model with focal loss.""" logger.info("๐Ÿš€ Starting model training...") - + model.to(device) optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5) criterion = FocalLoss() - - best_val_loss = float('inf') - + + best_val_loss = float("inf") + for epoch in range(epochs): model.train() total_loss = 0 - + for batch in tqdm(train_data, desc=f"Epoch {epoch + 1}/{epochs}"): input_ids = batch["input_ids"].to(device) attention_mask = batch["attention_mask"].to(device) labels = batch["labels"].to(device) - + optimizer.zero_grad() outputs = model(input_ids, attention_mask) loss = criterion(outputs, labels) loss.backward() optimizer.step() - + total_loss += loss.item() - + # Validation model.eval() val_loss = 0 @@ -306,122 +330,132 @@ def train_model(model, train_data, val_data, device, epochs=10): input_ids = batch["input_ids"].to(device) attention_mask = batch["attention_mask"].to(device) labels = batch["labels"].to(device) - + outputs = model(input_ids, attention_mask) loss = criterion(outputs, labels) val_loss += loss.item() - + avg_train_loss = total_loss / len(train_data) avg_val_loss = val_loss / len(val_data) - - logger.info(f"Epoch {epoch + 1}: Train Loss: {avg_train_loss:.4f}, Val Loss: {avg_val_loss:.4f}") - + + logger.info( + f"Epoch {epoch + 1}: Train Loss: {avg_train_loss:.4f}, Val Loss: {avg_val_loss:.4f}" + ) + # Save best model if avg_val_loss < best_val_loss: best_val_loss = avg_val_loss torch.save(model.state_dict(), "best_focal_model.pth") logger.info(f"โœ… Saved best model with val loss: {best_val_loss:.4f}") - + return model def evaluate_model(model, test_data, device): """Evaluate the model with different thresholds.""" logger.info("๐Ÿ“Š Evaluating model with different thresholds...") - + model.eval() all_predictions = [] all_labels = [] - + with torch.no_grad(): for batch in test_data: input_ids = batch["input_ids"].to(device) attention_mask = batch["attention_mask"].to(device) labels = batch["labels"].to(device) - + outputs = model(input_ids, attention_mask) predictions = torch.sigmoid(outputs) - + all_predictions.append(predictions.cpu().numpy()) all_labels.append(labels.cpu().numpy()) - + all_predictions = np.concatenate(all_predictions, axis=0) all_labels = np.concatenate(all_labels, axis=0) - + # Test different thresholds thresholds = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] best_f1 = 0 best_threshold = 0.5 - + for threshold in thresholds: binary_predictions = (all_predictions > threshold).astype(int) - + # Calculate metrics - f1 = f1_score(all_labels, binary_predictions, average='weighted', zero_division=0) - precision = precision_score(all_labels, binary_predictions, average='weighted', zero_division=0) - recall = recall_score(all_labels, binary_predictions, average='weighted', zero_division=0) - - logger.info(f"Threshold {threshold}: F1={f1:.4f}, Precision={precision:.4f}, Recall={recall:.4f}") - + f1 = f1_score(all_labels, binary_predictions, average="weighted", zero_division=0) + precision = precision_score( + all_labels, binary_predictions, average="weighted", zero_division=0 + ) + recall = recall_score(all_labels, binary_predictions, average="weighted", zero_division=0) + + logger.info( + f"Threshold {threshold}: F1={f1:.4f}, Precision={precision:.4f}, Recall={recall:.4f}" + ) + if f1 > best_f1: best_f1 = f1 best_threshold = threshold - + logger.info(f"๐ŸŽฏ Best threshold: {best_threshold} with F1: {best_f1:.4f}") - + # Final evaluation with best threshold binary_predictions = (all_predictions > best_threshold).astype(int) - final_f1 = f1_score(all_labels, binary_predictions, average='weighted', zero_division=0) - final_precision = precision_score(all_labels, binary_predictions, average='weighted', zero_division=0) - final_recall = recall_score(all_labels, binary_predictions, average='weighted', zero_division=0) - - logger.info(f"๐Ÿ† Final Results - F1: {final_f1:.4f}, Precision: {final_precision:.4f}, Recall: {final_recall:.4f}") - + final_f1 = f1_score(all_labels, binary_predictions, average="weighted", zero_division=0) + final_precision = precision_score( + all_labels, binary_predictions, average="weighted", zero_division=0 + ) + final_recall = recall_score(all_labels, binary_predictions, average="weighted", zero_division=0) + + logger.info( + f"๐Ÿ† Final Results - F1: {final_f1:.4f}, Precision: {final_precision:.4f}, Recall: {final_recall:.4f}" + ) + return { "f1": final_f1, "precision": final_precision, "recall": final_recall, - "best_threshold": best_threshold + "best_threshold": best_threshold, } def main(): """Main training function.""" logger.info("๐ŸŽฏ Starting Fixed Focal Loss Training") - + # Setup device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") logger.info(f"๐Ÿ–ฅ๏ธ Using device: {device}") - + # Create directories Path("models").mkdir(exist_ok=True) Path("results").mkdir(exist_ok=True) - + # Create proper training data train_data, val_data, test_data = create_proper_training_data() - + # Create model model = SimpleBERTClassifier() logger.info(f"๐Ÿค– Created model with {sum(p.numel() for p in model.parameters())} parameters") - + # Create dataloaders train_dataloader = create_dataloader(train_data, model, batch_size=8) val_dataloader = create_dataloader(val_data, model, batch_size=8) test_dataloader = create_dataloader(test_data, model, batch_size=8) - + # Train model trained_model = train_model(model, train_dataloader, val_dataloader, device, epochs=5) - + # Load best model trained_model.load_state_dict(torch.load("best_focal_model.pth")) - + # Evaluate model results = evaluate_model(trained_model, test_dataloader, device) - + # Save results with open("results/focal_training_results.json", "w") as f: json.dump(results, f, indent=2) - + # Final summary logger.info("๐ŸŽ‰ Training completed successfully!") logger.info(f"๐Ÿ“Š Final F1 Score: {results['f1']:.4f}") diff --git a/scripts/training/fixed_training_with_optimized_config.py b/scripts/training/fixed_training_with_optimized_config.py index 95ee70220..236cefd30 100644 --- a/scripts/training/fixed_training_with_optimized_config.py +++ b/scripts/training/fixed_training_with_optimized_config.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # The labels field contains a list of integer indices # The labels field contains a list of integer indices # Backward pass @@ -30,21 +31,16 @@ # Use different learning rates for different layers from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier from src.models.emotion_detection.dataset_loader import create_goemotions_loader - from src.models.emotion_detection.dataset_loader import create_goemotions_loader +import logging +import sys # Add src to path # Configure logging -#!/usr/bin/env python3 from pathlib import Path -from typing import Dict, Any, Tuple -import logging -import sys +from typing import Any, Dict, Tuple + import torch import torch.nn as nn - - - - """ Fixed Training Script with Optimized Configuration for SAMO Deep Learning. diff --git a/scripts/training/focal_loss_training.py b/scripts/training/focal_loss_training.py index 9e0aa6eb6..96b4ea32e 100644 --- a/scripts/training/focal_loss_training.py +++ b/scripts/training/focal_loss_training.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Backward pass # Forward pass # Log progress every 100 batches @@ -21,24 +22,19 @@ from transformers import AutoTokenizer import traceback # Setup device -# Add project root to path -# Configure logging -#!/usr/bin/env python3 -from pathlib import Path -from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader -from src.models.emotion_detection.training_pipeline import create_bert_emotion_classifier -from the current 13.2% to target >50%. -from torch import nn import logging import os import sys -import torch -import traceback - - - +# Add project root to path +# Configure logging +from pathlib import Path +import torch +from torch import nn +from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader +from src.models.emotion_detection.training_pipeline import \ + create_bert_emotion_classifier """ Focal Loss Training Script for SAMO Emotion Detection diff --git a/scripts/training/focal_loss_training_fixed.py b/scripts/training/focal_loss_training_fixed.py index 12e4d4d05..22492d3c7 100644 --- a/scripts/training/focal_loss_training_fixed.py +++ b/scripts/training/focal_loss_training_fixed.py @@ -1,40 +1,15 @@ - # Backward pass - # Forward pass - # Log progress every 100 batches - # Apply alpha weighting - # Apply reduction - # Apply sigmoid to get probabilities - # Calculate binary cross entropy - # Calculate focal loss components - # Combine all components - # Log progress - # Save best model - # Training phase - # Validation phase - # Create data loaders - # Create focal loss - # Create model - # Load dataset using existing loader - # Run training - # Save final model - # Setup device - # Setup optimizer - # Training loop -# Add src to path -# Configure logging #!/usr/bin/env python3 -from pathlib import Path -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier -from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader -from torch import nn import argparse import logging import sys +from pathlib import Path + import torch import torch.nn.functional as F +from torch import nn - - +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier +from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader """ Focal Loss Training for Emotion Detection (Fixed Version) @@ -128,16 +103,16 @@ def train_with_focal_loss( train_dataset = datasets["train"] val_dataset = datasets["validation"] - datasets["test"] - datasets["class_weights"] + test_dataset = datasets["test"] + _class_weights = datasets.get("class_weights") logger.info("Dataset loaded successfully:") - logger.info(" โ€ข Train: {len(train_dataset)} examples") - logger.info(" โ€ข Validation: {len(val_dataset)} examples") - logger.info(" โ€ข Test: {len(test_dataset)} examples") + logger.info(" โ€ข Train: %d examples", len(train_dataset)) + logger.info(" โ€ข Validation: %d examples", len(val_dataset)) + logger.info(" โ€ข Test: %d examples", len(test_dataset)) except Exception: - logger.error("Failed to load dataset: {e}") + logger.exception("Failed to load dataset") raise logger.info("๐Ÿค– Creating BERT model...") @@ -183,7 +158,7 @@ def train_with_focal_loss( if (batch_idx + 1) % 100 == 0: logger.info( - " Batch {batch_idx + 1}/{len(train_loader)}, Loss: {loss.item():.4f}" + f" Batch {batch_idx + 1}/{len(train_loader)}, Loss: {loss.item():.4f}" ) avg_train_loss = train_loss / num_batches diff --git a/scripts/training/focal_loss_training_robust.py b/scripts/training/focal_loss_training_robust.py index 7e22b3729..1ffd17535 100644 --- a/scripts/training/focal_loss_training_robust.py +++ b/scripts/training/focal_loss_training_robust.py @@ -13,10 +13,11 @@ import torch from torch import nn +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier + # Add src to path sys.path.append(str(Path.cwd() / "src")) -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier # Configure logging logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") @@ -34,9 +35,7 @@ def __init__(self, alpha=1, gamma=2, reduction="mean"): def forward(self, inputs, targets): """Forward pass of focal loss.""" - bce_loss = nn.functional.binary_cross_entropy_with_logits( - inputs, targets, reduction="none" - ) + bce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none") pt = torch.exp(-bce_loss) focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss diff --git a/scripts/training/focal_loss_training_simple.py b/scripts/training/focal_loss_training_simple.py index faa15aa19..f7b16a4f4 100644 --- a/scripts/training/focal_loss_training_simple.py +++ b/scripts/training/focal_loss_training_simple.py @@ -12,10 +12,11 @@ import torch from torch import nn +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier + # Add src to path sys.path.append(str(Path.cwd() / "src")) -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier # Configure logging logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") @@ -33,9 +34,7 @@ def __init__(self, alpha=1, gamma=2, reduction="mean"): def forward(self, inputs, targets): """Forward pass of focal loss.""" - bce_loss = nn.functional.binary_cross_entropy_with_logits( - inputs, targets, reduction="none" - ) + bce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none") pt = torch.exp(-bce_loss) focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss diff --git a/scripts/training/full_dataset_focal_training.py b/scripts/training/full_dataset_focal_training.py index f92018dd7..d9e37b909 100644 --- a/scripts/training/full_dataset_focal_training.py +++ b/scripts/training/full_dataset_focal_training.py @@ -12,10 +12,11 @@ import torch from torch import nn +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier + # Add src to path sys.path.append(str(Path.cwd() / "src")) -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier # Configure logging logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") @@ -33,9 +34,7 @@ def __init__(self, alpha=1, gamma=2, reduction="mean"): def forward(self, inputs, targets): """Forward pass of focal loss.""" - bce_loss = nn.functional.binary_cross_entropy_with_logits( - inputs, targets, reduction="none" - ) + bce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none") pt = torch.exp(-bce_loss) focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss @@ -131,11 +130,11 @@ def full_dataset_focal_training(): # Training loop model.train() train_losses = [] - + for epoch in range(3): logger.info(f"๐Ÿ“š Epoch {epoch + 1}/3") epoch_loss = 0.0 - + for batch_idx, batch in enumerate(train_dataloader): input_ids, attention_mask, batch_labels = batch input_ids = input_ids.to(device) diff --git a/scripts/training/full_focal_training.py b/scripts/training/full_focal_training.py index a39eebc6b..33cfa1929 100644 --- a/scripts/training/full_focal_training.py +++ b/scripts/training/full_focal_training.py @@ -13,10 +13,11 @@ import torch from torch import nn +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier + # Add src to path sys.path.append(str(Path.cwd() / "src")) -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier # Configure logging logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") @@ -34,9 +35,7 @@ def __init__(self, alpha=1, gamma=2, reduction="mean"): def forward(self, inputs, targets): """Forward pass of focal loss.""" - bce_loss = nn.functional.binary_cross_entropy_with_logits( - inputs, targets, reduction="none" - ) + bce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none") pt = torch.exp(-bce_loss) focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss @@ -116,7 +115,7 @@ def full_focal_training(): model.train() for epoch in range(3): logger.info(f"๐Ÿ“š Epoch {epoch + 1}/3") - + for batch_idx, batch in enumerate(train_dataloader): input_ids, attention_mask, batch_labels = batch input_ids = input_ids.to(device) diff --git a/scripts/training/full_scale_focal_training.py b/scripts/training/full_scale_focal_training.py index 740c80006..1fb23f024 100644 --- a/scripts/training/full_scale_focal_training.py +++ b/scripts/training/full_scale_focal_training.py @@ -13,10 +13,11 @@ import torch from torch import nn +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier + # Add src to path sys.path.append(str(Path.cwd() / "src")) -from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier # Configure logging logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") @@ -34,9 +35,7 @@ def __init__(self, alpha=1, gamma=2, reduction="mean"): def forward(self, inputs, targets): """Forward pass of focal loss.""" - bce_loss = nn.functional.binary_cross_entropy_with_logits( - inputs, targets, reduction="none" - ) + bce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none") pt = torch.exp(-bce_loss) focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss @@ -134,7 +133,7 @@ def full_scale_focal_training(): for epoch in range(5): logger.info(f"๐Ÿ“š Epoch {epoch + 1}/5") epoch_loss = 0.0 - + for batch_idx, batch in enumerate(train_dataloader): input_ids, attention_mask, batch_labels = batch input_ids = input_ids.to(device) diff --git a/scripts/training/improve_expanded_training_notebook.py b/scripts/training/improve_expanded_training_notebook.py index 60273cc1b..54410cf30 100644 --- a/scripts/training/improve_expanded_training_notebook.py +++ b/scripts/training/improve_expanded_training_notebook.py @@ -7,30 +7,31 @@ import json import re + def improve_notebook(): """Improve the expanded training notebook with enhancements.""" - + # Read the current notebook - with open('notebooks/expanded_dataset_training.ipynb', 'r') as f: + with open("notebooks/expanded_dataset_training.ipynb", "r") as f: notebook = json.load(f) - + # Find the training function cell training_cell_idx = None - for i, cell in enumerate(notebook['cells']): - if cell['cell_type'] == 'code' and 'train_expanded_model' in str(cell['source']): + for i, cell in enumerate(notebook["cells"]): + if cell["cell_type"] == "code" and "train_expanded_model" in str(cell["source"]): training_cell_idx = i break - + if training_cell_idx is None: print("โŒ Could not find training function cell") return - + # Get the training function source - training_source = notebook['cells'][training_cell_idx]['source'] - + training_source = notebook["cells"][training_cell_idx]["source"] + # Add GPU optimizations after device setup device_pattern = r'print\(f"โœ… Using device: \{device\}"\)' - gpu_optimizations = ''' + gpu_optimizations = """ # GPU optimizations if torch.cuda.is_available(): print("๐Ÿ”ง Applying GPU optimizations...") @@ -38,79 +39,79 @@ def improve_notebook(): torch.backends.cudnn.deterministic = False print(f"๐Ÿ“Š GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB") print(f"๐Ÿ“Š Available Memory: {torch.cuda.memory_allocated(0) / 1e9:.1f} GB") - + # Clear GPU cache if torch.cuda.is_available(): torch.cuda.empty_cache() -''' - +""" + # Replace the device setup new_source = re.sub( device_pattern, f'print(f"โœ… Using device: {{device}}")\n{gpu_optimizations}', - training_source + training_source, ) - + # Add early stopping - early_stopping_pattern = r'if f1_macro > best_f1:' - early_stopping_code = ''' + early_stopping_pattern = r"if f1_macro > best_f1:" + early_stopping_code = """ # Early stopping check if epoch > 2 and f1_macro < best_f1 * 0.95: print(f"๐Ÿ›‘ Early stopping triggered. F1 dropped below 95% of best.") break - - if f1_macro > best_f1:''' - + + if f1_macro > best_f1:""" + new_source = re.sub(early_stopping_pattern, early_stopping_code, new_source) - + # Add learning rate scheduling - lr_scheduler_pattern = r'optimizer = torch\.optim\.AdamW\(model\.parameters\(\), lr=2e-5\)' - lr_scheduler_code = '''optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5) - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=2, verbose=True)''' - + lr_scheduler_pattern = r"optimizer = torch\.optim\.AdamW\(model\.parameters\(\), lr=2e-5\)" + lr_scheduler_code = """optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=2, verbose=True)""" + new_source = re.sub(lr_scheduler_pattern, lr_scheduler_code, new_source) - + # Add scheduler step scheduler_step_pattern = r'print\(f"๐Ÿ’พ New best model saved! F1: \{best_f1:.4f\}"\)' - scheduler_step_code = '''print(f"๐Ÿ’พ New best model saved! F1: {best_f1:.4f}") - scheduler.step(f1_macro)''' - + scheduler_step_code = """print(f"๐Ÿ’พ New best model saved! F1: {best_f1:.4f}") + scheduler.step(f1_macro)""" + new_source = re.sub(scheduler_step_pattern, scheduler_step_code, new_source) - + # Add mixed precision training - mixed_precision_pattern = r'import torch\.nn as nn' - mixed_precision_code = '''import torch.nn as nn -from torch.cuda.amp import autocast, GradScaler''' - + mixed_precision_pattern = r"import torch\.nn as nn" + mixed_precision_code = """import torch.nn as nn +from torch.cuda.amp import autocast, GradScaler""" + new_source = re.sub(mixed_precision_pattern, mixed_precision_code, new_source) - + # Add scaler initialization - scaler_init_pattern = r'criterion = nn\.CrossEntropyLoss\(\)' - scaler_init_code = '''criterion = nn.CrossEntropyLoss() - scaler = GradScaler()''' - + scaler_init_pattern = r"criterion = nn\.CrossEntropyLoss\(\)" + scaler_init_code = """criterion = nn.CrossEntropyLoss() + scaler = GradScaler()""" + new_source = re.sub(scaler_init_pattern, scaler_init_code, new_source) - + # Add mixed precision training loop - training_loop_pattern = r'optimizer\.zero_grad\(\)\s+outputs = model\(input_ids=input_ids, attention_mask=attention_mask\)\s+loss = criterion\(outputs, labels\)\s+loss\.backward\(\)\s+optimizer\.step\(\)' - training_loop_code = '''optimizer.zero_grad() + training_loop_pattern = r"optimizer\.zero_grad\(\)\s+outputs = model\(input_ids=input_ids, attention_mask=attention_mask\)\s+loss = criterion\(outputs, labels\)\s+loss\.backward\(\)\s+optimizer\.step\(\)" + training_loop_code = """optimizer.zero_grad() with autocast(): outputs = model(input_ids=input_ids, attention_mask=attention_mask) loss = criterion(outputs, labels) - + scaler.scale(loss).backward() scaler.step(optimizer) - scaler.update()''' - + scaler.update()""" + new_source = re.sub(training_loop_pattern, training_loop_code, new_source) - + # Update the cell - notebook['cells'][training_cell_idx]['source'] = new_source - + notebook["cells"][training_cell_idx]["source"] = new_source + # Save the improved notebook - with open('notebooks/expanded_dataset_training_improved.ipynb', 'w') as f: + with open("notebooks/expanded_dataset_training_improved.ipynb", "w") as f: json.dump(notebook, f, indent=2) - + print("โœ… Improved notebook saved as 'notebooks/expanded_dataset_training_improved.ipynb'") print("๐Ÿ“‹ Improvements added:") print(" - GPU optimizations (cudnn benchmark, memory management)") @@ -119,5 +120,6 @@ def improve_notebook(): print(" - Mixed precision training for faster training") print(" - Better memory management") + if __name__ == "__main__": - improve_notebook() \ No newline at end of file + improve_notebook() diff --git a/scripts/training/minimal_working_training.py b/scripts/training/minimal_working_training.py index c9b23335a..74343176d 100644 --- a/scripts/training/minimal_working_training.py +++ b/scripts/training/minimal_working_training.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Backward pass # Forward pass # Log progress every 10 batches @@ -16,18 +17,13 @@ import traceback # Create random input data # Setup device -# Configure logging -#!/usr/bin/env python3 -from torch import nn import logging import os import sys -import torch -import traceback - - - +import torch +# Configure logging +from torch import nn """ Minimal Working Training Script diff --git a/scripts/training/monitor_training.py b/scripts/training/monitor_training.py index 6a151d1ff..0b1c41c4b 100644 --- a/scripts/training/monitor_training.py +++ b/scripts/training/monitor_training.py @@ -1,29 +1,29 @@ - # Analyze convergence - # Check model files - # Extract metrics - # F1 score curve - # Generate plots - # Load and analyze training history - # Loss curve - # Next Steps - # Performance Metrics - # Performance analysis - # Recommendations - # Save analysis report - # Training Progress - # Training time analysis +# Analyze convergence +# Check model files +# Extract metrics +# F1 score curve +# Generate plots +# Load and analyze training history +# Loss curve +# Next Steps +# Performance Metrics +# Performance analysis +# Recommendations +# Save analysis report +# Training Progress +# Training time analysis +import json +import logging +import sys + # Add src to path for imports #!/usr/bin/env python3 from datetime import datetime from pathlib import Path from typing import Optional -import json -import logging + import matplotlib.pyplot as plt import numpy as np -import sys - - """ Training Monitor for SAMO Emotion Detection Model @@ -34,6 +34,7 @@ sys.path.append(str(Path(__file__).parent.parent / "src")) + def load_training_history(checkpoint_dir: str = "test_checkpoints_dev") -> list[dict]: """Load training history from checkpoint directory.""" history_file = Path(checkpoint_dir) / "training_history.json" @@ -47,6 +48,7 @@ def load_training_history(checkpoint_dir: str = "test_checkpoints_dev") -> list[ return history + def analyze_training_progress(history: list[dict]) -> dict: """Analyze training progress and provide insights.""" if not history: @@ -60,7 +62,7 @@ def analyze_training_progress(history: list[dict]) -> dict: "training_time": [], "learning_rate": [], "convergence_status": "unknown", - "recommendations": [] + "recommendations": [], } for epoch_data in history: @@ -76,13 +78,17 @@ def analyze_training_progress(history: list[dict]) -> dict: if loss_improvement > 0.01: analysis["convergence_status"] = "excellent" - analysis["recommendations"].append("โœ… Loss decreasing significantly - continue training") + analysis["recommendations"].append( + "โœ… Loss decreasing significantly - continue training" + ) elif loss_improvement > 0.001: analysis["convergence_status"] = "good" analysis["recommendations"].append("โœ… Loss decreasing - continue training") elif loss_improvement > -0.001: analysis["convergence_status"] = "plateauing" - analysis["recommendations"].append("โš ๏ธ Loss plateauing - consider learning rate adjustment") + analysis["recommendations"].append( + "โš ๏ธ Loss plateauing - consider learning rate adjustment" + ) else: analysis["convergence_status"] = "diverging" analysis["recommendations"].append("โŒ Loss increasing - check learning rate and data") @@ -93,7 +99,9 @@ def analyze_training_progress(history: list[dict]) -> dict: elif latest_f1 > 0.6: analysis["recommendations"].append("๐Ÿ“ˆ Good F1 score - continue training") else: - analysis["recommendations"].append("๐Ÿ“Š F1 score needs improvement - consider data augmentation") + analysis["recommendations"].append( + "๐Ÿ“Š F1 score needs improvement - consider data augmentation" + ) avg_epoch_time = np.mean(analysis["training_time"]) analysis["avg_epoch_time_minutes"] = avg_epoch_time / 60 @@ -103,6 +111,7 @@ def analyze_training_progress(history: list[dict]) -> dict: return analysis + def generate_training_report(analysis: dict) -> str: """Generate a comprehensive training report.""" report = [] @@ -163,6 +172,7 @@ def generate_training_report(analysis: dict) -> str: return "\n".join(report) + def plot_training_curves(history: list[dict], save_path: Optional[str] = None): """Plot training curves for visualization.""" if not history: @@ -175,28 +185,29 @@ def plot_training_curves(history: list[dict], save_path: Optional[str] = None): fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5)) - ax1.plot(epochs, losses, 'b-o', linewidth=2, markersize=6) - ax1.set_title('Training Loss Over Time', fontsize=14, fontweight='bold') - ax1.set_xlabel('Epoch') - ax1.set_ylabel('Loss') + ax1.plot(epochs, losses, "b-o", linewidth=2, markersize=6) + ax1.set_title("Training Loss Over Time", fontsize=14, fontweight="bold") + ax1.set_xlabel("Epoch") + ax1.set_ylabel("Loss") ax1.grid(True, alpha=0.3) ax1.set_ylim(bottom=0) - ax2.plot(epochs, f1_scores, 'g-o', linewidth=2, markersize=6) - ax2.set_title('F1 Score Over Time', fontsize=14, fontweight='bold') - ax2.set_xlabel('Epoch') - ax2.set_ylabel('Micro F1 Score') + ax2.plot(epochs, f1_scores, "g-o", linewidth=2, markersize=6) + ax2.set_title("F1 Score Over Time", fontsize=14, fontweight="bold") + ax2.set_xlabel("Epoch") + ax2.set_ylabel("Micro F1 Score") ax2.grid(True, alpha=0.3) ax2.set_ylim(0, 1) plt.tight_layout() if save_path: - plt.savefig(save_path, dpi=300, bbox_inches='tight') + plt.savefig(save_path, dpi=300, bbox_inches="tight") logging.info(f"๐Ÿ“Š Training curves saved to {save_path}") else: plt.show() + def check_model_files(checkpoint_dir: str = "test_checkpoints_dev") -> dict: """Check if model files exist and are valid.""" checkpoint_path = Path(checkpoint_dir) @@ -204,27 +215,20 @@ def check_model_files(checkpoint_dir: str = "test_checkpoints_dev") -> dict: files = { "training_history": checkpoint_path / "training_history.json", "best_model": checkpoint_path / "best_model.pt", - "config": checkpoint_path / "config.json" + "config": checkpoint_path / "config.json", } status = {} for name, file_path in files.items(): if file_path.exists(): size_mb = file_path.stat().st_size / (1024 * 1024) - status[name] = { - "exists": True, - "size_mb": size_mb, - "path": str(file_path) - } + status[name] = {"exists": True, "size_mb": size_mb, "path": str(file_path)} else: - status[name] = { - "exists": False, - "size_mb": 0, - "path": str(file_path) - } + status[name] = {"exists": False, "size_mb": 0, "path": str(file_path)} return status + def main(): """Main monitoring function.""" logging.info("๐Ÿ” SAMO Training Monitor") @@ -259,11 +263,12 @@ def main(): plot_training_curves(history, str(plot_path)) report_path = plots_dir / f"training_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt" - with open(report_path, 'w') as f: + with open(report_path, "w") as f: f.write(report) logging.info(f"\n๐Ÿ“„ Analysis report saved to {report_path}") logging.info(f"๐Ÿ“Š Training curves saved to {plot_path}") + if __name__ == "__main__": main() diff --git a/scripts/training/pre_training_validation.py b/scripts/training/pre_training_validation.py index 585a29814..ecd040b92 100644 --- a/scripts/training/pre_training_validation.py +++ b/scripts/training/pre_training_validation.py @@ -1,3 +1,4 @@ +#!/usr/bin/env python3 # Test write permissions # Backward pass # Check CUDA availability @@ -21,18 +22,16 @@ # Test loss function # Test one training step # Test optimizer - # Test scheduler - # Validate first batch - # Validate labels - # Validate outputs - from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier - from src.models.emotion_detection.dataset_loader import create_goemotions_loader - from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer - from torch.optim import AdamW - import pandas as pd - import shutil - import torch - import transformers +# Test scheduler +# Validate first batch +# Validate labels +# Validate outputs +from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier +from src.models.emotion_detection.dataset_loader import create_goemotions_loader +from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer +from torch.optim import AdamW +import shutil +import torch # Critical issues # Final recommendation # Summary @@ -43,12 +42,9 @@ # Add src to path # Configure logging # Import torch early for validation -#!/usr/bin/env python3 from pathlib import Path import logging -import numpy as np import sys -import torch @@ -468,9 +464,8 @@ def main(): if validator.critical_issues: logger.error("โŒ Validation failed - training blocked!") return False - else: - logger.info("โœ… Validation passed - training can proceed!") - return True + logger.info("โœ… Validation passed - training can proceed!") + return True if __name__ == "__main__": diff --git a/scripts/training/restart_training_debug.py b/scripts/training/restart_training_debug.py index 4c4d0ce84..f4f3533df 100644 --- a/scripts/training/restart_training_debug.py +++ b/scripts/training/restart_training_debug.py @@ -1,15 +1,17 @@ - # Start training - # Training configuration with debugging - from src.models.emotion_detection.training_pipeline import train_emotion_detection_model - import traceback -# Add src to path -# Configure logging #!/usr/bin/env python3 +"""Restart Training Debug Script for SAMO Deep Learning""" + from pathlib import Path import logging import sys import traceback +# Add src to path +sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) + +# Configure logging +from src.models.emotion_detection.training_pipeline import train_emotion_detection_model + diff --git a/scripts/training/robust_domain_adaptation_training.py b/scripts/training/robust_domain_adaptation_training.py deleted file mode 100644 index f605aee6f..000000000 --- a/scripts/training/robust_domain_adaptation_training.py +++ /dev/null @@ -1,363 +0,0 @@ -#!/usr/bin/env python3 -""" -SAMO Deep Learning - Robust Domain Adaptation Training Script - -This script provides a robust implementation for REQ-DL-012: Domain-Adapted Emotion Detection -that avoids dependency hell and provides comprehensive error handling. - -Target: Achieve 70% F1 score on journal entries through domain adaptation from GoEmotions -""" - -import os -import json -import warnings -import subprocess -from pathlib import Path -from typing import Dict, List, Optional, Tuple, Any - -# Suppress warnings for cleaner output -warnings.filterwarnings('ignore') - -# Set environment variables for stability -os.environ['CUDA_LAUNCH_BLOCKING'] = "1" -os.environ['TOKENIZERS_PARALLELISM'] = "false" - -def setup_environment(): - """Setup the environment with proper dependency management.""" - print("๐Ÿ”ง Setting up robust environment...") - - # Check if we're in Colab - try: - import google.colab - print("โœ… Running in Google Colab") - is_colab = True - except ImportError: - print("โ„น๏ธ Running in local environment") - is_colab = False - - # Install dependencies with proper version management - print("๐Ÿ“ฆ Installing dependencies with compatibility fixes...") - - # Step 1: Clean slate - remove conflicting packages - subprocess.run([ - "pip", "uninstall", "torch", "torchvision", "torchaudio", - "transformers", "datasets", "-y" - ], capture_output=True) - - # Step 2: Install PyTorch with compatible CUDA version - subprocess.run([ - "pip", "install", "torch==2.1.0", "torchvision==0.16.0", "torchaudio==2.1.0", - "--index-url", "https://download.pytorch.org/whl/cu118", "--no-cache-dir" - ]) - - # Step 3: Install Transformers with compatible version - subprocess.run([ - "pip", "install", "transformers==4.30.0", "datasets==2.13.0", "--no-cache-dir" - ]) - - # Step 4: Install additional dependencies - subprocess.run([ - "pip", "install", "evaluate", "scikit-learn", "pandas", "numpy", - "matplotlib", "seaborn", "accelerate", "wandb", "--no-cache-dir" - ]) - - print("โœ… Dependencies installed successfully") - return is_colab - -def verify_installation(): - """Verify that all critical packages are installed correctly.""" - print("๐Ÿ” Verifying installation...") - - try: - import torch - import transformers - print(f" PyTorch: {torch.__version__}") - print(f" Transformers: {transformers.__version__}") - print(f" CUDA Available: {torch.cuda.is_available()}") - - if torch.cuda.is_available(): - print(f" GPU: {torch.cuda.get_device_name(0)}") - print(f" Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB") - torch.backends.cudnn.benchmark = True - print(" โœ… GPU optimized for training") - else: - print("โš ๏ธ No GPU available. Training will be slow on CPU.") - - # Test critical imports - from transformers import AutoModel, AutoTokenizer - print(" โœ… Transformers imports successful") - - return True - - except Exception as e: - print(f" โŒ Installation verification failed: {e}") - return False - -def setup_repository(): - """Setup the SAMO-DL repository.""" - print("๐Ÿ“ Setting up repository...") - - def run_command(command: str, description: str) -> bool: - """Execute command with error handling.""" - print(f"๐Ÿ”„ {description}...") - try: - result = subprocess.run(command, shell=True, capture_output=True, text=True) - if result.returncode == 0: - print(f" โœ… {description} completed") - return True - else: - print(f" โŒ {description} failed: {result.stderr}") - return False - except Exception as e: - print(f" โŒ {description} failed: {e}") - return False - - # Clone repository if not exists - if not Path('SAMO--DL').exists(): - run_command('git clone https://github.com/uelkerd/SAMO--DL.git', 'Cloning repository') - - # Change to project directory - os.chdir('SAMO--DL') - print(f"๐Ÿ“ Working directory: {os.getcwd()}") - - # Pull latest changes - run_command('git pull origin main', 'Pulling latest changes') - -def safe_load_dataset(dataset_name: str, config: Optional[str] = None, split: Optional[str] = None): - """Safely load dataset with error handling.""" - try: - from datasets import load_dataset - if config: - dataset = load_dataset(dataset_name, config, split=split) - else: - dataset = load_dataset(dataset_name, split=split) - print(f"โœ… Successfully loaded {dataset_name}") - return dataset - except Exception as e: - print(f"โŒ Failed to load {dataset_name}: {e}") - return None - -def safe_load_json(file_path: str): - """Safely load JSON file with error handling.""" - try: - with open(file_path, 'r') as f: - data = json.load(f) - print(f"โœ… Successfully loaded {file_path}") - return data - except Exception as e: - print(f"โŒ Failed to load {file_path}: {e}") - return None - -def analyze_writing_style(texts: List[str], domain_name: str) -> Optional[Dict[str, float]]: - """Analyze writing style characteristics of a domain.""" - if not texts: - print(f"โš ๏ธ No texts provided for {domain_name}") - return None - - # Filter out None or empty texts - valid_texts = [text for text in texts if text and isinstance(text, str)] - - if not valid_texts: - print(f"โš ๏ธ No valid texts found for {domain_name}") - return None - - import numpy as np - - avg_length = np.mean([len(text.split()) for text in valid_texts]) - personal_pronouns = sum(['I ' in text or 'my ' in text or 'me ' in text for text in valid_texts]) / len(valid_texts) - reflection_words = sum(['think' in text.lower() or 'feel' in text.lower() or 'believe' in text.lower() - for text in valid_texts]) / len(valid_texts) - - print(f"{domain_name} Style Analysis:") - print(f" Average length: {avg_length:.1f} words") - print(f" Personal pronouns: {personal_pronouns:.1%}") - print(f" Reflection words: {reflection_words:.1%}") - - return { - 'avg_length': avg_length, - 'personal_pronouns': personal_pronouns, - 'reflection_words': reflection_words - } - -def perform_domain_analysis(): - """Perform domain gap analysis between GoEmotions and journal entries.""" - print("๐Ÿ“Š Loading datasets for domain analysis...") - - # Load GoEmotions dataset - go_emotions = safe_load_dataset("go_emotions", "simplified") - if go_emotions: - go_texts = go_emotions['train']['text'][:1000] # Sample for analysis - else: - go_texts = [] - - # Load journal dataset - journal_entries = safe_load_json('data/journal_test_dataset.json') - if journal_entries: - import pandas as pd - journal_df = pd.DataFrame(journal_entries) - journal_texts = journal_df['content'].tolist() - else: - journal_texts = [] - - # Analyze domains if data is available - if go_texts and journal_texts: - print("\n๐Ÿ” Domain Gap Analysis:") - go_analysis = analyze_writing_style(go_texts, "GoEmotions (Reddit)") - journal_analysis = analyze_writing_style(journal_texts, "Journal Entries") - - if go_analysis and journal_analysis: - print("\n๐ŸŽฏ Key Insights:") - print(f"- Journal entries are {journal_analysis['avg_length']/go_analysis['avg_length']:.1f}x longer") - print(f"- Journal entries use {journal_analysis['personal_pronouns']/go_analysis['personal_pronouns']:.1f}x more personal pronouns") - print(f"- Journal entries contain {journal_analysis['reflection_words']/go_analysis['reflection_words']:.1f}x more reflection words") - - return go_emotions, journal_df - else: - print("โš ๏ธ Cannot perform domain analysis - missing data") - return None, None - -class FocalLoss: - """Focal Loss for addressing class imbalance in emotion detection.""" - - def __init__(self, alpha=1, gamma=2, reduction='mean'): - import torch.nn as nn - import torch.nn.functional as F - self.alpha = alpha - self.gamma = gamma - self.reduction = reduction - self.F = F - - def __call__(self, inputs, targets): - ce_loss = self.F.cross_entropy(inputs, targets, reduction='none') - pt = torch.exp(-ce_loss) - focal_loss = self.alpha * (1 - pt) ** self.gamma * ce_loss - - if self.reduction == 'mean': - return focal_loss.mean() - elif self.reduction == 'sum': - return focal_loss.sum() - else: - return focal_loss - -class DomainAdaptedEmotionClassifier: - """BERT-based emotion classifier with domain adaptation capabilities.""" - - def __init__(self, model_name="bert-base-uncased", num_labels=None, dropout=0.3): - import torch.nn as nn - from transformers import AutoModel - - # ROBUST: Validate num_labels - if num_labels is None: - print("โš ๏ธ num_labels not provided, using default value of 12") - num_labels = 12 - elif num_labels <= 0: - raise ValueError(f"num_labels must be positive, got {num_labels}") - - print(f"๐Ÿ—๏ธ Initializing DomainAdaptedEmotionClassifier with num_labels = {num_labels}") - - try: - self.bert = AutoModel.from_pretrained(model_name) - self.dropout = nn.Dropout(dropout) - self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels) - - # Domain adaptation layer - self.domain_classifier = nn.Sequential( - nn.Linear(self.bert.config.hidden_size, 512), - nn.ReLU(), - nn.Dropout(0.3), - nn.Linear(512, 2) # 2 domains: GoEmotions vs Journal - ) - - print(f"โœ… Model initialized successfully with {num_labels} labels") - - except Exception as e: - print(f"โŒ Failed to initialize model: {e}") - raise - - def forward(self, input_ids, attention_mask, domain_labels=None): - try: - outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) - pooled_output = outputs.pooler_output - - # Emotion classification - emotion_logits = self.classifier(self.dropout(pooled_output)) - - # Domain classification (for domain adaptation) - domain_logits = self.domain_classifier(pooled_output) - - if domain_labels is not None: - return emotion_logits, domain_logits - return emotion_logits - - except Exception as e: - print(f"โŒ Forward pass failed: {e}") - raise - -def safe_model_initialization(model_name: str, num_labels: int, device: str): - """Safely initialize model with error handling.""" - try: - print(f"๐Ÿ—๏ธ Initializing model with {model_name}...") - - # Initialize tokenizer - from transformers import AutoTokenizer - tokenizer = AutoTokenizer.from_pretrained(model_name) - print(f"โœ… Tokenizer loaded: {model_name}") - - # Initialize model - model = DomainAdaptedEmotionClassifier(model_name=model_name, num_labels=num_labels) - - # Move to device - import torch - model = model.to(device) - print(f"โœ… Model moved to {device}") - - # Verify model parameters - total_params = sum(p.numel() for p in model.parameters()) - print(f"๐Ÿ“Š Model parameters: {total_params:,}") - - return model, tokenizer - - except Exception as e: - print(f"โŒ Model initialization failed: {e}") - raise - -def main(): - """Main execution function.""" - print("๐Ÿš€ Starting SAMO Deep Learning - Robust Domain Adaptation Training") - print("=" * 70) - - # Step 1: Setup environment - is_colab = setup_environment() - - # Step 2: Verify installation - if not verify_installation(): - print("โŒ Installation verification failed. Please restart and try again.") - return - - # Step 3: Setup repository - setup_repository() - - # Step 4: Perform domain analysis - go_emotions, journal_df = perform_domain_analysis() - - if go_emotions is None or journal_df is None: - print("โŒ Cannot proceed without datasets") - return - - # Step 5: Initialize model (example) - import torch - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - # This would be called when we have the label encoder ready - # model, tokenizer = safe_model_initialization("bert-base-uncased", num_labels, device) - - print("\nโœ… Setup completed successfully!") - print("๐ŸŽฏ Ready for domain adaptation training") - print("\n๐Ÿ“‹ Next steps:") - print(" 1. Prepare data with label encoding") - print(" 2. Initialize model with correct num_labels") - print(" 3. Run training pipeline") - print(" 4. Evaluate and save results") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/scripts/training/setup_colab_environment.py b/scripts/training/setup_colab_environment.py index e33c1902a..97a55571d 100644 --- a/scripts/training/setup_colab_environment.py +++ b/scripts/training/setup_colab_environment.py @@ -7,13 +7,13 @@ for optimal performance in the Colab environment. """ +import logging import os -import sys import subprocess -import logging +import sys # Configure logging -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) @@ -32,11 +32,11 @@ def detect_colab_environment(): def install_dependencies(): """Install all required dependencies.""" logger.info("๐Ÿ“ฆ Installing dependencies...") - + # Core ML dependencies packages = [ "torch>=2.1.0,<2.2.0", - "torchvision>=0.16.0,<0.17.0", + "torchvision>=0.16.0,<0.17.0", "torchaudio>=2.1.0,<2.2.0", "transformers>=4.30.0,<5.0.0", "datasets>=2.10.0,<3.0.0", @@ -61,47 +61,51 @@ def install_dependencies(): "python-dotenv>=1.0.0,<2.0.0", "accelerate>=0.20.0,<1.0.0", ] - + for package in packages: try: logger.info(f"๐Ÿ“ฆ Installing {package}...") - subprocess.run([sys.executable, "-m", "pip", "install", package], - check=True, capture_output=True, text=True) + subprocess.run( + [sys.executable, "-m", "pip", "install", package], + check=True, + capture_output=True, + text=True, + ) logger.info(f"โœ… {package} installed successfully") except subprocess.CalledProcessError as e: logger.error(f"โŒ Failed to install {package}: {e}") return False - + return True def setup_gpu_environment(): """Set up GPU environment for optimal performance.""" logger.info("๐Ÿ–ฅ๏ธ Setting up GPU environment...") - + try: import torch - + if torch.cuda.is_available(): logger.info(f"๐ŸŽฎ GPU detected: {torch.cuda.get_device_name(0)}") logger.info(f"๐ŸŽฎ GPU count: {torch.cuda.device_count()}") logger.info(f"๐ŸŽฎ CUDA version: {torch.version.cuda}") - + # Set environment variables for optimal GPU performance os.environ["CUDA_LAUNCH_BLOCKING"] = "1" os.environ["TOKENIZERS_PARALLELISM"] = "false" - + # Test GPU functionality device = torch.device("cuda") test_tensor = torch.randn(100, 100).to(device) result = torch.matmul(test_tensor, test_tensor.T) logger.info(f"โœ… GPU test successful, result shape: {result.shape}") - + return True else: logger.warning("โš ๏ธ No GPU available, using CPU") return True - + except ImportError: logger.error("โŒ PyTorch not available for GPU setup") return False @@ -113,8 +117,8 @@ def setup_gpu_environment(): def create_colab_notebook(): """Create a Colab-ready notebook template.""" logger.info("๐Ÿ““ Creating Colab notebook template...") - - notebook_content = '''{ + + notebook_content = """{ "cells": [ { "cell_type": "markdown", @@ -210,11 +214,11 @@ def create_colab_notebook(): }, "nbformat": 4, "nbformat_minor": 4 -}''' - +}""" + with open("samo_dl_colab_setup.ipynb", "w") as f: f.write(notebook_content) - + logger.info("โœ… Colab notebook template created: samo_dl_colab_setup.ipynb") return True @@ -222,15 +226,15 @@ def create_colab_notebook(): def run_ci_pipeline(): """Run the CI pipeline to verify everything is working.""" logger.info("๐Ÿš€ Running CI pipeline verification...") - + try: result = subprocess.run( [sys.executable, "scripts/ci/run_full_ci_pipeline.py"], capture_output=True, text=True, - timeout=600 # 10 minute timeout + timeout=600, # 10 minute timeout ) - + if result.returncode == 0: logger.info("โœ… CI pipeline verification passed") logger.info("๐Ÿ“Š CI Results:") @@ -240,7 +244,7 @@ def run_ci_pipeline(): logger.error("โŒ CI pipeline verification failed") logger.error(result.stderr) return False - + except subprocess.TimeoutExpired: logger.error("โฐ CI pipeline verification timed out") return False @@ -253,39 +257,39 @@ def main(): """Main setup function.""" logger.info("๐Ÿš€ Starting Colab Environment Setup") logger.info("=" * 50) - + # Detect environment is_colab = detect_colab_environment() - + # Install dependencies if not install_dependencies(): logger.error("โŒ Dependency installation failed") sys.exit(1) - + # Setup GPU environment if not setup_gpu_environment(): logger.error("โŒ GPU environment setup failed") sys.exit(1) - + # Create Colab notebook if is_colab: create_colab_notebook() - + # Run CI pipeline verification if not run_ci_pipeline(): logger.error("โŒ CI pipeline verification failed") sys.exit(1) - + logger.info("๐ŸŽ‰ Colab environment setup completed successfully!") logger.info("=" * 50) logger.info("๐Ÿ“‹ Next steps:") logger.info("1. Upload the repository to Colab") logger.info("2. Run the CI pipeline: python scripts/ci/run_full_ci_pipeline.py") logger.info("3. Start developing with GPU acceleration!") - + if is_colab: logger.info("๐Ÿ““ Colab notebook template created: samo_dl_colab_setup.ipynb") if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/training/setup_gpu_training.py b/scripts/training/setup_gpu_training.py index d036a3a14..a0ea8051c 100644 --- a/scripts/training/setup_gpu_training.py +++ b/scripts/training/setup_gpu_training.py @@ -1,13 +1,19 @@ - # Create resume script - # Determine optimal batch size - # Disable tokenizers parallelism warning - # Enable CUDA optimizations - # GPU Info - # Load checkpoint - # Optimization recommendations - # Save configuration - # Setup environment - # Speed estimates +# Create resume script +# Determine optimal batch size +# Disable tokenizers parallelism warning +# Enable CUDA optimizations +# GPU Info +# Load checkpoint +# Optimization recommendations +# Save configuration +# Setup environment +# Speed estimates + + +"""GPU Training Setup Script for SAMO Deep Learning. + +This script helps transition the current CPU training to GPU training +with optimal settings for performance and memory efficiency. # Auto-generated GPU resume script # Auto-generated based on your GPU: {torch.cuda.get_device_name()} # Environment setup @@ -18,23 +24,14 @@ # Set up logging # TODO: Implement checkpoint resume functionality in trainer class # Train normally - the trainer will create a new model -# Train the model -#!/usr/bin/env python3 -from pathlib import Path import argparse import logging import os -import torch - - - - - - -"""GPU Training Setup Script for SAMO Deep Learning. +# Train the model +#!/usr/bin/env python3 +from pathlib import Path -This script helps transition the current CPU training to GPU training -with optimal settings for performance and memory efficiency. +import torch Usage: python scripts/setup_gpu_training.py --check diff --git a/scripts/training/simple_vertex_training.py b/scripts/training/simple_vertex_training.py index 30e48b844..8e8500337 100644 --- a/scripts/training/simple_vertex_training.py +++ b/scripts/training/simple_vertex_training.py @@ -39,6 +39,7 @@ def simple_vertex_training(): config_dir.mkdir(parents=True, exist_ok=True) import json + with open(config_dir / "training_config.json", "w") as f: json.dump(config, f, indent=2) diff --git a/scripts/training/simple_working_training.py b/scripts/training/simple_working_training.py index 2a4e5a871..201bed6fc 100644 --- a/scripts/training/simple_working_training.py +++ b/scripts/training/simple_working_training.py @@ -1,33 +1,20 @@ - # Backward pass - # Forward pass - # Log progress every 100 batches - # Save model - # Log progress - # Save best model - # Training phase - # Validation phase - # BCE loss - # Create data loaders - # Create focal loss - # Create model - # Focal loss components - # Load dataset - # Setup optimizer - # Training loop - import traceback - # Setup device -# Add project root to path -# Configure logging #!/usr/bin/env python3 +"""Simple Working Training Script for SAMO Deep Learning""" + from pathlib import Path +import sys +import traceback + +# Add project root to path +sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) + +# Configure logging from src.models.emotion_detection.dataset_loader import GoEmotionsDataLoader from src.models.emotion_detection.training_pipeline import create_bert_emotion_classifier from torch import nn import logging import os -import sys import torch -import traceback @@ -67,15 +54,13 @@ def forward(self, inputs, targets): if self.reduction == "mean": return focal_loss.mean() - elif self.reduction == "sum": + if self.reduction == "sum": return focal_loss.sum() - else: - return focal_loss + return focal_loss def train_simple_model(): """Train a simple BERT model with focal loss.""" - logger.info("๐Ÿš€ Starting Simple Working Training") logger.info(" โ€ข Focal Loss: alpha=0.25, gamma=2.0") logger.info(" โ€ข Learning Rate: 2e-05") diff --git a/scripts/training/summarize_comprehensive_notebook.py b/scripts/training/summarize_comprehensive_notebook.py index fdaf4daca..755baab44 100644 --- a/scripts/training/summarize_comprehensive_notebook.py +++ b/scripts/training/summarize_comprehensive_notebook.py @@ -9,30 +9,31 @@ import json + def summarize_comprehensive_notebook(): """Summarize the comprehensive notebook.""" - + # Read the notebook - with open('notebooks/COMPREHENSIVE_ULTIMATE_TRAINING_COLAB.ipynb', 'r') as f: + with open("notebooks/COMPREHENSIVE_ULTIMATE_TRAINING_COLAB.ipynb", "r") as f: notebook = json.load(f) - + print("๐Ÿš€ COMPREHENSIVE ULTIMATE TRAINING NOTEBOOK SUMMARY") print("=" * 60) print() - + # Count cells by type - markdown_cells = [cell for cell in notebook['cells'] if cell['cell_type'] == 'markdown'] - code_cells = [cell for cell in notebook['cells'] if cell['cell_type'] == 'code'] - - print(f"๐Ÿ“Š NOTEBOOK STATISTICS:") + markdown_cells = [cell for cell in notebook["cells"] if cell["cell_type"] == "markdown"] + code_cells = [cell for cell in notebook["cells"] if cell["cell_type"] == "code"] + + print("๐Ÿ“Š NOTEBOOK STATISTICS:") print(f" Total cells: {len(notebook['cells'])}") print(f" Markdown cells: {len(markdown_cells)}") print(f" Code cells: {len(code_cells)}") print() - + print("๐ŸŽฏ ALL FEATURES INCLUDED:") print("=" * 40) - + features = [ "โœ… Configuration preservation (prevents 8.3% vs 75% discrepancy)", "โœ… Focal loss (handles class imbalance)", @@ -48,16 +49,16 @@ def summarize_comprehensive_notebook(): "โœ… Model saving with verification", "โœ… Complete training pipeline", "โœ… Evaluation and metrics", - "โœ… Unseen data testing" + "โœ… Unseen data testing", ] - + for feature in features: print(f" {feature}") - + print() print("๐Ÿ“‹ CELL BREAKDOWN:") print("=" * 30) - + cell_titles = [ "Title and Overview", "Package Installation", @@ -76,12 +77,12 @@ def summarize_comprehensive_notebook(): "Training Execution", "Evaluation and Validation", "Advanced Validation and Bias Analysis", - "Model Saving with Verification" + "Model Saving with Verification", ] - + for i, title in enumerate(cell_titles, 1): print(f" {i:2d}. {title}") - + print() print("๐ŸŽฏ KEY ADVANTAGES:") print("=" * 30) @@ -93,18 +94,19 @@ def summarize_comprehensive_notebook(): "๐Ÿ” Advanced validation and bias analysis", "๐Ÿ’พ Proper model saving with configuration verification", "๐Ÿš€ Ready for production deployment", - "๐Ÿ“‹ Complete training pipeline from start to finish" + "๐Ÿ“‹ Complete training pipeline from start to finish", ] - + for advantage in advantages: print(f" {advantage}") - + print() print("๐Ÿ“ FILE LOCATION:") - print(f" notebooks/COMPREHENSIVE_ULTIMATE_TRAINING_COLAB.ipynb") + print(" notebooks/COMPREHENSIVE_ULTIMATE_TRAINING_COLAB.ipynb") print() print("๐Ÿš€ READY TO USE!") print(" Download, upload to Colab, set GPU runtime, and run!") + if __name__ == "__main__": - summarize_comprehensive_notebook() \ No newline at end of file + summarize_comprehensive_notebook() diff --git a/scripts/training/summarize_ultimate_notebook.py b/scripts/training/summarize_ultimate_notebook.py index d6c83271e..b50081f2c 100644 --- a/scripts/training/summarize_ultimate_notebook.py +++ b/scripts/training/summarize_ultimate_notebook.py @@ -8,23 +8,24 @@ import json + def summarize_notebook(): """Summarize the ultimate notebook contents.""" - + print("๐Ÿš€ ULTIMATE BULLETPROOF TRAINING NOTEBOOK SUMMARY") print("=" * 60) print() - + # Read the notebook - with open('notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb', 'r') as f: + with open("notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb", "r") as f: notebook = json.load(f) - + print("๐Ÿ“‹ NOTEBOOK OVERVIEW:") print(" ๐Ÿ“ File: notebooks/ULTIMATE_BULLETPROOF_TRAINING_COLAB.ipynb") print(f" ๐Ÿ“Š Total cells: {len(notebook['cells'])}") print(" ๐ŸŽฏ Target: 75-85% F1 score with consistent performance") print() - + print("โœ… ALL FEATURES INCLUDED:") print(" ๐Ÿ”ง Configuration preservation (prevents 8.3% vs 75% discrepancy)") print(" ๐ŸŽฏ Focal loss implementation (handles class imbalance)") @@ -33,32 +34,32 @@ def summarize_notebook(): print(" ๐Ÿงช Advanced validation (proper testing)") print(" ๐Ÿ’พ Model saving with verification") print() - + print("๐Ÿ” CELL BREAKDOWN:") cell_count = 0 - for cell in notebook['cells']: + for cell in notebook["cells"]: cell_count += 1 - if cell['cell_type'] == 'markdown': + if cell["cell_type"] == "markdown": # Extract the first line of markdown - first_line = cell['source'][0].strip() if cell['source'] else "" - if first_line.startswith('#'): + first_line = cell["source"][0].strip() if cell["source"] else "" + if first_line.startswith("#"): print(f" {cell_count:2d}. ๐Ÿ“ {first_line}") - elif cell['cell_type'] == 'code': + elif cell["cell_type"] == "code": # Look for key functions/classes - code_text = ''.join(cell['source']) - if 'FocalLoss' in code_text: + code_text = "".join(cell["source"]) + if "FocalLoss" in code_text: print(f" {cell_count:2d}. ๐ŸŽฏ Focal Loss Implementation") - elif 'WeightedLossTrainer' in code_text: + elif "WeightedLossTrainer" in code_text: print(f" {cell_count:2d}. โš–๏ธ Weighted Loss Trainer") - elif 'augment_text' in code_text: + elif "augment_text" in code_text: print(f" {cell_count:2d}. ๐Ÿ“Š Data Augmentation") - elif 'compute_metrics' in code_text: + elif "compute_metrics" in code_text: print(f" {cell_count:2d}. ๐Ÿ“ˆ Compute Metrics") - elif 'trainer.train()' in code_text: + elif "trainer.train()" in code_text: print(f" {cell_count:2d}. ๐Ÿš€ Training Execution") - elif 'model.save_pretrained' in code_text: + elif "model.save_pretrained" in code_text: print(f" {cell_count:2d}. ๐Ÿ’พ Model Saving with Verification") - + print() print("๐ŸŽฏ KEY IMPROVEMENTS FROM PREVIOUS ITERATIONS:") print(" โœ… Fixed model configuration preservation") @@ -68,7 +69,7 @@ def summarize_notebook(): print(" โœ… Advanced validation on diverse examples") print(" โœ… Comprehensive model saving with verification") print() - + print("๐Ÿ“‹ USAGE INSTRUCTIONS:") print(" 1. Download the notebook file") print(" 2. Upload to Google Colab") @@ -76,7 +77,7 @@ def summarize_notebook(): print(" 4. Run all cells") print(" 5. Expect 75-85% F1 score!") print() - + print("๐Ÿ”ง TECHNICAL SPECIFICATIONS:") print(" ๐Ÿ—๏ธ Model: j-hartmann/emotion-english-distilroberta-base") print(" ๐ŸŽฏ Emotions: 12 classes (anxious, calm, content, excited, etc.)") @@ -85,12 +86,13 @@ def summarize_notebook(): print(" ๐Ÿงช Validation: Advanced testing on diverse examples") print(" ๐Ÿ’พ Output: Verified model with proper configuration") print() - + print("๐ŸŽ‰ THIS IS THE ULTIMATE BULLETPROOF VERSION!") print(" Combines ALL successful techniques from previous iterations") print(" Addresses ALL known issues and limitations") print(" Designed for reliable, consistent performance") print(" Ready for production deployment") + if __name__ == "__main__": - summarize_notebook() \ No newline at end of file + summarize_notebook() diff --git a/scripts/training/test_quick_training.py b/scripts/training/test_quick_training.py index e634e87b8..4338b97ce 100644 --- a/scripts/training/test_quick_training.py +++ b/scripts/training/test_quick_training.py @@ -1,30 +1,33 @@ - # Create trainer and load small dataset - # Find best threshold - # Load a pre-trained model if available, otherwise skip - # Load model - # Overall assessment - # Prepare small dataset - # Run training with development mode enabled - # Success criteria - # Test different thresholds - # Validate results - # Summary - # Test 1: Development mode training - # Test 2: Threshold tuning -# Add src to path -# Configure logging -#!/usr/bin/env python3 -from src.models.emotion_detection.bert_classifier import evaluate_emotion_classifier -from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer -from src.models.emotion_detection.training_pipeline import train_emotion_detection_model -from pathlib import Path +# Create trainer and load small dataset +# Find best threshold +# Load a pre-trained model if available, otherwise skip +# Load model +# Overall assessment +# Prepare small dataset +# Run training with development mode enabled +# Success criteria +# Test different thresholds +# Validate results +# Summary +# Test 1: Development mode training +# Test 2: Threshold tuning + import logging import sys import time -import torch +from pathlib import Path +import torch +# Configure logging +#!/usr/bin/env python3 +from src.models.emotion_detection.bert_classifier import evaluate_emotion_classifier +# Add src to path +from src.models.emotion_detection.training_pipeline import ( + EmotionDetectionTrainer, + train_emotion_detection_model, +) """Quick Training Test Script for SAMO Emotion Detection. @@ -130,7 +133,6 @@ def test_threshold_tuning(): logger.info("No pre-trained model found, skipping threshold tuning test") return True - checkpoint = torch.load(model_path, map_location="cpu", weights_only=False) trainer.model.load_state_dict(checkpoint["model_state_dict"]) diff --git a/scripts/training/validate_improved_notebook.py b/scripts/training/validate_improved_notebook.py index eda4c6a03..2bf70e098 100644 --- a/scripts/training/validate_improved_notebook.py +++ b/scripts/training/validate_improved_notebook.py @@ -6,35 +6,36 @@ import json + def validate_notebook(): """Validate the improved notebook for Colab execution.""" - + print("๐Ÿ” Validating improved notebook...") - + # Load the notebook try: - with open('notebooks/expanded_dataset_training_improved.ipynb', 'r') as f: + with open("notebooks/expanded_dataset_training_improved.ipynb", "r") as f: notebook = json.load(f) print("โœ… Notebook JSON is valid") except Exception as e: print(f"โŒ Notebook JSON error: {e}") return False - + # Check notebook structure - cells = notebook['cells'] + cells = notebook["cells"] print(f"๐Ÿ“Š Notebook has {len(cells)} cells") - + # Validate cell types - markdown_cells = [c for c in cells if c['cell_type'] == 'markdown'] - code_cells = [c for c in cells if c['cell_type'] == 'code'] - + markdown_cells = [c for c in cells if c["cell_type"] == "markdown"] + code_cells = [c for c in cells if c["cell_type"] == "code"] + print(f"๐Ÿ“ Markdown cells: {len(markdown_cells)}") print(f"๐Ÿ’ป Code cells: {len(code_cells)}") - + # Check for critical components - cell_sources = [str(c.get('source', '')) for c in cells] - all_source = ' '.join(cell_sources) - + cell_sources = [str(c.get("source", "")) for c in cells] + all_source = " ".join(cell_sources) + # Critical checks checks = [ ("Repository cloning", "git clone https://github.com/uelkerd/SAMO--DL.git"), @@ -48,17 +49,17 @@ def validate_notebook(): ("Model testing", "test_new_model"), ("Results download", "files.download"), ] - + print("\n๐Ÿ” Critical component checks:") all_passed = True - + for check_name, check_content in checks: if check_content in all_source: print(f" โœ… {check_name}") else: print(f" โŒ {check_name}") all_passed = False - + # Check for JSON syntax issues print("\n๐Ÿ” JSON syntax validation:") try: @@ -69,7 +70,7 @@ def validate_notebook(): except Exception as e: print(f" โŒ JSON escaping issues: {e}") all_passed = False - + # Check for GPU optimizations gpu_optimizations = [ "torch.backends.cudnn.benchmark = True", @@ -77,9 +78,9 @@ def validate_notebook(): "torch.cuda.empty_cache()", "non_blocking=True", "num_workers=2", - "pin_memory=True" + "pin_memory=True", ] - + print("\n๐Ÿ” GPU optimization checks:") for opt in gpu_optimizations: if opt in all_source: @@ -87,7 +88,7 @@ def validate_notebook(): else: print(f" โŒ {opt}") all_passed = False - + # Check for training optimizations training_optimizations = [ "GradScaler()", @@ -96,9 +97,9 @@ def validate_notebook(): "scaler.step(optimizer)", "scaler.update()", "ReduceLROnPlateau", - "Early stopping triggered" + "Early stopping triggered", ] - + print("\n๐Ÿ” Training optimization checks:") for opt in training_optimizations: if opt in all_source: @@ -106,14 +107,14 @@ def validate_notebook(): else: print(f" โŒ {opt}") all_passed = False - + # Summary - print(f"\n๐Ÿ“Š Validation Summary:") + print("\n๐Ÿ“Š Validation Summary:") print(f" Total cells: {len(cells)}") print(f" Code cells: {len(code_cells)}") print(f" Markdown cells: {len(markdown_cells)}") print(f" All checks passed: {'โœ…' if all_passed else 'โŒ'}") - + if all_passed: print("\n๐ŸŽ‰ Notebook is ready for Colab execution!") print("๐Ÿ“‹ Next steps:") @@ -123,8 +124,9 @@ def validate_notebook(): print(" 4. Expect 75-85% F1 score!") else: print("\nโš ๏ธ Notebook needs fixes before Colab execution") - + return all_passed + if __name__ == "__main__": - validate_notebook() \ No newline at end of file + validate_notebook() diff --git a/scripts/training/vertex_ai_training.py b/scripts/training/vertex_ai_training.py index 1b80e5ace..fbcb13938 100644 --- a/scripts/training/vertex_ai_training.py +++ b/scripts/training/vertex_ai_training.py @@ -4,23 +4,24 @@ Runs training/validation on Vertex AI and standardizes logging and path bootstrap. """ -from pathlib import Path import argparse import logging +import os import sys import traceback +from pathlib import Path + import transformers -import os -# Import project-specific modules -from src.models.emotion_detection.dataset_loader import create_goemotions_loader from src.models.emotion_detection.bert_classifier import ( - WeightedBCELoss, create_bert_emotion_classifier -) -from src.models.emotion_detection.training_pipeline import ( - EmotionDetectionTrainer + WeightedBCELoss, + create_bert_emotion_classifier, ) +# Import project-specific modules +from src.models.emotion_detection.dataset_loader import create_goemotions_loader +from src.models.emotion_detection.training_pipeline import EmotionDetectionTrainer + # Ensure project root on path try: PROJECT_ROOT = Path(__file__).resolve().parents[2] @@ -39,99 +40,46 @@ format="%(asctime)s - %(levelname)s - %(message)s", handlers=[ logging.StreamHandler(sys.stdout), - logging.FileHandler("/app/logs/vertex_training.log") - ] + logging.FileHandler("/app/logs/vertex_training.log"), + ], ) logger = logging.getLogger(__name__) def parse_arguments(): """Parse command line arguments.""" - parser = argparse.ArgumentParser( - description="Vertex AI Training for SAMO Deep Learning" - ) + parser = argparse.ArgumentParser(description="Vertex AI Training for SAMO Deep Learning") + parser.add_argument("--model_name", default="bert-base-uncased", help="Hugging Face model name") + parser.add_argument("--batch_size", type=int, default=16, help="Training batch size") parser.add_argument( - "--model_name", - default="bert-base-uncased", - help="Hugging Face model name" - ) - parser.add_argument( - "--batch_size", - type=int, - default=16, - help="Training batch size" - ) - parser.add_argument( - "--learning_rate", - type=float, - default=2e-6, - help="Learning rate (optimized for stability)" + "--learning_rate", type=float, default=2e-6, help="Learning rate (optimized for stability)" ) + parser.add_argument("--num_epochs", type=int, default=3, help="Number of training epochs") + parser.add_argument("--max_length", type=int, default=512, help="Maximum sequence length") parser.add_argument( - "--num_epochs", - type=int, - default=3, - help="Number of training epochs" - ) - parser.add_argument( - "--max_length", - type=int, - default=512, - help="Maximum sequence length" - ) - parser.add_argument( - "--freeze_bert_layers", - type=int, - default=6, - help="Number of BERT layers to freeze" + "--freeze_bert_layers", type=int, default=6, help="Number of BERT layers to freeze" ) parser.add_argument( - "--use_focal_loss", - action="store_true", - help="Use focal loss instead of BCE" - ) - parser.add_argument( - "--class_weights", - action="store_true", - help="Use class weights for imbalanced data" - ) - parser.add_argument( - "--dev_mode", - action="store_true", - help="Run in development mode" + "--use_focal_loss", action="store_true", help="Use focal loss instead of BCE" ) parser.add_argument( - "--debug_mode", - action="store_true", - help="Enable debugging mode" + "--class_weights", action="store_true", help="Use class weights for imbalanced data" ) + parser.add_argument("--dev_mode", action="store_true", help="Run in development mode") + parser.add_argument("--debug_mode", action="store_true", help="Enable debugging mode") + parser.add_argument("--validation_mode", action="store_true", help="Run validation only") parser.add_argument( - "--validation_mode", - action="store_true", - help="Run validation only" - ) - parser.add_argument( - "--check_data_distribution", - action="store_true", - help="Check data distribution" - ) - parser.add_argument( - "--check_model_architecture", - action="store_true", - help="Check model architecture" + "--check_data_distribution", action="store_true", help="Check data distribution" ) parser.add_argument( - "--check_loss_function", - action="store_true", - help="Check loss function" + "--check_model_architecture", action="store_true", help="Check model architecture" ) + parser.add_argument("--check_loss_function", action="store_true", help="Check loss function") parser.add_argument( - "--check_training_config", - action="store_true", - help="Check training configuration" + "--check_training_config", action="store_true", help="Check training configuration" ) return parser.parse_args() @@ -143,6 +91,7 @@ def validate_environment(): try: import torch + logger.info("โœ… PyTorch: %s", torch.__version__) logger.info("โœ… Transformers: %s", transformers.__version__) logger.info("โœ… Vertex AI: Available") @@ -231,6 +180,7 @@ def validate_model_architecture(): try: import torch + model, loss_fn = create_bert_emotion_classifier( model_name="bert-base-uncased", class_weights=None, @@ -284,6 +234,7 @@ def validate_loss_function(): try: import torch import torch.nn.functional as F + batch_size = 4 num_classes = 28 @@ -294,18 +245,14 @@ def validate_loss_function(): loss_fn = WeightedBCELoss() loss1 = loss_fn(logits, labels) - bce_manual = F.binary_cross_entropy_with_logits( - logits, labels, reduction="mean" - ) + bce_manual = F.binary_cross_entropy_with_logits(logits, labels, reduction="mean") logger.info("โœ… Mixed labels loss: %.8f", loss1.item()) logger.info("โœ… Manual BCE loss: %.8f", bce_manual.item()) loss_diff = abs(loss1.item() - bce_manual.item()) if loss_diff > 1.0: - logger.warning( - "โš ๏ธ Large difference between custom and manual loss: %s", loss_diff - ) + logger.warning("โš ๏ธ Large difference between custom and manual loss: %s", loss_diff) labels_all_pos = torch.ones(batch_size, num_classes) loss2 = loss_fn(logits, labels_all_pos) @@ -351,9 +298,7 @@ def validate_training_config(args): if not args.use_focal_loss and not args.class_weights: logger.warning("โš ๏ธ No class balancing strategy") - logger.warning( - " Consider using focal loss or class weights for imbalanced data" - ) + logger.warning(" Consider using focal loss or class weights for imbalanced data") return True @@ -412,9 +357,7 @@ def _build_validation_list(args): validations.append(("Loss Function", validate_loss_function)) if args.check_training_config: - validations.append( - ("Training Config", lambda: validate_training_config(args)) - ) + validations.append(("Training Config", lambda: validate_training_config(args))) if not validations: validations = [ @@ -432,9 +375,9 @@ def _run_validations(validations): results = {} for name, validation_func in validations: - logger.info("\n%s", "="*40) + logger.info("\n%s", "=" * 40) logger.info("Running: %s", name) - logger.info("%s", "="*40) + logger.info("%s", "=" * 40) try: validation_func() # Will raise exception on failure @@ -453,9 +396,9 @@ def _print_validation_summary(results): passed = sum(results.values()) total = len(results) - logger.info("\n%s", "="*50) + logger.info("\n%s", "=" * 50) logger.info("๐Ÿ“Š VALIDATION SUMMARY") - logger.info("%s", "="*50) + logger.info("%s", "=" * 50) logger.info("Total checks: %d", total) logger.info("Passed: %d", passed) logger.info("Failed: %d", total - passed) diff --git a/scripts/training/vertex_automl_training.py b/scripts/training/vertex_automl_training.py index 5126387ee..3ec243e35 100644 --- a/scripts/training/vertex_automl_training.py +++ b/scripts/training/vertex_automl_training.py @@ -1,32 +1,31 @@ - # If no emotion column found, use the last column (typically labels) - # Save results to GCS - # Step 1: Load metadata - # Step 2: Create dataset - # Step 3: Train model - # Step 4: Monitor training - # Step 5: Deploy model - # Step 6: Save results - # Configure training job - # Create dataset - # Download first few lines to check structure - # Find the target column (should be the emotion labels column) - # Get model evaluation - # Get the correct target column - # Initialize Vertex AI - # Look for emotion-related columns - # Start training - # Initialize and run training -# Configure logging -#!/usr/bin/env python3 -from datetime import datetime -from google.cloud import aiplatform -from google.cloud import storage +# If no emotion column found, use the last column (typically labels) +# Save results to GCS +# Step 1: Load metadata +# Step 2: Create dataset +# Step 3: Train model +# Step 4: Monitor training +# Step 5: Deploy model +# Step 6: Save results +# Configure training job +# Create dataset +# Download first few lines to check structure +# Find the target column (should be the emotion labels column) +# Get model evaluation +# Get the correct target column +# Initialize Vertex AI +# Look for emotion-related columns +# Start training +# Initialize and run training import json import logging import sys import time +# Configure logging +#!/usr/bin/env python3 +from datetime import datetime +from google.cloud import aiplatform, storage """ SAMO Vertex AI AutoML Training Pipeline diff --git a/scripts/training/working_training_script.py b/scripts/training/working_training_script.py index a6d2e852b..905f5f200 100644 --- a/scripts/training/working_training_script.py +++ b/scripts/training/working_training_script.py @@ -1,23 +1,17 @@ - # Backward pass - # Check for 0.0000 loss - # Create dummy batch - # Forward pass - # Step 1: Create model (this worked in validation) - # Step 2: Create optimizer with reduced learning rate - # Step 3: Test forward pass (this worked in validation) - # Step 4: Simple training loop with dummy data - from src.models.emotion_detection.bert_classifier import create_bert_emotion_classifier - import traceback -# Add src to path -# Configure logging #!/usr/bin/env python3 +"""Working Training Script for SAMO Deep Learning""" + from pathlib import Path import logging import sys import torch -import torch.nn as nn import traceback +# Add src to path +sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) + +# Configure logging + diff --git a/scripts/validation/check_dependencies.py b/scripts/validation/check_dependencies.py index f1f8149d8..f23b37dc6 100644 --- a/scripts/validation/check_dependencies.py +++ b/scripts/validation/check_dependencies.py @@ -9,104 +9,105 @@ import re import sys from pathlib import Path -from typing import Set, List, Dict +from typing import Set + class DependencyChecker: """Checker for dependency usage in the codebase.""" - + def __init__(self, requirements_path: str = "requirements.txt"): self.requirements_path = Path(requirements_path) self.project_root = Path(__file__).parent.parent.parent self.unused_deps = [] self.missing_deps = [] - + def check_dependencies(self) -> bool: """Check if all dependencies are used in the codebase.""" print("๐Ÿ” Checking dependency usage...") - + # Read requirements.txt if not self.requirements_path.exists(): print(f"โŒ Requirements file not found: {self.requirements_path}") return False - + required_deps = self._parse_requirements() used_deps = self._find_used_dependencies() - + # Check for unused dependencies for dep in required_deps: if dep not in used_deps: self.unused_deps.append(dep) - + # Check for missing dependencies (optional) # This would require more complex analysis - + return len(self.unused_deps) == 0 - + def _parse_requirements(self) -> Set[str]: """Parse requirements.txt and extract package names.""" deps = set() - - with open(self.requirements_path, 'r') as f: + + with open(self.requirements_path, "r") as f: for line in f: line = line.strip() - if line and not line.startswith('#'): + if line and not line.startswith("#"): # Extract package name (remove version constraints) - package = re.split(r'[<>=!~]', line)[0].strip() + package = re.split(r"[<>=!~]", line)[0].strip() deps.add(package) - + return deps - + def _find_used_dependencies(self) -> Set[str]: """Find all dependencies used in the codebase.""" used_deps = set() - + # Common Python file extensions - python_extensions = {'.py', '.pyx', '.pyi'} - + python_extensions = {".py", ".pyx", ".pyi"} + # Directories to scan - scan_dirs = ['src', 'scripts', 'tests', 'deployment'] - + scan_dirs = ["src", "scripts", "tests", "deployment"] + for scan_dir in scan_dirs: dir_path = self.project_root / scan_dir if dir_path.exists(): - for file_path in dir_path.rglob('*'): + for file_path in dir_path.rglob("*"): if file_path.suffix in python_extensions: self._scan_file_for_imports(file_path, used_deps) - + return used_deps - + def _scan_file_for_imports(self, file_path: Path, used_deps: Set[str]) -> None: """Scan a Python file for import statements.""" try: - with open(file_path, 'r', encoding='utf-8') as f: + with open(file_path, "r", encoding="utf-8") as f: content = f.read() - + # Find import statements import_patterns = [ - r'^import\s+(\w+)', - r'^from\s+(\w+)', - r'^\s+import\s+(\w+)', - r'^\s+from\s+(\w+)' + r"^import\s+(\w+)", + r"^from\s+(\w+)", + r"^\s+import\s+(\w+)", + r"^\s+from\s+(\w+)", ] - + for pattern in import_patterns: matches = re.findall(pattern, content, re.MULTILINE) for match in matches: # Handle multi-import statements - packages = [p.strip() for p in match.split(',')] + packages = [p.strip() for p in match.split(",")] for package in packages: # Extract base package name - base_package = package.split('.')[0] + base_package = package.split(".")[0] used_deps.add(base_package) - + except Exception as e: print(f"โš ๏ธ Warning: Could not scan {file_path}: {e}") - + def print_results(self) -> None: """Print dependency check results.""" - print(f"\n๐Ÿ“Š Dependency Usage Check Results") + print("\n๐Ÿ“Š Dependency Usage Check Results") print("=" * 50) - + if self.unused_deps: print(f"\nโš ๏ธ Potentially Unused Dependencies ({len(self.unused_deps)}):") for dep in sorted(self.unused_deps): @@ -114,27 +115,28 @@ def print_results(self) -> None: print("\n๐Ÿ’ก Consider removing these dependencies if they're not needed.") else: print("\nโœ… All dependencies appear to be used in the codebase!") - + if self.missing_deps: print(f"\nโŒ Missing Dependencies ({len(self.missing_deps)}):") for dep in sorted(self.missing_deps): print(f" - {dep}") + def main(): """Main function to run dependency usage check.""" checker = DependencyChecker() - + if checker.check_dependencies(): checker.print_results() if checker.unused_deps: print("\nโš ๏ธ Found potentially unused dependencies") return 0 # Don't fail the build, just warn - else: - print("\nโœ… Dependency usage check passed!") - return 0 - else: - checker.print_results() - return 1 + + print("\nโœ… Dependency usage check passed!") + return 0 + checker.print_results() + return 1 + if __name__ == "__main__": - sys.exit(main()) \ No newline at end of file + sys.exit(main()) diff --git a/scripts/validation/validate_security_config.py b/scripts/validation/validate_security_config.py index 9d438eee0..77648c3b8 100644 --- a/scripts/validation/validate_security_config.py +++ b/scripts/validation/validate_security_config.py @@ -6,232 +6,236 @@ settings are present and valid according to the security schema. """ -import yaml import sys from pathlib import Path -from typing import Dict, Any, List +from typing import Any, Dict + +import yaml + class SecurityConfigValidator: """Validator for security configuration files.""" - + def __init__(self, config_path: str = "configs/security.yaml"): self.config_path = Path(config_path) self.errors = [] self.warnings = [] - + def validate(self) -> bool: """Validate the security configuration file.""" print("๐Ÿ” Validating security configuration...") - + # Check if file exists if not self.config_path.exists(): self.errors.append(f"Security configuration file not found: {self.config_path}") return False - + try: - with open(self.config_path, 'r') as f: + with open(self.config_path, "r") as f: config = yaml.safe_load(f) except yaml.YAMLError as e: self.errors.append(f"Invalid YAML in security configuration: {e}") return False - + # Validate required sections self._validate_required_sections(config) - + # Validate API security settings - self._validate_api_security(config.get('api', {})) - + self._validate_api_security(config.get("api", {})) + # Validate security headers - self._validate_security_headers(config.get('security_headers', {})) - + self._validate_security_headers(config.get("security_headers", {})) + # Validate logging configuration - self._validate_logging(config.get('logging', {})) - + self._validate_logging(config.get("logging", {})) + # Validate environment settings - self._validate_environment(config.get('environment', {})) - + self._validate_environment(config.get("environment", {})) + # Validate dependency security - self._validate_dependencies(config.get('dependencies', {})) - + self._validate_dependencies(config.get("dependencies", {})) + # Validate model security - self._validate_model_security(config.get('model', {})) - + self._validate_model_security(config.get("model", {})) + # Validate database security - self._validate_database_security(config.get('database', {})) - + self._validate_database_security(config.get("database", {})) + # Validate deployment security - self._validate_deployment_security(config.get('deployment', {})) - + self._validate_deployment_security(config.get("deployment", {})) + return len(self.errors) == 0 - + def _validate_required_sections(self, config: Dict[str, Any]) -> None: """Validate that all required sections are present.""" required_sections = [ - 'api', 'security_headers', 'logging', 'environment', - 'dependencies', 'model', 'database', 'deployment' + "api", + "security_headers", + "logging", + "environment", + "dependencies", + "model", + "database", + "deployment", ] - + for section in required_sections: if section not in config: self.errors.append(f"Missing required section: {section}") - + def _validate_api_security(self, api_config: Dict[str, Any]) -> None: """Validate API security configuration.""" if not api_config: self.errors.append("API configuration is empty") return - + # Check rate limiting - rate_limiting = api_config.get('rate_limiting', {}) - if not rate_limiting.get('enabled', False): + rate_limiting = api_config.get("rate_limiting", {}) + if not rate_limiting.get("enabled", False): self.warnings.append("Rate limiting is disabled - security risk") - + # Check CORS - cors = api_config.get('cors', {}) - if not cors.get('enabled', False): + cors = api_config.get("cors", {}) + if not cors.get("enabled", False): self.warnings.append("CORS is disabled - may cause issues") - + # Check authentication - auth = api_config.get('authentication', {}) - if not auth.get('enabled', False): + auth = api_config.get("authentication", {}) + if not auth.get("enabled", False): self.errors.append("Authentication is disabled - security risk") - + # Check input validation - input_validation = api_config.get('input_validation', {}) + input_validation = api_config.get("input_validation", {}) if not input_validation: self.errors.append("Input validation configuration is missing") - + def _validate_security_headers(self, headers_config: Dict[str, Any]) -> None: """Validate security headers configuration.""" - if not headers_config.get('enabled', False): + if not headers_config.get("enabled", False): self.warnings.append("Security headers are disabled") return - - headers = headers_config.get('headers', {}) - required_headers = [ - 'X-Content-Type-Options', - 'X-Frame-Options', - 'X-XSS-Protection' - ] - + + headers = headers_config.get("headers", {}) + required_headers = ["X-Content-Type-Options", "X-Frame-Options", "X-XSS-Protection"] + for header in required_headers: if header not in headers: self.warnings.append(f"Missing recommended security header: {header}") - + def _validate_logging(self, logging_config: Dict[str, Any]) -> None: """Validate logging configuration.""" if not logging_config: self.errors.append("Logging configuration is missing") return - + # Check security events logging - security_events = logging_config.get('security_events', {}) - if not security_events.get('enabled', False): + security_events = logging_config.get("security_events", {}) + if not security_events.get("enabled", False): self.warnings.append("Security events logging is disabled") - + # Check request logging - requests = logging_config.get('requests', {}) - if not requests.get('enabled', False): + requests = logging_config.get("requests", {}) + if not requests.get("enabled", False): self.warnings.append("Request logging is disabled") - + # Check error logging - errors = logging_config.get('errors', {}) - if not errors.get('enabled', False): + errors = logging_config.get("errors", {}) + if not errors.get("enabled", False): self.warnings.append("Error logging is disabled") - + def _validate_environment(self, env_config: Dict[str, Any]) -> None: """Validate environment configuration.""" if not env_config: self.errors.append("Environment configuration is missing") return - + # Check required environment variables - required_vars = env_config.get('required_vars', []) + required_vars = env_config.get("required_vars", []) if not required_vars: self.warnings.append("No required environment variables specified") - + # Check sensitive variables - sensitive_vars = env_config.get('sensitive_vars', []) + sensitive_vars = env_config.get("sensitive_vars", []) if not sensitive_vars: self.warnings.append("No sensitive variables specified for masking") - + # Check environment-specific settings - for env in ['production', 'development', 'testing']: + for env in ["production", "development", "testing"]: env_settings = env_config.get(env, {}) if not env_settings: self.warnings.append(f"No settings specified for {env} environment") - + def _validate_dependencies(self, deps_config: Dict[str, Any]) -> None: """Validate dependency security configuration.""" if not deps_config: self.errors.append("Dependency security configuration is missing") return - - scanning = deps_config.get('scanning', {}) - if not scanning.get('enabled', False): + + scanning = deps_config.get("scanning", {}) + if not scanning.get("enabled", False): self.warnings.append("Dependency security scanning is disabled") - - tools = scanning.get('tools', []) + + tools = scanning.get("tools", []) if not tools: self.warnings.append("No security scanning tools specified") - + def _validate_model_security(self, model_config: Dict[str, Any]) -> None: """Validate model security configuration.""" if not model_config: self.errors.append("Model security configuration is missing") return - - loading = model_config.get('loading', {}) - if not loading.get('validate_model_files', False): + + loading = model_config.get("loading", {}) + if not loading.get("validate_model_files", False): self.warnings.append("Model file validation is disabled") - - inference = model_config.get('inference', {}) + + inference = model_config.get("inference", {}) if not inference: self.warnings.append("Model inference security settings are missing") - + def _validate_database_security(self, db_config: Dict[str, Any]) -> None: """Validate database security configuration.""" if not db_config: self.errors.append("Database security configuration is missing") return - - connection = db_config.get('connection', {}) - if not connection.get('use_ssl', False): + + connection = db_config.get("connection", {}) + if not connection.get("use_ssl", False): self.errors.append("Database SSL is disabled - security risk") - - data_protection = db_config.get('data_protection', {}) - if not data_protection.get('encrypt_sensitive_data', False): + + data_protection = db_config.get("data_protection", {}) + if not data_protection.get("encrypt_sensitive_data", False): self.warnings.append("Sensitive data encryption is disabled") - + def _validate_deployment_security(self, deploy_config: Dict[str, Any]) -> None: """Validate deployment security configuration.""" if not deploy_config: self.errors.append("Deployment security configuration is missing") return - - container = deploy_config.get('container', {}) - if not container.get('run_as_non_root', False): + + container = deploy_config.get("container", {}) + if not container.get("run_as_non_root", False): self.errors.append("Container not configured to run as non-root - security risk") - - network = deploy_config.get('network', {}) - if not network.get('use_https', False): + + network = deploy_config.get("network", {}) + if not network.get("use_https", False): self.errors.append("HTTPS is disabled - security risk") - + def print_results(self) -> None: """Print validation results.""" - print(f"\n๐Ÿ“Š Security Configuration Validation Results") + print("\n๐Ÿ“Š Security Configuration Validation Results") print("=" * 50) - + if self.errors: print(f"\nโŒ Errors ({len(self.errors)}):") for error in self.errors: print(f" - {error}") - + if self.warnings: print(f"\nโš ๏ธ Warnings ({len(self.warnings)}):") for warning in self.warnings: print(f" - {warning}") - + if not self.errors and not self.warnings: print("\nโœ… Security configuration is valid!") elif not self.errors: @@ -239,10 +243,11 @@ def print_results(self) -> None: else: print(f"\nโŒ Configuration has {len(self.errors)} errors that must be fixed") + def main(): """Main function to run security configuration validation.""" validator = SecurityConfigValidator() - + if validator.validate(): validator.print_results() if validator.errors: @@ -253,5 +258,6 @@ def main(): validator.print_results() sys.exit(1) + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/src/api_rate_limiter.py b/src/api_rate_limiter.py index 8ec1995bf..b7ff2e54a 100644 --- a/src/api_rate_limiter.py +++ b/src/api_rate_limiter.py @@ -6,21 +6,24 @@ Includes security features. """ -import time -import threading -from collections import defaultdict, deque -from typing import Dict, Deque, Optional, Tuple, Set -import logging import hashlib import ipaddress +import logging +import threading +import time +from collections import defaultdict, deque from dataclasses import dataclass +from typing import Deque, Dict, Optional, Set, Tuple + from starlette.middleware.base import BaseHTTPMiddleware logger = logging.getLogger(__name__) + @dataclass class RateLimitConfig: """Rate limiting configuration.""" + requests_per_minute: int = 60 burst_size: int = 10 window_size_seconds: int = 60 @@ -45,6 +48,7 @@ class RateLimitConfig: # -------- Path exclusion helpers -------- + def _normalize_path(path: str) -> str: """Normalize path for matching. @@ -73,10 +77,7 @@ def _build_exclusions(excluded_paths: Optional[Set[str]]) -> Set[str]: "/redoc", "/openapi.json", } - return { - _normalize_path(p) - for p in (default_exclusions | (excluded_paths or set())) - } + return {_normalize_path(p) for p in (default_exclusions | (excluded_paths or set()))} def _is_excluded_path(request_path: str, normalized_exclusions: Set[str]) -> bool: @@ -129,6 +130,7 @@ async def dispatch(self, request, call_next): # type: ignore[override] allowed, reason, meta = self._limiter.allow_request(client_ip, user_agent) if not allowed: from fastapi.responses import JSONResponse + return JSONResponse( status_code=429, content={ @@ -162,7 +164,7 @@ class TokenBucketRateLimiter: def __init__(self, config: RateLimitConfig): self.config = config self.buckets: Dict[str, float] = defaultdict(lambda: config.burst_size) - self.last_refill: Dict[str, float] = defaultdict(lambda: time.time()) + self.last_refill: Dict[str, float] = defaultdict(time.time) self.blocked_clients: Dict[str, float] = {} self.concurrent_requests: Dict[str, int] = defaultdict(int) self.request_history: Dict[str, Deque] = defaultdict(lambda: deque(maxlen=100)) @@ -186,21 +188,11 @@ def _is_ip_allowed(self, client_ip: str) -> bool: try: # Validate IP; exception will be raised if invalid ipaddress.ip_address(client_ip) - if ( - self.config.enable_ip_blacklist - and client_ip in self.config.blacklisted_ips - ): - logger.warning( - "Blocked request from blacklisted IP: %s", client_ip - ) + if self.config.enable_ip_blacklist and client_ip in self.config.blacklisted_ips: + logger.warning("Blocked request from blacklisted IP: %s", client_ip) return False - if ( - self.config.enable_ip_whitelist - and client_ip not in self.config.whitelisted_ips - ): - logger.warning( - "Blocked request from non-whitelisted IP: %s", client_ip - ) + if self.config.enable_ip_whitelist and client_ip not in self.config.whitelisted_ips: + logger.warning("Blocked request from non-whitelisted IP: %s", client_ip) return False return True except ValueError: @@ -223,17 +215,44 @@ def _analyze_user_agent(self, user_agent: str) -> int: ua_lower = user_agent.lower() high_risk_patterns = [ - 'sqlmap', 'nikto', 'nmap', 'scanner', 'crawler', 'spider', - 'bot', 'automation', 'script', 'python-requests', 'curl', - 'wget', 'httrack', 'grabber', 'harvester' + "sqlmap", + "nikto", + "nmap", + "scanner", + "crawler", + "spider", + "bot", + "automation", + "script", + "python-requests", + "curl", + "wget", + "httrack", + "grabber", + "harvester", ] medium_risk_patterns = [ - 'headless', 'phantom', 'selenium', 'webdriver', 'automated', - 'testing', 'monitoring', 'healthcheck', 'pingdom', 'uptimerobot' + "headless", + "phantom", + "selenium", + "webdriver", + "automated", + "testing", + "monitoring", + "healthcheck", + "pingdom", + "uptimerobot", ] low_risk_patterns = [ - 'bot', 'crawler', 'spider', 'indexer', 'feed', 'rss', - 'aggregator', 'monitor', 'checker' + "bot", + "crawler", + "spider", + "indexer", + "feed", + "rss", + "aggregator", + "monitor", + "checker", ] score = ( @@ -242,9 +261,8 @@ def _analyze_user_agent(self, user_agent: str) -> int: + 1 * sum(1 for p in low_risk_patterns if p in ua_lower) ) - if ( - any(p in ua_lower for p in ["bot", "crawler"]) and - any(p in ua_lower for p in ["python", "curl", "wget"]) + if any(p in ua_lower for p in ["bot", "crawler"]) and any( + p in ua_lower for p in ["python", "curl", "wget"] ): score += 2 @@ -287,8 +305,7 @@ def _calculate_request_regular_interval_score(recent_history: list) -> int: if len(recent_history) < 5: return 0 intervals = [ - recent_history[i] - recent_history[i - 1] - for i in range(1, len(recent_history)) + recent_history[i] - recent_history[i - 1] for i in range(1, len(recent_history)) ] if len(intervals) < 3: return 0 @@ -297,13 +314,9 @@ def _calculate_request_regular_interval_score(recent_history: list) -> int: return 3 if (var < 0.1 and avg < 2.0) else 0 @staticmethod - def _calculate_sustained_volume_score( - recent_history: list, current_time: float - ) -> int: + def _calculate_sustained_volume_score(recent_history: list, current_time: float) -> int: """Score sustained high request volume over the last minute.""" - minute_count = sum( - 1 for t in recent_history if current_time - t <= 60.0 - ) + minute_count = sum(1 for t in recent_history if current_time - t <= 60.0) return 2 if minute_count > 50 else 0 def _detect_abuse( @@ -317,24 +330,24 @@ def _detect_abuse( current_time = time.time() while history and current_time - history[0] > 3600: history.popleft() - recent_requests = [ - t for t in history - if current_time - t <= self.config.rapid_fire_window - ] + recent_requests = [t for t in history if current_time - t <= self.config.rapid_fire_window] if len(recent_requests) > self.config.rapid_fire_threshold: logger.warning( "Rate-based abuse detected: %d requests in %ss from %s", - len(recent_requests), self.config.rapid_fire_window, client_ip, + len(recent_requests), + self.config.rapid_fire_window, + client_ip, ) return True minute_requests = [ - t for t in history - if current_time - t <= self.config.sustained_rate_window + t for t in history if current_time - t <= self.config.sustained_rate_window ] if len(minute_requests) > self.config.sustained_rate_threshold: logger.warning( "Rate-based abuse detected: %d requests in %ss from %s", - len(minute_requests), self.config.sustained_rate_window, client_ip, + len(minute_requests), + self.config.sustained_rate_window, + client_ip, ) return True if self.config.enable_user_agent_analysis: @@ -385,48 +398,63 @@ def allow_request( return False, "IP not allowed", {"ip": client_ip} client_key = self._get_client_key(client_ip, user_agent) if self._is_client_blocked(client_key): - return False, "Client blocked", { - "client_key": client_key, - "ip": client_ip, - } - if ( - self.concurrent_requests[client_key] - >= self.config.max_concurrent_requests - ): - return False, "Too many concurrent requests", { - "client_key": client_key, - "concurrent": self.concurrent_requests[client_key], - "max": self.config.max_concurrent_requests, - } - if self._detect_abuse(client_key, client_ip, user_agent): - self.blocked_clients[client_key] = ( - time.time() + self.config.block_duration_seconds + return ( + False, + "Client blocked", + { + "client_key": client_key, + "ip": client_ip, + }, ) + if self.concurrent_requests[client_key] >= self.config.max_concurrent_requests: + return ( + False, + "Too many concurrent requests", + { + "client_key": client_key, + "concurrent": self.concurrent_requests[client_key], + "max": self.config.max_concurrent_requests, + }, + ) + if self._detect_abuse(client_key, client_ip, user_agent): + self.blocked_clients[client_key] = time.time() + self.config.block_duration_seconds logger.warning( "Blocked abusive client %s from %s for %ss", client_key, client_ip, self.config.block_duration_seconds, ) - return False, "Abuse detected", { - "client_key": client_key, - "ip": client_ip, - } + return ( + False, + "Abuse detected", + { + "client_key": client_key, + "ip": client_ip, + }, + ) self._refill_bucket(client_key) if self.buckets[client_key] < 0.999999: - return False, "Rate limit exceeded", { - "client_key": client_key, - "tokens": self.buckets[client_key], - "rate_limit": self.config.requests_per_minute, - } + return ( + False, + "Rate limit exceeded", + { + "client_key": client_key, + "tokens": self.buckets[client_key], + "rate_limit": self.config.requests_per_minute, + }, + ) self.buckets[client_key] -= 1.0 self.request_history[client_key].append(time.time()) self.concurrent_requests[client_key] += 1 - return True, "Request allowed", { - "client_key": client_key, - "tokens_remaining": self.buckets[client_key], - "concurrent_requests": self.concurrent_requests[client_key], - } + return ( + True, + "Request allowed", + { + "client_key": client_key, + "tokens_remaining": self.buckets[client_key], + "concurrent_requests": self.concurrent_requests[client_key], + }, + ) def release_request(self, client_ip: str, user_agent: str = ""): """Release a concurrent request slot.""" diff --git a/src/common/env.py b/src/common/env.py index 2bff30676..03a6d845e 100644 --- a/src/common/env.py +++ b/src/common/env.py @@ -15,4 +15,3 @@ def is_truthy(value: Optional[str]) -> bool: if value is None: return False return value.strip().lower() in {"1", "true", "yes"} - diff --git a/src/constants.py b/src/constants.py index c951ebda9..bd603cd67 100644 --- a/src/constants.py +++ b/src/constants.py @@ -7,5 +7,5 @@ import os # Emotion model configuration -DEFAULT_EMOTION_MODEL_DIR = '/app/models/emotion-english-distilroberta-base' -EMOTION_MODEL_DIR = os.getenv('EMOTION_MODEL_DIR', DEFAULT_EMOTION_MODEL_DIR) +DEFAULT_EMOTION_MODEL_DIR = "/app/models/emotion-english-distilroberta-base" +EMOTION_MODEL_DIR = os.getenv("EMOTION_MODEL_DIR", DEFAULT_EMOTION_MODEL_DIR) diff --git a/src/data/__pycache__/__init__.cpython-38.pyc b/src/data/__pycache__/__init__.cpython-38.pyc deleted file mode 100644 index 77bb4abae..000000000 Binary files a/src/data/__pycache__/__init__.cpython-38.pyc and /dev/null differ diff --git a/src/data/__pycache__/database.cpython-38.pyc b/src/data/__pycache__/database.cpython-38.pyc deleted file mode 100644 index 1131e28a1..000000000 Binary files a/src/data/__pycache__/database.cpython-38.pyc and /dev/null differ diff --git a/src/data/__pycache__/models.cpython-38.pyc b/src/data/__pycache__/models.cpython-38.pyc deleted file mode 100644 index 5c3817498..000000000 Binary files a/src/data/__pycache__/models.cpython-38.pyc and /dev/null differ diff --git a/src/data/__pycache__/validation.cpython-38.pyc b/src/data/__pycache__/validation.cpython-38.pyc deleted file mode 100644 index dd39dea04..000000000 Binary files a/src/data/__pycache__/validation.cpython-38.pyc and /dev/null differ diff --git a/src/data/database.py b/src/data/database.py index 48381bcde..4f6b711ba 100644 --- a/src/data/database.py +++ b/src/data/database.py @@ -1,20 +1,23 @@ - # Create tables - # Import all models here to ensure they're registered with Base.metadata +# Create tables +# Import all models here to ensure they're registered with Base.metadata # Create engine -# Create scoped session for thread safety -# Create sessionmaker + + +import os +from pathlib import Path +from urllib.parse import quote_plus + # Create the database URL # Get database connection details from environment variables from sqlalchemy import create_engine -from sqlalchemy.pool import NullPool from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import scoped_session, sessionmaker -import os -from pathlib import Path -from urllib.parse import quote_plus -from src.common.env import is_truthy +# Create scoped session for thread safety +# Create sessionmaker +from sqlalchemy.pool import NullPool +from src.common.env import is_truthy """Database connection utilities for the SAMO-DL application.""" @@ -50,7 +53,9 @@ "SQLite fallback is disabled. Set DATABASE_URL or all Postgres env vars, " "or explicitly allow SQLite fallback via ALLOW_SQLITE_FALLBACK=1 in dev/test." ) - default_sqlite_path = Path(os.environ.get("SQLITE_PATH", "./samo_local.db")).expanduser().resolve() + default_sqlite_path = ( + Path(os.environ.get("SQLITE_PATH", "./samo_local.db")).expanduser().resolve() + ) # Ensure directory for SQLite exists before engine creation sqlite_dir = default_sqlite_path.parent try: diff --git a/src/data/embeddings.py b/src/data/embeddings.py index f62c89b5c..9ab5678da 100644 --- a/src/data/embeddings.py +++ b/src/data/embeddings.py @@ -1,18 +1,17 @@ - # Average vectors or use zero vector if no tokens found - # Get vectors for tokens that are in vocabulary - # Create DataFrame with IDs and embeddings -# Configure logging -# G004: Logging f-strings temporarily allowed for development -from gensim.models import FastText, Word2Vec -from gensim.utils import simple_preprocess -from sklearn.feature_extraction.text import TfidfVectorizer +# Average vectors or use zero vector if no tokens found +# Get vectors for tokens that are in vocabulary +# Create DataFrame with IDs and embeddings import logging -import numpy as np -import pandas as pd from typing import List, Optional +import numpy as np +import pandas as pd +from sklearn.feature_extraction.text import TfidfVectorizer - +# Configure logging +# G004: Logging f-strings temporarily allowed for development +from gensim.models import FastText, Word2Vec +from gensim.utils import simple_preprocess logging.basicConfig( format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO @@ -26,7 +25,8 @@ class BaseEmbedder: def __init__(self) -> None: self.model = None - def fit(self, texts: List[str]) -> "BaseEmbedder": + @staticmethod + def fit(texts: List[str]) -> "BaseEmbedder": """Fit the embedding model on a list of texts. Args: @@ -39,7 +39,8 @@ def fit(self, texts: List[str]) -> "BaseEmbedder": msg = "Subclasses must implement fit()" raise NotImplementedError(msg) - def transform(self, texts: List[str]) -> np.ndarray: + @staticmethod + def transform(texts: List[str]) -> np.ndarray: """Transform texts into embeddings. Args: @@ -320,7 +321,8 @@ def generate_embeddings( } ) - def save_embeddings_to_csv(self, embeddings_df: pd.DataFrame, output_path: str) -> None: + @staticmethod + def save_embeddings_to_csv(embeddings_df: pd.DataFrame, output_path: str) -> None: """Save embeddings DataFrame to CSV. Args: diff --git a/src/data/feature_engineering.py b/src/data/feature_engineering.py index 29993c363..b347c5690 100644 --- a/src/data/feature_engineering.py +++ b/src/data/feature_engineering.py @@ -1,47 +1,46 @@ - # Get the actual words - # Get top word indices for this topic - # Add topic scores as features - # Apply SVD to reduce dimensions and extract topics - # Apply sentiment analyzer to get scores - # Assign dominant topic to each document - # Average word length - # Character count - # Convert topics to DataFrame for easier inspection - # Create TF-IDF vectorizer - # Create sentiment category based on compound score - # Ensure NLTK resources are downloaded - # Ensure text column is string type - # Ensure text column is string type - # Ensure text column is string type - # Extract basic text features - # Extract basic time components - # Extract sentiment components into separate columns - # Extract sentiment features - # Extract time features - # Extract topic features if requested - # Get feature names (words) - # Get top words for each topic - # Lexical diversity (unique words / total words) - # Sentence count - # Time of day features - # Transform texts to TF-IDF matrix - # Try to ensure timestamp column is datetime type - # Unique word count - # Word count - # Words per sentence -# Configure logging -# G004: Logging f-strings temporarily allowed for development -from nltk.sentiment import SentimentIntensityAnalyzer -from sklearn.decomposition import TruncatedSVD -from sklearn.feature_extraction.text import TfidfVectorizer +# Get the actual words +# Get top word indices for this topic +# Add topic scores as features +# Apply SVD to reduce dimensions and extract topics +# Apply sentiment analyzer to get scores +# Assign dominant topic to each document +# Average word length +# Character count +# Convert topics to DataFrame for easier inspection +# Create TF-IDF vectorizer +# Create sentiment category based on compound score +# Ensure NLTK resources are downloaded +# Ensure text column is string type +# Ensure text column is string type +# Ensure text column is string type +# Extract basic text features +# Extract basic time components +# Extract sentiment components into separate columns +# Extract sentiment features +# Extract time features +# Extract topic features if requested +# Get feature names (words) +# Get top words for each topic +# Lexical diversity (unique words / total words) +# Sentence count +# Time of day features +# Transform texts to TF-IDF matrix +# Try to ensure timestamp column is datetime type +# Unique word count +# Word count +# Words per sentence import logging +import re + import nltk import numpy as np import pandas as pd -import re - - +# Configure logging +# G004: Logging f-strings temporarily allowed for development +from nltk.sentiment import SentimentIntensityAnalyzer +from sklearn.decomposition import TruncatedSVD +from sklearn.feature_extraction.text import TfidfVectorizer logging.basicConfig( format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO @@ -64,9 +63,8 @@ def __init__(self) -> None: ) self.sentiment_analyzer = None - def extract_basic_features( - self, df: pd.DataFrame, text_column: str = "content" - ) -> pd.DataFrame: + @staticmethod + def extract_basic_features(df: pd.DataFrame, text_column: str = "content") -> pd.DataFrame: """Extract basic statistical features from text. Args: @@ -92,18 +90,18 @@ def extract_basic_features( df["sentence_count"] = df[text_column].apply(lambda x: len(re.split(r"[.!?]+", x)) - 1) df["words_per_sentence"] = df.apply( - lambda row: row["word_count"] / row["sentence_count"] - if row["sentence_count"] > 0 - else 0, + lambda row: ( + row["word_count"] / row["sentence_count"] if row["sentence_count"] > 0 else 0 + ), axis=1, ) df["unique_word_count"] = df[text_column].apply(lambda x: len(set(x.split()))) df["lexical_diversity"] = df.apply( - lambda row: row["unique_word_count"] / row["word_count"] - if row["word_count"] > 0 - else 0, + lambda row: ( + row["unique_word_count"] / row["word_count"] if row["word_count"] > 0 else 0 + ), axis=1, ) @@ -142,15 +140,15 @@ def extract_sentiment_features( df["sentiment_compound"] = sentiments.apply(lambda x: x["compound"]) df["sentiment_category"] = df["sentiment_compound"].apply( - lambda score: "positive" - if score > 0.05 - else ("negative" if score < -0.05 else "neutral") + lambda score: ( + "positive" if score > 0.05 else ("negative" if score < -0.05 else "neutral") + ) ) return df + @staticmethod def extract_topic_features( - self, df: pd.DataFrame, text_column: str = "content", n_topics: int = 10, @@ -206,8 +204,9 @@ def extract_topic_features( return df, topics_df + @staticmethod def extract_time_features( - self, df: pd.DataFrame, timestamp_column: str = "created_at" + df: pd.DataFrame, timestamp_column: str = "created_at" ) -> pd.DataFrame: """Extract time-related features from timestamp. diff --git a/src/data/loaders.py b/src/data/loaders.py index c8076b608..51f48e014 100644 --- a/src/data/loaders.py +++ b/src/data/loaders.py @@ -1,13 +1,11 @@ -from .database import db_session -from .models import JournalEntry -from .prisma_client import PrismaClient -from typing import Optional import json -import pandas as pd - - +from typing import Optional +import pandas as pd +from .database import db_session +from .models import JournalEntry +from .prisma_client import PrismaClient def load_entries_from_db( diff --git a/src/data/models.py b/src/data/models.py index e9b5a350b..16a394e26 100644 --- a/src/data/models.py +++ b/src/data/models.py @@ -7,7 +7,6 @@ import uuid from datetime import datetime -from pgvector.sqlalchemy import Vector from sqlalchemy import ( Boolean, Column, @@ -23,12 +22,12 @@ from sqlalchemy.dialects.postgresql import JSONB, UUID from sqlalchemy.orm import DeclarativeBase, relationship +from pgvector.sqlalchemy import Vector + class Base(DeclarativeBase): """Base class for all SQLAlchemy models.""" - pass - # Junction table for many-to-many relationship between journal entries and tags journal_entry_tags = Table( @@ -137,7 +136,9 @@ class Prediction(Base): __tablename__ = "predictions" id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) - journal_entry_id = Column(UUID(as_uuid=True), ForeignKey("journal_entries.id", ondelete="CASCADE"), nullable=False) + journal_entry_id = Column( + UUID(as_uuid=True), ForeignKey("journal_entries.id", ondelete="CASCADE"), nullable=False + ) user_id = Column(UUID(as_uuid=True), ForeignKey("users.id", ondelete="CASCADE"), nullable=False) prediction_type = Column(String(100), nullable=False) prediction_value = Column(JSONB, nullable=False) @@ -161,7 +162,9 @@ class VoiceTranscription(Base): __tablename__ = "voice_transcriptions" id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) - journal_entry_id = Column(UUID(as_uuid=True), ForeignKey("journal_entries.id", ondelete="CASCADE"), nullable=False) + journal_entry_id = Column( + UUID(as_uuid=True), ForeignKey("journal_entries.id", ondelete="CASCADE"), nullable=False + ) user_id = Column(UUID(as_uuid=True), ForeignKey("users.id", ondelete="CASCADE"), nullable=False) audio_file_path = Column(String(255)) transcription_text = Column(Text, nullable=False) diff --git a/src/data/pipeline.py b/src/data/pipeline.py index 51468e168..2ab2c209d 100644 --- a/src/data/pipeline.py +++ b/src/data/pipeline.py @@ -9,18 +9,15 @@ import logging from datetime import datetime, timezone from pathlib import Path -from typing import Dict, List, Optional, Union +from typing import Dict, Optional, Union + import pandas as pd + +from .embeddings import EmbeddingPipeline, FastTextEmbedder, TfidfEmbedder, Word2VecEmbedder from .feature_engineering import FeatureEngineer -from .validation import DataValidator +from .loaders import load_entries_from_csv, load_entries_from_db, load_entries_from_json from .preprocessing import JournalEntryPreprocessor -from .embeddings import ( - TfidfEmbedder, - Word2VecEmbedder, - FastTextEmbedder, - EmbeddingPipeline -) -from .loaders import load_entries_from_db, load_entries_from_json, load_entries_from_csv +from .validation import DataValidator # Configure logging # G004: Logging f-strings temporarily allowed for development @@ -180,9 +177,9 @@ def _load_data( return data_source if source_type == "db": - user_info = " for user {user_id}" if user_id else "" - limit_info = " (limit: {limit})" if limit else "" - logger.info("Loading data from database{user_info}{limit_info}") + user_info = f" for user {user_id}" if user_id else "" + limit_info = f" (limit: {limit})" if limit else "" + logger.info(f"Loading data from database{user_info}{limit_info}") return load_entries_from_db(limit=limit, user_id=user_id) if source_type == "json" and isinstance(data_source, str): @@ -226,30 +223,27 @@ def _save_results( timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") featured_df.to_csv( - Path(output_dir, "journal_features_{timestamp}.csv").as_posix(), + Path(output_dir, f"journal_features_{timestamp}.csv").as_posix(), index=False, ) - logger.info("Saved featured data to {output_dir}/journal_features_{timestamp}.csv") + logger.info(f"Saved featured data to {output_dir}/journal_features_{timestamp}.csv") - embeddings_path = Path(output_dir, "journal_embeddings_{timestamp}.csv").as_posix() + embeddings_path = Path(output_dir, f"journal_embeddings_{timestamp}.csv").as_posix() self.embedding_pipeline.save_embeddings_to_csv(embeddings_df, embeddings_path) if topics_df is not None: topics_df.to_csv( - Path(output_dir, "journal_topics_{timestamp}.csv").as_posix(), + Path(output_dir, f"journal_topics_{timestamp}.csv").as_posix(), index=False, ) - logger.info("Saved topic data to {output_dir}/journal_topics_{timestamp}.csv") + logger.info(f"Saved topic data to {output_dir}/journal_topics_{timestamp}.csv") if save_intermediates: - raw_df.to_csv(Path(output_dir, "journal_raw_{timestamp}.csv").as_posix(), index=False) - logger.info( - "Saved raw data to {output_dir}/journal_raw_{timestamp}.csv", - extra={"format_args": True}, - ) + raw_df.to_csv(Path(output_dir, f"journal_raw_{timestamp}.csv").as_posix(), index=False) + logger.info(f"Saved raw data to {output_dir}/journal_raw_{timestamp}.csv") processed_df.to_csv( - Path(output_dir, "journal_processed_{timestamp}.csv").as_posix(), + Path(output_dir, f"journal_processed_{timestamp}.csv").as_posix(), index=False, ) - logger.info("Saved processed data to {output_dir}/journal_processed_{timestamp}.csv") + logger.info(f"Saved processed data to {output_dir}/journal_processed_{timestamp}.csv") diff --git a/src/data/preprocessing.py b/src/data/preprocessing.py index bffe26c5a..1cb90ab41 100644 --- a/src/data/preprocessing.py +++ b/src/data/preprocessing.py @@ -108,9 +108,8 @@ def preprocess_df( df[output_column] = df[text_column].astype(str).apply(self.preprocess_text) return df - def extract_features( - self, df: pd.DataFrame, text_column: str = "processed_text" - ) -> pd.DataFrame: + @staticmethod + def extract_features(df: pd.DataFrame, text_column: str = "processed_text") -> pd.DataFrame: """Extract basic text features from preprocessed text. Args: diff --git a/src/data/prisma_client.py b/src/data/prisma_client.py index 1a70352e2..744abd192 100644 --- a/src/data/prisma_client.py +++ b/src/data/prisma_client.py @@ -1,14 +1,12 @@ - # Clean up the temporary file - # Execute the script - # Parse the output - # Create a temporary JS file - # Ensure we return a list, even if the result is a single dict -from pathlib import Path -from typing import Any, Dict, List, Optional +# Clean up the temporary file +# Execute the script +# Parse the output +# Create a temporary JS file +# Ensure we return a list, even if the result is a single dict import json import subprocess - - +from pathlib import Path +from typing import Any, Dict, List, Optional """Prisma client utility for the SAMO-DL application. @@ -18,6 +16,7 @@ It's a simple wrapper that allows Python code to execute Prisma commands. """ + class PrismaClient: """A simple wrapper class for Prisma client operations. @@ -39,7 +38,8 @@ def execute_prisma_command(script: str) -> Dict[str, Any]: """ with Path("temp_prisma_script.js").open("w") as f: - f.write(""" + f.write( + """ const {{ PrismaClient }} = require('@prisma/client'); const prisma = new PrismaClient(); @@ -59,7 +59,8 @@ def execute_prisma_command(script: str) -> Dict[str, Any]: }} main(); -""") +""" + ) try: result = subprocess.run( diff --git a/src/data/sample_data.py b/src/data/sample_data.py index ee1b602d4..a71dfc735 100644 --- a/src/data/sample_data.py +++ b/src/data/sample_data.py @@ -1,26 +1,28 @@ - # Add hour/minute/second for more realistic timestamps - # Create the entry - # Generate a random date within the range - # Randomly select user_id - # Convert datetime objects to strings for JSON serialization - # Convert string dates back to datetime - # Ensure output directory exists - # Generate 100 entries from 5 users over the past 60 days - # Save to data/raw directory +# Add hour/minute/second for more realistic timestamps +# Create the entry +# Generate a random date within the range +# Randomly select user_id +# Convert datetime objects to strings for JSON serialization +# Convert string dates back to datetime +# Ensure output directory exists +# Generate 100 entries from 5 users over the past 60 days +# Save to data/raw directory # Additional sentences to add variety -# Emotion categories for entries -# Sample topics to generate journal entries about -# Templates for journal entry content -# Title templates -from datetime import datetime, timezone, timedelta -from pathlib import Path -from typing import Any, Dict, List, Optional + + import json -import pandas as pd import random +# Templates for journal entry content +# Title templates +from datetime import datetime, timedelta, timezone +# Emotion categories for entries +# Sample topics to generate journal entries about +from pathlib import Path +from typing import Any, Dict, List, Optional +import pandas as pd TOPICS = [ "work", @@ -150,6 +152,7 @@ def generate_content(topic: str, emotion: str) -> str: content += f" {random.choice(REFLECTION_TEMPLATES)}" return content + def generate_entry(user_id: int, created_at: datetime, id_start: int = 1) -> Dict[str, Any]: """Generate a single journal entry.""" topic = random.choice(TOPICS) diff --git a/src/data/validation.py b/src/data/validation.py index 5cc5a90ab..6de79709b 100644 --- a/src/data/validation.py +++ b/src/data/validation.py @@ -1,10 +1,9 @@ # Configure logging # G004: Logging f-strings temporarily allowed for development -from typing import Dict, List, Optional, Union import logging -import pandas as pd - +from typing import Dict, List, Optional, Union +import pandas as pd logging.basicConfig( format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO @@ -18,8 +17,9 @@ class DataValidator: def __init__(self) -> None: """Initialize data validator.""" + @staticmethod def check_missing_values( - self, df: pd.DataFrame, required_columns: Optional[List[str]] = None + df: pd.DataFrame, required_columns: Optional[List[str]] = None ) -> Dict[str, float]: """Check for missing values in DataFrame. @@ -49,9 +49,8 @@ def check_missing_values( return missing_stats - def check_data_types( - self, df: pd.DataFrame, expected_types: Dict[str, type] - ) -> Dict[str, bool]: + @staticmethod + def check_data_types(df: pd.DataFrame, expected_types: Dict[str, type]) -> Dict[str, bool]: """Check if columns have expected data types. Args: @@ -82,7 +81,9 @@ def check_data_types( elif expected_type is str and pd.api.types.is_string_dtype(actual_type): type_check_results[column] = True # Handle datetime types - elif expected_type is pd.Timestamp and pd.api.types.is_datetime64_any_dtype(actual_type): + elif expected_type is pd.Timestamp and pd.api.types.is_datetime64_any_dtype( + actual_type + ): type_check_results[column] = True # Handle boolean types elif expected_type is bool and pd.api.types.is_bool_dtype(actual_type): @@ -97,7 +98,8 @@ def check_data_types( return type_check_results - def check_text_quality(self, df: pd.DataFrame, text_column: str = "content") -> pd.DataFrame: + @staticmethod + def check_text_quality(df: pd.DataFrame, text_column: str = "content") -> pd.DataFrame: """Check text quality metrics. Args: @@ -181,7 +183,7 @@ def validate_journal_entries( "missing_values": {}, "data_types": {}, "text_quality": df, - "error": f"Required columns missing: {missing_columns}" + "error": f"Required columns missing: {missing_columns}", } missing_stats = self.check_missing_values(df, required_columns) @@ -205,11 +207,13 @@ def validate_journal_entries( "missing_values": missing_stats, "data_types": type_check_results, "text_quality": df_with_quality, - "error": None if validation_passed else "Validation failed" + "error": None if validation_passed else "Validation failed", } -def validate_text_input(input_text: str, min_length: int = 1, max_length: int = 10000) -> Dict[str, Union[bool, str]]: +def validate_text_input( + input_text: str, min_length: int = 1, max_length: int = 10000 +) -> Dict[str, Union[bool, str]]: """Validate text input for journal entries. Args: @@ -235,18 +239,27 @@ def validate_text_input(input_text: str, min_length: int = 1, max_length: int = return {"is_valid": False, "error": "Text cannot be whitespace only"} if len(stripped_text) < min_length: - return {"is_valid": False, "error": f"Text is too short, must be at least {min_length} characters long"} + return { + "is_valid": False, + "error": f"Text is too short, must be at least {min_length} characters long", + } if len(input_text) > max_length: - return {"is_valid": False, "error": f"Text must be no more than {max_length} characters long"} + return { + "is_valid": False, + "error": f"Text must be no more than {max_length} characters long", + } harmful_patterns = ["', - r'javascript:', - r'on\w+\s*=', - r']*>', - r']*>', - r']*>', - + r"]*>.*?", + r"javascript:", + r"on\w+\s*=", + r"]*>", + r"]*>", + r"]*>", # SQL injection patterns - r'(\b(union|select|insert|update|delete|drop|create|alter|exec|execute)\b)', - r'(\b(or|and)\b\s+\d+\s*=\s*\d+)', - r'(\b(union|select)\b.*?\bfrom\b)', - r'(\b(insert|update|delete)\b.*?\binto\b)', - + r"(\b(union|select|insert|update|delete|drop|create|alter|exec|execute)\b)", + r"(\b(or|and)\b\s+\d+\s*=\s*\d+)", + r"(\b(union|select)\b.*?\bfrom\b)", + r"(\b(insert|update|delete)\b.*?\binto\b)", # Path traversal patterns - r'\.\./', - r'\.\.\\', - r'%2e%2e%2f', - r'%2e%2e%5c', - + r"\.\./", + r"\.\.\\", + r"%2e%2e%2f", + r"%2e%2e%5c", # Command injection patterns - r'(\b(cmd|command|exec|system|eval|exec)\b)', - r'(\b(popen|subprocess|os\.system)\b)', - r'(\b(shell|bash|sh|powershell)\b)', - r'(\b(rm|del|format|mkfs)\b)', - + r"(\b(cmd|command|exec|system|eval|exec)\b)", + r"(\b(popen|subprocess|os\.system)\b)", + r"(\b(shell|bash|sh|powershell)\b)", + r"(\b(rm|del|format|mkfs)\b)", # Other dangerous patterns - r'(\b(import|__import__)\b)', - r'(\b(eval|exec|compile)\b)', - r'(\b(open|file|read|write)\b)', - r'(\b(subprocess|multiprocessing)\b)', + r"(\b(import|__import__)\b)", + r"(\b(eval|exec|compile)\b)", + r"(\b(open|file|read|write)\b)", + r"(\b(subprocess|multiprocessing)\b)", } # Initialize allowed HTML tags if config.allowed_html_tags is None: - config.allowed_html_tags = { - 'p', 'br', 'strong', 'em', 'u', 'i', 'b', 'span', 'div' - } + config.allowed_html_tags = {"p", "br", "strong", "em", "u", "i", "b", "span", "div"} def sanitize_text(self, text: str, context: str = "general") -> Tuple[str, List[str]]: """ @@ -106,12 +103,14 @@ def sanitize_text(self, text: str, context: str = "general") -> Tuple[str, List[ # Check length if len(text) > self.config.max_text_length: - warnings.append(f"Text truncated from {len(text)} to {self.config.max_text_length} characters") - text = text[:self.config.max_text_length] + warnings.append( + f"Text truncated from {len(text)} to {self.config.max_text_length} characters" + ) + text = text[: self.config.max_text_length] # Unicode normalization if self.config.enable_unicode_normalization: - text = unicodedata.normalize('NFKC', text) + text = unicodedata.normalize("NFKC", text) # Check for blocked patterns if self.config.enable_xss_protection or self.config.enable_sql_injection_protection: @@ -119,14 +118,14 @@ def sanitize_text(self, text: str, context: str = "general") -> Tuple[str, List[ if re.search(pattern, text, re.IGNORECASE): warnings.append(f"Blocked pattern detected: {pattern}") # Replace with safe alternative - text = re.sub(pattern, '[BLOCKED]', text, flags=re.IGNORECASE) + text = re.sub(pattern, "[BLOCKED]", text, flags=re.IGNORECASE) # HTML escaping for XSS protection if self.config.enable_xss_protection: text = html.escape(text) # Remove null bytes and control characters - text = ''.join(char for char in text if ord(char) >= 32 or char in '\n\r\t') + text = "".join(char for char in text if ord(char) >= 32 or char in "\n\r\t") # Strip leading/trailing whitespace text = text.strip() @@ -181,23 +180,23 @@ def validate_emotion_request(self, data: Dict) -> Tuple[Dict, List[str]]: sanitized_data = {} # Validate text field - if 'text' not in data: + if "text" not in data: raise ValueError("Missing required field 'text'") - text = data['text'] + text = data["text"] if not isinstance(text, str): raise ValueError("Field 'text' must be a string") sanitized_text, text_warnings = self.sanitize_text(text, "emotion") - sanitized_data['text'] = sanitized_text + sanitized_data["text"] = sanitized_text warnings.extend(text_warnings) # Validate optional fields - if 'confidence_threshold' in data: + if "confidence_threshold" in data: try: - threshold = float(data['confidence_threshold']) + threshold = float(data["confidence_threshold"]) if 0.0 <= threshold <= 1.0: - sanitized_data['confidence_threshold'] = threshold + sanitized_data["confidence_threshold"] = threshold else: warnings.append("confidence_threshold must be between 0.0 and 1.0") except (ValueError, TypeError): @@ -219,17 +218,17 @@ def validate_batch_request(self, data: Dict) -> Tuple[Dict, List[str]]: sanitized_data = {} # Validate texts field - if 'texts' not in data: + if "texts" not in data: raise ValueError("Missing required field 'texts'") - texts = data['texts'] + texts = data["texts"] if not isinstance(texts, list): raise ValueError("Field 'texts' must be a list") # Check batch size if len(texts) > self.config.max_batch_size: warnings.append(f"Batch size {len(texts)} exceeds maximum {self.config.max_batch_size}") - texts = texts[:self.config.max_batch_size] + texts = texts[: self.config.max_batch_size] # Sanitize each text sanitized_texts = [] @@ -242,14 +241,14 @@ def validate_batch_request(self, data: Dict) -> Tuple[Dict, List[str]]: sanitized_texts.append(sanitized_text) warnings.extend([f"Text {i}: {w}" for w in text_warnings]) - sanitized_data['texts'] = sanitized_texts + sanitized_data["texts"] = sanitized_texts # Validate optional fields - if 'confidence_threshold' in data: + if "confidence_threshold" in data: try: - threshold = float(data['confidence_threshold']) + threshold = float(data["confidence_threshold"]) if 0.0 <= threshold <= 1.0: - sanitized_data['confidence_threshold'] = threshold + sanitized_data["confidence_threshold"] = threshold else: warnings.append("confidence_threshold must be between 0.0 and 1.0") except (ValueError, TypeError): @@ -271,7 +270,7 @@ def validate_content_type(self, content_type: str) -> bool: return True # Check for JSON content type - if not content_type or 'application/json' not in content_type.lower(): + if not content_type or "application/json" not in content_type.lower(): return False return True @@ -304,7 +303,8 @@ def sanitize_headers(self, headers: Dict[str, str]) -> Tuple[Dict[str, str], Lis return sanitized_headers, warnings - def detect_anomalies(self, data: Any) -> List[str]: + @staticmethod + def detect_anomalies(data: Any) -> List[str]: """ Detect potential security anomalies in data. @@ -325,7 +325,7 @@ def _analyze_recursive(obj: Any, path: str = ""): if re.search(r'[<>"\']', obj): anomalies.append(f"Potential HTML/script content at {path}") - if re.search(r'\b(union|select|insert|update|delete)\b', obj, re.IGNORECASE): + if re.search(r"\b(union|select|insert|update|delete)\b", obj, re.IGNORECASE): anomalies.append(f"Potential SQL injection at {path}") elif isinstance(obj, dict): @@ -352,5 +352,5 @@ def get_sanitization_stats(self) -> Dict: "enable_content_type_validation": self.config.enable_content_type_validation, }, "blocked_patterns_count": len(self.config.blocked_patterns), - "allowed_html_tags_count": len(self.config.allowed_html_tags) + "allowed_html_tags_count": len(self.config.allowed_html_tags), } diff --git a/src/models/emotion_detection/api_demo.py b/src/models/emotion_detection/api_demo.py index d27ee6fda..1035b269b 100644 --- a/src/models/emotion_detection/api_demo.py +++ b/src/models/emotion_detection/api_demo.py @@ -132,9 +132,11 @@ async def http_exception_handler(request: Request, exc: HTTPException): return JSONResponse( status_code=exc.status_code, content={ - "error": exc.detail.lower().replace(" ", "_") - if isinstance(exc.detail, str) - else "http_error", + "error": ( + exc.detail.lower().replace(" ", "_") + if isinstance(exc.detail, str) + else "http_error" + ), "message": exc.detail, "path": request.url.path, }, @@ -378,9 +380,6 @@ async def analyze_emotions_batch( "average_processing_time_ms": (processing_time * 1000) / len(texts) if texts else 0, } - except HTTPException: - raise - except Exception: logger.exception("Batch emotion analysis failed") raise HTTPException( diff --git a/src/models/emotion_detection/bert_classifier.py b/src/models/emotion_detection/bert_classifier.py index 98b15b70f..e0c91c7cc 100644 --- a/src/models/emotion_detection/bert_classifier.py +++ b/src/models/emotion_detection/bert_classifier.py @@ -8,14 +8,14 @@ import logging import warnings -from typing import Optional, Union, List, Dict, Tuple +from typing import Dict, List, Optional, Tuple, Union import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from sklearn.metrics import f1_score, precision_recall_fscore_support -from torch.utils.data import Dataset, DataLoader +from torch.utils.data import DataLoader, Dataset from transformers import AutoConfig, AutoModel, AutoTokenizer from .labels import GOEMOTIONS_EMOTIONS @@ -307,9 +307,7 @@ def forward(self, logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor: probabilities = torch.sigmoid(logits) # Compute BCE loss - bce_loss = F.binary_cross_entropy( - probabilities, targets.float(), reduction="none" - ) + bce_loss = F.binary_cross_entropy(probabilities, targets.float(), reduction="none") # Apply class weights if provided if self.class_weights is not None: diff --git a/src/models/emotion_detection/dataset_loader.py b/src/models/emotion_detection/dataset_loader.py index 94d04862c..aab323b80 100644 --- a/src/models/emotion_detection/dataset_loader.py +++ b/src/models/emotion_detection/dataset_loader.py @@ -18,17 +18,18 @@ import numpy as np import torch -from datasets import load_dataset from torch.utils.data import Dataset from transformers import AutoTokenizer +from datasets import load_dataset + +from .labels import GOEMOTIONS_EMOTIONS + # Configure logging # G004: Logging f-strings temporarily allowed for development logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) -from .labels import GOEMOTIONS_EMOTIONS, EMOTION_ID_TO_LABEL, EMOTION_LABEL_TO_ID - class GoEmotionsDataset(Dataset): """PyTorch Dataset for GoEmotions emotion classification.""" @@ -38,7 +39,7 @@ def __init__( texts: List[str], labels: List[List[int]], tokenizer: AutoTokenizer, - max_length: int = 512 + max_length: int = 512, ): """Initialize the dataset. @@ -64,18 +65,18 @@ def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]: encoding = self.tokenizer( text, truncation=True, - padding='max_length', + padding="max_length", max_length=self.max_length, - return_tensors='pt' + return_tensors="pt", ) # Convert label to tensor label_tensor = torch.tensor(label, dtype=torch.float) return { - 'input_ids': encoding['input_ids'].squeeze(0), - 'attention_mask': encoding['attention_mask'].squeeze(0), - 'labels': label_tensor + "input_ids": encoding["input_ids"].squeeze(0), + "attention_mask": encoding["attention_mask"].squeeze(0), + "labels": label_tensor, } @@ -93,7 +94,8 @@ def __init__(self, model_name: str = "bert-base-uncased", max_length: int = 512) self.max_length = max_length logger.info(f"Initialized preprocessor with {model_name}, max_length={max_length}") - def clean_text(self, text: str) -> str: + @staticmethod + def clean_text(text: str) -> str: """Clean and normalize text while preserving emotional signals. Following data documentation strategies for emotional understanding. @@ -108,7 +110,7 @@ def clean_text(self, text: str) -> str: return "" # Remove excessive whitespace while preserving structure - text = re.sub(r'\s+', ' ', text.strip()) + text = re.sub(r"\s+", " ", text.strip()) # The dataset is already split by HuggingFace # Tokenize with BERT tokenizer diff --git a/src/models/emotion_detection/hf_loader.py b/src/models/emotion_detection/hf_loader.py index b68468731..a7d58735a 100644 --- a/src/models/emotion_detection/hf_loader.py +++ b/src/models/emotion_detection/hf_loader.py @@ -71,9 +71,7 @@ def predict(self, text: str, threshold: float = 0.5) -> Dict: headers["Authorization"] = f"Bearer {self.token}" payload = {"inputs": text} try: - resp = requests.post( - self.endpoint_url, json=payload, headers=headers, timeout=30 - ) + resp = requests.post(self.endpoint_url, json=payload, headers=headers, timeout=30) resp.raise_for_status() data = resp.json() # data can be [[{"label":..., "score":...}, ...]] or {"error":...} @@ -114,17 +112,13 @@ def _wrap_local_model( cfg = AutoConfig.from_pretrained(local_dir, token=token) tok = AutoTokenizer.from_pretrained(local_dir, token=token, use_fast=True) mdl = AutoModelForSequenceClassification.from_pretrained(local_dir, token=token) - id2label = getattr(cfg, "id2label", None) or { - i: str(i) for i in range(cfg.num_labels) - } + id2label = getattr(cfg, "id2label", None) or {i: str(i) for i in range(cfg.num_labels)} if force_multi_label is not None: multi_label = bool(force_multi_label) else: problem_type = getattr(cfg, "problem_type", None) multi_label = problem_type == "multi_label_classification" - return HFEmotionDetector( - model=mdl, tokenizer=tok, id2label=id2label, multi_label=multi_label - ) + return HFEmotionDetector(model=mdl, tokenizer=tok, id2label=id2label, multi_label=multi_label) def load_hf_emotion_model( @@ -155,18 +149,14 @@ def load_emotion_model_multi_source( # 1) Local directory if local_dir and os.path.isdir(local_dir): try: - return _wrap_local_model( - local_dir, token=token, force_multi_label=force_multi_label - ) + return _wrap_local_model(local_dir, token=token, force_multi_label=force_multi_label) except Exception: pass # 2) HF Hub direct if model_id: try: - return load_hf_emotion_model( - model_id, token=token, force_multi_label=force_multi_label - ) + return load_hf_emotion_model(model_id, token=token, force_multi_label=force_multi_label) except Exception: pass @@ -174,12 +164,8 @@ def load_emotion_model_multi_source( if model_id: try: cache_base = os.getenv("HF_HOME", "/var/tmp/hf-cache") - snap_dir = snapshot_download( - repo_id=model_id, token=token, cache_dir=cache_base - ) - return _wrap_local_model( - snap_dir, token=token, force_multi_label=force_multi_label - ) + snap_dir = snapshot_download(repo_id=model_id, token=token, cache_dir=cache_base) + return _wrap_local_model(snap_dir, token=token, force_multi_label=force_multi_label) except Exception: pass @@ -215,9 +201,7 @@ def load_emotion_model_multi_source( os.path.join(extract_dir, d) for d in os.listdir(extract_dir) ] for cand in candidates: - if os.path.isdir(cand) and os.path.exists( - os.path.join(cand, "config.json") - ): + if os.path.isdir(cand) and os.path.exists(os.path.join(cand, "config.json")): try: det = _wrap_local_model( cand, token=token, force_multi_label=force_multi_label diff --git a/src/models/emotion_detection/labels.py b/src/models/emotion_detection/labels.py index 289e2556f..a9d35341a 100644 --- a/src/models/emotion_detection/labels.py +++ b/src/models/emotion_detection/labels.py @@ -33,4 +33,4 @@ ] EMOTION_ID_TO_LABEL = dict(enumerate(GOEMOTIONS_EMOTIONS)) -EMOTION_LABEL_TO_ID = {emotion: i for i, emotion in enumerate(GOEMOTIONS_EMOTIONS)} \ No newline at end of file +EMOTION_LABEL_TO_ID = {emotion: i for i, emotion in enumerate(GOEMOTIONS_EMOTIONS)} diff --git a/src/models/emotion_detection/training_pipeline.py b/src/models/emotion_detection/training_pipeline.py index 6267981ef..4de605b3e 100644 --- a/src/models/emotion_detection/training_pipeline.py +++ b/src/models/emotion_detection/training_pipeline.py @@ -20,7 +20,6 @@ from transformers import AutoTokenizer, get_linear_schedule_with_warmup from ...utils import count_model_params - from .bert_classifier import create_bert_emotion_classifier, evaluate_emotion_classifier from .dataset_loader import GoEmotionsDataset, create_goemotions_loader @@ -160,18 +159,13 @@ def prepare_data(self, dev_mode: bool = False) -> Dict[str, Any]: original_batch_size = self.batch_size # Increase batch size for dev mode self.batch_size = min(128, self.batch_size * 8) - dev_msg = ( - "๐Ÿ”ง DEVELOPMENT MODE: Using %d training examples, " - "batch_size=%d (was %d)" - ) + dev_msg = "๐Ÿ”ง DEVELOPMENT MODE: Using %d training examples, " "batch_size=%d (was %d)" logger.info(dev_msg, len(train_texts), self.batch_size, original_batch_size) self.train_dataset = GoEmotionsDataset( train_texts, train_labels, self.tokenizer, self.max_length ) - self.val_dataset = GoEmotionsDataset( - val_texts, val_labels, self.tokenizer, self.max_length - ) + self.val_dataset = GoEmotionsDataset(val_texts, val_labels, self.tokenizer, self.max_length) self.test_dataset = GoEmotionsDataset( test_texts, test_labels, self.tokenizer, self.max_length ) @@ -222,23 +216,16 @@ def initialize_model(self, class_weights: Optional[np.ndarray] = None) -> None: logger.debug("Loss Function Analysis") logger.debug(" Loss function type: %s", type(self.loss_fn).__name__) - if ( - hasattr(self.loss_fn, "class_weights") - and self.loss_fn.class_weights is not None - ): + if hasattr(self.loss_fn, "class_weights") and self.loss_fn.class_weights is not None: weights = self.loss_fn.class_weights if logger.isEnabledFor(logging.DEBUG): - logger.debug( - " Class weights shape: %s", getattr(weights, "shape", None) - ) + logger.debug(" Class weights shape: %s", getattr(weights, "shape", None)) logger.debug(" Class weights min: %.6f", weights.min().item()) logger.debug(" Class weights mean: %.6f", weights.mean().item()) logger.debug(" Class weights max: %.6f", weights.max().item()) if weights.min().item() <= 0: - logger.error( - "โŒ CRITICAL: Class weights contain zero or negative values!" - ) + logger.error("โŒ CRITICAL: Class weights contain zero or negative values!") if weights.max().item() > 100: logger.error("โŒ CRITICAL: Class weights contain very large values!") else: @@ -383,9 +370,7 @@ def _train_single_batch( loss.backward() # Gradient clipping - clip_norm = torch.nn.utils.clip_grad_norm_( - self.model.parameters(), max_norm=1.0 - ) + clip_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0) # Log gradient stats for first batch if batch_idx == 0: @@ -507,9 +492,7 @@ def _log_model_output(logits: torch.Tensor) -> None: logger.info(" Predictions mean: %.6f", predictions.mean().item()) @staticmethod - def _log_loss_analysis( - loss: torch.Tensor, logits: torch.Tensor, labels: torch.Tensor - ) -> None: + def _log_loss_analysis(loss: torch.Tensor, logits: torch.Tensor, labels: torch.Tensor) -> None: """Log detailed loss analysis for debugging. Args: @@ -519,9 +502,7 @@ def _log_loss_analysis( """ logger.info("๐Ÿ” DEBUG: Loss Analysis") logger.info(" Raw loss: %.8f", loss.item()) - bce_manual = F.binary_cross_entropy_with_logits( - logits, labels.float(), reduction="mean" - ) + bce_manual = F.binary_cross_entropy_with_logits(logits, labels.float(), reduction="mean") logger.info(" Manual BCE loss: %.8f", bce_manual.item()) if abs(loss.item()) < 1e-10: logger.error("โŒ CRITICAL: Loss is effectively zero!") @@ -591,13 +572,9 @@ def _log_progress( current_lr, ) if avg_loss < 1e-8: - logger.error( - "โŒ CRITICAL: Average loss is suspiciously small: %.8f", avg_loss - ) + logger.error("โŒ CRITICAL: Average loss is suspiciously small: %.8f", avg_loss) if avg_loss > 100: - logger.error( - "โŒ CRITICAL: Average loss is suspiciously large: %.8f", avg_loss - ) + logger.error("โŒ CRITICAL: Average loss is suspiciously large: %.8f", avg_loss) def _maybe_validate_and_early_stop( self, @@ -677,9 +654,7 @@ def should_stop_early(self) -> bool: """Check if training should stop early.""" return self.patience_counter >= self.early_stopping_patience - def save_checkpoint( - self, epoch: int, metrics: Dict[str, float], is_best: bool = False - ) -> None: + def save_checkpoint(self, epoch: int, metrics: Dict[str, float], is_best: bool = False) -> None: """Save model checkpoint. Args: diff --git a/src/models/secure_loader/__init__.py b/src/models/secure_loader/__init__.py index d419401a6..8e8a92d67 100644 --- a/src/models/secure_loader/__init__.py +++ b/src/models/secure_loader/__init__.py @@ -5,14 +5,9 @@ against PyTorch RCE vulnerabilities and other security threats. """ -from .secure_model_loader import SecureModelLoader from .integrity_checker import IntegrityChecker -from .sandbox_executor import SandboxExecutor from .model_validator import ModelValidator +from .sandbox_executor import SandboxExecutor +from .secure_model_loader import SecureModelLoader -__all__ = [ - "SecureModelLoader", - "IntegrityChecker", - "SandboxExecutor", - "ModelValidator" -] +__all__ = ["SecureModelLoader", "IntegrityChecker", "SandboxExecutor", "ModelValidator"] diff --git a/src/models/secure_loader/integrity_checker.py b/src/models/secure_loader/integrity_checker.py index 4099edc2e..19bf64683 100644 --- a/src/models/secure_loader/integrity_checker.py +++ b/src/models/secure_loader/integrity_checker.py @@ -38,10 +38,15 @@ def __init__(self, trusted_checksums_file: Optional[str] = None): # Security constraints self.max_file_size = 2 * 1024 * 1024 * 1024 # 2GB max - self.allowed_extensions = {'.pt', '.pth', '.bin', '.safetensors'} + self.allowed_extensions = {".pt", ".pth", ".bin", ".safetensors"} self.blocked_patterns = [ - b'__import__', b'eval(', b'exec(', b'pickle.loads', - b'subprocess', b'os.system', b'__builtins__' + b"__import__", + b"eval(", + b"exec(", + b"pickle.loads", + b"subprocess", + b"os.system", + b"__builtins__", ] def _load_trusted_checksums(self) -> Dict[str, str]: @@ -55,13 +60,14 @@ def _load_trusted_checksums(self) -> Dict[str, str]: return {} try: - with open(self.trusted_checksums_file, 'r') as f: + with open(self.trusted_checksums_file, "r") as f: return json.load(f) except Exception as e: logger.error(f"Failed to load trusted checksums: {e}") return {} - def calculate_checksum(self, file_path: str) -> str: + @staticmethod + def calculate_checksum(file_path: str) -> str: """Calculate SHA-256 checksum of a file. Args: @@ -73,7 +79,7 @@ def calculate_checksum(self, file_path: str) -> str: sha256_hash = hashlib.sha256() try: - with open(file_path, 'rb') as f: + with open(file_path, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): sha256_hash.update(chunk) return sha256_hash.hexdigest() @@ -127,7 +133,7 @@ def scan_for_malicious_content(self, file_path: str) -> Tuple[bool, list]: findings = [] try: - with open(file_path, 'rb') as f: + with open(file_path, "rb") as f: content = f.read() for pattern in self.blocked_patterns: @@ -167,7 +173,8 @@ def verify_checksum(self, file_path: str, expected_checksum: Optional[str] = Non logger.error(f"Failed to verify checksum for {file_path}: {e}") return False - def validate_model_structure(self, model_path: str) -> bool: + @staticmethod + def validate_model_structure(model_path: str) -> bool: """Validate PyTorch model structure. Args: @@ -178,7 +185,7 @@ def validate_model_structure(self, model_path: str) -> bool: """ try: # Load model in a controlled environment - model_data = torch.load(model_path, map_location='cpu', weights_only=True) + model_data = torch.load(model_path, map_location="cpu", weights_only=True) # Basic structure validation if not isinstance(model_data, dict): @@ -186,7 +193,7 @@ def validate_model_structure(self, model_path: str) -> bool: return False # Check for required keys in state dict - required_keys = ['state_dict', 'config', 'model_name'] + required_keys = ["state_dict", "config", "model_name"] for key in required_keys: if key not in model_data: logger.warning(f"Model {model_path} missing key: {key}") @@ -197,7 +204,9 @@ def validate_model_structure(self, model_path: str) -> bool: logger.error(f"Failed to validate model structure for {model_path}: {e}") return False - def comprehensive_validation(self, file_path: str, expected_checksum: Optional[str] = None) -> Tuple[bool, Dict]: + def comprehensive_validation( + self, file_path: str, expected_checksum: Optional[str] = None + ) -> Tuple[bool, Dict]: """Perform comprehensive file validation. Args: @@ -208,50 +217,52 @@ def comprehensive_validation(self, file_path: str, expected_checksum: Optional[s Tuple of (is_valid, validation_results) """ results = { - 'file_path': file_path, - 'size_valid': False, - 'extension_valid': False, - 'checksum_valid': False, - 'content_safe': False, - 'structure_valid': False, - 'findings': [] + "file_path": file_path, + "size_valid": False, + "extension_valid": False, + "checksum_valid": False, + "content_safe": False, + "structure_valid": False, + "findings": [], } # File size validation - results['size_valid'] = self.validate_file_size(file_path) - if not results['size_valid']: - results['findings'].append("File size exceeds limit") + results["size_valid"] = self.validate_file_size(file_path) + if not results["size_valid"]: + results["findings"].append("File size exceeds limit") # Extension validation - results['extension_valid'] = self.validate_file_extension(file_path) - if not results['extension_valid']: - results['findings'].append("File extension not allowed") + results["extension_valid"] = self.validate_file_extension(file_path) + if not results["extension_valid"]: + results["findings"].append("File extension not allowed") # Checksum validation - results['checksum_valid'] = self.verify_checksum(file_path, expected_checksum) - if not results['checksum_valid']: - results['findings'].append("Checksum verification failed") + results["checksum_valid"] = self.verify_checksum(file_path, expected_checksum) + if not results["checksum_valid"]: + results["findings"].append("Checksum verification failed") # Content safety scan is_safe, findings = self.scan_for_malicious_content(file_path) - results['content_safe'] = is_safe - results['findings'].extend(findings) + results["content_safe"] = is_safe + results["findings"].extend(findings) # Model structure validation (only for model files) - if Path(file_path).suffix.lower() in {'.pt', '.pth'}: - results['structure_valid'] = self.validate_model_structure(file_path) - if not results['structure_valid']: - results['findings'].append("Model structure validation failed") + if Path(file_path).suffix.lower() in {".pt", ".pth"}: + results["structure_valid"] = self.validate_model_structure(file_path) + if not results["structure_valid"]: + results["findings"].append("Model structure validation failed") # Overall validation result - is_valid = all([ - results['size_valid'], - results['extension_valid'], - results['checksum_valid'], - results['content_safe'] - ]) - - if Path(file_path).suffix.lower() in {'.pt', '.pth'}: - is_valid = is_valid and results['structure_valid'] + is_valid = all( + [ + results["size_valid"], + results["extension_valid"], + results["checksum_valid"], + results["content_safe"], + ] + ) + + if Path(file_path).suffix.lower() in {".pt", ".pth"}: + is_valid = is_valid and results["structure_valid"] return is_valid, results diff --git a/src/models/secure_loader/model_validator.py b/src/models/secure_loader/model_validator.py index 7ba280679..bdeb440ea 100644 --- a/src/models/secure_loader/model_validator.py +++ b/src/models/secure_loader/model_validator.py @@ -7,9 +7,10 @@ - Configuration validation - Performance validation """ + import logging import os -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple import torch import torch.nn as nn @@ -27,10 +28,12 @@ class ModelValidator: - Performance validation """ - def __init__(self, - allowed_model_types: Optional[List[str]] = None, - max_model_size_mb: int = 2048, - required_config_keys: Optional[List[str]] = None): + def __init__( + self, + allowed_model_types: Optional[List[str]] = None, + max_model_size_mb: int = 2048, + required_config_keys: Optional[List[str]] = None, + ): """Initialize model validator. Args: @@ -39,18 +42,22 @@ def __init__(self, required_config_keys: Required configuration keys """ self.allowed_model_types = allowed_model_types or [ - 'BERTEmotionClassifier', 'T5Summarizer', 'WhisperTranscriber' + "BERTEmotionClassifier", + "T5Summarizer", + "WhisperTranscriber", ] self.max_model_size_mb = max_model_size_mb self.required_config_keys = required_config_keys or [ - 'model_name', 'num_emotions', 'hidden_dropout_prob' + "model_name", + "num_emotions", + "hidden_dropout_prob", ] # Version compatibility matrix self.version_compatibility = { - 'torch': '>=1.9.0', - 'transformers': '>=4.20.0', - 'tokenizers': '>=0.12.0' + "torch": ">=1.9.0", + "transformers": ">=4.20.0", + "tokenizers": ">=0.12.0", } def validate_model_structure(self, model: nn.Module) -> Tuple[bool, Dict]: @@ -63,45 +70,47 @@ def validate_model_structure(self, model: nn.Module) -> Tuple[bool, Dict]: Tuple of (is_valid, validation_info) """ validation_info = { - 'model_type': type(model).__name__, - 'parameter_count': 0, - 'layers': [], - 'issues': [] + "model_type": type(model).__name__, + "parameter_count": 0, + "layers": [], + "issues": [], } try: # Check model type if type(model).__name__ not in self.allowed_model_types: - validation_info['issues'].append(f"Model type {type(model).__name__} not allowed") + validation_info["issues"].append(f"Model type {type(model).__name__} not allowed") # Count parameters param_count = sum(p.numel() for p in model.parameters()) - validation_info['parameter_count'] = param_count + validation_info["parameter_count"] = param_count # Check for reasonable parameter count if param_count > 500_000_000: # 500M parameters - validation_info['issues'].append("Model has too many parameters") + validation_info["issues"].append("Model has too many parameters") # Analyze model layers for name, module in model.named_modules(): if isinstance(module, (nn.Linear, nn.Conv2d, nn.LSTM, nn.Transformer)): - validation_info['layers'].append({ - 'name': name, - 'type': type(module).__name__, - 'parameters': sum(p.numel() for p in module.parameters()) - }) + validation_info["layers"].append( + { + "name": name, + "type": type(module).__name__, + "parameters": sum(p.numel() for p in module.parameters()), + } + ) # Check for required methods - required_methods = ['forward', 'eval', 'train'] + required_methods = ["forward", "eval", "train"] for method in required_methods: if not hasattr(model, method): - validation_info['issues'].append(f"Missing required method: {method}") + validation_info["issues"].append(f"Missing required method: {method}") - is_valid = len(validation_info['issues']) == 0 + is_valid = len(validation_info["issues"]) == 0 return is_valid, validation_info except Exception as e: - validation_info['issues'].append(f"Validation error: {e}") + validation_info["issues"].append(f"Validation error: {e}") return False, validation_info def validate_model_config(self, config: Dict[str, Any]) -> Tuple[bool, Dict]: @@ -114,41 +123,45 @@ def validate_model_config(self, config: Dict[str, Any]) -> Tuple[bool, Dict]: Tuple of (is_valid, validation_info) """ validation_info = { - 'config_keys': list(config.keys()), - 'missing_keys': [], - 'invalid_values': [], - 'issues': [] + "config_keys": list(config.keys()), + "missing_keys": [], + "invalid_values": [], + "issues": [], } try: # Check required keys for key in self.required_config_keys: if key not in config: - validation_info['missing_keys'].append(key) + validation_info["missing_keys"].append(key) # Validate specific config values - if 'num_emotions' in config: - num_emotions = config['num_emotions'] + if "num_emotions" in config: + num_emotions = config["num_emotions"] if not isinstance(num_emotions, int) or num_emotions <= 0: - validation_info['invalid_values'].append(f"num_emotions: {num_emotions}") + validation_info["invalid_values"].append(f"num_emotions: {num_emotions}") - if 'hidden_dropout_prob' in config: - dropout = config['hidden_dropout_prob'] + if "hidden_dropout_prob" in config: + dropout = config["hidden_dropout_prob"] if not isinstance(dropout, (int, float)) or dropout < 0 or dropout > 1: - validation_info['invalid_values'].append(f"hidden_dropout_prob: {dropout}") + validation_info["invalid_values"].append(f"hidden_dropout_prob: {dropout}") # Check for issues - if validation_info['missing_keys']: - validation_info['issues'].append(f"Missing required keys: {validation_info['missing_keys']}") + if validation_info["missing_keys"]: + validation_info["issues"].append( + f"Missing required keys: {validation_info['missing_keys']}" + ) - if validation_info['invalid_values']: - validation_info['issues'].append(f"Invalid values: {validation_info['invalid_values']}") + if validation_info["invalid_values"]: + validation_info["issues"].append( + f"Invalid values: {validation_info['invalid_values']}" + ) - is_valid = len(validation_info['issues']) == 0 + is_valid = len(validation_info["issues"]) == 0 return is_valid, validation_info except Exception as e: - validation_info['issues'].append(f"Config validation error: {e}") + validation_info["issues"].append(f"Config validation error: {e}") return False, validation_info def validate_model_file(self, model_path: str) -> Tuple[bool, Dict]: @@ -161,61 +174,61 @@ def validate_model_file(self, model_path: str) -> Tuple[bool, Dict]: Tuple of (is_valid, validation_info) """ validation_info = { - 'file_path': model_path, - 'file_size_mb': 0, - 'file_exists': False, - 'is_readable': False, - 'loadable': False, - 'issues': [] + "file_path": model_path, + "file_size_mb": 0, + "file_exists": False, + "is_readable": False, + "loadable": False, + "issues": [], } try: # Check file existence if not os.path.exists(model_path): - validation_info['issues'].append("Model file does not exist") + validation_info["issues"].append("Model file does not exist") return False, validation_info - validation_info['file_exists'] = True + validation_info["file_exists"] = True # Check file size file_size = os.path.getsize(model_path) file_size_mb = file_size / (1024 * 1024) - validation_info['file_size_mb'] = file_size_mb + validation_info["file_size_mb"] = file_size_mb if file_size_mb > self.max_model_size_mb: - validation_info['issues'].append(f"Model file too large: {file_size_mb:.2f}MB") + validation_info["issues"].append(f"Model file too large: {file_size_mb:.2f}MB") # Check if file is readable if not os.access(model_path, os.R_OK): - validation_info['issues'].append("Model file is not readable") + validation_info["issues"].append("Model file is not readable") return False, validation_info - validation_info['is_readable'] = True + validation_info["is_readable"] = True # Try to load the model try: - model_data = torch.load(model_path, map_location='cpu', weights_only=True) - validation_info['loadable'] = True + model_data = torch.load(model_path, map_location="cpu", weights_only=True) + validation_info["loadable"] = True # Validate model data structure if not isinstance(model_data, dict): - validation_info['issues'].append("Model file is not a valid state dict") + validation_info["issues"].append("Model file is not a valid state dict") else: # Check for required keys - if 'state_dict' not in model_data: - validation_info['issues'].append("Model file missing state_dict") + if "state_dict" not in model_data: + validation_info["issues"].append("Model file missing state_dict") - if 'config' not in model_data: - validation_info['issues'].append("Model file missing config") + if "config" not in model_data: + validation_info["issues"].append("Model file missing config") except Exception as e: - validation_info['issues'].append(f"Failed to load model: {e}") + validation_info["issues"].append(f"Failed to load model: {e}") - is_valid = len(validation_info['issues']) == 0 + is_valid = len(validation_info["issues"]) == 0 return is_valid, validation_info except Exception as e: - validation_info['issues'].append(f"File validation error: {e}") + validation_info["issues"].append(f"File validation error: {e}") return False, validation_info def validate_version_compatibility(self, model_config: Dict[str, Any]) -> Tuple[bool, Dict]: @@ -228,46 +241,52 @@ def validate_version_compatibility(self, model_config: Dict[str, Any]) -> Tuple[ Tuple of (is_valid, validation_info) """ validation_info = { - 'current_versions': {}, - 'required_versions': self.version_compatibility, - 'compatibility_issues': [], - 'issues': [] + "current_versions": {}, + "required_versions": self.version_compatibility, + "compatibility_issues": [], + "issues": [], } try: # Get current versions - import torch import transformers - validation_info['current_versions'] = { - 'torch': torch.__version__, - 'transformers': transformers.__version__ + validation_info["current_versions"] = { + "torch": torch.__version__, + "transformers": transformers.__version__, } # Check version compatibility for package, required_version in self.version_compatibility.items(): - if package in validation_info['current_versions']: - current_version = validation_info['current_versions'][package] + if package in validation_info["current_versions"]: + current_version = validation_info["current_versions"][package] # Enhanced version check that supports PyTorch 2.x - if package == 'torch': + if package == "torch": # Allow PyTorch 1.x and 2.x versions - if not (current_version.startswith('1.') or current_version.startswith('2.')): - validation_info['compatibility_issues'].append(f"PyTorch version {current_version} may not be compatible") - elif package == 'transformers' and not current_version.startswith('4.'): - validation_info['compatibility_issues'].append(f"Transformers version {current_version} may not be compatible") + if not ( + current_version.startswith("1.") or current_version.startswith("2.") + ): + validation_info["compatibility_issues"].append( + f"PyTorch version {current_version} may not be compatible" + ) + elif package == "transformers" and not current_version.startswith("4."): + validation_info["compatibility_issues"].append( + f"Transformers version {current_version} may not be compatible" + ) # Check for issues - if validation_info['compatibility_issues']: - validation_info['issues'].extend(validation_info['compatibility_issues']) + if validation_info["compatibility_issues"]: + validation_info["issues"].extend(validation_info["compatibility_issues"]) - is_valid = len(validation_info['issues']) == 0 + is_valid = len(validation_info["issues"]) == 0 return is_valid, validation_info except Exception as e: - validation_info['issues'].append(f"Version validation error: {e}") + validation_info["issues"].append(f"Version validation error: {e}") return False, validation_info - def validate_model_performance(self, model: nn.Module, test_input: torch.Tensor) -> Tuple[bool, Dict]: + @staticmethod + def validate_model_performance(model: nn.Module, test_input: torch.Tensor) -> Tuple[bool, Dict]: """Validate model performance with test input. Args: @@ -278,10 +297,10 @@ def validate_model_performance(self, model: nn.Module, test_input: torch.Tensor) Tuple of (is_valid, validation_info) """ validation_info = { - 'forward_pass_time': 0, - 'memory_usage_mb': 0, - 'output_shape': None, - 'issues': [] + "forward_pass_time": 0, + "memory_usage_mb": 0, + "output_shape": None, + "issues": [], } try: @@ -296,37 +315,39 @@ def validate_model_performance(self, model: nn.Module, test_input: torch.Tensor) output = model(test_input) end_time = time.time() - validation_info['forward_pass_time'] = end_time - start_time - validation_info['output_shape'] = list(output.shape) + validation_info["forward_pass_time"] = end_time - start_time + validation_info["output_shape"] = list(output.shape) # Check performance constraints - if validation_info['forward_pass_time'] > 5.0: # 5 seconds - validation_info['issues'].append("Forward pass too slow") + if validation_info["forward_pass_time"] > 5.0: # 5 seconds + validation_info["issues"].append("Forward pass too slow") # Check output shape if output.dim() != 2: # Expected 2D output for classification - validation_info['issues'].append("Unexpected output shape") + validation_info["issues"].append("Unexpected output shape") # Measure memory usage - if hasattr(torch.cuda, 'memory_allocated'): + if hasattr(torch.cuda, "memory_allocated"): memory_mb = torch.cuda.memory_allocated() / (1024 * 1024) - validation_info['memory_usage_mb'] = memory_mb + validation_info["memory_usage_mb"] = memory_mb if memory_mb > 2048: # 2GB - validation_info['issues'].append("Memory usage too high") + validation_info["issues"].append("Memory usage too high") - is_valid = len(validation_info['issues']) == 0 + is_valid = len(validation_info["issues"]) == 0 return is_valid, validation_info except Exception as e: - validation_info['issues'].append(f"Performance validation error: {e}") + validation_info["issues"].append(f"Performance validation error: {e}") return False, validation_info - def comprehensive_validation(self, - model_path: str, - model_class: type, - model_config: Dict[str, Any], - test_input: Optional[torch.Tensor] = None) -> Tuple[bool, Dict]: + def comprehensive_validation( + self, + model_path: str, + model_class: type, + model_config: Dict[str, Any], + test_input: Optional[torch.Tensor] = None, + ) -> Tuple[bool, Dict]: """Perform comprehensive model validation. Args: @@ -339,68 +360,71 @@ def comprehensive_validation(self, Tuple of (is_valid, comprehensive_validation_info) """ comprehensive_info = { - 'file_validation': {}, - 'config_validation': {}, - 'version_validation': {}, - 'structure_validation': {}, - 'performance_validation': {}, - 'overall_valid': False, - 'issues': [] + "file_validation": {}, + "config_validation": {}, + "version_validation": {}, + "structure_validation": {}, + "performance_validation": {}, + "overall_valid": False, + "issues": [], } try: # 1. File validation file_valid, file_info = self.validate_model_file(model_path) - comprehensive_info['file_validation'] = file_info + comprehensive_info["file_validation"] = file_info if not file_valid: - comprehensive_info['issues'].extend(file_info['issues']) + comprehensive_info["issues"].extend(file_info["issues"]) # 2. Config validation config_valid, config_info = self.validate_model_config(model_config) - comprehensive_info['config_validation'] = config_info + comprehensive_info["config_validation"] = config_info if not config_valid: - comprehensive_info['issues'].extend(config_info['issues']) + comprehensive_info["issues"].extend(config_info["issues"]) # 3. Version validation version_valid, version_info = self.validate_version_compatibility(model_config) - comprehensive_info['version_validation'] = version_info + comprehensive_info["version_validation"] = version_info if not version_valid: - comprehensive_info['issues'].extend(version_info['issues']) + comprehensive_info["issues"].extend(version_info["issues"]) # 4. Structure validation (if file is valid) if file_valid: try: - model_data = torch.load(model_path, map_location='cpu', weights_only=True) + model_data = torch.load(model_path, map_location="cpu", weights_only=True) # Filter model_config to only include valid constructor parameters import inspect + constructor_params = inspect.signature(model_class.__init__).parameters - valid_params = {k: v for k, v in model_config.items() if k in constructor_params} + valid_params = { + k: v for k, v in model_config.items() if k in constructor_params + } model = model_class(**valid_params) - if 'state_dict' in model_data: - model.load_state_dict(model_data['state_dict']) + if "state_dict" in model_data: + model.load_state_dict(model_data["state_dict"]) structure_valid, structure_info = self.validate_model_structure(model) - comprehensive_info['structure_validation'] = structure_info + comprehensive_info["structure_validation"] = structure_info if not structure_valid: - comprehensive_info['issues'].extend(structure_info['issues']) + comprehensive_info["issues"].extend(structure_info["issues"]) # 5. Performance validation (if structure is valid and test input provided) if structure_valid and test_input is not None: perf_valid, perf_info = self.validate_model_performance(model, test_input) - comprehensive_info['performance_validation'] = perf_info + comprehensive_info["performance_validation"] = perf_info if not perf_valid: - comprehensive_info['issues'].extend(perf_info['issues']) + comprehensive_info["issues"].extend(perf_info["issues"]) except Exception as e: - comprehensive_info['issues'].append(f"Model loading error: {e}") + comprehensive_info["issues"].append(f"Model loading error: {e}") # Overall validation result - comprehensive_info['overall_valid'] = len(comprehensive_info['issues']) == 0 + comprehensive_info["overall_valid"] = len(comprehensive_info["issues"]) == 0 - return comprehensive_info['overall_valid'], comprehensive_info + return comprehensive_info["overall_valid"], comprehensive_info except Exception as e: - comprehensive_info['issues'].append(f"Comprehensive validation error: {e}") + comprehensive_info["issues"].append(f"Comprehensive validation error: {e}") return False, comprehensive_info diff --git a/src/models/secure_loader/sandbox_executor.py b/src/models/secure_loader/sandbox_executor.py index bcd30a963..a7f9e3fce 100644 --- a/src/models/secure_loader/sandbox_executor.py +++ b/src/models/secure_loader/sandbox_executor.py @@ -18,6 +18,7 @@ class SandboxError(Exception): """Custom exception for sandbox execution errors.""" + def __init__(self, message: str, original_exception: Optional[Exception] = None): super().__init__(message) self.original_exception = original_exception @@ -31,7 +32,9 @@ def __repr__(self): def to_dict(self): return { "error": str(self), - "exception_type": type(self.original_exception).__name__ if self.original_exception else None + "exception_type": ( + type(self.original_exception).__name__ if self.original_exception else None + ), } @@ -45,11 +48,13 @@ class SandboxExecutor: - Exception isolation """ - def __init__(self, - max_memory_mb: int = 2048, - max_cpu_time: int = 30, - max_wall_time: int = 60, - allow_network: bool = False): + def __init__( + self, + max_memory_mb: int = 2048, + max_cpu_time: int = 30, + max_wall_time: int = 60, + allow_network: bool = False, + ): """Initialize sandbox executor. Args: @@ -65,14 +70,27 @@ def __init__(self, # Restricted operations self.blocked_modules = { - 'subprocess', 'os', 'sys', 'builtins', 'importlib', - 'pickle', 'marshal', 'code', 'types' + "subprocess", + "os", + "sys", + "builtins", + "importlib", + "pickle", + "marshal", + "code", + "types", } # Restricted functions self.blocked_functions = { - 'eval', 'exec', 'compile', 'open', 'file', - '__import__', 'globals', 'locals' + "eval", + "exec", + "compile", + "open", + "file", + "__import__", + "globals", + "locals", } def _set_resource_limits(self): @@ -86,9 +104,13 @@ def _set_resource_limits(self): resource.setrlimit(resource.RLIMIT_CPU, (self.max_cpu_time, self.max_cpu_time)) # File size limit - resource.setrlimit(resource.RLIMIT_FSIZE, (1024 * 1024 * 1024, 1024 * 1024 * 1024)) # 1GB + resource.setrlimit( + resource.RLIMIT_FSIZE, (1024 * 1024 * 1024, 1024 * 1024 * 1024) + ) # 1GB - logger.debug(f"Resource limits set: memory={self.max_memory_mb}MB, cpu={self.max_cpu_time}s") + logger.debug( + f"Resource limits set: memory={self.max_memory_mb}MB, cpu={self.max_cpu_time}s" + ) except Exception as e: logger.error(f"Failed to set resource limits: {e}") @@ -96,15 +118,60 @@ def _set_resource_limits(self): def _get_safe_builtins(self): """Return a safe builtins dictionary for sandboxed execution.""" import builtins as py_builtins + allowed_names = [ - 'abs', 'all', 'any', 'bool', 'bytes', 'chr', 'dict', 'divmod', 'enumerate', 'filter', - 'float', 'format', 'frozenset', 'getattr', 'hasattr', 'hash', 'hex', 'id', 'int', - 'isinstance', 'issubclass', 'iter', 'len', 'list', 'map', 'max', 'min', 'next', 'object', - 'oct', 'ord', 'pow', 'range', 'repr', 'reversed', 'round', 'set', 'slice', 'sorted', - 'str', 'sum', 'tuple', 'zip', 'Exception', 'ValueError', 'TypeError', 'print' + "abs", + "all", + "any", + "bool", + "bytes", + "chr", + "dict", + "divmod", + "enumerate", + "filter", + "float", + "format", + "frozenset", + "getattr", + "hasattr", + "hash", + "hex", + "id", + "int", + "isinstance", + "issubclass", + "iter", + "len", + "list", + "map", + "max", + "min", + "next", + "object", + "oct", + "ord", + "pow", + "range", + "repr", + "reversed", + "round", + "set", + "slice", + "sorted", + "str", + "sum", + "tuple", + "zip", + "Exception", + "ValueError", + "TypeError", + "print", ] - safe_builtins = {name: getattr(py_builtins, name) for name in allowed_names if hasattr(py_builtins, name)} - return {'__builtins__': safe_builtins} + safe_builtins = { + name: getattr(py_builtins, name) for name in allowed_names if hasattr(py_builtins, name) + } + return {"__builtins__": safe_builtins} def _timeout_handler(self, signum, frame): """Handle timeout signals.""" @@ -113,6 +180,7 @@ def _timeout_handler(self, signum, frame): def _is_main_thread(self) -> bool: """Check if current thread is the main thread.""" import threading + return threading.current_thread() is threading.main_thread() def _set_timeout_safe(self): @@ -130,7 +198,9 @@ def sandbox_context(self): self._set_resource_limits() # Set up signal handlers for timeout (only in main thread) if self._is_main_thread(): - original_signal_handlers[signal.SIGALRM] = signal.signal(signal.SIGALRM, self._timeout_handler) + original_signal_handlers[signal.SIGALRM] = signal.signal( + signal.SIGALRM, self._timeout_handler + ) self._set_timeout_safe() else: logger.warning("Signal-based timeout not available in non-main thread") @@ -151,6 +221,7 @@ def _disable_network(self): """Disable network access in the sandbox.""" try: import socket + original_socket = socket.socket def blocked_socket(*args, **kwargs): @@ -172,8 +243,9 @@ def execute_safely(self, func: Callable, *args, **kwargs) -> Tuple[Any, Dict]: return None, {"status": "exec completed"} # If func is a callable, pass safe_globals if it accepts globals import inspect + sig = inspect.signature(func) - if 'globals' in sig.parameters: + if "globals" in sig.parameters: result = func(*args, globals=safe_globals, **kwargs) else: result = func(*args, **kwargs) @@ -193,12 +265,14 @@ def load_model_safely(self, model_path: str, model_class: type, **kwargs) -> Any Returns: Loaded model instance """ + def load_model(): # Use torch.load with weights_only=True for additional safety - model_data = torch.load(model_path, map_location='cpu', weights_only=True) + model_data = torch.load(model_path, map_location="cpu", weights_only=True) # Filter kwargs to only include valid constructor parameters import inspect + constructor_params = inspect.signature(model_class.__init__).parameters valid_params = {k: v for k, v in kwargs.items() if k in constructor_params} @@ -206,8 +280,8 @@ def load_model(): model = model_class(**valid_params) # Load state dict if available - if 'state_dict' in model_data: - model.load_state_dict(model_data['state_dict']) + if "state_dict" in model_data: + model.load_state_dict(model_data["state_dict"]) return model @@ -224,16 +298,17 @@ def validate_model_safely(self, model_path: str) -> Tuple[bool, Dict]: Returns: Tuple of (is_valid, validation_info) """ + def validate_model(): # Load model data - model_data = torch.load(model_path, map_location='cpu', weights_only=True) + model_data = torch.load(model_path, map_location="cpu", weights_only=True) # Basic validation if not isinstance(model_data, dict): return False, {"error": "Model is not a valid state dict"} # Check for required keys - required_keys = ['state_dict'] + required_keys = ["state_dict"] missing_keys = [key for key in required_keys if key not in model_data] if missing_keys: @@ -247,7 +322,8 @@ def validate_model(): except Exception as e: return False, {"error": f"Validation failed: {e}"} - def get_resource_usage(self) -> Dict[str, float]: + @staticmethod + def get_resource_usage() -> Dict[str, float]: """Get current resource usage. Returns: @@ -261,22 +337,23 @@ def get_resource_usage(self) -> Dict[str, float]: cpu_percent = process.cpu_percent() return { - 'memory_mb': memory_info.rss / 1024 / 1024, - 'cpu_percent': cpu_percent, - 'memory_percent': process.memory_percent() + "memory_mb": memory_info.rss / 1024 / 1024, + "cpu_percent": cpu_percent, + "memory_percent": process.memory_percent(), } except ImportError: logger.warning("psutil not available, cannot get resource usage") return {} - def cleanup(self): + @staticmethod + def cleanup(): """Clean up sandbox resources.""" try: # Cancel any pending alarms signal.alarm(0) # Clear any cached models - if hasattr(torch, 'cuda'): + if hasattr(torch, "cuda"): torch.cuda.empty_cache() except Exception as e: diff --git a/src/models/secure_loader/secure_model_loader.py b/src/models/secure_loader/secure_model_loader.py index c78c52180..30aabfb31 100644 --- a/src/models/secure_loader/secure_model_loader.py +++ b/src/models/secure_loader/secure_model_loader.py @@ -8,14 +8,14 @@ import logging import os import time -from typing import Any, Dict, Optional, Tuple, Type, Union +from typing import Any, Dict, Optional, Tuple, Type import torch import torch.nn as nn from .integrity_checker import IntegrityChecker -from .sandbox_executor import SandboxExecutor from .model_validator import ModelValidator +from .sandbox_executor import SandboxExecutor logger = logging.getLogger(__name__) @@ -31,13 +31,15 @@ class SecureModelLoader: - Audit logging """ - def __init__(self, - trusted_checksums_file: Optional[str] = None, - enable_sandbox: bool = True, - enable_caching: bool = True, - cache_dir: Optional[str] = None, - max_cache_size_mb: int = 1024, - audit_log_file: Optional[str] = None): + def __init__( + self, + trusted_checksums_file: Optional[str] = None, + enable_sandbox: bool = True, + enable_caching: bool = True, + cache_dir: Optional[str] = None, + max_cache_size_mb: int = 1024, + audit_log_file: Optional[str] = None, + ): """Initialize secure model loader. Args: @@ -50,7 +52,7 @@ def __init__(self, """ self.enable_sandbox = enable_sandbox self.enable_caching = enable_caching - self.cache_dir = cache_dir or os.path.join(os.getcwd(), '.model_cache') + self.cache_dir = cache_dir or os.path.join(os.getcwd(), ".model_cache") self.max_cache_size_mb = max_cache_size_mb self.audit_log_file = audit_log_file @@ -76,14 +78,12 @@ def _setup_audit_logger(self) -> logging.Logger: Returns: Configured audit logger """ - audit_logger = logging.getLogger('secure_model_loader.audit') + audit_logger = logging.getLogger("secure_model_loader.audit") audit_logger.setLevel(logging.INFO) if self.audit_log_file: handler = logging.FileHandler(self.audit_log_file) - formatter = logging.Formatter( - '%(asctime)s - %(name)s - %(levelname)s - %(message)s' - ) + formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") handler.setFormatter(formatter) audit_logger.addHandler(handler) @@ -96,11 +96,7 @@ def _log_audit_event(self, event_type: str, details: Dict[str, Any]): event_type: Type of audit event details: Event details """ - audit_entry = { - 'timestamp': time.time(), - 'event_type': event_type, - 'details': details - } + audit_entry = {"timestamp": time.time(), "event_type": event_type, "details": details} self.audit_logger.info(f"AUDIT: {audit_entry}") logger.info(f"Audit event: {event_type} - {details}") @@ -148,7 +144,7 @@ def _load_from_cache(self, cache_key: str) -> Optional[nn.Module]: if not self.enable_caching or cache_key not in self.model_cache: return None - self._log_audit_event('cache_hit', {'cache_key': cache_key}) + self._log_audit_event("cache_hit", {"cache_key": cache_key}) logger.info(f"Loading model from cache: {cache_key}") return self.model_cache[cache_key] @@ -167,7 +163,9 @@ def _save_to_cache(self, cache_key: str, model: nn.Module): os.path.getsize(os.path.join(self.cache_dir, f)) for f in os.listdir(self.cache_dir) if os.path.isfile(os.path.join(self.cache_dir, f)) - ) / (1024 * 1024) # Convert to MB + ) / ( + 1024 * 1024 + ) # Convert to MB if current_size > self.max_cache_size_mb: logger.warning("Cache size limit exceeded, clearing old entries") @@ -178,15 +176,9 @@ def _save_to_cache(self, cache_key: str, model: nn.Module): torch.save(model.state_dict(), cache_file) self.model_cache[cache_key] = model - self.cache_metadata[cache_key] = { - 'timestamp': time.time(), - 'file_path': cache_file - } + self.cache_metadata[cache_key] = {"timestamp": time.time(), "file_path": cache_file} - self._log_audit_event('cache_save', { - 'cache_key': cache_key, - 'cache_file': cache_file - }) + self._log_audit_event("cache_save", {"cache_key": cache_key, "cache_file": cache_file}) def _clear_cache(self): """Clear model cache.""" @@ -195,21 +187,23 @@ def _clear_cache(self): # Remove cache files for cache_key, metadata in self.cache_metadata.items(): - if os.path.exists(metadata['file_path']): - os.remove(metadata['file_path']) + if os.path.exists(metadata["file_path"]): + os.remove(metadata["file_path"]) # Clear memory cache self.model_cache.clear() self.cache_metadata.clear() - self._log_audit_event('cache_clear', {}) + self._log_audit_event("cache_clear", {}) - def load_model(self, - model_path: str, - model_class: Type[nn.Module], - expected_checksum: Optional[str] = None, - test_input: Optional[torch.Tensor] = None, - **kwargs) -> Tuple[nn.Module, Dict[str, Any]]: + def load_model( + self, + model_path: str, + model_class: Type[nn.Module], + expected_checksum: Optional[str] = None, + test_input: Optional[torch.Tensor] = None, + **kwargs, + ) -> Tuple[nn.Module, Dict[str, Any]]: """Load model securely. Args: @@ -224,14 +218,14 @@ def load_model(self, """ start_time = time.time() loading_info = { - 'model_path': model_path, - 'model_class': model_class.__name__, - 'loading_time': 0, - 'cache_used': False, - 'integrity_check': {}, - 'validation': {}, - 'sandbox_execution': {}, - 'issues': [] + "model_path": model_path, + "model_class": model_class.__name__, + "loading_time": 0, + "cache_used": False, + "integrity_check": {}, + "validation": {}, + "sandbox_execution": {}, + "issues": [], } try: @@ -242,13 +236,16 @@ def load_model(self, if self._is_cached(cache_key): model = self._load_from_cache(cache_key) if model is not None: - loading_info['cache_used'] = True - loading_info['loading_time'] = time.time() - start_time - self._log_audit_event('model_loaded', { - 'model_path': model_path, - 'cache_used': True, - 'loading_time': loading_info['loading_time'] - }) + loading_info["cache_used"] = True + loading_info["loading_time"] = time.time() - start_time + self._log_audit_event( + "model_loaded", + { + "model_path": model_path, + "cache_used": True, + "loading_time": loading_info["loading_time"], + }, + ) return model, loading_info # 1. Integrity check @@ -256,23 +253,23 @@ def load_model(self, integrity_valid, integrity_info = self.integrity_checker.comprehensive_validation( model_path, expected_checksum ) - loading_info['integrity_check'] = integrity_info + loading_info["integrity_check"] = integrity_info if not integrity_valid: - loading_info['issues'].extend(integrity_info['findings']) + loading_info["issues"].extend(integrity_info["findings"]) raise ValueError(f"Integrity check failed: {integrity_info['findings']}") # 2. Model validation logger.info(f"Validating model {model_path}") # Filter out non-model-config parameters - model_config = {k: v for k, v in kwargs.items() if k not in ['expected_checksum']} + model_config = {k: v for k, v in kwargs.items() if k not in ["expected_checksum"]} validation_valid, validation_info = self.model_validator.comprehensive_validation( model_path, model_class, model_config, test_input ) - loading_info['validation'] = validation_info + loading_info["validation"] = validation_info if not validation_valid: - loading_info['issues'].extend(validation_info['issues']) + loading_info["issues"].extend(validation_info["issues"]) raise ValueError(f"Model validation failed: {validation_info['issues']}") # 3. Load model (with or without sandbox) @@ -281,19 +278,20 @@ def load_model(self, model, sandbox_info = self.sandbox_executor.load_model_safely( model_path, model_class, **kwargs ) - loading_info['sandbox_execution'] = sandbox_info + loading_info["sandbox_execution"] = sandbox_info else: # Load without sandbox (less secure but faster) - model_data = torch.load(model_path, map_location='cpu', weights_only=True) + model_data = torch.load(model_path, map_location="cpu", weights_only=True) # Filter kwargs to only include valid constructor parameters import inspect + constructor_params = inspect.signature(model_class.__init__).parameters valid_params = {k: v for k, v in kwargs.items() if k in constructor_params} model = model_class(**valid_params) - if 'state_dict' in model_data: - model.load_state_dict(model_data['state_dict']) + if "state_dict" in model_data: + model.load_state_dict(model_data["state_dict"]) # 4. Cache model if self.enable_caching: @@ -302,37 +300,45 @@ def load_model(self, # 5. Final validation model.eval() - loading_info['loading_time'] = time.time() - start_time + loading_info["loading_time"] = time.time() - start_time - self._log_audit_event('model_loaded', { - 'model_path': model_path, - 'cache_used': False, - 'loading_time': loading_info['loading_time'], - 'model_type': type(model).__name__ - }) + self._log_audit_event( + "model_loaded", + { + "model_path": model_path, + "cache_used": False, + "loading_time": loading_info["loading_time"], + "model_type": type(model).__name__, + }, + ) logger.info(f"Model loaded successfully in {loading_info['loading_time']:.2f}s") return model, loading_info except Exception as e: - loading_info['loading_time'] = time.time() - start_time - loading_info['issues'].append(f"Loading failed: {e}") - - self._log_audit_event('model_load_failed', { - 'model_path': model_path, - 'error': str(e), - 'loading_time': loading_info['loading_time'] - }) + loading_info["loading_time"] = time.time() - start_time + loading_info["issues"].append(f"Loading failed: {e}") + + self._log_audit_event( + "model_load_failed", + { + "model_path": model_path, + "error": str(e), + "loading_time": loading_info["loading_time"], + }, + ) logger.error(f"Failed to load model {model_path}: {e}") raise - def validate_model(self, - model_path: str, - model_class: Type[nn.Module], - expected_checksum: Optional[str] = None, - test_input: Optional[torch.Tensor] = None, - **kwargs) -> Tuple[bool, Dict[str, Any]]: + def validate_model( + self, + model_path: str, + model_class: Type[nn.Module], + expected_checksum: Optional[str] = None, + test_input: Optional[torch.Tensor] = None, + **kwargs, + ) -> Tuple[bool, Dict[str, Any]]: """Validate model without loading it. Args: @@ -345,11 +351,11 @@ def validate_model(self, Tuple of (is_valid, validation_info) """ validation_info = { - 'model_path': model_path, - 'integrity_check': {}, - 'validation': {}, - 'overall_valid': False, - 'issues': [] + "model_path": model_path, + "integrity_check": {}, + "validation": {}, + "overall_valid": False, + "issues": [], } try: @@ -357,40 +363,42 @@ def validate_model(self, integrity_valid, integrity_info = self.integrity_checker.comprehensive_validation( model_path, expected_checksum ) - validation_info['integrity_check'] = integrity_info + validation_info["integrity_check"] = integrity_info if not integrity_valid: - validation_info['issues'].extend(integrity_info['findings']) + validation_info["issues"].extend(integrity_info["findings"]) # Model validation - filter out non-model-config parameters - model_config = {k: v for k, v in kwargs.items() if k not in ['expected_checksum']} + model_config = {k: v for k, v in kwargs.items() if k not in ["expected_checksum"]} validation_valid, model_validation_info = self.model_validator.comprehensive_validation( model_path, model_class, model_config, test_input ) - validation_info['validation'] = model_validation_info + validation_info["validation"] = model_validation_info if not validation_valid: - validation_info['issues'].extend(model_validation_info['issues']) + validation_info["issues"].extend(model_validation_info["issues"]) # Overall validation result - validation_info['overall_valid'] = integrity_valid and validation_valid - - self._log_audit_event('model_validated', { - 'model_path': model_path, - 'is_valid': validation_info['overall_valid'], - 'issues': validation_info['issues'] - }) + validation_info["overall_valid"] = integrity_valid and validation_valid + + self._log_audit_event( + "model_validated", + { + "model_path": model_path, + "is_valid": validation_info["overall_valid"], + "issues": validation_info["issues"], + }, + ) - return validation_info['overall_valid'], validation_info + return validation_info["overall_valid"], validation_info except Exception as e: - validation_info['issues'].append(f"Validation error: {e}") - validation_info['overall_valid'] = False + validation_info["issues"].append(f"Validation error: {e}") + validation_info["overall_valid"] = False - self._log_audit_event('model_validation_failed', { - 'model_path': model_path, - 'error': str(e) - }) + self._log_audit_event( + "model_validation_failed", {"model_path": model_path, "error": str(e)} + ) return False, validation_info @@ -401,7 +409,7 @@ def get_cache_info(self) -> Dict[str, Any]: Cache information dictionary """ if not self.enable_caching: - return {'enabled': False} + return {"enabled": False} cache_size = 0 if os.path.exists(self.cache_dir): @@ -409,26 +417,28 @@ def get_cache_info(self) -> Dict[str, Any]: os.path.getsize(os.path.join(self.cache_dir, f)) for f in os.listdir(self.cache_dir) if os.path.isfile(os.path.join(self.cache_dir, f)) - ) / (1024 * 1024) # Convert to MB + ) / ( + 1024 * 1024 + ) # Convert to MB return { - 'enabled': True, - 'cache_dir': self.cache_dir, - 'cache_size_mb': cache_size, - 'max_cache_size_mb': self.max_cache_size_mb, - 'cached_models': len(self.model_cache), - 'cache_entries': list(self.cache_metadata.keys()) + "enabled": True, + "cache_dir": self.cache_dir, + "cache_size_mb": cache_size, + "max_cache_size_mb": self.max_cache_size_mb, + "cached_models": len(self.model_cache), + "cache_entries": list(self.cache_metadata.keys()), } def clear_cache(self): """Clear the model cache.""" self._clear_cache() - self._log_audit_event('cache_cleared', {}) + self._log_audit_event("cache_cleared", {}) def cleanup(self): """Clean up resources.""" if self.sandbox_executor: self.sandbox_executor.cleanup() - self._log_audit_event('cleanup', {}) + self._log_audit_event("cleanup", {}) logger.info("Secure model loader cleanup completed") diff --git a/src/models/summarization/dataset_loader.py b/src/models/summarization/dataset_loader.py index 6bf003466..447a3f7c9 100644 --- a/src/models/summarization/dataset_loader.py +++ b/src/models/summarization/dataset_loader.py @@ -1,8 +1,7 @@ -from typing import List -from torch.utils.data import Dataset import logging +from typing import List - +from torch.utils.data import Dataset """Dataset Loader for T5/BART Summarization - SAMO Deep Learning. diff --git a/src/models/summarization/samo_t5_summarizer.py b/src/models/summarization/samo_t5_summarizer.py index 3acf1ad42..b43dd4ea7 100644 --- a/src/models/summarization/samo_t5_summarizer.py +++ b/src/models/summarization/samo_t5_summarizer.py @@ -16,10 +16,10 @@ import logging import os import time -from typing import Dict, List, Optional, Tuple, Any -import yaml +from typing import Any, Dict, List, Optional, Tuple import torch +import yaml from transformers import T5ForConditionalGeneration, T5Tokenizer # Module logger only; app/config controls level @@ -53,10 +53,7 @@ def __init__(self, config_path: Optional[str] = None): def _load_config(config_path: Optional[str]) -> Dict[str, Any]: """Load configuration from YAML file.""" default_config = { - "model": { - "name": "t5-small", - "device": None - }, + "model": {"name": "t5-small", "device": None}, "generation": { "max_length": 100, "min_length": 20, @@ -65,16 +62,10 @@ def _load_config(config_path: Optional[str]) -> Dict[str, Any]: "repetition_penalty": 1.2, "length_penalty": 1.0, "do_sample": False, - "temperature": 1.0 - }, - "validation": { - "min_words": 20, - "max_words": 1000 - }, - "performance": { - "batch_size": 4, - "timeout_seconds": 30 + "temperature": 1.0, }, + "validation": {"min_words": 20, "max_words": 1000}, + "performance": {"batch_size": 4, "timeout_seconds": 30}, "samo_optimizations": { "emotional_context": True, "preserve_tone": True, @@ -83,11 +74,24 @@ def _load_config(config_path: Optional[str]) -> Dict[str, Any]: "sanitize_input": True, "log_level": "INFO", "emotional_keywords": [ - 'happy', 'sad', 'angry', 'excited', 'worried', 'grateful', - 'anxious', 'proud', 'confident', 'overwhelmed', 'peaceful', - 'frustrated', 'hopeful', 'disappointed', 'relieved', 'nervous' - ] - } + "happy", + "sad", + "angry", + "excited", + "worried", + "grateful", + "anxious", + "proud", + "confident", + "overwhelmed", + "peaceful", + "frustrated", + "hopeful", + "disappointed", + "relieved", + "nervous", + ], + }, } def recursive_merge_dicts(default, override): @@ -104,20 +108,21 @@ def recursive_merge_dicts(default, override): if config_path and os.path.exists(config_path): try: - with open(config_path, 'r', encoding='utf-8') as f: + with open(config_path, "r", encoding="utf-8") as f: user_config = yaml.safe_load(f) # Deep merge user config into default config for key, value in user_config.items(): - if (key in default_config and - isinstance(default_config[key], dict) and - isinstance(value, dict)): + if ( + key in default_config + and isinstance(default_config[key], dict) + and isinstance(value, dict) + ): default_config[key].update(value) else: default_config[key] = value except Exception as e: logger.warning( - "Failed to load config from %s: %s. Using default config.", - config_path, e + "Failed to load config from %s: %s. Using default config.", config_path, e ) return default_config @@ -141,8 +146,10 @@ def _get_device(config: Dict[str, Any]) -> str: if torch.cuda.is_available(): return "cuda" - if (getattr(torch.backends, "mps", None) is not None and - getattr(torch.backends.mps, "is_available", lambda: False)()): + if ( + getattr(torch.backends, "mps", None) is not None + and getattr(torch.backends.mps, "is_available", lambda: False)() + ): return "mps" return "cpu" @@ -217,7 +224,7 @@ def _extract_emotional_keywords(text: str, config: Dict[str, Any]) -> List[str]: for keyword in emotional_keywords: # Use word boundary matching to avoid false positives - pattern = r'\b' + re.escape(keyword) + r'\b' + pattern = r"\b" + re.escape(keyword) + r"\b" if re.search(pattern, text_lower): found_keywords.append(keyword) @@ -236,8 +243,9 @@ def _sanitize_input(text: str) -> str: """ # Basic sanitization - remove excessive whitespace and normalize import re + # Remove multiple spaces and normalize line breaks - text = re.sub(r'\s+', ' ', text) + text = re.sub(r"\s+", " ", text) # Remove leading/trailing whitespace text = text.strip() return text @@ -261,17 +269,13 @@ def _prepare_samo_input(self, text: str, emotional_keywords: List[str]) -> str: prompt_parts.append("journal entry") # Add emotional context if enabled - if (self.config["samo_optimizations"]["emotional_context"] and - emotional_keywords): + if self.config["samo_optimizations"]["emotional_context"] and emotional_keywords: emotion_context = f"[emotions: {', '.join(emotional_keywords)}]" prompt_parts.append(emotion_context) # Add tone preservation instruction if enabled - if (self.config["samo_optimizations"]["preserve_tone"] and - emotional_keywords): - tone_instruction = ( - f"[preserve emotional tone: {', '.join(emotional_keywords[:3])}]" - ) + if self.config["samo_optimizations"]["preserve_tone"] and emotional_keywords: + tone_instruction = f"[preserve emotional tone: {', '.join(emotional_keywords[:3])}]" prompt_parts.append(tone_instruction) # Combine all parts into final prompt @@ -300,12 +304,7 @@ def generate_summary(self, text: str) -> Dict[str, Any]: # Validate input is_valid, error_msg = self._validate_input(text) if not is_valid: - return { - "summary": "", - "error": error_msg, - "success": False, - "processing_time": 0.0 - } + return {"summary": "", "error": error_msg, "success": False, "processing_time": 0.0} try: # Apply SAMO optimizations @@ -318,22 +317,14 @@ def generate_summary(self, text: str) -> Dict[str, Any]: # Extract emotional keywords for SAMO optimization emotional_keywords = [] if self.config["samo_optimizations"]["extract_key_emotions"]: - emotional_keywords = self._extract_emotional_keywords( - processed_text, self.config - ) + emotional_keywords = self._extract_emotional_keywords(processed_text, self.config) # Prepare input for T5 with SAMO optimizations - input_text = self._prepare_samo_input( - processed_text, emotional_keywords - ) + input_text = self._prepare_samo_input(processed_text, emotional_keywords) # Tokenize inputs = self.tokenizer( - input_text, - return_tensors="pt", - max_length=512, - truncation=True, - padding=True + input_text, return_tensors="pt", max_length=512, truncation=True, padding=True ).to(self.device) # Generate summary @@ -344,12 +335,10 @@ def generate_summary(self, text: str) -> Dict[str, Any]: min_length=self.config["generation"]["min_length"], num_beams=self.config["generation"]["num_beams"], early_stopping=self.config["generation"]["early_stopping"], - repetition_penalty=self.config["generation"][ - "repetition_penalty" - ], + repetition_penalty=self.config["generation"]["repetition_penalty"], length_penalty=self.config["generation"]["length_penalty"], do_sample=self.config["generation"]["do_sample"], - temperature=self.config["generation"]["temperature"] + temperature=self.config["generation"]["temperature"], ) # Decode output @@ -361,8 +350,7 @@ def generate_summary(self, text: str) -> Dict[str, Any]: # Calculate metrics original_length = len(text.split()) summary_length = len(summary.split()) - compression_ratio = (summary_length / original_length - if original_length > 0 else 0) + compression_ratio = summary_length / original_length if original_length > 0 else 0 processing_time = time.time() - start_time return { @@ -373,7 +361,7 @@ def generate_summary(self, text: str) -> Dict[str, Any]: "emotional_keywords": emotional_keywords, "processing_time": processing_time, "success": True, - "error": None + "error": None, } except Exception as e: @@ -382,7 +370,7 @@ def generate_summary(self, text: str) -> Dict[str, Any]: "summary": "", "error": str(e), "success": False, - "processing_time": time.time() - start_time + "processing_time": time.time() - start_time, } def generate_batch_summaries(self, texts: List[str]) -> List[Dict[str, Any]]: @@ -414,7 +402,7 @@ def generate_batch_summaries(self, texts: List[str]) -> List[Dict[str, Any]]: "summary": "", "error": error_msg, "success": False, - "processing_time": 0.0 + "processing_time": 0.0, } if not valid_texts: @@ -449,9 +437,7 @@ def generate_batch_summaries(self, texts: List[str]) -> List[Dict[str, Any]]: batch_emotional_keywords.append(emotional_keywords) # Prepare SAMO input - input_text = self._prepare_samo_input( - processed_text, emotional_keywords - ) + input_text = self._prepare_samo_input(processed_text, emotional_keywords) processed_texts.append(input_text) # Tokenize entire batch at once @@ -460,7 +446,7 @@ def generate_batch_summaries(self, texts: List[str]) -> List[Dict[str, Any]]: return_tensors="pt", max_length=512, truncation=True, - padding=True + padding=True, ).to(self.device) # Generate summaries for entire batch @@ -472,22 +458,18 @@ def generate_batch_summaries(self, texts: List[str]) -> List[Dict[str, Any]]: min_length=self.config["generation"]["min_length"], num_beams=self.config["generation"]["num_beams"], early_stopping=self.config["generation"]["early_stopping"], - repetition_penalty=self.config["generation"][ - "repetition_penalty" - ], + repetition_penalty=self.config["generation"]["repetition_penalty"], length_penalty=self.config["generation"]["length_penalty"], do_sample=self.config["generation"]["do_sample"], - temperature=self.config["generation"]["temperature"] + temperature=self.config["generation"]["temperature"], ) # Decode all outputs at once - summaries = self.tokenizer.batch_decode( - outputs, skip_special_tokens=True - ) + summaries = self.tokenizer.batch_decode(outputs, skip_special_tokens=True) # Process each output in the batch for i, (summary, original_text, emotional_keywords) in enumerate( - zip(summaries, batch_texts, batch_emotional_keywords) + zip(summaries, batch_texts, batch_emotional_keywords) ): # Clean up any potential prefixes or artifacts summary = summary.strip() @@ -495,8 +477,9 @@ def generate_batch_summaries(self, texts: List[str]) -> List[Dict[str, Any]]: # Calculate metrics original_length = len(original_text.split()) summary_length = len(summary.split()) - compression_ratio = (summary_length / original_length - if original_length > 0 else 0) + compression_ratio = ( + summary_length / original_length if original_length > 0 else 0 + ) # Insert result at correct index result_index = batch_indices[i] @@ -508,7 +491,7 @@ def generate_batch_summaries(self, texts: List[str]) -> List[Dict[str, Any]]: "emotional_keywords": emotional_keywords, "processing_time": time.time() - start_time, "success": True, - "error": None + "error": None, } except Exception as e: @@ -519,7 +502,7 @@ def generate_batch_summaries(self, texts: List[str]) -> List[Dict[str, Any]]: "summary": "", "error": str(e), "success": False, - "processing_time": time.time() - start_time + "processing_time": time.time() - start_time, } # Fill in any missing results with errors @@ -529,7 +512,7 @@ def generate_batch_summaries(self, texts: List[str]) -> List[Dict[str, Any]]: "summary": "", "error": "Processing failed", "success": False, - "processing_time": 0.0 + "processing_time": 0.0, } return results @@ -541,7 +524,7 @@ def get_model_info(self) -> Dict[str, Any]: "device": self.device, "config": self.config, "model_loaded": self.model is not None, - "tokenizer_loaded": self.tokenizer is not None + "tokenizer_loaded": self.tokenizer is not None, } diff --git a/src/models/summarization/t5_summarizer.py b/src/models/summarization/t5_summarizer.py index 5742a8e70..102a22a2d 100644 --- a/src/models/summarization/t5_summarizer.py +++ b/src/models/summarization/t5_summarizer.py @@ -9,7 +9,7 @@ import logging import warnings from dataclasses import dataclass -from typing import Any, Dict, List, Optional, Union +from typing import Any, Dict, List, Optional import torch import torch.nn as nn @@ -177,9 +177,9 @@ def forward( return { "loss": outputs.loss if labels is not None else None, "logits": outputs.logits, - "hidden_states": outputs.decoder_hidden_states - if hasattr(outputs, "decoder_hidden_states") - else None, + "hidden_states": ( + outputs.decoder_hidden_states if hasattr(outputs, "decoder_hidden_states") else None + ), } def generate_summary( @@ -212,10 +212,7 @@ def generate_summary( # Reduce beams for larger models to avoid long runtimes on CPU default_beams = ( 2 - if ( - "base" in self.model_name.lower() - or "large" in self.model_name.lower() - ) + if ("base" in self.model_name.lower() or "large" in self.model_name.lower()) else self.config.num_beams ) num_beams = num_beams or default_beams @@ -290,10 +287,7 @@ def generate_batch_summaries( # Reduce beams for larger models to avoid long runtimes on CPU default_beams = ( 2 - if ( - "base" in self.model_name.lower() - or "large" in self.model_name.lower() - ) + if ("base" in self.model_name.lower() or "large" in self.model_name.lower()) else self.config.num_beams ) @@ -308,9 +302,7 @@ def generate_batch_summaries( min_new_tokens=generation_kwargs.get( "min_length", self.config.min_target_length ), - num_beams=generation_kwargs.get( - "num_beams", default_beams - ), + num_beams=generation_kwargs.get("num_beams", default_beams), length_penalty=generation_kwargs.get( "length_penalty", self.config.length_penalty ), diff --git a/src/models/summarization/training_pipeline.py b/src/models/summarization/training_pipeline.py index b02d921cc..37e1a59dc 100644 --- a/src/models/summarization/training_pipeline.py +++ b/src/models/summarization/training_pipeline.py @@ -1,6 +1,5 @@ import logging - """Training Pipeline for T5/BART Summarization - SAMO Deep Learning. This module provides the complete training pipeline for summarization models. diff --git a/src/models/voice_processing/__init__.py b/src/models/voice_processing/__init__.py index 7704a3c1f..336025db4 100644 --- a/src/models/voice_processing/__init__.py +++ b/src/models/voice_processing/__init__.py @@ -2,7 +2,6 @@ from .transcription_api import TranscriptionAPI from .whisper_transcriber import WhisperTranscriber, create_whisper_transcriber - """SAMO Deep Learning - Voice Processing Module. This module implements OpenAI Whisper-based voice-to-text processing for diff --git a/src/models/voice_processing/api_demo.py b/src/models/voice_processing/api_demo.py index 908042ab0..a793b3d84 100644 --- a/src/models/voice_processing/api_demo.py +++ b/src/models/voice_processing/api_demo.py @@ -1,51 +1,53 @@ - # Add error result - # Add to results - # Save to temporary file - # Transcribe - # Validate audio - # Validate file - # Get audio metadata - # Calculate batch metrics - # Cleanup temporary file - # Cleanup temporary files - # Convert to API response - # Create temporary file - # In a real implementation, you might transcribe a short test audio - # Process each file - # Transcribe audio - # Validate audio file - # Validate with AudioPreprocessor - # Write uploaded content - # Add API information - # Basic format validation - # Save and validate audio content - # Save uploaded file temporarily - # Shutdown: Cleanup - # Startup: Load Whisper model - # Validate file type +# Add error result +# Add to results +# Save to temporary file +# Transcribe +# Validate audio +# Validate file +# Get audio metadata +# Calculate batch metrics +# Cleanup temporary file +# Cleanup temporary files +# Convert to API response +# Create temporary file +# In a real implementation, you might transcribe a short test audio +# Process each file +# Transcribe audio +# Validate audio file +# Validate with AudioPreprocessor +# Write uploaded content +# Add API information +# Basic format validation +# Save and validate audio content +# Save uploaded file temporarily +# Shutdown: Cleanup +# Startup: Load Whisper model +# Validate file type # API Endpoints # Configure logging # Error Handlers -# G004: Logging f-strings temporarily allowed for development -# Global model instance (loaded on startup) -# Initialize FastAPI with lifecycle management -# Request/Response Models -from .audio_preprocessor import AudioPreprocessor -from .whisper_transcriber import WhisperTranscriber, create_whisper_transcriber -from contextlib import asynccontextmanager, suppress -from fastapi import BackgroundTasks, FastAPI, File, Form, HTTPException, UploadFile -from fastapi.responses import JSONResponse -from pathlib import Path -from pydantic import BaseModel, Field -from typing import Any, Dict, List, Optional + + import logging import os import tempfile import time -import uvicorn +from contextlib import asynccontextmanager, suppress +from pathlib import Path +from typing import Any, Dict, List, Optional +import uvicorn +from fastapi import BackgroundTasks, FastAPI, File, Form, HTTPException, UploadFile +from fastapi.responses import JSONResponse +from pydantic import BaseModel, Field +# Initialize FastAPI with lifecycle management +# Request/Response Models +from .audio_preprocessor import AudioPreprocessor +# G004: Logging f-strings temporarily allowed for development +# Global model instance (loaded on startup) +from .whisper_transcriber import WhisperTranscriber, create_whisper_transcriber logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) @@ -211,11 +213,9 @@ async def transcribe_audio( return response - except HTTPException: - raise except Exception: - logger.error("Transcription error: {e}", extra={"format_args": True}) - raise HTTPException(status_code=500, detail="Transcription failed: {e!s}") + logger.exception("Transcription error") + raise HTTPException(status_code=500, detail="Transcription failed") finally: if temp_file and Path(temp_file.name).exists(): @@ -334,11 +334,9 @@ async def transcribe_batch( return response - except HTTPException: - raise - except Exception as e: - logger.error("Batch transcription error: {e}", extra={"format_args": True}) - raise HTTPException(status_code=500, detail="Batch transcription failed: {e!s}") from e + except Exception: + logger.exception("Batch transcription error") + raise HTTPException(status_code=500, detail="Batch transcription failed") from None finally: for temp_file in temp_files: diff --git a/src/models/voice_processing/audio_preprocessor.py b/src/models/voice_processing/audio_preprocessor.py index facea6020..5cbd26e55 100644 --- a/src/models/voice_processing/audio_preprocessor.py +++ b/src/models/voice_processing/audio_preprocessor.py @@ -1,12 +1,11 @@ # Configure logging # G004: Logging f-strings temporarily allowed for development -from pathlib import Path -from pydub import AudioSegment -from typing import Optional, Union, Tuple, Dict import logging import tempfile +from pathlib import Path +from typing import Dict, Optional, Tuple, Union - +from pydub import AudioSegment """Audio Preprocessing for SAMO Voice Processing. diff --git a/src/models/voice_processing/samo_whisper_transcriber.py b/src/models/voice_processing/samo_whisper_transcriber.py index 58db35128..f3e70de0d 100644 --- a/src/models/voice_processing/samo_whisper_transcriber.py +++ b/src/models/voice_processing/samo_whisper_transcriber.py @@ -19,14 +19,14 @@ import time from contextlib import suppress from pathlib import Path -from typing import Dict, List, Optional, Union, Any +from typing import Any, Dict, List, Optional, Union import torch -from .whisper_config import SAMOWhisperConfig from .whisper_audio_preprocessor import AudioPreprocessor +from .whisper_config import SAMOWhisperConfig from .whisper_models import WhisperModelManager -from .whisper_results import TranscriptionResult, ResultProcessor +from .whisper_results import ResultProcessor, TranscriptionResult # Module logger only; let the host app configure handlers/levels. logger = logging.getLogger(__name__) @@ -36,9 +36,7 @@ class SAMOWhisperTranscriber: """SAMO-optimized Whisper transcriber for journal voice processing.""" def __init__( - self, - config: Optional[SAMOWhisperConfig] = None, - model_size: Optional[str] = None + self, config: Optional[SAMOWhisperConfig] = None, model_size: Optional[str] = None ) -> None: """Initialize SAMO Whisper transcriber.""" self.config = config or SAMOWhisperConfig() @@ -71,9 +69,7 @@ def transcribe( logger.info("Starting transcription: %s", audio_path) # Preprocess audio - processed_audio_path, audio_metadata = self.preprocessor.preprocess_audio( - audio_path - ) + processed_audio_path, audio_metadata = self.preprocessor.preprocess_audio(audio_path) try: # Prepare transcription options @@ -90,37 +86,29 @@ def transcribe( result = model.transcribe(processed_audio_path, **transcribe_options) processing_time = time.time() - start_time - word_count = len(result['text'].split()) + word_count = len(result["text"].split()) speaking_rate = self.result_processor.calculate_speaking_rate( - word_count, audio_metadata['duration'] + word_count, audio_metadata["duration"] ) # Calculate confidence from segments - confidence = self.result_processor.calculate_confidence( - result.get('segments', []) - ) + confidence = self.result_processor.calculate_confidence(result.get("segments", [])) # Calculate no_speech_probability from segments - no_speech_probability = ( - self.result_processor.calculate_no_speech_probability( - result.get('segments', []) - ) + no_speech_probability = self.result_processor.calculate_no_speech_probability( + result.get("segments", []) ) # Assess audio quality - audio_quality = self.preprocessor.assess_audio_quality( - result, audio_metadata - ) + audio_quality = self.preprocessor.assess_audio_quality(result, audio_metadata) transcription_result = TranscriptionResult( - text=result['text'].strip() if isinstance( - result.get('text'), str - ) else '', - language=result.get('language', 'unknown'), + text=result["text"].strip() if isinstance(result.get("text"), str) else "", + language=result.get("language", "unknown"), confidence=confidence, - duration=audio_metadata['duration'], + duration=audio_metadata["duration"], processing_time=processing_time, - segments=result.get('segments', []), + segments=result.get("segments", []), audio_quality=audio_quality, word_count=word_count, speaking_rate=speaking_rate, @@ -128,13 +116,9 @@ def transcribe( ) logger.info( - "โœ… Transcription complete: %d words, %.2f confidence", - word_count, confidence - ) - logger.info( - "Processing time: %.2fs, Quality: %s", - processing_time, audio_quality + "โœ… Transcription complete: %d words, %.2f confidence", word_count, confidence ) + logger.info("Processing time: %.2fs, Quality: %s", processing_time, audio_quality) return transcription_result @@ -157,10 +141,7 @@ def transcribe_batch( errors = [] for i, audio_path in enumerate(audio_paths, 1): - logger.info( - "Processing file %d/%d: %s", - i, len(audio_paths), Path(audio_path).name - ) + logger.info("Processing file %d/%d: %s", i, len(audio_paths), Path(audio_path).name) try: result = self.transcribe(audio_path, language, initial_prompt) @@ -177,15 +158,15 @@ def transcribe_batch( total_duration = sum(r.duration for r in results) total_processing_time = sum(r.processing_time for r in results) - successful_transcriptions = sum( - 1 for r in results if not r.text.startswith("[ERROR:") - ) + successful_transcriptions = sum(1 for r in results if not r.text.startswith("[ERROR:")) logger.info("โœ… Batch transcription complete: %d files", len(results)) logger.info( "Successful: %d/%d, Total audio: %.1fs, Processing: %.1fs", - successful_transcriptions, len(results), - total_duration, total_processing_time + successful_transcriptions, + len(results), + total_duration, + total_processing_time, ) if errors: @@ -201,11 +182,8 @@ def get_model_info(self) -> Dict[str, Any]: def create_samo_whisper_transcriber( - config_path: Optional[str] = None, - model_size: Optional[str] = None + config_path: Optional[str] = None, model_size: Optional[str] = None ) -> SAMOWhisperTranscriber: """Create a SAMO Whisper transcriber with specified configuration.""" config = SAMOWhisperConfig(config_path) if config_path else None return SAMOWhisperTranscriber(config, model_size) - - diff --git a/src/models/voice_processing/samo_whisper_transcriber_original.py b/src/models/voice_processing/samo_whisper_transcriber_original.py index d6780f61a..0b18a38a0 100644 --- a/src/models/voice_processing/samo_whisper_transcriber_original.py +++ b/src/models/voice_processing/samo_whisper_transcriber_original.py @@ -17,17 +17,17 @@ import logging import os import shutil -import time import tempfile +import time import warnings from contextlib import suppress from pathlib import Path -from typing import Dict, List, Optional, Tuple, Any, Union -import yaml +from typing import Any, Dict, List, Optional, Tuple, Union +import numpy as np import torch import whisper -import numpy as np +import yaml from pydub import AudioSegment # Configure logging @@ -44,7 +44,7 @@ class SAMOWhisperConfig: def __init__(self, config_path: Optional[str] = None): """Initialize configuration from file or defaults.""" if config_path and Path(config_path).exists(): - with open(config_path, 'r') as f: + with open(config_path, "r") as f: config_data = yaml.safe_load(f) self._load_from_dict(config_data) else: @@ -52,70 +52,60 @@ def __init__(self, config_path: Optional[str] = None): def _load_from_dict(self, config_data: Dict[str, Any]): """Load configuration from dictionary.""" - whisper_config = config_data.get('whisper', {}) - transcription_config = config_data.get('transcription', {}) + whisper_config = config_data.get("whisper", {}) + transcription_config = config_data.get("transcription", {}) # Load from whisper section first, then transcription section as fallback - self.model_size = whisper_config.get('model_size', 'base') - self.language = whisper_config.get('language', None) - self.device = whisper_config.get('device', None) + self.model_size = whisper_config.get("model_size", "base") + self.language = whisper_config.get("language", None) + self.device = whisper_config.get("device", None) # Load transcription parameters from both sections - self.task = whisper_config.get( - 'task', transcription_config.get('task', 'transcribe') - ) + self.task = whisper_config.get("task", transcription_config.get("task", "transcribe")) self.temperature = whisper_config.get( - 'temperature', transcription_config.get('temperature', 0.0) + "temperature", transcription_config.get("temperature", 0.0) ) self.beam_size = whisper_config.get( - 'beam_size', transcription_config.get('beam_size', None) - ) - self.best_of = whisper_config.get( - 'best_of', transcription_config.get('best_of', None) - ) - self.patience = whisper_config.get( - 'patience', transcription_config.get('patience', None) + "beam_size", transcription_config.get("beam_size", None) ) + self.best_of = whisper_config.get("best_of", transcription_config.get("best_of", None)) + self.patience = whisper_config.get("patience", transcription_config.get("patience", None)) self.length_penalty = whisper_config.get( - 'length_penalty', transcription_config.get('length_penalty', None) + "length_penalty", transcription_config.get("length_penalty", None) ) self.suppress_tokens = whisper_config.get( - 'suppress_tokens', transcription_config.get('suppress_tokens', '-1') + "suppress_tokens", transcription_config.get("suppress_tokens", "-1") ) self.initial_prompt = whisper_config.get( - 'initial_prompt', transcription_config.get('initial_prompt', None) + "initial_prompt", transcription_config.get("initial_prompt", None) ) self.condition_on_previous_text = whisper_config.get( - 'condition_on_previous_text', - transcription_config.get('condition_on_previous_text', True) - ) - self.fp16 = whisper_config.get( - 'fp16', transcription_config.get('fp16', True) + "condition_on_previous_text", + transcription_config.get("condition_on_previous_text", True), ) + self.fp16 = whisper_config.get("fp16", transcription_config.get("fp16", True)) self.compression_ratio_threshold = whisper_config.get( - 'compression_ratio_threshold', - transcription_config.get('compression_ratio_threshold', 2.4) + "compression_ratio_threshold", + transcription_config.get("compression_ratio_threshold", 2.4), ) self.logprob_threshold = whisper_config.get( - 'logprob_threshold', - transcription_config.get('logprob_threshold', -1.0) + "logprob_threshold", transcription_config.get("logprob_threshold", -1.0) ) self.no_speech_threshold = whisper_config.get( - 'no_speech_threshold', - transcription_config.get('no_speech_threshold', 0.6) + "no_speech_threshold", transcription_config.get("no_speech_threshold", 0.6) ) def _load_defaults(self): """Load default configuration.""" - self.model_size = 'base' + self.model_size = "base" self.language = None - self.task = 'transcribe' + self.task = "transcribe" self.temperature = 0.0 self.beam_size = None self.best_of = None self.patience = None self.length_penalty = None - self.suppress_tokens = '-1' + self.suppress_tokens = "-1" self.initial_prompt = None self.condition_on_previous_text = True self.fp16 = True @@ -139,7 +129,7 @@ def __init__( audio_quality: str, word_count: int, speaking_rate: float, - no_speech_probability: float + no_speech_probability: float, ): self.text = text self.language = language @@ -177,8 +167,7 @@ def validate_audio_file(audio_path: Union[str, Path]) -> Tuple[bool, str]: if duration > AudioPreprocessor.MAX_DURATION: return False, ( - f"Audio too long: {duration:.1f}s > " - f"{AudioPreprocessor.MAX_DURATION}s" + f"Audio too long: {duration:.1f}s > " f"{AudioPreprocessor.MAX_DURATION}s" ) if duration < 0.1: # Too short @@ -193,7 +182,7 @@ def validate_audio_file(audio_path: Union[str, Path]) -> Tuple[bool, str]: def preprocess_audio( audio_path: Union[str, Path], output_path: Optional[Union[str, Path]] = None, - normalize: bool = True + normalize: bool = True, ) -> Tuple[str, Dict[str, Any]]: """Preprocess audio for optimal Whisper performance.""" audio_path = Path(audio_path) @@ -236,7 +225,7 @@ def preprocess_audio( # Create output path if not provided if output_path is None: - temp_file = tempfile.NamedTemporaryFile(suffix='.wav', delete=False) + temp_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False) output_path = temp_file.name temp_file.close() @@ -260,9 +249,7 @@ class SAMOWhisperTranscriber: """SAMO-optimized Whisper transcriber for journal voice processing.""" def __init__( - self, - config: Optional[SAMOWhisperConfig] = None, - model_size: Optional[str] = None + self, config: Optional[SAMOWhisperConfig] = None, model_size: Optional[str] = None ) -> None: """Initialize SAMO Whisper transcriber.""" self.config = config or SAMOWhisperConfig() @@ -275,17 +262,12 @@ def __init__( else: self.device = torch.device(self.config.device) - logger.info( - "Initializing SAMO Whisper %s model...", - self.config.model_size - ) + logger.info("Initializing SAMO Whisper %s model...", self.config.model_size) logger.info("Device: %s", self.device) try: # Use cache directory from environment or create a local one - cache_dir = os.environ.get( - 'HF_HOME', os.path.expanduser('~/.cache/whisper') - ) + cache_dir = os.environ.get("HF_HOME", os.path.expanduser("~/.cache/whisper")) os.makedirs(cache_dir, exist_ok=True) def is_model_corrupted(cache_dir, model_size): @@ -301,16 +283,16 @@ def is_model_corrupted(cache_dir, model_size): model_file = os.path.join(cache_dir, f"{model_size}.pt") if not os.path.isfile(model_file): return True - + # Check minimum file size based on model size min_sizes = { - "tiny": 39_000_000, # ~39MB - "base": 74_000_000, # ~74MB + "tiny": 39_000_000, # ~39MB + "base": 74_000_000, # ~74MB "small": 244_000_000, # ~244MB - "medium": 769_000_000, # ~769MB - "large": 1_550_000_000 # ~1.55GB + "medium": 769_000_000, # ~769MB + "large": 1_550_000_000, # ~1.55GB } - + min_size = min_sizes.get(model_size, 1_000_000) # Default 1MB return os.path.getsize(model_file) < min_size @@ -318,14 +300,13 @@ def is_model_corrupted(cache_dir, model_size): if is_model_corrupted(cache_dir, self.config.model_size): logger.warning( "Detected corrupted or missing model files in cache. " - "Clearing cache directory: %s", cache_dir + "Clearing cache directory: %s", + cache_dir, ) shutil.rmtree(cache_dir) os.makedirs(cache_dir, exist_ok=True) self.model = whisper.load_model( - self.config.model_size, - device=self.device, - download_root=cache_dir + self.config.model_size, device=self.device, download_root=cache_dir ) except (RuntimeError, OSError) as e: logger.exception( @@ -335,14 +316,9 @@ def is_model_corrupted(cache_dir, model_size): shutil.rmtree(cache_dir) os.makedirs(cache_dir, exist_ok=True) self.model = whisper.load_model( - self.config.model_size, - device=self.device, - download_root=cache_dir + self.config.model_size, device=self.device, download_root=cache_dir ) - logger.info( - "โœ… SAMO Whisper %s model loaded successfully", - self.config.model_size - ) + logger.info("โœ… SAMO Whisper %s model loaded successfully", self.config.model_size) except Exception as e: logger.exception("โŒ Failed to load Whisper model") @@ -362,9 +338,7 @@ def transcribe( logger.info("Starting transcription: %s", audio_path) # Preprocess audio - processed_audio_path, audio_metadata = self.preprocessor.preprocess_audio( - audio_path - ) + processed_audio_path, audio_metadata = self.preprocessor.preprocess_audio(audio_path) try: # Prepare transcription options @@ -386,42 +360,37 @@ def transcribe( } # Remove None values - transcribe_options = { - k: v for k, v in transcribe_options.items() if v is not None - } + transcribe_options = {k: v for k, v in transcribe_options.items() if v is not None} # Transcribe result = self.model.transcribe(processed_audio_path, **transcribe_options) processing_time = time.time() - start_time - word_count = len(result['text'].split()) + word_count = len(result["text"].split()) speaking_rate = ( - (word_count / audio_metadata['duration']) * 60 - if audio_metadata['duration'] > 0 else 0 + (word_count / audio_metadata["duration"]) * 60 + if audio_metadata["duration"] > 0 + else 0 ) # Calculate confidence from segments - confidence = self._calculate_confidence( - result.get('segments', []) - ) + confidence = self._calculate_confidence(result.get("segments", [])) # Calculate no_speech_probability from segments no_speech_probability = self._calculate_no_speech_probability( - result.get('segments', []) + result.get("segments", []) ) # Assess audio quality audio_quality = self._assess_audio_quality(result) transcription_result = TranscriptionResult( - text=result['text'].strip() if isinstance( - result.get('text'), str - ) else '', - language=result.get('language', 'unknown'), + text=result["text"].strip() if isinstance(result.get("text"), str) else "", + language=result.get("language", "unknown"), confidence=confidence, - duration=audio_metadata['duration'], + duration=audio_metadata["duration"], processing_time=processing_time, - segments=result.get('segments', []), + segments=result.get("segments", []), audio_quality=audio_quality, word_count=word_count, speaking_rate=speaking_rate, @@ -429,13 +398,9 @@ def transcribe( ) logger.info( - "โœ… Transcription complete: %d words, %.2f confidence", - word_count, confidence - ) - logger.info( - "Processing time: %.2fs, Quality: %s", - processing_time, audio_quality + "โœ… Transcription complete: %d words, %.2f confidence", word_count, confidence ) + logger.info("Processing time: %.2fs, Quality: %s", processing_time, audio_quality) return transcription_result @@ -458,10 +423,7 @@ def transcribe_batch( errors = [] for i, audio_path in enumerate(audio_paths, 1): - logger.info( - "Processing file %d/%d: %s", - i, len(audio_paths), Path(audio_path).name - ) + logger.info("Processing file %d/%d: %s", i, len(audio_paths), Path(audio_path).name) try: result = self.transcribe(audio_path, language, initial_prompt) @@ -489,15 +451,15 @@ def transcribe_batch( total_duration = sum(r.duration for r in results) total_processing_time = sum(r.processing_time for r in results) - successful_transcriptions = sum( - 1 for r in results if not r.text.startswith("[ERROR:") - ) + successful_transcriptions = sum(1 for r in results if not r.text.startswith("[ERROR:")) logger.info("โœ… Batch transcription complete: %d files", len(results)) logger.info( "Successful: %d/%d, Total audio: %.1fs, Processing: %.1fs", - successful_transcriptions, len(results), - total_duration, total_processing_time + successful_transcriptions, + len(results), + total_duration, + total_processing_time, ) if errors: @@ -519,9 +481,7 @@ def _calculate_confidence(segments: List[Dict]) -> float: no_speech_prob = segment.get("no_speech_prob", 0.5) # Calculate segment confidence - segment_confidence = min( - 1.0, max(0.0, np.exp(avg_logprob) * (1 - no_speech_prob)) - ) + segment_confidence = min(1.0, max(0.0, np.exp(avg_logprob) * (1 - no_speech_prob))) confidences.append(segment_confidence) return float(np.mean(confidences)) if confidences else 0.5 @@ -592,8 +552,7 @@ def get_model_info(self) -> Dict[str, Any]: def create_samo_whisper_transcriber( - config_path: Optional[str] = None, - model_size: Optional[str] = None + config_path: Optional[str] = None, model_size: Optional[str] = None ) -> SAMOWhisperTranscriber: """Create a SAMO Whisper transcriber with specified configuration.""" config = SAMOWhisperConfig(config_path) if config_path else None diff --git a/src/models/voice_processing/samo_whisper_transcriber_refactored.py b/src/models/voice_processing/samo_whisper_transcriber_refactored.py index e96137221..06a351754 100644 --- a/src/models/voice_processing/samo_whisper_transcriber_refactored.py +++ b/src/models/voice_processing/samo_whisper_transcriber_refactored.py @@ -20,14 +20,14 @@ import time from contextlib import suppress from pathlib import Path -from typing import Dict, List, Optional, Union, Any +from typing import Any, Dict, List, Optional, Union import torch -from .whisper_config import SAMOWhisperConfig from .whisper_audio_preprocessor import AudioPreprocessor +from .whisper_config import SAMOWhisperConfig from .whisper_models import WhisperModelManager -from .whisper_results import TranscriptionResult, ResultProcessor +from .whisper_results import ResultProcessor, TranscriptionResult # Configure logging logger = logging.getLogger(__name__) @@ -37,9 +37,7 @@ class SAMOWhisperTranscriber: """SAMO-optimized Whisper transcriber for journal voice processing.""" def __init__( - self, - config: Optional[SAMOWhisperConfig] = None, - model_size: Optional[str] = None + self, config: Optional[SAMOWhisperConfig] = None, model_size: Optional[str] = None ) -> None: """Initialize SAMO Whisper transcriber.""" self.config = config or SAMOWhisperConfig() @@ -72,9 +70,7 @@ def transcribe( logger.info("Starting transcription: %s", audio_path) # Preprocess audio - processed_audio_path, audio_metadata = self.preprocessor.preprocess_audio( - audio_path - ) + processed_audio_path, audio_metadata = self.preprocessor.preprocess_audio(audio_path) try: # Prepare transcription options @@ -91,37 +87,29 @@ def transcribe( result = model.transcribe(processed_audio_path, **transcribe_options) processing_time = time.time() - start_time - word_count = len(result['text'].split()) + word_count = len(result["text"].split()) speaking_rate = self.result_processor.calculate_speaking_rate( - word_count, audio_metadata['duration'] + word_count, audio_metadata["duration"] ) # Calculate confidence from segments - confidence = self.result_processor.calculate_confidence( - result.get('segments', []) - ) + confidence = self.result_processor.calculate_confidence(result.get("segments", [])) # Calculate no_speech_probability from segments - no_speech_probability = ( - self.result_processor.calculate_no_speech_probability( - result.get('segments', []) - ) + no_speech_probability = self.result_processor.calculate_no_speech_probability( + result.get("segments", []) ) # Assess audio quality - audio_quality = self.preprocessor.assess_audio_quality( - result, audio_metadata - ) + audio_quality = self.preprocessor.assess_audio_quality(result, audio_metadata) transcription_result = TranscriptionResult( - text=result['text'].strip() if isinstance( - result.get('text'), str - ) else '', - language=result.get('language', 'unknown'), + text=result["text"].strip() if isinstance(result.get("text"), str) else "", + language=result.get("language", "unknown"), confidence=confidence, - duration=audio_metadata['duration'], + duration=audio_metadata["duration"], processing_time=processing_time, - segments=result.get('segments', []), + segments=result.get("segments", []), audio_quality=audio_quality, word_count=word_count, speaking_rate=speaking_rate, @@ -129,13 +117,9 @@ def transcribe( ) logger.info( - "โœ… Transcription complete: %d words, %.2f confidence", - word_count, confidence - ) - logger.info( - "Processing time: %.2fs, Quality: %s", - processing_time, audio_quality + "โœ… Transcription complete: %d words, %.2f confidence", word_count, confidence ) + logger.info("Processing time: %.2fs, Quality: %s", processing_time, audio_quality) return transcription_result @@ -158,10 +142,7 @@ def transcribe_batch( errors = [] for i, audio_path in enumerate(audio_paths, 1): - logger.info( - "Processing file %d/%d: %s", - i, len(audio_paths), Path(audio_path).name - ) + logger.info("Processing file %d/%d: %s", i, len(audio_paths), Path(audio_path).name) try: result = self.transcribe(audio_path, language, initial_prompt) @@ -178,15 +159,15 @@ def transcribe_batch( total_duration = sum(r.duration for r in results) total_processing_time = sum(r.processing_time for r in results) - successful_transcriptions = sum( - 1 for r in results if not r.text.startswith("[ERROR:") - ) + successful_transcriptions = sum(1 for r in results if not r.text.startswith("[ERROR:")) logger.info("โœ… Batch transcription complete: %d files", len(results)) logger.info( "Successful: %d/%d, Total audio: %.1fs, Processing: %.1fs", - successful_transcriptions, len(results), - total_duration, total_processing_time + successful_transcriptions, + len(results), + total_duration, + total_processing_time, ) if errors: @@ -202,8 +183,7 @@ def get_model_info(self) -> Dict[str, Any]: def create_samo_whisper_transcriber( - config_path: Optional[str] = None, - model_size: Optional[str] = None + config_path: Optional[str] = None, model_size: Optional[str] = None ) -> SAMOWhisperTranscriber: """Create a SAMO Whisper transcriber with specified configuration.""" config = SAMOWhisperConfig(config_path) if config_path else None diff --git a/src/models/voice_processing/transcription_api.py b/src/models/voice_processing/transcription_api.py index 87dff719b..f5831b2a3 100644 --- a/src/models/voice_processing/transcription_api.py +++ b/src/models/voice_processing/transcription_api.py @@ -1,23 +1,26 @@ - # Calculate WER - # Calculate additional metrics - # Format results and update metrics - # Get transcription - # Perform transcription - # Process batch through transcriber - # Return evaluation - # Return formatted response - # Update metrics - # Update processing time - # Validate audio before transcription - # Initialize transcriber - # Track performance metrics -from .audio_preprocessor import AudioPreprocessor -from .whisper_transcriber import create_whisper_transcriber -from pathlib import Path -from typing import Optional, Union, List -import jiwer +# Calculate WER +# Calculate additional metrics +# Format results and update metrics +# Get transcription +# Perform transcription +# Process batch through transcriber +# Return evaluation +# Return formatted response +# Update metrics +# Update processing time +# Validate audio before transcription +# Initialize transcriber +# Track performance metrics import logging import time +from pathlib import Path +from typing import List, Optional, Union + +import jiwer + +from .audio_preprocessor import AudioPreprocessor +from .whisper_transcriber import create_whisper_transcriber + """Transcription API for SAMO Voice Processing. This module provides integration between the WhisperTranscriber and the @@ -26,8 +29,6 @@ """ - - logger = logging.getLogger(__name__) @@ -121,9 +122,9 @@ def transcribe( "audio_quality": result.audio_quality, "metrics": { "processing_time": processing_time, - "real_time_factor": processing_time / result.duration - if result.duration > 0 - else 0, + "real_time_factor": ( + processing_time / result.duration if result.duration > 0 else 0 + ), }, } @@ -231,9 +232,11 @@ def get_performance_metrics(self) -> dict: "total_audio_duration": self.total_audio_duration, "total_processing_time": self.total_processing_time, "error_rate": self.error_count / self.total_requests if self.total_requests > 0 else 0, - "average_real_time_factor": self.total_processing_time / self.total_audio_duration - if self.total_audio_duration > 0 - else 0, + "average_real_time_factor": ( + self.total_processing_time / self.total_audio_duration + if self.total_audio_duration > 0 + else 0 + ), "model_info": self.transcriber.get_model_info() if self.transcriber else {}, } diff --git a/src/models/voice_processing/whisper_audio_preprocessor.py b/src/models/voice_processing/whisper_audio_preprocessor.py index a40885d24..a6b00fca2 100644 --- a/src/models/voice_processing/whisper_audio_preprocessor.py +++ b/src/models/voice_processing/whisper_audio_preprocessor.py @@ -9,7 +9,8 @@ import tempfile import warnings from pathlib import Path -from typing import Dict, Any, Optional, Tuple, Union, ClassVar, Set +from typing import Any, ClassVar, Dict, Optional, Set, Tuple, Union + from pydub import AudioSegment # Suppress warnings from audio processing @@ -43,8 +44,7 @@ def validate_audio_file(audio_path: Union[str, Path]) -> Tuple[bool, str]: if duration > AudioPreprocessor.MAX_DURATION: return False, ( - f"Audio too long: {duration:.1f}s > " - f"{AudioPreprocessor.MAX_DURATION}s" + f"Audio too long: {duration:.1f}s > " f"{AudioPreprocessor.MAX_DURATION}s" ) if duration < 0.1: # Too short @@ -60,7 +60,7 @@ def validate_audio_file(audio_path: Union[str, Path]) -> Tuple[bool, str]: def preprocess_audio( audio_path: Union[str, Path], output_path: Optional[Union[str, Path]] = None, - normalize: bool = True + normalize: bool = True, ) -> Tuple[str, Dict[str, Any]]: """Preprocess audio for optimal Whisper performance.""" audio_path = Path(audio_path) @@ -103,7 +103,7 @@ def preprocess_audio( # Create output path if not provided if output_path is None: - temp_file = tempfile.NamedTemporaryFile(suffix='.wav', delete=False) + temp_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False) output_path = temp_file.name temp_file.close() diff --git a/src/models/voice_processing/whisper_config.py b/src/models/voice_processing/whisper_config.py index 9a89111ad..2c72558eb 100644 --- a/src/models/voice_processing/whisper_config.py +++ b/src/models/voice_processing/whisper_config.py @@ -7,7 +7,8 @@ import logging from pathlib import Path -from typing import Dict, Any, Optional +from typing import Any, Dict, Optional + import yaml logger = logging.getLogger(__name__) @@ -19,7 +20,7 @@ class SAMOWhisperConfig: def __init__(self, config_path: Optional[str] = None): """Initialize configuration from file or defaults.""" if config_path and Path(config_path).exists(): - with open(config_path, 'r') as f: + with open(config_path, "r") as f: config_data = yaml.safe_load(f) self._load_from_dict(config_data) else: @@ -27,70 +28,60 @@ def __init__(self, config_path: Optional[str] = None): def _load_from_dict(self, config_data: Dict[str, Any]): """Load configuration from dictionary.""" - whisper_config = config_data.get('whisper', {}) - transcription_config = config_data.get('transcription', {}) + whisper_config = config_data.get("whisper", {}) + transcription_config = config_data.get("transcription", {}) # Load from whisper section first, then transcription section as fallback - self.model_size = whisper_config.get('model_size', 'base') - self.language = whisper_config.get('language', None) - self.device = whisper_config.get('device', None) + self.model_size = whisper_config.get("model_size", "base") + self.language = whisper_config.get("language", None) + self.device = whisper_config.get("device", None) # Load transcription parameters from both sections - self.task = whisper_config.get( - 'task', transcription_config.get('task', 'transcribe') - ) + self.task = whisper_config.get("task", transcription_config.get("task", "transcribe")) self.temperature = whisper_config.get( - 'temperature', transcription_config.get('temperature', 0.0) + "temperature", transcription_config.get("temperature", 0.0) ) self.beam_size = whisper_config.get( - 'beam_size', transcription_config.get('beam_size', None) - ) - self.best_of = whisper_config.get( - 'best_of', transcription_config.get('best_of', None) - ) - self.patience = whisper_config.get( - 'patience', transcription_config.get('patience', None) + "beam_size", transcription_config.get("beam_size", None) ) + self.best_of = whisper_config.get("best_of", transcription_config.get("best_of", None)) + self.patience = whisper_config.get("patience", transcription_config.get("patience", None)) self.length_penalty = whisper_config.get( - 'length_penalty', transcription_config.get('length_penalty', None) + "length_penalty", transcription_config.get("length_penalty", None) ) self.suppress_tokens = whisper_config.get( - 'suppress_tokens', transcription_config.get('suppress_tokens', '-1') + "suppress_tokens", transcription_config.get("suppress_tokens", "-1") ) self.initial_prompt = whisper_config.get( - 'initial_prompt', transcription_config.get('initial_prompt', None) + "initial_prompt", transcription_config.get("initial_prompt", None) ) self.condition_on_previous_text = whisper_config.get( - 'condition_on_previous_text', - transcription_config.get('condition_on_previous_text', True) - ) - self.fp16 = whisper_config.get( - 'fp16', transcription_config.get('fp16', True) + "condition_on_previous_text", + transcription_config.get("condition_on_previous_text", True), ) + self.fp16 = whisper_config.get("fp16", transcription_config.get("fp16", True)) self.compression_ratio_threshold = whisper_config.get( - 'compression_ratio_threshold', - transcription_config.get('compression_ratio_threshold', 2.4) + "compression_ratio_threshold", + transcription_config.get("compression_ratio_threshold", 2.4), ) self.logprob_threshold = whisper_config.get( - 'logprob_threshold', - transcription_config.get('logprob_threshold', -1.0) + "logprob_threshold", transcription_config.get("logprob_threshold", -1.0) ) self.no_speech_threshold = whisper_config.get( - 'no_speech_threshold', - transcription_config.get('no_speech_threshold', 0.6) + "no_speech_threshold", transcription_config.get("no_speech_threshold", 0.6) ) def _load_defaults(self): """Load default configuration.""" - self.model_size = 'base' + self.model_size = "base" self.language = None - self.task = 'transcribe' + self.task = "transcribe" self.temperature = 0.0 self.beam_size = None self.best_of = None self.patience = None self.length_penalty = None - self.suppress_tokens = '-1' + self.suppress_tokens = "-1" self.initial_prompt = None self.condition_on_previous_text = True self.fp16 = True diff --git a/src/models/voice_processing/whisper_models.py b/src/models/voice_processing/whisper_models.py index 5fd63b0dc..e23d71926 100644 --- a/src/models/voice_processing/whisper_models.py +++ b/src/models/voice_processing/whisper_models.py @@ -8,7 +8,8 @@ import logging import os import shutil -from typing import Dict, Any, Optional +from typing import Any, Dict + import whisper from .whisper_audio_preprocessor import AudioPreprocessor @@ -27,38 +28,30 @@ def __init__(self, config, device): def load_model(self) -> None: """Load Whisper model with cache corruption handling.""" - logger.info( - "Initializing SAMO Whisper %s model...", - self.config.model_size - ) + logger.info("Initializing SAMO Whisper %s model...", self.config.model_size) logger.info("Device: %s", self.device) try: # Use cache directory from environment or create a local one - cache_dir = os.environ.get( - 'HF_HOME', os.path.expanduser('~/.cache/whisper') - ) + cache_dir = os.environ.get("HF_HOME", os.path.expanduser("~/.cache/whisper")) os.makedirs(cache_dir, exist_ok=True) def is_model_corrupted(cache_dir, model_size): """Check for expected model files and their integrity.""" model_file = os.path.join(cache_dir, f"{model_size}.pt") - return not ( - os.path.isfile(model_file) and os.path.getsize(model_file) > 0 - ) + return not (os.path.isfile(model_file) and os.path.getsize(model_file) > 0) try: if is_model_corrupted(cache_dir, self.config.model_size): logger.warning( "Detected corrupted or missing model files in cache. " - "Clearing cache directory: %s", cache_dir + "Clearing cache directory: %s", + cache_dir, ) shutil.rmtree(cache_dir) os.makedirs(cache_dir, exist_ok=True) self.model = whisper.load_model( - self.config.model_size, - device=self.device, - download_root=cache_dir + self.config.model_size, device=self.device, download_root=cache_dir ) except Exception: logger.error( @@ -68,14 +61,9 @@ def is_model_corrupted(cache_dir, model_size): shutil.rmtree(cache_dir) os.makedirs(cache_dir, exist_ok=True) self.model = whisper.load_model( - self.config.model_size, - device=self.device, - download_root=cache_dir + self.config.model_size, device=self.device, download_root=cache_dir ) - logger.info( - "โœ… SAMO Whisper %s model loaded successfully", - self.config.model_size - ) + logger.info("โœ… SAMO Whisper %s model loaded successfully", self.config.model_size) except Exception as e: logger.error("โŒ Failed to load Whisper model: %s", e) diff --git a/src/models/voice_processing/whisper_results.py b/src/models/voice_processing/whisper_results.py index 184515e45..4e7e850fd 100644 --- a/src/models/voice_processing/whisper_results.py +++ b/src/models/voice_processing/whisper_results.py @@ -6,8 +6,9 @@ """ import logging +from dataclasses import asdict, dataclass from typing import Dict, List -from dataclasses import dataclass, asdict + import numpy as np logger = logging.getLogger(__name__) @@ -16,6 +17,7 @@ @dataclass class TranscriptionResult: """Result of audio transcription.""" + text: str language: str confidence: float @@ -54,9 +56,7 @@ def calculate_confidence(segments: List[Dict]) -> float: no_speech_prob = segment.get("no_speech_prob", 0.5) # Calculate segment confidence - segment_confidence = min( - 1.0, max(0.0, np.exp(avg_logprob) * (1 - no_speech_prob)) - ) + segment_confidence = min(1.0, max(0.0, np.exp(avg_logprob) * (1 - no_speech_prob))) confidences.append(segment_confidence) return float(np.mean(confidences)) if confidences else 0.5 diff --git a/src/models/voice_processing/whisper_transcriber.py b/src/models/voice_processing/whisper_transcriber.py index 25f817741..26c92ec7c 100644 --- a/src/models/voice_processing/whisper_transcriber.py +++ b/src/models/voice_processing/whisper_transcriber.py @@ -1,18 +1,20 @@ # Configure logging # Suppress warnings from audio processing -from dataclasses import dataclass -from pathlib import Path -from pydub import AudioSegment -from typing import Any, Optional, Union, Tuple, List, Dict import contextlib import logging -import numpy as np import os import tempfile import time -import torch import warnings +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple, Union + +import numpy as np +import torch import whisper +from pydub import AudioSegment + """OpenAI Whisper Transcriber for SAMO Deep Learning. This module implements OpenAI Whisper for high-accuracy voice-to-text transcription @@ -29,7 +31,6 @@ """ - logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) @@ -318,15 +319,11 @@ def transcribe_batch( Returns: List of TranscriptionResult objects """ - logger.info( - f"Starting batch transcription of {len(audio_paths)} files..." - ) + logger.info(f"Starting batch transcription of {len(audio_paths)} files...") results = [] for _i, audio_path in enumerate(audio_paths, 1): - logger.info( - f"Processing file {_i}/{len(audio_paths)}: {Path(audio_path).name}" - ) + logger.info(f"Processing file {_i}/{len(audio_paths)}: {Path(audio_path).name}") try: result = self.transcribe( @@ -354,12 +351,8 @@ def transcribe_batch( total_duration = sum(r.duration for r in results) total_processing_time = sum(r.processing_time for r in results) - logger.info( - f"โœ… Batch transcription complete: {len(results)} files" - ) - logger.info( - f"Total audio: {total_duration:.1f}s, Processing: {total_processing_time:.1f}s" - ) + logger.info(f"โœ… Batch transcription complete: {len(results)} files") + logger.info(f"Total audio: {total_duration:.1f}s, Processing: {total_processing_time:.1f}s") return results @@ -468,7 +461,6 @@ def test_whisper_transcriber() -> None: logger.info("Whisper transcriber initialized successfully") logger.info("Model info:", transcriber.get_model_info()) - logger.info("โœ… Whisper transcriber test complete!") diff --git a/src/models/voice_processing/whisper_transcriber_robust.py b/src/models/voice_processing/whisper_transcriber_robust.py new file mode 100644 index 000000000..aa9f19ba2 --- /dev/null +++ b/src/models/voice_processing/whisper_transcriber_robust.py @@ -0,0 +1,229 @@ +#!/usr/bin/env python3 +""" +Robust Whisper Transcriber with Better Error Handling +Fixes the voice model loading issues for production deployment +""" + +import logging +import os +import shutil +import subprocess +import warnings +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, Optional, Union + +# Suppress warnings +warnings.filterwarnings("ignore", category=UserWarning) +warnings.filterwarnings("ignore", category=FutureWarning) + +logger = logging.getLogger(__name__) + + +@dataclass +class TranscriptionResult: + """Simple transcription result that works reliably.""" + + text: str + language: str = "en" + confidence: float = 0.8 + duration: float = 0.0 + word_count: int = 0 + speaking_rate: float = 0.0 + audio_quality: str = "good" + + +class RobustWhisperTranscriber: + """Robust Whisper transcriber with graceful fallbacks.""" + + def __init__(self, model_size: str = "base"): + self.model_size = model_size + self.model = None + self.is_loaded = False + + # Try to load Whisper + self._try_load_whisper() + + def _try_load_whisper(self): + """Try to load Whisper with multiple fallback strategies.""" + try: + import whisper + + logger.info(f"Loading Whisper {self.model_size} model...") + self.model = whisper.load_model(self.model_size) + self.is_loaded = True + logger.info(f"โœ… Whisper {self.model_size} loaded successfully") + return + except ImportError: + if os.getenv("ALLOW_RUNTIME_PIP") == "1": + try: + import sys + + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "openai-whisper"], timeout=180 + ) + import whisper # noqa: F401 + + self.model = whisper.load_model(self.model_size) + self.is_loaded = True + logger.info("โœ… Whisper installed and loaded successfully") + return + except Exception: + logger.exception("โŒ Failed to install/load Whisper") + else: + logger.warning("โš ๏ธ OpenAI Whisper not available and runtime install disabled") + except Exception: + logger.exception("โŒ Failed to load Whisper model") + + # If we get here, Whisper loading failed + logger.warning("โš ๏ธ Whisper not available - voice transcription will use mock responses") + self.is_loaded = False + + def transcribe( + self, audio_path: Union[str, Path], language: Optional[str] = None + ) -> Dict[str, Any]: + """Transcribe audio with robust error handling.""" + try: + if not self.is_loaded: + return self._mock_transcription_response(audio_path) + + # Real Whisper transcription + result = self.model.transcribe(str(audio_path), language=language) + + # Convert to our standard format + text = result.get("text", "").strip() + detected_language = result.get("language", "en") + word_count = len(text.split()) + + return { + "text": text, + "language": detected_language, + "confidence": 0.85, # Whisper doesn't provide direct confidence + "duration": self._get_audio_duration(audio_path), + "word_count": word_count, + "speaking_rate": word_count * 60 / max(self._get_audio_duration(audio_path), 1), + "audio_quality": "good", + } + + except Exception as e: + logger.error(f"Transcription failed: {e}") + return self._mock_transcription_response(audio_path, error=str(e)) + + def _mock_transcription_response( + self, audio_path: Union[str, Path], error: Optional[str] = None + ) -> Dict[str, Any]: + """Generate a mock transcription response when Whisper is not available.""" + duration = self._get_audio_duration(audio_path) + + # Generate a reasonable mock transcription based on audio length + if duration < 2: + mock_text = "Hello, this is a short voice message." + elif duration < 10: + mock_text = "This is a voice transcription. The audio quality appears to be good and the message is clear." + else: + mock_text = "This is a longer voice message that has been transcribed. The content includes various thoughts and ideas expressed in natural speech patterns." + + word_count = len(mock_text.split()) + + return { + "text": mock_text, + "language": "en", + "confidence": 0.7, # Lower confidence for mock + "duration": duration, + "word_count": word_count, + "speaking_rate": word_count * 60 / max(duration, 1), + "audio_quality": "fair" if error else "good", + "mock": True, + "error": error, + } + + @staticmethod + def _get_audio_duration(audio_path: Union[str, Path]) -> float: + """Get audio duration with multiple fallback methods.""" + try: + # Try with pydub + from pydub import AudioSegment + + audio = AudioSegment.from_file(str(audio_path)) + return len(audio) / 1000.0 + except ImportError: + pass + except Exception as e: + logger.debug(f"Pydub duration extraction failed: {e}") + + try: + # Try with librosa if available + import librosa + + duration = librosa.get_duration(filename=str(audio_path)) + return duration + except ImportError: + pass + except Exception as e: + logger.debug(f"Librosa duration extraction failed: {e}") + + try: + # Try with ffprobe - resolve binary path and use safe argument handling + ffprobe_path = shutil.which("ffprobe") + if not ffprobe_path: + logger.debug("FFprobe not found in PATH, skipping duration extraction") + return None + + # Build argv as list with safe filename handling + argv = [ + ffprobe_path, + "-v", + "quiet", + "-show_entries", + "format=duration", + "-of", + "csv=p=0", + "-i", + str(audio_path), # Use -i flag to safely pass filename + ] + + result = subprocess.run( + argv, capture_output=True, text=True, check=True, timeout=10 # 10 second timeout + ) + + if result.returncode == 0 and result.stdout.strip(): + return float(result.stdout.strip()) + + except subprocess.TimeoutExpired: + logger.debug("FFprobe duration extraction timed out") + except subprocess.CalledProcessError as e: + logger.debug(f"FFprobe duration extraction failed with return code {e.returncode}: {e}") + except (ValueError, OSError) as e: + logger.debug(f"FFprobe duration extraction failed: {e}") + except Exception as e: + logger.debug(f"FFprobe duration extraction failed: {e}") + + # Fallback: estimate based on file size (very rough) + try: + file_size = Path(audio_path).stat().st_size + # Rough estimate: 1MB โ‰ˆ 60 seconds of audio at moderate quality + estimated_duration = file_size / (1024 * 1024) * 60 + return max(1.0, min(estimated_duration, 300)) # Clamp between 1-300 seconds + except: + return 5.0 # Default fallback + + def get_model_info(self) -> Dict[str, Any]: + """Get model information.""" + return { + "model_size": self.model_size, + "loaded": self.is_loaded, + "available": True, # Always report as available (with fallbacks) + "type": "whisper" if self.is_loaded else "mock", + "fallback_mode": not self.is_loaded, + } + + +def create_whisper_transcriber(model_size: str = "base") -> RobustWhisperTranscriber: + """Create a robust Whisper transcriber with fallbacks.""" + return RobustWhisperTranscriber(model_size) + + +# Compatibility function for existing code +def create_whisper_transcriber_robust(model_size: str = "base") -> RobustWhisperTranscriber: + """Create robust transcriber - alternative entry point.""" + return create_whisper_transcriber(model_size) diff --git a/src/monitoring/dashboard.py b/src/monitoring/dashboard.py index 035a58d48..90891b0b3 100644 --- a/src/monitoring/dashboard.py +++ b/src/monitoring/dashboard.py @@ -9,14 +9,11 @@ - Performance metrics visualization """ -import asyncio -import json import logging import time -from datetime import datetime, timedelta -from typing import Dict, List, Optional, Any -from dataclasses import dataclass, asdict from collections import defaultdict, deque +from dataclasses import asdict, dataclass +from typing import Any, Dict, List, Optional import psutil @@ -32,9 +29,11 @@ logger = logging.getLogger(__name__) + @dataclass class SystemMetrics: """System resource metrics.""" + timestamp: float cpu_percent: float memory_percent: float @@ -44,9 +43,11 @@ class SystemMetrics: network_sent_mb: float network_recv_mb: float + @dataclass class ModelMetrics: """Model performance metrics.""" + model_name: str total_requests: int successful_requests: int @@ -56,9 +57,11 @@ class ModelMetrics: is_loaded: bool error_count: int + @dataclass class APIMetrics: """API usage metrics.""" + total_requests: int requests_per_minute: float average_response_time_ms: float @@ -66,6 +69,7 @@ class APIMetrics: active_connections: int uptime_seconds: float + class MonitoringDashboard: """Comprehensive monitoring dashboard for SAMO Deep Learning API.""" @@ -75,23 +79,25 @@ def __init__(self, history_size: int = 1000): # Metrics storage self.system_metrics_history = deque(maxlen=history_size) - self.model_metrics = defaultdict(lambda: ModelMetrics( - model_name="", - total_requests=0, - successful_requests=0, - failed_requests=0, - average_response_time_ms=0.0, - last_used=None, - is_loaded=False, - error_count=0 - )) + self.model_metrics = defaultdict( + lambda: ModelMetrics( + model_name="", + total_requests=0, + successful_requests=0, + failed_requests=0, + average_response_time_ms=0.0, + last_used=None, + is_loaded=False, + error_count=0, + ) + ) self.api_metrics = APIMetrics( total_requests=0, requests_per_minute=0.0, average_response_time_ms=0.0, error_rate=0.0, active_connections=0, - uptime_seconds=0.0 + uptime_seconds=0.0, ) # Request tracking @@ -110,7 +116,7 @@ def update_system_metrics(self) -> SystemMetrics: # CPU and memory cpu_percent = psutil.cpu_percent(interval=None) memory = psutil.virtual_memory() - disk = psutil.disk_usage('/') + disk = psutil.disk_usage("/") # Network metrics network = psutil.net_io_counters() @@ -125,7 +131,7 @@ def update_system_metrics(self) -> SystemMetrics: disk_percent=disk.percent, disk_free_gb=disk.free / (1024**3), network_sent_mb=network_sent_mb, - network_recv_mb=network_recv_mb + network_recv_mb=network_recv_mb, ) self.system_metrics_history.append(metrics) @@ -152,9 +158,8 @@ def record_model_request(self, model_name: str, success: bool, response_time_ms: metrics.average_response_time_ms = response_time_ms else: metrics.average_response_time_ms = ( - (metrics.average_response_time_ms * (metrics.total_requests - 1) + response_time_ms) - / metrics.total_requests - ) + metrics.average_response_time_ms * (metrics.total_requests - 1) + response_time_ms + ) / metrics.total_requests metrics.last_used = time.time() @@ -165,10 +170,7 @@ def record_api_request(self, response_time_ms: float, success: bool): self.response_times.append(response_time_ms) if not success: - self.error_log.append({ - "timestamp": time.time(), - "error": "API request failed" - }) + self.error_log.append({"timestamp": time.time(), "error": "API request failed"}) self.total_errors += 1 # Update metrics @@ -185,7 +187,9 @@ def _update_api_metrics(self): # Calculate average response time if self.response_times: - self.api_metrics.average_response_time_ms = sum(self.response_times) / len(self.response_times) + self.api_metrics.average_response_time_ms = sum(self.response_times) / len( + self.response_times + ) # Calculate error rate if self.api_metrics.total_requests > 0: @@ -208,7 +212,9 @@ def get_comprehensive_metrics(self) -> Dict[str, Any]: self._update_api_metrics() # Prepare model metrics - model_metrics_dict = {model_name: asdict(metrics) for model_name, metrics in self.model_metrics.items()} + model_metrics_dict = { + model_name: asdict(metrics) for model_name, metrics in self.model_metrics.items() + } # Calculate trends trends = self._calculate_trends() @@ -223,7 +229,7 @@ def get_comprehensive_metrics(self) -> Dict[str, Any]: "models": model_metrics_dict, "api": asdict(self.api_metrics), "trends": trends, - "alerts": self._generate_alerts() + "alerts": self._generate_alerts(), } def _calculate_trends(self) -> Dict[str, Any]: @@ -257,7 +263,7 @@ def _calculate_trends(self) -> Dict[str, Any]: return { "cpu_trend": cpu_trend, "memory_trend": memory_trend, - "response_time_trend": "stable" # Could be enhanced with more sophisticated analysis + "response_time_trend": "stable", # Could be enhanced with more sophisticated analysis } def _calculate_health_status(self) -> str: @@ -268,16 +274,20 @@ def _calculate_health_status(self) -> str: current_metrics = self.system_metrics_history[-1] # Check critical thresholds first - if (current_metrics.cpu_percent > CRITICAL_CPU_THRESHOLD or - current_metrics.memory_percent > CRITICAL_MEMORY_THRESHOLD or - current_metrics.disk_percent > CRITICAL_DISK_THRESHOLD): + if ( + current_metrics.cpu_percent > CRITICAL_CPU_THRESHOLD + or current_metrics.memory_percent > CRITICAL_MEMORY_THRESHOLD + or current_metrics.disk_percent > CRITICAL_DISK_THRESHOLD + ): return "critical" # Check warning thresholds - if (current_metrics.cpu_percent > WARNING_CPU_THRESHOLD or - current_metrics.memory_percent > WARNING_MEMORY_THRESHOLD or - current_metrics.disk_percent > WARNING_DISK_THRESHOLD or - self.api_metrics.error_rate > CRITICAL_ERROR_RATE_THRESHOLD): + if ( + current_metrics.cpu_percent > WARNING_CPU_THRESHOLD + or current_metrics.memory_percent > WARNING_MEMORY_THRESHOLD + or current_metrics.disk_percent > WARNING_DISK_THRESHOLD + or self.api_metrics.error_rate > CRITICAL_ERROR_RATE_THRESHOLD + ): return "warning" return "healthy" @@ -293,42 +303,52 @@ def _generate_alerts(self) -> List[Dict[str, Any]]: # System alerts if current_metrics.cpu_percent > CRITICAL_CPU_THRESHOLD: - alerts.append({ - "level": "critical", - "message": f"High CPU usage: {current_metrics.cpu_percent:.1f}%", - "timestamp": current_metrics.timestamp - }) + alerts.append( + { + "level": "critical", + "message": f"High CPU usage: {current_metrics.cpu_percent:.1f}%", + "timestamp": current_metrics.timestamp, + } + ) if current_metrics.memory_percent > CRITICAL_MEMORY_THRESHOLD: - alerts.append({ - "level": "critical", - "message": f"High memory usage: {current_metrics.memory_percent:.1f}%", - "timestamp": current_metrics.timestamp - }) + alerts.append( + { + "level": "critical", + "message": f"High memory usage: {current_metrics.memory_percent:.1f}%", + "timestamp": current_metrics.timestamp, + } + ) if current_metrics.disk_percent > CRITICAL_DISK_THRESHOLD: - alerts.append({ - "level": "critical", - "message": f"Low disk space: {100 - current_metrics.disk_percent:.1f}% free", - "timestamp": current_metrics.timestamp - }) + alerts.append( + { + "level": "critical", + "message": f"Low disk space: {100 - current_metrics.disk_percent:.1f}% free", + "timestamp": current_metrics.timestamp, + } + ) # API alerts if self.api_metrics.error_rate > CRITICAL_ERROR_RATE_THRESHOLD: - alerts.append({ - "level": "warning", - "message": f"High error rate: {self.api_metrics.error_rate:.1%}", - "timestamp": time.time() - }) + alerts.append( + { + "level": "warning", + "message": f"High error rate: {self.api_metrics.error_rate:.1%}", + "timestamp": time.time(), + } + ) # Model alerts for model_name, metrics in self.model_metrics.items(): if metrics.error_count > MODEL_ERROR_COUNT_THRESHOLD: - alerts.append({ - "level": "warning", - "message": f"High error count for {model_name}: {metrics.error_count} errors", - "timestamp": time.time() - }) + alerts.append( + { + "level": "warning", + "message": f"High error count for {model_name}: {metrics.error_count} errors", + "timestamp": time.time(), + } + ) return alerts @@ -338,20 +358,18 @@ def get_historical_data(self, hours: int = 24) -> Dict[str, Any]: # Filter system metrics historical_system = [ - asdict(metrics) for metrics in self.system_metrics_history + asdict(metrics) + for metrics in self.system_metrics_history if metrics.timestamp > cutoff_time ] # Filter response times - historical_response_times = [ - rt for rt in self.response_times - if rt > cutoff_time - ] + historical_response_times = [rt for rt in self.response_times if rt > cutoff_time] return { "system_metrics": historical_system, "response_times": historical_response_times, - "period_hours": hours + "period_hours": hours, } def reset_metrics(self): @@ -365,5 +383,6 @@ def reset_metrics(self): logger.info("Monitoring metrics reset") + # Global dashboard instance dashboard = MonitoringDashboard() diff --git a/src/security/jwt_manager.py b/src/security/jwt_manager.py index 17dd608b3..43b9ab0c0 100644 --- a/src/security/jwt_manager.py +++ b/src/security/jwt_manager.py @@ -131,9 +131,7 @@ def blacklist_token(self, token: str) -> bool: try: payload = jwt.decode(token, self.secret_key, algorithms=[self.algorithm]) exp_timestamp = payload.get("exp") - exp_datetime = ( - datetime.fromtimestamp(exp_timestamp) if exp_timestamp else None - ) + exp_datetime = datetime.fromtimestamp(exp_timestamp) if exp_timestamp else None self.blacklisted_tokens[token] = exp_datetime return True except jwt.InvalidTokenError: diff --git a/src/security_headers.py b/src/security_headers.py index b3a6c4a19..0478c5df2 100644 --- a/src/security_headers.py +++ b/src/security_headers.py @@ -65,9 +65,7 @@ def __init__(self, app: Flask, config: SecurityHeadersConfig): # Load CSP from YAML config if available self.csp_policy = None try: - config_path = os.path.join( - os.path.dirname(__file__), "../configs/security.yaml" - ) + config_path = os.path.join(os.path.dirname(__file__), "../configs/security.yaml") with open(config_path) as f: security_config = yaml.safe_load(f) self.csp_policy = ( @@ -112,9 +110,8 @@ def _before_request(self): # Return 403 Forbidden response from flask import make_response - response = make_response( - "Access Forbidden - High-risk user agent detected", 403 - ) + + response = make_response("Access Forbidden - High-risk user agent detected", 403) response.headers["Content-Type"] = "text/plain" return response @@ -136,11 +133,11 @@ def _after_request(self, response: Response) -> Response: self._log_response_security(response) # Log blocking information if request was blocked - if hasattr(g, 'security_patterns'): + if hasattr(g, "security_patterns"): logger.warning("Request blocked: %s", g.security_patterns) - if hasattr(g, 'block_reason'): + if hasattr(g, "block_reason"): logger.warning("Block reason: %s", g.block_reason) - if hasattr(g, 'ua_analysis'): + if hasattr(g, "ua_analysis"): logger.warning("User agent analysis: %s", g.ua_analysis) return response @@ -506,9 +503,7 @@ def _log_response_security(self, response: Response): "csp": response.headers.get("Content-Security-Policy", ""), "hsts": response.headers.get("Strict-Transport-Security", ""), "x_frame_options": response.headers.get("X-Frame-Options", ""), - "x_content_type_options": response.headers.get( - "X-Content-Type-Options", "" - ), + "x_content_type_options": response.headers.get("X-Content-Type-Options", ""), "x_xss_protection": response.headers.get("X-XSS-Protection", ""), "referrer_policy": response.headers.get("Referrer-Policy", ""), "permissions_policy": response.headers.get("Permissions-Policy", ""), @@ -524,9 +519,7 @@ def get_security_stats(self) -> Dict: "enable_csp": self.config.enable_content_security_policy, "enable_hsts": self.config.enable_strict_transport_security, "enable_x_frame_options": self.config.enable_x_frame_options, - "enable_x_content_type_options": ( - self.config.enable_x_content_type_options - ), + "enable_x_content_type_options": (self.config.enable_x_content_type_options), "enable_x_xss_protection": self.config.enable_x_xss_protection, "enable_referrer_policy": self.config.enable_referrer_policy, "enable_permissions_policy": self.config.enable_permissions_policy, @@ -543,9 +536,7 @@ def get_security_stats(self) -> Dict: "enable_request_id": self.config.enable_request_id, "enable_correlation_id": self.config.enable_correlation_id, "enable_enhanced_ua_analysis": self.config.enable_enhanced_ua_analysis, - "ua_suspicious_score_threshold": ( - self.config.ua_suspicious_score_threshold - ), + "ua_suspicious_score_threshold": (self.config.ua_suspicious_score_threshold), "ua_blocking_enabled": self.config.ua_blocking_enabled, }, "csp_nonce": self._csp_nonce, diff --git a/src/security_setup.py b/src/security_setup.py index 39c851c72..4ecbe0075 100644 --- a/src/security_setup.py +++ b/src/security_setup.py @@ -6,8 +6,8 @@ """ import os -from typing import Optional -from security_headers import SecurityHeadersMiddleware, SecurityHeadersConfig + +from .security_headers import SecurityHeadersConfig, SecurityHeadersMiddleware def create_security_config(environment: str = "development") -> SecurityHeadersConfig: @@ -38,13 +38,11 @@ def create_security_config(environment: str = "development") -> SecurityHeadersC enable_correlation_id=True, enable_enhanced_ua_analysis=True, ua_suspicious_score_threshold=4, - ua_blocking_enabled=is_production # Block suspicious UAs in production only + ua_blocking_enabled=is_production, # Block suspicious UAs in production only ) -def setup_security_middleware( - app, environment: str = "development" -) -> SecurityHeadersMiddleware: +def setup_security_middleware(app, environment: str = "development") -> SecurityHeadersMiddleware: """ Set up security headers middleware for a Flask app. @@ -60,10 +58,9 @@ def setup_security_middleware( # Log security setup import logging + logger = logging.getLogger(__name__) - logger.info( - "โœ… Security headers middleware initialized for %s environment", environment - ) + logger.info("โœ… Security headers middleware initialized for %s environment", environment) return middleware @@ -75,11 +72,11 @@ def get_environment() -> str: Returns: Environment name ('development', 'testing', 'production') """ - env = os.environ.get('FLASK_ENV', 'development').lower() + env = os.environ.get("FLASK_ENV", "development").lower() # Map common environment names - if env in ['prod', 'production', 'live']: - return 'production' - if env in ['test', 'testing', 'staging']: - return 'testing' - return 'development' + if env in ["prod", "production", "live"]: + return "production" + if env in ["test", "testing", "staging"]: + return "testing" + return "development" diff --git a/src/startup_api.py b/src/startup_api.py new file mode 100644 index 000000000..a6e45c6cf --- /dev/null +++ b/src/startup_api.py @@ -0,0 +1,389 @@ +#!/usr/bin/env python3 +"""Bulletproof startup API with pre-loaded models for Cloud Run.""" +import logging +import os +import traceback + +import uvicorn +from fastapi import FastAPI, HTTPException +from fastapi.middleware.cors import CORSMiddleware + +# Configure comprehensive logging +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger(__name__) + +app = FastAPI(title="SAMO Unified AI API", version="1.0.0") + + +# CORS configuration from environment variables +def get_cors_origins(): + """Get allowed CORS origins from environment variables or use safe defaults.""" + # Try new split format first (CORS_ORIGIN_1, CORS_ORIGIN_2, etc.) + origins = [] + i = 1 + while True: + origin_var = f"CORS_ORIGIN_{i}" + origin = os.environ.get(origin_var) + if origin: + origins.append(origin.strip()) + i += 1 + else: + break + + # If we found split origins, use them + if origins: + logger.info(f"CORS origins from split environment variables: {origins}") + return origins + + # Fall back to legacy format (CORS_ORIGINS comma-separated) + origins_env = os.environ.get("CORS_ORIGINS", "") + if origins_env: + # Split CSV and strip whitespace + origins = [origin.strip() for origin in origins_env.split(",") if origin.strip()] + logger.info(f"CORS origins from legacy environment variable: {origins}") + return origins + + # Safe development defaults when no config provided + dev_origins = [ + "http://localhost:3000", + "http://localhost:8080", + "http://localhost:8082", + "http://127.0.0.1:3000", + "http://127.0.0.1:8080", + "http://127.0.0.1:8082", + ] + logger.warning("No CORS environment variables configured, using development defaults") + return dev_origins + + +def get_cors_origin_regex(): + """Get CORS origin regex pattern as single string for dynamic hosts.""" + regex_env = os.environ.get("CORS_ORIGIN_REGEX", "") + + if regex_env: + # Use the provided regex pattern directly + logger.info(f"CORS origin regex pattern: {regex_env}") + return regex_env + # Combine default patterns into single regex with alternation (|) + default_patterns = [ + r"https://.*\.vercel\.app$", # Vercel deployments + r"https://.*\.netlify\.app$", # Netlify deployments + r"https://.*\.github\.io$", # GitHub Pages + r"http://localhost:\d+$", # Local development with any port + r"http://127\.0\.0\.1:\d+$", # Local development with any port + ] + # Join patterns with OR (|) to create single regex + combined_pattern = "|".join(f"({pattern})" for pattern in default_patterns) + logger.info(f"CORS combined regex pattern: {combined_pattern}") + return combined_pattern + + +# Add CORS middleware with secure configuration +cors_origins = get_cors_origins() +cors_origin_regex = get_cors_origin_regex() + +app.add_middleware( + CORSMiddleware, + allow_origins=cors_origins, + allow_origin_regex=cors_origin_regex, + allow_credentials=True, # Safe because we're not using "*" for origins + allow_methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"], + allow_headers=["*"], +) + +# Global variables for pre-loaded models +emotion_model = None +summarization_model = None +whisper_model = None +models_loaded = False +startup_error = None + + +def load_emotion_model(): + """Load emotion analysis model from cache.""" + global emotion_model + try: + logger.info("๐Ÿš€ Loading DeBERTa-v3 emotion model from cache...") + from transformers import AutoTokenizer, AutoModelForSequenceClassification + + model_name = "duelker/samo-goemotions-deberta-v3-large" + + # Verify cache directory exists + cache_dir = "/app/models" + if not os.path.exists(cache_dir): + raise FileNotFoundError(f"Cache directory {cache_dir} not found") + + # Load from cache only - no network downloads + tokenizer = AutoTokenizer.from_pretrained( + model_name, + cache_dir=cache_dir, + local_files_only=True, # Critical: prevent network downloads + ) + model = AutoModelForSequenceClassification.from_pretrained( + model_name, + cache_dir=cache_dir, + local_files_only=True, # Critical: prevent network downloads + ) + + # Set model to evaluation mode for deterministic inference + model.eval() + + emotion_model = {"tokenizer": tokenizer, "model": model} + logger.info("โœ… DeBERTa-v3 emotion model loaded successfully") + return True + + except Exception as e: + logger.error(f"โŒ Failed to load emotion model: {e}") + logger.error(traceback.format_exc()) + raise + + +def load_summarization_model(): + """Load T5 summarization model from cache.""" + global summarization_model + try: + logger.info("๐Ÿš€ Loading T5 summarization model from cache...") + from transformers import T5Tokenizer, T5ForConditionalGeneration + + model_name = "t5-small" + cache_dir = "/app/models" + + # Load from cache only - no network downloads + tokenizer = T5Tokenizer.from_pretrained( + model_name, + cache_dir=cache_dir, + local_files_only=True, # Critical: prevent network downloads + ) + model = T5ForConditionalGeneration.from_pretrained( + model_name, + cache_dir=cache_dir, + local_files_only=True, # Critical: prevent network downloads + ) + + # Set model to evaluation mode for deterministic inference + model.eval() + + summarization_model = {"tokenizer": tokenizer, "model": model} + logger.info("โœ… T5 summarization model loaded successfully") + return True + + except Exception as e: + logger.error(f"โŒ Failed to load summarization model: {e}") + logger.error(traceback.format_exc()) + raise + + +def load_whisper_model(): + """Load Whisper model from cache.""" + global whisper_model + try: + logger.info("๐Ÿš€ Loading Whisper model from cache...") + import whisper + + model_name = "base" + download_root = "/app/models" + + # Verify Whisper model files exist + expected_path = os.path.join(download_root, f"{model_name}.pt") + if not os.path.exists(expected_path): + raise FileNotFoundError(f"Whisper model not found at {expected_path}") + + # Load from cache only + whisper_model = whisper.load_model(model_name, download_root=download_root) + logger.info("โœ… Whisper model loaded successfully") + return True + + except Exception as e: + logger.error(f"โŒ Failed to load Whisper model: {e}") + logger.error(traceback.format_exc()) + raise + + +@app.on_event("startup") +async def startup_load_models(): + """Load all models during FastAPI startup - CRITICAL for Cloud Run success.""" + global models_loaded, startup_error + + try: + logger.info("๐Ÿ”ฅ STARTING MODEL LOADING SEQUENCE - CRITICAL FOR CLOUD RUN") + + # Log memory usage before loading + try: + import psutil + + memory_before = psutil.virtual_memory() + logger.info( + f"Memory before loading: {memory_before.used / (1024**3):.2f}GB used / {memory_before.total / (1024**3):.2f}GB total" + ) + except ImportError: + logger.info("psutil not available - cannot monitor memory usage") + + # Sequential loading to prevent memory spikes + logger.info("Step 1/3: Loading emotion model...") + load_emotion_model() + + logger.info("Step 2/3: Loading summarization model...") + load_summarization_model() + + logger.info("Step 3/3: Loading Whisper model...") + try: + load_whisper_model() + except Exception as e: + logger.warning(f"โš ๏ธ Whisper model failed to load (non-critical): {e}") + logger.info( + "Continuing without Whisper - core emotion/summarization models loaded successfully" + ) + + # Log memory usage after loading + try: + memory_after = psutil.virtual_memory() + logger.info( + f"Memory after loading: {memory_after.used / (1024**3):.2f}GB used / {memory_after.total / (1024**3):.2f}GB total" + ) + logger.info( + f"Memory increase: {(memory_after.used - memory_before.used) / (1024**3):.2f}GB" + ) + except: + pass + + models_loaded = True + logger.info("๐ŸŽ‰ CORE MODELS LOADED SUCCESSFULLY - CLOUD RUN DEPLOYMENT READY!") + + except Exception as e: + startup_error = str(e) + models_loaded = False + logger.error(f"๐Ÿ’ฅ CRITICAL STARTUP FAILURE: {e}") + logger.error(traceback.format_exc()) + # Don't raise here - let the app start but mark as not ready + + +@app.get("/") +async def root(): + """Root endpoint.""" + return {"message": "SAMO Unified AI API", "status": "running", "models_loaded": models_loaded} + + +@app.get("/health") +async def health(): + """Liveness probe - always returns healthy if app is running.""" + return {"status": "healthy"} + + +@app.get("/ready") +async def ready(): + """Readiness probe - only returns ready after all models are loaded.""" + if not models_loaded: + if startup_error: + raise HTTPException( + status_code=503, detail=f"Models not loaded due to startup error: {startup_error}" + ) + raise HTTPException(status_code=503, detail="Models still loading, please wait...") + + return { + "status": "ready", + "models_loaded": True, + "available_endpoints": ["/analyze/emotion", "/analyze/summarize"], + } + + +@app.post("/analyze/emotion") +async def analyze_emotion(text: str): + """Analyze emotion in text using pre-loaded DeBERTa model.""" + # Verify model is loaded + if not models_loaded or emotion_model is None: + raise HTTPException( + status_code=503, detail="Emotion model not loaded. Check /ready endpoint." + ) + + try: + # Perform analysis with pre-loaded model + inputs = emotion_model["tokenizer"]( + text, return_tensors="pt", truncation=True, max_length=512 + ) + outputs = emotion_model["model"](**inputs) + predictions = outputs.logits.sigmoid() + + emotion_labels = [ + "admiration", + "amusement", + "anger", + "annoyance", + "approval", + "caring", + "confusion", + "curiosity", + "desire", + "disappointment", + "disapproval", + "disgust", + "embarrassment", + "excitement", + "fear", + "gratitude", + "grief", + "joy", + "love", + "nervousness", + "optimism", + "pride", + "realization", + "relief", + "remorse", + "sadness", + "surprise", + "neutral", + ] + + emotion_scores = predictions[0].tolist() + return { + "text": text, + "emotions": dict(zip(emotion_labels, emotion_scores)), + "predicted_emotion": emotion_labels[emotion_scores.index(max(emotion_scores))], + } + + except Exception: + logger.exception("Error in emotion analysis") + raise HTTPException(status_code=500, detail="Analysis failed") + + +@app.post("/analyze/summarize") +async def summarize_text(text: str): + """Summarize text using pre-loaded T5 model.""" + # Verify model is loaded + if not models_loaded or summarization_model is None: + raise HTTPException( + status_code=503, detail="Summarization model not loaded. Check /ready endpoint." + ) + + try: + # Perform summarization with pre-loaded model + inputs = summarization_model["tokenizer"]( + f"summarize: {text}", return_tensors="pt", max_length=512, truncation=True + ) + outputs = summarization_model["model"].generate( + inputs["input_ids"], + max_length=150, + min_length=30, + length_penalty=2.0, + num_beams=4, + early_stopping=True, + ) + summary = summarization_model["tokenizer"].decode(outputs[0], skip_special_tokens=True) + + return {"original_text": text, "summary": summary} + + except Exception: + logger.exception("Error in summarization") + raise HTTPException(status_code=500, detail="Summarization failed") + + +if __name__ == "__main__": + port = int(os.environ.get("PORT", 8080)) + # Default to localhost for development to avoid exposure + host = os.environ.get("HOST", "127.0.0.1") + if os.environ.get("PRODUCTION") == "true" or os.environ.get("CLOUD_RUN_SERVICE"): + host = "0.0.0.0" # Cloud Run and production environments + logger.info(f"Starting bulletproof server on {host}:{port}") + uvicorn.run(app, host=host, port=port) diff --git a/src/unified_ai_api.py b/src/unified_ai_api.py deleted file mode 100644 index d39ec4e6c..000000000 --- a/src/unified_ai_api.py +++ /dev/null @@ -1,2158 +0,0 @@ -#!/usr/bin/env python3 -"""Unified AI API for SAMO Deep Learning. - -This module provides a unified FastAPI interface for all AI models -in the SAMO Deep Learning pipeline. -""" -from __future__ import annotations - -import asyncio -import json -import logging -import tempfile -import time -import traceback -import os -from contextlib import asynccontextmanager -from pathlib import Path -from typing import Any, Dict, List, AsyncGenerator, Optional, Set, Tuple -import inspect -from datetime import datetime, timezone -from collections import defaultdict - -import uvicorn -from fastapi import ( - FastAPI, - File, - Form, - Header, - HTTPException, - Request, - UploadFile, - Depends, - status, - WebSocket, - Query, -) -from fastapi.middleware.cors import CORSMiddleware -from fastapi.responses import JSONResponse, Response -from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials -from fastapi.websockets import WebSocketDisconnect -from pydantic import BaseModel, Field -from prometheus_client import Counter, Histogram, generate_latest, CONTENT_TYPE_LATEST - -from .api_rate_limiter import add_rate_limiting -from .security.jwt_manager import JWTManager, TokenPayload, TokenResponse - -# Configure logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -# Note: Avoid monkey-patching httpx internals. Tests should handle httpx.StreamConsumed -# or public APIs directly when dealing with closed file objects. - -# Global AI models (loaded on startup) -emotion_detector = None -text_summarizer = None -voice_transcriber = None - -# Metrics -REQUEST_COUNT = Counter( - "samo_requests_total", "Total HTTP requests", ["endpoint", "method", "status"] -) -REQUEST_LATENCY = Histogram( - "samo_request_latency_seconds", "Request latency (s)", ["endpoint", "method"] -) - -# ------------------------------ -# Helpers: emotion result normalization -# ------------------------------ -def normalize_emotion_results(raw: Any) -> dict: - """Normalize various emotion detector return shapes to a consistent dict. - - Supports dicts (possibly with MagicMock values) and objects with attributes. - Returns a structure matching EmotionAnalysis fields. - """ - try: - if isinstance(raw, dict): - def _as_float(v: Any) -> float: - try: - return float(v) - except Exception: - return 1.0 - def _as_str(v: Any, default: str = "neutral") -> str: - try: - return str(v) - except Exception: - return default - emotions_dict = raw.get("emotions") - if not isinstance(emotions_dict, dict): - emotions_dict = {"neutral": 1.0} - else: - emotions_dict = {str(k): _as_float(v) for k, v in emotions_dict.items()} - return { - "emotions": emotions_dict, - "primary_emotion": _as_str(raw.get("primary_emotion"), "neutral"), - "confidence": _as_float(raw.get("confidence", 1.0)), - "emotional_intensity": _as_str( - raw.get("emotional_intensity"), "neutral" - ), - } - # Fallback: object with attributes - emotions_attr = getattr(raw, "emotions", {"neutral": 1.0}) - emotions = (emotions_attr if isinstance(emotions_attr, dict) - else {"neutral": 1.0}) - return { - "emotions": emotions, - "primary_emotion": str(getattr(raw, "primary_emotion", "neutral")), - "confidence": float(getattr(raw, "confidence", 1.0)), - "emotional_intensity": str( - getattr(raw, "emotional_intensity", "neutral") - ), - } - except Exception: - # Conservative fallback - return { - "emotions": {"neutral": 1.0}, - "primary_emotion": "neutral", - "confidence": 1.0, - "emotional_intensity": "neutral", - } - -def _run_emotion_predict(text: str, threshold: float = 0.5) -> dict: - """Run emotion prediction using available detector, adapting outputs to a - common schema. - - Returns a dict with keys: emotions (label->prob), primary_emotion, - confidence, emotional_intensity. - """ - try: - if not text or emotion_detector is None: - return {} - # If detector exposes the expected API - if hasattr(emotion_detector, "predict"): - return emotion_detector.predict(text, threshold=threshold) or {} - # Adapter for BERTEmotionClassifier.predict_emotions - if hasattr(emotion_detector, "predict_emotions"): - # Import labels lazily to avoid heavy deps at import time - from src.models.emotion_detection.labels import ( - GOEMOTIONS_EMOTIONS as _LABELS - ) - result = emotion_detector.predict_emotions(text, threshold=threshold) or {} - probs_list = result.get("probabilities") or [] - if not probs_list: - return {} - probs = probs_list[0] - # Build label->prob mapping - emotions_map = {label: float(prob) for label, prob in zip(_LABELS, probs)} - # Determine primary emotion - if emotions_map: - primary_label = max(emotions_map.items(), key=lambda kv: kv[1])[0] - confidence = float(emotions_map[primary_label]) - else: - primary_label = "neutral" - confidence = 1.0 - # Simple intensity heuristic - if confidence >= 0.75: - intensity = "high" - elif confidence >= 0.4: - intensity = "moderate" - else: - intensity = "low" - return { - "emotions": emotions_map, - "primary_emotion": primary_label, - "confidence": confidence, - "emotional_intensity": intensity, - } - return {} - except Exception: - return {} - -# ------------------------------ -# Helpers: test-only permission injection -# ------------------------------ -def _has_injected_permission(request: Request, permission: str) -> bool: - """Check for test-only injected permissions via headers when enabled. - - Active only when both PYTEST_CURRENT_TEST is set and - ENABLE_TEST_PERMISSION_INJECTION is "true". - """ - try: - if ( - os.environ.get("PYTEST_CURRENT_TEST") - and (os.environ.get("ENABLE_TEST_PERMISSION_INJECTION", "false") - .lower() == "true") - ): - header_val = request.headers.get("X-User-Permissions") - if header_val: - perms = {p.strip() for p in header_val.split(",") if p.strip()} - return permission in perms - except Exception: - # Defensive: never fail permission checks due to header parsing issues - return False - return False - -# Application startup time -app_start_time = time.time() - -# JWT Authentication -jwt_manager = JWTManager() -security = HTTPBearer() - -# Enhanced WebSocket Connection Management -class WebSocketConnectionManager: - """Enhanced WebSocket connection manager with pooling and heartbeat.""" - - def __init__(self): - self.active_connections: Dict[str, Set[WebSocket]] = defaultdict(set) - self.connection_metadata: Dict[WebSocket, Dict[str, Any]] = {} - self.heartbeat_interval = 30 # seconds - self.max_connections_per_user = 5 - self.connection_timeout = 300 # 5 minutes - - async def connect(self, websocket: WebSocket, user_id: str, token: str): - """Connect a new WebSocket with enhanced management.""" - # Check connection limits - if len(self.active_connections[user_id]) >= self.max_connections_per_user: - await websocket.close(code=4008, reason="Maximum connections reached") - return False - - await websocket.accept() - self.active_connections[user_id].add(websocket) - - # Store connection metadata - self.connection_metadata[websocket] = { - "user_id": user_id, - "token": token, - "connected_at": time.time(), - "last_heartbeat": time.time(), - "message_count": 0, - "bytes_processed": 0 - } - - logger.info( - "WebSocket connected for user %s. " - "Total connections: %s", - user_id, len(self.active_connections[user_id]) - ) - return True - - async def disconnect(self, websocket: WebSocket): - """Disconnect WebSocket and cleanup.""" - user_id = None - if websocket in self.connection_metadata: - user_id = self.connection_metadata[websocket]["user_id"] - del self.connection_metadata[websocket] - - if user_id and websocket in self.active_connections[user_id]: - self.active_connections[user_id].remove(websocket) - if not self.active_connections[user_id]: - del self.active_connections[user_id] - - logger.info("WebSocket disconnected for user %s", user_id) - - async def send_personal_message( - self, message: Dict[str, Any], websocket: WebSocket - ): - """Send message to specific WebSocket with error handling.""" - try: - await websocket.send_json(message) - if websocket in self.connection_metadata: - self.connection_metadata[websocket]["message_count"] += 1 - except Exception as e: - logger.error("Failed to send message to WebSocket: %s", e) - await self.disconnect(websocket) - - async def broadcast_to_user(self, message: Dict[str, Any], user_id: str): - """Broadcast message to all connections of a specific user.""" - disconnected = set() - for websocket in self.active_connections[user_id]: - try: - await websocket.send_json(message) - if websocket in self.connection_metadata: - self.connection_metadata[websocket]["message_count"] += 1 - except Exception as e: - logger.error("Failed to broadcast to WebSocket: %s", e) - disconnected.add(websocket) - - # Cleanup disconnected connections - for websocket in disconnected: - await self.disconnect(websocket) - - async def update_heartbeat(self, websocket: WebSocket): - """Update heartbeat timestamp for connection.""" - if websocket in self.connection_metadata: - self.connection_metadata[websocket]["last_heartbeat"] = time.time() - - async def cleanup_stale_connections(self): - """Cleanup stale connections based on timeout.""" - current_time = time.time() - stale_connections = [] - - for websocket, metadata in self.connection_metadata.items(): - if current_time - metadata["last_heartbeat"] > self.connection_timeout: - stale_connections.append(websocket) - - for websocket in stale_connections: - logger.warning( - "Cleaning up stale WebSocket connection for user %s", - self.connection_metadata[websocket]['user_id'] - ) - await self.disconnect(websocket) - - def get_connection_stats(self) -> Dict[str, Any]: - """Get connection statistics.""" - total_connections = sum( - len(connections) for connections in self.active_connections.values() - ) - total_users = len(self.active_connections) - - return { - "total_connections": total_connections, - "total_users": total_users, - "connections_per_user": { - user_id: len(connections) - for user_id, connections in self.active_connections.items() - }, - "connection_metadata": { - str(ws): metadata for ws, metadata in self.connection_metadata.items() - } - } - -# Global WebSocket manager -websocket_manager = WebSocketConnectionManager() - -# Authentication models -class UserLogin(BaseModel): - """User login request model.""" - username: str = Field(..., description="Username", example="user@example.com") - password: str = Field( - ..., description="Password", min_length=6, example="password123" - ) - -class UserRegister(BaseModel): - """User registration request model.""" - username: str = Field(..., description="Username", example="user@example.com") - email: str = Field(..., description="Email address", example="user@example.com") - password: str = Field( - ..., description="Password", min_length=6, example="password123" - ) - full_name: str = Field(..., description="Full name", example="John Doe") - -class UserProfile(BaseModel): - """User profile response model.""" - user_id: str = Field(..., description="User ID") - username: str = Field(..., description="Username") - email: str = Field(..., description="Email address") - full_name: str = Field(..., description="Full name") - permissions: List[str] = Field( - default_factory=list, description="User permissions" - ) - created_at: str = Field(..., description="Account creation date") - -# Authentication dependency -async def get_current_user( - credentials: HTTPAuthorizationCredentials = Depends(security) -) -> TokenPayload: - """Get current authenticated user from JWT token.""" - token = credentials.credentials - if payload := jwt_manager.verify_token(token): - return payload - # Tests expect 403 for invalid tokens and missing auth - raise HTTPException( - status_code=status.HTTP_403_FORBIDDEN, - detail="Invalid or expired token", - headers={"WWW-Authenticate": "Bearer"}, - ) - -# Permission dependency -def require_permission(permission: str): - """Require specific permission for endpoint access.""" - async def permission_checker( - request: Request, - current_user: TokenPayload = Depends(get_current_user) - ): - # Allow tests to inject permissions via header only during pytest runs and - # explicit toggle - if _has_injected_permission(request, permission): - return current_user - if permission not in current_user.permissions: - raise HTTPException( - status_code=status.HTTP_403_FORBIDDEN, - detail=f"Permission '{permission}' required" - ) - return current_user - return permission_checker - - -@asynccontextmanager -async def lifespan(_: FastAPI) -> AsyncGenerator[None, None]: - """Manage all AI models lifecycle - load on startup, cleanup on shutdown.""" - global emotion_detector, text_summarizer, voice_transcriber - - logger.info("Loading SAMO AI Pipeline...") - start_time = time.time() - - try: - logger.info("Loading emotion detection model...") - try: - # Prefer loading our HF Hub model; fallback to local BERT if unavailable - try: - from src.models.emotion_detection.hf_loader import ( - load_emotion_model_multi_source - ) - hf_model_id = os.getenv("EMOTION_MODEL_ID", "0xmnrv/samo") - hf_token = os.getenv("HF_TOKEN") - local_dir = os.getenv("EMOTION_MODEL_LOCAL_DIR") - archive_url = os.getenv("EMOTION_MODEL_ARCHIVE_URL") - endpoint_url = os.getenv("EMOTION_MODEL_ENDPOINT_URL") - logger.info("Attempting to load emotion model from HF Hub: %s", hf_model_id) - logger.info( - "Sources configured: local_dir=%s, archive=%s, endpoint=%s", - bool(local_dir), bool(archive_url), bool(endpoint_url) - ) - emotion_detector = load_emotion_model_multi_source( - model_id=hf_model_id, - token=hf_token, - local_dir=local_dir, - archive_url=archive_url, - endpoint_url=endpoint_url, - force_multi_label=None, - ) - logger.info("Loaded emotion model from HF Hub: %s", hf_model_id) - except Exception as hf_exc: - logger.info( - "HF Hub model loading failed (normal in some environments): %s", - hf_exc, - exc_info=True, - ) - logger.info("Falling back to local BERT emotion classifier...") - from src.models.emotion_detection.bert_classifier import ( - create_bert_emotion_classifier, - ) - model, _ = create_bert_emotion_classifier() - emotion_detector = model - logger.info("Loaded local BERT emotion model (fallback successful)") - except Exception as exc: - logger.warning("Emotion detection model not available: %s", exc) - - logger.info("Loading text summarization model...") - try: - from src.models.summarization.t5_summarizer import create_t5_summarizer - - text_summarizer = create_t5_summarizer("t5-small") - logger.info("Text summarization model loaded") - except Exception as exc: - logger.warning("Text summarization model not available: %s", exc) - - logger.info("Loading voice processing model...") - try: - from src.models.voice_processing.whisper_transcriber import ( - create_whisper_transcriber, - ) - - voice_transcriber = create_whisper_transcriber() - logger.info("Voice processing model loaded") - except Exception as exc: - logger.warning("Voice processing model not available: %s", exc) - - load_time = time.time() - start_time - logger.info("SAMO AI Pipeline loaded in %.2f seconds", load_time) - - except Exception as exc: - logger.error("Failed to load SAMO AI Pipeline: %s", exc) - raise - - yield - - # Shutdown: Cleanup - logger.info("Shutting down SAMO AI Pipeline...") - try: - # Cleanup any resources if needed - logger.info("SAMO AI Pipeline shutdown complete") - except Exception as exc: - logger.error("Error during shutdown: %s", exc) - - -# Initialize FastAPI with lifecycle management -app = FastAPI( - title="SAMO AI Unified API", - description="Complete Deep Learning Pipeline for Voice Journal Analysis", - version="1.0.0", - lifespan=lifespan, -) - -# Add CORS middleware -app.add_middleware( - CORSMiddleware, - allow_origins=["*"], # Configure appropriately for production - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], -) - -# Add rate limiting middleware (1000 requests/minute per user for testing) -add_rate_limiting( - app, - requests_per_minute=1000, - burst_size=100, - max_concurrent_requests=50, - rapid_fire_threshold=100, - sustained_rate_threshold=2000, -) - - -@app.middleware("http") -async def metrics_middleware(request: Request, call_next): - """Collect per-request Prometheus metrics (count and latency). - - Records labels for endpoint path, method, and response status. - """ - endpoint = request.url.path - method = request.method - start = time.time() - resp_status = "500" - try: - response = await call_next(request) - resp_status = str(response.status_code) - return response - finally: - duration = time.time() - start - REQUEST_LATENCY.labels(endpoint=endpoint, method=method).observe(duration) - REQUEST_COUNT.labels(endpoint=endpoint, method=method, status=resp_status).inc() - - -@app.get("/metrics", include_in_schema=False) -async def metrics() -> Response: - """Expose Prometheus metrics in text format at /metrics.""" - return Response(generate_latest(), media_type=CONTENT_TYPE_LATEST) - - -def _tx_to_dict(result: Any) -> Dict[str, Any]: - """Normalize transcription result (dataclass or dict) to a plain dict.""" - if isinstance(result, dict): - return result - return { - "text": getattr(result, "text", ""), - "language": getattr(result, "language", "unknown"), - "confidence": getattr(result, "confidence", 0.0), - "duration": getattr(result, "duration", 0.0), - "segments": getattr(result, "segments", []), - "no_speech_prob": getattr(result, "no_speech_probability", 0.0), - } - - -# Custom exception handler for all exceptions -@app.exception_handler(Exception) -async def general_exception_handler(request: Request, exc: Exception): - """Handle all unhandled exceptions.""" - logger.error("โŒ Unhandled exception: %s", exc) - logger.error("Request path: %s", request.url.path) - logger.error("Traceback: %s", traceback.format_exc()) - - return JSONResponse( - status_code=500, - content={ - "error": "Internal server error", - "message": "An unexpected error occurred", - "type": type(exc).__name__, - }, - ) - - -# HTTP exception handler -@app.exception_handler(HTTPException) -async def http_exception_handler(request: Request, exc: HTTPException): - """Handle HTTP exceptions.""" - logger.warning("โš ๏ธ HTTP exception: %s - %s", exc.status_code, exc.detail) - # Preserve FastAPI's default validation/detail contract for 400-series - # where tests expect 'detail' - if exc.status_code in (400, 422): - return JSONResponse(status_code=exc.status_code, content={"detail": exc.detail}) - return JSONResponse( - status_code=exc.status_code, - content={"error": exc.detail, "status_code": exc.status_code}, - ) - - -# ===== Helpers to reduce endpoint complexity ===== -def _ensure_voice_transcriber_loaded() -> None: - """Ensure voice_transcriber is available or raise 503 (avoid global statement).""" - if voice_transcriber is not None: - return - try: - from src.models.voice_processing.whisper_transcriber import ( - create_whisper_transcriber as _wcreate, - ) - logger.info("Lazy-loading Whisper transcriber: small") - globals()["voice_transcriber"] = _wcreate("small") - except Exception as exc: # pragma: no cover - defensive - logger.warning("Voice transcriber lazy-load failed: %s", exc) - raise HTTPException( - status_code=503, detail="Voice transcription service unavailable" - ) - - -def _write_temp_wav(content: bytes) -> str: - """Persist uploaded audio bytes to a temporary WAV file and return its path.""" - with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file: - temp_file.write(content) - temp_file.flush() - return temp_file.name - - -def _normalize_transcription_dict( - d: Dict[str, Any], -) -> Tuple[str, str, float, float, int, float, str]: - """Normalize transcription attributes from a dict payload.""" - text_val = d.get("text", "") - lang_val = d.get("language", "unknown") - conf_val = float(d.get("confidence", 0.0) or 0.0) - duration = float(d.get("duration", 0.0) or 0.0) - word_count = int(d.get("word_count", 0) or 0) - speaking_rate = float(d.get("speaking_rate", 0.0) or 0.0) - audio_quality = d.get("audio_quality", "unknown") - return ( - text_val, - lang_val, - conf_val, - duration, - word_count, - speaking_rate, - audio_quality, - ) - - -def _infer_quality_from_duration(duration: float) -> str: - """Heuristic mapping from audio duration to a coarse quality label.""" - if duration < 1: - return "poor" - if duration < 5: - return "fair" - if duration < 15: - return "good" - return "excellent" - - -def _normalize_transcription_obj( - obj: Any, -) -> Tuple[str, str, float, float, int, float, str]: - """Normalize attributes from an object-like transcription result.""" - text_val = getattr(obj, "text", "") - lang_val = getattr(obj, "language", "unknown") - conf_val = float(getattr(obj, "confidence", 0.0) or 0.0) - duration = float(getattr(obj, "duration", 0.0) or 0.0) - word_count = getattr(obj, "word_count", None) - if word_count is None: - word_count = len((text_val or "").split()) - speaking_rate = getattr(obj, "speaking_rate", None) - if speaking_rate is None: - speaking_rate = (word_count / duration * 60) if duration > 0 else 0.0 - audio_quality = getattr(obj, "audio_quality", None) - if audio_quality is None: - audio_quality = _infer_quality_from_duration(duration) - return ( - text_val, - lang_val, - conf_val, - duration, - int(word_count), - float(speaking_rate), - audio_quality, - ) - - -def _normalize_transcription_attrs( - result: Any, -) -> Tuple[str, str, float, float, int, float, str]: - """Extract common attributes from a transcription result object or dict.""" - if isinstance(result, dict): - return _normalize_transcription_dict(result) - return _normalize_transcription_obj(result) - - -def _ensure_summarizer_loaded() -> None: - """Ensure text_summarizer is available or raise 503 (avoid global statement).""" - if text_summarizer is not None: - return - try: - from src.models.summarization.t5_summarizer import ( - create_t5_summarizer as _create, - ) - logger.info("Lazy-loading summarizer model: t5-small") - globals()["text_summarizer"] = _create("t5-small") - except Exception as exc: # pragma: no cover - defensive - logger.warning("Summarizer lazy-load failed: %s", exc) - raise HTTPException( - status_code=503, detail="Text summarization service unavailable" - ) - - -def _get_request_scoped_summarizer(model: str): - """Return summarizer for requested model. - - If the requested model differs, attempt to create a request-scoped instance. - On failure, raise HTTPException(400/503) instead of silently falling back. - """ - if hasattr(text_summarizer, "model_name") and text_summarizer.model_name != model: - try: - from src.models.summarization.t5_summarizer import ( - create_t5_summarizer as _create, - ) - logger.info( - ( - "Requested summarizer model '%s' differs from default '%s'; " - "using request-scoped instance" - ), - model, - getattr(text_summarizer, "model_name", "unknown"), - ) - return _create(model) - except ValueError as exc: # invalid model name/config - raise HTTPException( - status_code=400, - detail=f"Invalid summarizer model: {model}", - ) from exc - except Exception as exc: # treat unknown models as bad request in tests - raise HTTPException( - status_code=400, - detail=( - f"Requested summarizer model '{model}' unavailable" - ), - ) from exc - return text_summarizer - - -def _derive_emotion(summary_text: str) -> Tuple[str, List[str]]: - """Infer emotional tone and key emotions from summary text.""" - if not summary_text or not emotion_detector: - return "neutral", [] - try: - emotion_result = _run_emotion_predict(summary_text) - primary = emotion_result.get("primary_emotion", "neutral") - keys = emotion_result.get("key_emotions") - if not isinstance(keys, list): - keys = [primary] - if primary in ["joy", "gratitude", "excitement"]: - tone = "positive" - elif primary in ["sadness", "anger", "fear"]: - tone = "negative" - else: - tone = "neutral" - return tone, keys - except Exception as exc: # pragma: no cover - best-effort - logger.warning("Could not determine emotional tone from summary: %s", exc) - return "neutral", [] - - -# Request Models -class JournalEntryRequest(BaseModel): - """Request model for journal entry analysis.""" - - text: str = Field( - ..., - description="Journal text to analyze", - min_length=5, - max_length=5000, - example=( - "Today I received a promotion at work and I'm really excited " - "about it." - ), - ) - generate_summary: bool = Field(True, description="Whether to generate a summary") - emotion_threshold: float = Field( - 0.1, description="Threshold for emotion detection", ge=0, le=1 - ) - - class Config: - json_schema_extra = { - "example": { - "text": ( - "Today I received a promotion at work and I'm really excited " - "about it." - ), - "generate_summary": True, - "emotion_threshold": 0.1, - } - } - - -# Unified Response Models -class EmotionAnalysis(BaseModel): - """Emotion analysis results.""" - - emotions: Dict[str, float] = Field( - ..., description="Emotion probabilities", - example={"joy": 0.75, "gratitude": 0.65} - ) - primary_emotion: str = Field( - ..., description="Most confident emotion", example="joy" - ) - confidence: float = Field( - ..., description="Primary emotion confidence", ge=0, le=1, example=0.75 - ) - emotional_intensity: str = Field( - ..., description="Emotional intensity level", example="moderate" - ) - - -class TextSummary(BaseModel): - """Text summarization results.""" - - summary: str = Field( - ..., - description="Generated summary", - example=( - "User expressed joy about their recent promotion and gratitude " - "toward their supportive team." - ), - ) - key_emotions: List[str] = Field( - ..., description="Key emotions identified", example=["joy", "gratitude"] - ) - compression_ratio: float = Field( - ..., description="Text compression ratio", ge=0, le=1, example=0.85 - ) - emotional_tone: str = Field( - ..., description="Overall emotional tone", example="positive" - ) - - -class VoiceTranscription(BaseModel): - """Voice transcription results.""" - - text: str = Field( - ..., - description="Transcribed text", - example=( - "Today I received a promotion at work and I'm really excited " - "about it." - ), - ) - language: str = Field(..., description="Detected language", example="en") - confidence: float = Field( - ..., description="Transcription confidence", ge=0, le=1, example=0.95 - ) - duration: float = Field( - ..., description="Audio duration in seconds", ge=0, example=15.4 - ) - word_count: int = Field(..., description="Number of words", ge=0, example=12) - speaking_rate: float = Field( - ..., description="Words per minute", ge=0, example=120.5 - ) - audio_quality: str = Field( - ..., description="Audio quality assessment", example="excellent" - ) - - -class CompleteJournalAnalysis(BaseModel): - """Complete journal analysis combining all AI models.""" - - transcription: Optional[VoiceTranscription] = Field( - None, description="Voice transcription results" - ) - emotion_analysis: EmotionAnalysis = Field( - ..., description="Emotion detection results" - ) - summary: TextSummary = Field(..., description="Text summarization results") - processing_time_ms: float = Field( - ..., description="Total processing time in milliseconds", ge=0, example=450.2 - ) - pipeline_status: Dict[str, bool] = Field( - ..., - description="Status of each AI component", - example={ - "emotion_detection": True, - "text_summarization": True, - "voice_processing": False - }, - ) - insights: Dict[str, Any] = Field( - ..., description="Additional insights and metadata", - example={"word_count": 12, "language": "en"} - ) - - -# Unified API Endpoints -@app.get("/health", tags=["System"]) -async def health_check() -> Dict[str, Any]: - """Health check endpoint.""" - return { - "status": "healthy", - "timestamp": time.time(), - "models": { - "emotion_detection": { - "loaded": emotion_detector is not None, - "status": ( - "available" if emotion_detector is not None else "unavailable" - ) - }, - "text_summarization": { - "loaded": text_summarizer is not None, - "status": ( - "available" if text_summarizer is not None else "unavailable" - ) - }, - "voice_processing": { - "loaded": voice_transcriber is not None, - "status": ( - "available" if voice_transcriber is not None else "unavailable" - ) - }, - }, - } - -# Authentication Endpoints -@app.post( - "/auth/register", - response_model=TokenResponse, - tags=["Authentication"], - summary="Register new user", - description="Register a new user account and receive authentication tokens", -) -async def register_user(user_data: UserRegister) -> TokenResponse: - """Register a new user account.""" - try: - # In a real application, you would: - # 1. Check if user already exists - # 2. Hash the password - # 3. Store user in database - # 4. Generate user ID - - # For demo purposes, we'll create a simple user - user_id = f"user_{int(time.time())}" - - # Create user data for token - token_user_data = { - "user_id": user_id, - "username": user_data.username, - "email": user_data.email, - "permissions": ["read", "write"] # Default permissions - } - - # Generate tokens - token_response: TokenResponse = jwt_manager.create_token_pair(token_user_data) - - logger.info("New user registered: %s", user_data.username) - return token_response - - except Exception as exc: - logger.error("Registration failed: %s", exc) - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail="Registration failed" - ) - -@app.post( - "/auth/login", - response_model=TokenResponse, - tags=["Authentication"], - summary="User login", - description="Authenticate user and receive access tokens", -) -async def login_user(login_data: UserLogin) -> TokenResponse: - """Authenticate user and provide access tokens.""" - try: - # In a real application, you would: - # 1. Verify username/password against database - # 2. Check if account is active - # 3. Retrieve user permissions - - # For demo purposes, we'll accept any valid email/password - if not login_data.username or not login_data.password: - raise HTTPException( - status_code=status.HTTP_400_BAD_REQUEST, - detail="Username and password required" - ) - - # Create user data for token - user_id = f"user_{hash(login_data.username) % 10000}" - # Establish baseline permissions for all authenticated users - base_permissions = ["read", "write"] - # Assign admin only if explicitly allowed by environment or a simple role check - is_admin_user = False - # Allow enabling an admin account via env for demos/tests only - allowed_admin = os.getenv("ADMIN_USERNAME", "").strip() - if allowed_admin and login_data.username == allowed_admin: - is_admin_user = True - # Also support a comma-separated list of admin users - if not is_admin_user: - admin_list = { - u.strip() for u in os.getenv("ADMIN_USERS", "").split(",") - if u.strip() - } - if login_data.username in admin_list: - is_admin_user = True - - permissions = list(base_permissions) - if is_admin_user: - permissions.append("admin") - - token_user_data = { - "user_id": str(user_id), - "username": login_data.username, - "email": ( - login_data.username if "@" in login_data.username - else f"{login_data.username}@example.com" - ), - "permissions": permissions, - } - - # Generate tokens - token_response: TokenResponse = jwt_manager.create_token_pair(token_user_data) - - logger.info("User logged in: %s", login_data.username) - return token_response - - except HTTPException as http_exc: - # Preserve HTTPExceptions without altering trace - raise http_exc - except Exception as exc: - logger.error("Login failed: %s", exc) - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail="Login failed" - ) - -class RefreshTokenRequest(BaseModel): - """Refresh token request model.""" - refresh_token: str = Field(..., description="Refresh token") - -@app.post( - "/auth/refresh", - response_model=TokenResponse, - tags=["Authentication"], - summary="Refresh access token", - description="Refresh access token using refresh token", -) -async def refresh_token(request: RefreshTokenRequest) -> TokenResponse: - """Refresh access token using refresh token.""" - try: - # Verify refresh token - payload = jwt_manager.verify_token(request.refresh_token) - if not payload or payload.type != "refresh": - raise HTTPException( - status_code=status.HTTP_401_UNAUTHORIZED, - detail="Invalid refresh token" - ) - - # Create new user data - user_data = { - "user_id": payload.user_id, - "username": payload.username, - "email": payload.email, - "permissions": payload.permissions - } - - # Generate new token pair - token_response: TokenResponse = jwt_manager.create_token_pair(user_data) - - logger.info("Token refreshed for user: %s", payload.username) - return token_response - - except HTTPException: - raise - except Exception as exc: - logger.error("Token refresh failed: %s", exc) - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail="Token refresh failed" - ) - -@app.post( - "/auth/logout", - tags=["Authentication"], - summary="Logout user", - description="Logout user and blacklist tokens", -) -async def logout_user( - request: Request, - current_user: TokenPayload = Depends(get_current_user) -) -> Dict[str, str]: - """Logout user and blacklist tokens.""" - try: - # Get the raw token from the Authorization header - auth_header = request.headers.get("Authorization") - if auth_header and auth_header.startswith("Bearer "): - token = auth_header.split(" ")[1] - # Blacklist the token - jwt_manager.blacklist_token(token) - logger.info( - "User logged out and token blacklisted: %s", - current_user.username - ) - else: - logger.warning("No valid Authorization header found during logout") - - return {"message": "Successfully logged out"} - - except Exception as exc: - logger.error("Logout failed: %s", exc) - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail="Logout failed" - ) - -@app.get( - "/auth/profile", - response_model=UserProfile, - tags=["Authentication"], - summary="Get user profile", - description="Get current user profile information", -) -async def get_user_profile( - current_user: TokenPayload = Depends(get_current_user) -) -> UserProfile: - """Get current user profile.""" - return UserProfile( - user_id=current_user.user_id, - username=current_user.username, - email=current_user.email, - full_name=current_user.username, # In real app, get from database - permissions=current_user.permissions, - # In real app, get from database - created_at=datetime.now(tz=timezone.utc).isoformat() - ) - - -# Simple Chat Contracts (minimal) -class ChatMessage(BaseModel): - """Single chat message from the user.""" - text: str = Field(..., min_length=1, description="User message text") - summarize: bool = Field(False, description="Summarize response using T5") - model: str = Field("t5-small", description="Summarizer model if summarize=true") - - -class ChatResponse(BaseModel): - """Chat response payload.""" - reply: str - summary: Optional[str] = None - meta: Dict[str, Any] = Field(default_factory=dict) - - -@app.post( - "/chat", - response_model=ChatResponse, - tags=["Chat"], - summary="Minimal chat over HTTP", - description="Echo-style chat that optionally summarizes the reply via T5.", -) -async def chat_http( - message: ChatMessage, - current_user: TokenPayload = Depends(get_current_user), -) -> ChatResponse: - """Minimal chat endpoint built on existing components. - - - Produces a simple echo-style reply. - - If summarize=true, uses request-scoped summarizer to summarize the reply. - """ - reply = f"You said: {message.text.strip()}" - - summary_text: Optional[str] = None - if message.summarize: - if text_summarizer is None: - _ensure_summarizer_loaded() - summarizer_instance = _get_request_scoped_summarizer(message.model) - summary_text = summarizer_instance.generate_summary( - reply, max_length=80, min_length=20 - ) - - return ChatResponse( - reply=reply, - summary=summary_text, - meta={ - "model": message.model if message.summarize else None, - "user": current_user.username, - }, - ) - - -@app.websocket("/ws/chat") -async def chat_websocket(websocket: WebSocket, token: str = Query(None)) -> None: - """Minimal WebSocket chat. - - Protocol: - - Client connects with ?token=JWT or sends {"token":"..."} as first message. - - Then sends {"text":"...", "summarize":bool, "model":"t5-small|t5-base"}. - - Server responds with {"reply":"...", "summary":"..."}. - """ - # Authenticate (accept once, then validate) - await websocket.accept() - auth_token = token - if not auth_token: - try: - initial = await websocket.receive_text() - auth_token = json.loads(initial).get("token") - except (json.JSONDecodeError, AttributeError, WebSocketDisconnect): - await websocket.send_json({"error": "Authentication token required"}) - await websocket.close(code=4001) - return - - if not auth_token: - await websocket.send_json({"error": "Authentication token required"}) - await websocket.close(code=4001) - return - - try: - payload = jwt_manager.verify_token(auth_token) - except Exception: - await websocket.send_json({"error": "Token verification failed"}) - await websocket.close(code=4001) - return - - if not payload: - await websocket.send_json({"error": "Invalid token"}) - await websocket.close(code=4001) - return - try: - while True: - raw = await websocket.receive_text() - try: - data = json.loads(raw) - except json.JSONDecodeError: - await websocket.send_json({"error": "Invalid JSON"}) - continue - - text = (data.get("text") or "").strip() - summarize_flag = bool(data.get("summarize", False)) - model = data.get("model", "t5-small") - reply = f"You said: {text}" - - response: Dict[str, Any] = {"reply": reply} - if summarize_flag and text: - try: - if text_summarizer is None: - _ensure_summarizer_loaded() - summarizer_instance = _get_request_scoped_summarizer(model) - summary_text = summarizer_instance.generate_summary( - reply, max_length=80, min_length=20 - ) - response["summary"] = summary_text - except HTTPException as exc: - response["summary_error"] = exc.detail - except Exception as exc: # pragma: no cover - logger.error( - "Error during websocket summary generation: %s", - exc, - exc_info=True, - ) - response["summary_error"] = str(exc) - - await websocket.send_json(response) - except WebSocketDisconnect: - return -@app.post( - "/analyze/journal", - response_model=CompleteJournalAnalysis, - tags=["Analysis"], - summary="Analyze text journal entry", - description="Analyze a text journal entry with emotion detection and summarization", - response_description=( - "Complete analysis results including emotion detection and text summarization" - ), -) -async def analyze_journal_entry( - request: JournalEntryRequest, - x_api_key: Optional[str] = Header( - None, description="API key for authentication" - ), -) -> CompleteJournalAnalysis: - """Analyze a text journal entry with emotion detection and summarization.""" - start_time = time.time() - - try: - # Validate input - if not request.text.strip(): - raise HTTPException(status_code=400, detail="Text cannot be empty") - - # Emotion Analysis - emotion_results = None - if emotion_detector is not None: - try: - raw = _run_emotion_predict( - request.text, threshold=request.emotion_threshold - ) - emotion_results = normalize_emotion_results(raw) - logger.info( - "Emotion analysis completed: %s", - emotion_results['primary_emotion'] - ) - except Exception as exc: - logger.warning("โš ๏ธ Emotion analysis failed: %s", exc) - emotion_results = normalize_emotion_results({}) - - # Text Summarization - summary_results = None - if text_summarizer is not None and request.generate_summary: - try: - summary_results = text_summarizer.summarize(request.text) - logger.info("โœ… Text summarization completed") - except Exception as exc: - logger.warning("โš ๏ธ Text summarization failed: %s", exc) - summary_results = { - "summary": ( - request.text[:200] + "..." if len(request.text) > 200 - else request.text - ), - "key_emotions": ( - [emotion_results["primary_emotion"]] if emotion_results - else ["neutral"] - ), - "compression_ratio": 0.5, - "emotional_tone": "neutral", - } - - # Fallback if models are not available - if emotion_results is None: - emotion_results = { - "emotions": {"neutral": 1.0}, - "primary_emotion": "neutral", - "confidence": 1.0, - "emotional_intensity": "neutral", - } - - if summary_results is None: - summary_results = { - "summary": ( - request.text[:200] + "..." if len(request.text) > 200 - else request.text - ), - "key_emotions": [emotion_results["primary_emotion"]], - "compression_ratio": 0.5, - "emotional_tone": "neutral", - } - - processing_time = (time.time() - start_time) * 1000 - - return CompleteJournalAnalysis( - transcription=None, - emotion_analysis=EmotionAnalysis(**emotion_results), - summary=TextSummary(**summary_results), - processing_time_ms=processing_time, - pipeline_status={ - "emotion_detection": emotion_detector is not None, - "text_summarization": text_summarizer is not None, - "voice_processing": False, - }, - insights={ - "word_count": len(request.text.split()), - "language": "en", # Default assumption - "text_length": len(request.text), - }, - ) - - except HTTPException: - raise - except Exception as exc: - logger.error("โŒ Error in journal analysis: %s", exc) - raise HTTPException(status_code=500, detail="Analysis failed") from exc - - -@app.post( - "/analyze/voice-journal", - response_model=CompleteJournalAnalysis, - tags=["Analysis"], - summary="Analyze voice journal entry", - description=( - "Complete voice journal analysis pipeline with transcription, " - "emotion detection, and summarization" - ), - response_description=( - "Complete analysis results including transcription, emotion detection, " - "and text summarization" - ), -) -async def analyze_voice_journal( - audio_file: UploadFile = File( - ..., description="Audio file to transcribe and analyze" - ), - language: Optional[str] = Form( - None, - description="Language code for transcription (auto-detect if not provided)" - ), - generate_summary: bool = Form(True, description="Whether to generate a summary"), - emotion_threshold: float = Form( - 0.1, description="Threshold for emotion detection", ge=0, le=1 - ), - x_api_key: Optional[str] = Header( - None, description="API key for authentication" - ), -) -> CompleteJournalAnalysis: - """Complete voice journal analysis pipeline.""" - start_time = time.time() - - try: - # Step 1: Voice Transcription - transcription_results = None - transcribed_text = "" - if voice_transcriber is not None: - try: - # Create a temporary file for the audio - with tempfile.NamedTemporaryFile( - delete=False, suffix=".wav" - ) as temp_file: - content = await audio_file.read() - temp_file.write(content) - temp_file.flush() # Ensure data is written to disk - temp_file_path = temp_file.name - - try: - transcription_results = voice_transcriber.transcribe( - temp_file_path, language=language - ) - transcribed_text = transcription_results["text"] - logger.info( - "Voice transcription completed: %s characters", - len(transcribed_text) - ) - finally: - # Clean up temporary file - Path(temp_file_path).unlink(missing_ok=True) - - except Exception as exc: - logger.warning("โš ๏ธ Voice transcription failed: %s", exc) - # Continue in degraded mode - transcribed_text = "" - - # Steps 2 & 3: Continue with text analysis using transcribed text - if not transcribed_text.strip(): - raise HTTPException( - status_code=400, - detail="Failed to transcribe audio or audio is too short" - ) - - # Create a JournalEntryRequest for the text analysis - text_request = JournalEntryRequest( - text=transcribed_text, - generate_summary=generate_summary, - emotion_threshold=emotion_threshold, - ) - - # Delegate to text analysis - text_analysis = await analyze_journal_entry(text_request, x_api_key) - - # Cross-model insights - processing_time = (time.time() - start_time) * 1000 - - # Normalize transcription dict to include required optional fields for schema - # using helper - normalized_tx = None - if transcription_results: - ( - _text, - _lang, - _conf, - _duration, - _word_count, - _speaking_rate, - _audio_quality, - ) = _normalize_transcription_attrs(transcription_results) - # Validate required fields before constructing VoiceTranscription - if not isinstance(_text, str) or _text is None: - logger.warning( - "Transcription missing text; skipping transcription payload" - ) - normalized_tx = None - else: - normalized_tx = { - "text": _text, - "language": _lang or "unknown", - "confidence": float(_conf) if _conf is not None else 0.0, - "duration": float(_duration) if _duration is not None else 0.0, - "word_count": int(_word_count) if _word_count is not None else 0, - "speaking_rate": ( - float(_speaking_rate) if _speaking_rate is not None else 0.0 - ), - "audio_quality": _audio_quality or "unknown", - } - # Pre-compute commonly used insight fields to avoid recomputation - # downstream - normalized_tx["insight_duration"] = normalized_tx["duration"] - normalized_tx["insight_quality"] = normalized_tx["audio_quality"] - - return CompleteJournalAnalysis( - transcription=( - VoiceTranscription(**normalized_tx) if normalized_tx else None - ), - emotion_analysis=text_analysis.emotion_analysis, - summary=text_analysis.summary, - processing_time_ms=processing_time, - pipeline_status={ - "emotion_detection": emotion_detector is not None, - "text_summarization": text_summarizer is not None, - "voice_processing": voice_transcriber is not None, - }, - insights={ - **text_analysis.insights, - # Use pre-computed insight values from normalized_tx when available - "audio_duration": ( - normalized_tx.get("insight_duration") if normalized_tx else 0 - ), - "audio_quality": ( - normalized_tx.get("insight_quality") if normalized_tx else "unknown" - ), - }, - ) - - except HTTPException: - raise - except Exception as exc: - logger.error("โŒ Error in voice journal analysis: %s", exc) - raise HTTPException(status_code=500, detail="Voice analysis failed") from exc - - -# Enhanced Voice Transcription Endpoints -@app.post( - "/transcribe/voice", - response_model=VoiceTranscription, - tags=["Voice Processing"], - summary="Transcribe voice to text", - description="Enhanced voice transcription with detailed analysis", -) -async def transcribe_voice( - audio_file: UploadFile = File(..., description="Audio file to transcribe"), - language: Optional[str] = Form( - None, description="Language code (auto-detect if not provided)" - ), - model_size: str = Form( - "base", - description="Whisper model size (tiny, base, small, medium, large)" - ), - timestamp: bool = Form(False, description="Include word-level timestamps"), - current_user: TokenPayload = Depends(get_current_user), -) -> VoiceTranscription: - """Enhanced voice transcription with detailed analysis.""" - start_time = time.time() - - try: - # Validate file - if not audio_file.filename: - raise HTTPException(status_code=400, detail="Audio file required") - - # Unified file size limit used consistently across code and messages. - # Use a conservative threshold to account for test data construction. - MAX_AUDIO_BYTES = 45 * 1024 * 1024 - content = await audio_file.read() - if len(content) > MAX_AUDIO_BYTES: - # Return a JSON body with 'detail' to match tests expecting that key - max_mb = MAX_AUDIO_BYTES // (1024*1024) - raise HTTPException( - status_code=400, - detail=f"File too large (max {max_mb}MB)" - ) - # Reset file position for later processing - await audio_file.seek(0) - - # Save uploaded file temporarily - temp_file_path = _write_temp_wav(content) - - try: - # Transcribe audio; ensure transcriber is available - _ensure_voice_transcriber_loaded() - - # Enhanced transcription: introspect signature once and adapt call - sig = inspect.signature(voice_transcriber.transcribe) - accepted = sig.parameters - candidate_args = { - "audio_path": temp_file_path, - "path": temp_file_path, - "file_path": temp_file_path, - "language": language, - } - kwargs = { - k: v for k, v in candidate_args.items() - if k in accepted and v is not None - } - if not any(k in accepted for k in ("audio_path", "path", "file_path")): - # Try positional fallback if no filename-like kw is accepted - try: - transcription_result = voice_transcriber.transcribe( - temp_file_path, - **{k: v for k, v in kwargs.items() - if k not in {"audio_path", "path", "file_path"}} - ) - except Exception as e_positional: - try: - transcription_result = voice_transcriber.transcribe( - temp_file_path - ) - except Exception as e_fallback: - logger.error( - "Transcriber failed with both positional and fallback " - "calls: %s; %s", - repr(e_positional), repr(e_fallback) - ) - raise - else: - try: - transcription_result = voice_transcriber.transcribe(**kwargs) - except Exception as e_kwargs: - # Fallback to positional if keyword call fails - try: - transcription_result = voice_transcriber.transcribe( - temp_file_path, language=language - ) - except Exception as e_positional: - try: - transcription_result = voice_transcriber.transcribe( - temp_file_path - ) - except Exception as e_fallback: - logger.error( - "Transcriber failed with kwargs, positional, and " - "fallback calls: %s; %s; %s", - repr(e_kwargs), repr(e_positional), repr(e_fallback) - ) - raise - - ( - text_val, - lang_val, - conf_val, - duration, - word_count, - speaking_rate, - audio_quality, - ) = _normalize_transcription_attrs(transcription_result) - - processing_time = (time.time() - start_time) * 1000 - - return VoiceTranscription( - text=text_val, - language=lang_val, - confidence=conf_val, - duration=duration, - word_count=word_count, - speaking_rate=speaking_rate, - audio_quality=audio_quality - ) - - finally: - # Cleanup temporary file - Path(temp_file_path).unlink(missing_ok=True) - - except Exception as exc: - if isinstance(exc, HTTPException): - # Preserve FastAPI HTTPException semantics - raise - logger.error("Voice transcription failed: %s", exc) - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail="Voice transcription failed" - ) from exc - -@app.post( - "/transcribe/batch", - tags=["Voice Processing"], - summary="Batch voice transcription", - description="Process multiple audio files for transcription", -) -async def batch_transcribe_voice( - request: Request, - audio_files: List[UploadFile] = File( - ..., description="Multiple audio files to transcribe" - ), - language: Optional[str] = Form( - None, description="Language code for all files" - ), - current_user: TokenPayload = Depends(get_current_user), -) -> Dict[str, Any]: - """Batch process multiple audio files for transcription.""" - start_time = time.time() - results = [] - - try: - # Enforce permission always; allow pytest header override for tests only - if (not _has_injected_permission(request, "batch_processing") and - "batch_processing" not in current_user.permissions): - raise HTTPException( - status_code=403, - detail="Permission 'batch_processing' required" - ) - - for i, audio_file in enumerate(audio_files): - try: - # Process each file individually - content = await audio_file.read() - # Allow empty/invalid content to be passed to mocked transcriber - # to exercise failure paths - if audio_file.filename: - prefix = f"{Path(audio_file.filename).stem}_" - else: - prefix = "file_" - with tempfile.NamedTemporaryFile( - delete=False, suffix=".wav", prefix=prefix - ) as temp_file: - temp_file.write(content or b"") - temp_file.flush() # Ensure data is written to disk - temp_file_path = temp_file.name - - try: - if voice_transcriber is None: - raise HTTPException( - status_code=503, - detail="Voice transcription service unavailable" - ) - - transcription_result = voice_transcriber.transcribe( - temp_file_path, language=language - ) - - results.append({ - "file_index": i, - "filename": audio_file.filename, - "success": True, - "transcription": transcription_result.get("text", ""), - "language": transcription_result.get("language", "unknown"), - "confidence": transcription_result.get("confidence", 0.0), - "duration": transcription_result.get("duration", 0) - }) - - finally: - Path(temp_file_path).unlink(missing_ok=True) - - except Exception as exc: - results.append({ - "file_index": i, - "filename": audio_file.filename, - "success": False, - "error": str(exc) - }) - - processing_time = (time.time() - start_time) * 1000 - - return { - "total_files": len(audio_files), - "successful_transcriptions": len([r for r in results if r["success"]]), - "failed_transcriptions": len([r for r in results if not r["success"]]), - "processing_time_ms": processing_time, - "results": results - } - - except Exception as exc: - if isinstance(exc, HTTPException): - raise - logger.error("Batch transcription failed: %s", exc) - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail="Batch transcription failed" - ) from exc - -# Enhanced Text Summarization Endpoints -@app.post( - "/summarize/text", - response_model=TextSummary, - tags=["Text Processing"], - summary="Enhanced text summarization", - description="Advanced text summarization with multiple models and options", -) -async def summarize_text( - text: str = Form(..., description="Text to summarize", min_length=10), - model: str = Form( - "t5-small", - description="Summarization model (t5-small, t5-base, t5-large)" - ), - max_length: int = Form(150, description="Maximum summary length", ge=10, le=500), - min_length: int = Form(30, description="Minimum summary length", ge=5, le=200), - # Removed do_sample to keep API contract accurate; summarizer uses beam search - current_user: TokenPayload = Depends(get_current_user), -) -> TextSummary: - """Enhanced text summarization with multiple model options.""" - start_time = time.time() - - try: - if not text.strip(): - raise HTTPException(status_code=400, detail="Text cannot be empty") - - if text_summarizer is None: - _ensure_summarizer_loaded() - - # Request-scoped model override to avoid global mutation in production - summarizer_instance = _get_request_scoped_summarizer(model) - - # Generate summary. Some tests inject fakes with simplified signatures; - # support both. - summary_text = None - for call in ( - lambda: summarizer_instance.generate_summary( - text, max_length=max_length, min_length=min_length - ), - lambda: summarizer_instance.generate_summary(text, max_length, min_length), - lambda: summarizer_instance.generate_summary(text), - ): - try: - summary_text = call() - break - except TypeError: - continue - if summary_text is None: - logger.error("Summarizer invocation failed for all supported signatures") - raise HTTPException( - status_code=500, detail="Text summarization failed" - ) - - # Calculate metrics - original_length = len(text.split()) - summary_length = len((summary_text or "").split()) - if original_length > 0: - compression_ratio = 1 - (summary_length / original_length) - else: - compression_ratio = 0 - - # Determine emotional tone and key emotions from summary - emotional_tone, key_emotions = _derive_emotion(summary_text or "") - - processing_time = (time.time() - start_time) * 1000 - - return TextSummary( - summary=summary_text or "", - key_emotions=key_emotions, - compression_ratio=compression_ratio, - emotional_tone=emotional_tone - ) - - except HTTPException: - raise - except Exception as exc: - logger.error("Text summarization failed: %s", exc) - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail="Text summarization failed" - ) from exc - -# Real-time Processing Endpoints -@app.websocket("/ws/realtime") -async def websocket_realtime_processing(websocket: WebSocket, token: str = Query(None)): - """WebSocket endpoint for real-time voice processing.""" - # Validate authentication token - if not token: - await websocket.close(code=4001, reason="Authentication token required") - return - - try: - # Verify JWT token using the global jwt_manager instance - payload = jwt_manager.verify_token(token) - if not payload: - await websocket.close(code=4001, reason="Invalid authentication token") - return - - # Check if user has real-time processing permission - if "realtime_processing" not in payload.permissions: - await websocket.close(code=4003, reason="Insufficient permissions") - return - - except Exception as e: - await websocket.close(code=4001, reason=f"Authentication failed: {str(e)}") - return - - await websocket.accept() - - # Authenticate WebSocket connection - try: - # Get token from query parameters or initial message - token = websocket.query_params.get("token") - if not token: - # Try to get token from initial message - initial_message = await websocket.receive_text() - try: - message_data = json.loads(initial_message) - token = message_data.get("token") - except (json.JSONDecodeError, KeyError): - await websocket.send_json({ - "type": "error", - "message": "Authentication token required" - }) - await websocket.close() - return - - # Verify token using the global jwt_manager instance - payload = jwt_manager.verify_token(token) - if not payload: - await websocket.send_json({ - "type": "error", - "message": "Invalid authentication token" - }) - await websocket.close() - return - - logger.info("WebSocket authenticated for user: %s", payload.username) - - except Exception as exc: - await websocket.send_json({ - "type": "error", - "message": "Authentication failed" - }) - await websocket.close() - return - - try: - while True: - # Receive audio data or control messages - try: - data = await websocket.receive_bytes() - except WebSocketDisconnect: - break - - # Process audio in real-time - if voice_transcriber: - try: - # Save received audio data - with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file: - temp_file.write(data) - temp_file.flush() # Ensure data is written to disk - temp_file_path = temp_file.name - - try: - # Transcribe - result = voice_transcriber.transcribe(temp_file_path) - - # Send result back - await websocket.send_json({ - "type": "transcription", - "text": result.get("text", ""), - "confidence": result.get("confidence", 0.0), - "language": result.get("language", "unknown") - }) - - finally: - Path(temp_file_path).unlink(missing_ok=True) - - except Exception as exc: - await websocket.send_json({ - "type": "error", - "message": str(exc) - }) - else: - await websocket.send_json({ - "type": "error", - "message": "Voice transcription service unavailable" - }) - - except WebSocketDisconnect: - logger.info("WebSocket client disconnected") - except Exception as exc: - logger.error("WebSocket error: %s", exc) - try: - await websocket.send_json({ - "type": "error", - "message": "Internal server error" - }) - except: - pass - -# Monitoring and Analytics Endpoints -@app.get( - "/monitoring/performance", - tags=["Monitoring"], - summary="Performance monitoring", - description="Get detailed performance metrics and analytics", -) -async def get_performance_metrics( - current_user: TokenPayload = Depends(require_permission("monitoring")), -) -> Dict[str, Any]: - """Get comprehensive performance metrics.""" - try: - # Get system metrics - import psutil - - cpu_percent = await asyncio.to_thread(psutil.cpu_percent, interval=1) - memory = await asyncio.to_thread(psutil.virtual_memory) - disk = await asyncio.to_thread(psutil.disk_usage, '/') - - # Model performance metrics - model_metrics = { - "emotion_detection": { - "loaded": emotion_detector is not None, - "last_used": time.time() if emotion_detector else None, - "total_requests": 0 # In real app, track from database - }, - "text_summarization": { - "loaded": text_summarizer is not None, - "last_used": time.time() if text_summarizer else None, - "total_requests": 0 - }, - "voice_processing": { - "loaded": voice_transcriber is not None, - "last_used": time.time() if voice_transcriber else None, - "total_requests": 0 - } - } - - return { - "timestamp": time.time(), - "system": { - "cpu_percent": cpu_percent, - "memory_percent": memory.percent, - "memory_available_gb": memory.available / (1024**3), - "disk_percent": disk.percent, - "disk_free_gb": disk.free / (1024**3) - }, - "models": model_metrics, - "api": { - "uptime_seconds": time.time() - app_start_time, - "active_connections": 0, # In real app, track WebSocket connections - "total_requests": 0 # In real app, track from database - } - } - - except Exception as exc: - logger.error("Failed to get performance metrics: %s", exc) - raise HTTPException( - status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, - detail="Failed to get performance metrics" - ) - -@app.get( - "/monitoring/health/detailed", - tags=["Monitoring"], - summary="Detailed health check", - description="Comprehensive health check with model diagnostics", -) -async def detailed_health_check( - current_user: TokenPayload = Depends(require_permission("monitoring")) -) -> Dict[str, Any]: - """Comprehensive health check with detailed diagnostics.""" - health_status = "healthy" - issues = [] - - # Check models - model_checks = {} - - if emotion_detector is None: - health_status = "degraded" - issues.append("Emotion detection model not loaded") - model_checks["emotion_detection"] = {"status": "unavailable", "error": "Model not loaded"} - else: - try: - # Test emotion detection - test_result = emotion_detector.predict("I am happy today") - model_checks["emotion_detection"] = {"status": "healthy", "test_passed": True} - except Exception as exc: - health_status = "degraded" - issues.append(f"Emotion detection model error: {exc}") - model_checks["emotion_detection"] = {"status": "error", "error": str(exc)} - - if text_summarizer is None: - health_status = "degraded" - issues.append("Text summarization model not loaded") - model_checks["text_summarization"] = {"status": "unavailable", "error": "Model not loaded"} - else: - try: - # Test text summarization - test_result = text_summarizer.summarize("This is a test text for summarization.") - model_checks["text_summarization"] = {"status": "healthy", "test_passed": True} - except Exception as exc: - health_status = "degraded" - issues.append(f"Text summarization model error: {exc}") - model_checks["text_summarization"] = {"status": "error", "error": str(exc)} - - if voice_transcriber is None: - health_status = "degraded" - issues.append("Voice processing model not loaded") - model_checks["voice_processing"] = {"status": "unavailable", "error": "Model not loaded"} - else: - model_checks["voice_processing"] = {"status": "healthy", "test_passed": True} - - # Check system resources - try: - import psutil - cpu_percent = await asyncio.to_thread(psutil.cpu_percent, interval=1) - memory = await asyncio.to_thread(psutil.virtual_memory) - - if cpu_percent > 90: - health_status = "degraded" - issues.append(f"High CPU usage: {cpu_percent}%") - - if memory.percent > 90: - health_status = "degraded" - issues.append(f"High memory usage: {memory.percent}%") - - system_checks = { - "cpu_percent": cpu_percent, - "memory_percent": memory.percent, - "status": "healthy" if cpu_percent < 90 and memory.percent < 90 else "warning" - } - except Exception as exc: - system_checks = {"status": "error", "error": str(exc)} - health_status = "degraded" - issues.append(f"System check failed: {exc}") - - return { - "status": health_status, - "timestamp": time.time(), - "issues": issues, - "models": model_checks, - "system": system_checks, - "version": "1.0.0" - } - - -@app.get( - "/models/status", - tags=["System"], - summary="Get models status", - description="Get detailed status information about all AI models in the pipeline", -) -async def get_models_status() -> Dict[str, Any]: - """Get detailed status of all AI models.""" - return { - "emotion_detector": { - "loaded": emotion_detector is not None, - "model_type": "BERT + GoEmotions", - "capabilities": ["Multi-label emotion classification", "Emotion intensity analysis"], - "available": emotion_detector is not None, - "description": "Multi-label emotion classification", - }, - "text_summarizer": { - "loaded": text_summarizer is not None, - "model_type": "T5", - "capabilities": ["Text summarization", "Content compression"], - "available": text_summarizer is not None, - "description": "Text summarization and compression", - }, - "voice_transcriber": { - "loaded": voice_transcriber is not None, - "model_type": "OpenAI Whisper", - "capabilities": ["Speech-to-text transcription", "Language detection"], - "available": voice_transcriber is not None, - "description": "Speech-to-text transcription", - }, - "pipeline": { - "complete": all([emotion_detector, text_summarizer, voice_transcriber]), - "partial": any([emotion_detector, text_summarizer, voice_transcriber]), - "degraded_mode": not all([emotion_detector, text_summarizer, voice_transcriber]), - }, - } - - -@app.get( - "/", - tags=["System"], - summary="API information", - description="Get information about the API endpoints and capabilities", -) -async def root() -> Dict[str, Any]: - """Root endpoint with API information.""" - return { - "message": "SAMO AI Unified API is running", - "name": "SAMO AI Unified API", - "version": "1.0.0", - "description": "Complete Deep Learning Pipeline for Voice Journal Analysis", - "endpoints": { - "health": "/health", - "analyze_text": "/analyze/journal", - "analyze_voice": "/analyze/voice-journal", - "models_status": "/models/status", - }, - "capabilities": [ - "Voice-to-text transcription", - "Emotion detection and analysis", - "Text summarization", - "Complete journal processing pipeline", - ], - } - - -if __name__ == "__main__": - uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/src/utils.py b/src/utils.py index 509717b8f..70e24e04d 100644 --- a/src/utils.py +++ b/src/utils.py @@ -1,8 +1,8 @@ #!/usr/bin/env python3 """Utility functions for the SAMO-DL project.""" + import torch -from typing import Union def count_model_params(model: torch.nn.Module, only_trainable: bool = False) -> int: diff --git a/test_samo_t5_standalone.py b/test_samo_t5_standalone.py index 9b2497dcf..f15c3b847 100644 --- a/test_samo_t5_standalone.py +++ b/test_samo_t5_standalone.py @@ -19,25 +19,26 @@ # Alternative import path from src.models.summarization.samo_t5_summarizer import create_samo_t5_summarizer + def test_summarizer_initialization(): """Test summarizer initialization and model info.""" print("1. Initializing SAMO T5 Summarizer...") cfg_path = str((Path(__file__).resolve().parent / "configs" / "samo_t5_config.yaml")) summarizer = create_samo_t5_summarizer(cfg_path) print("โœ… Summarizer initialized successfully") - + # Test model info print("\n2. Checking model information...") model_info = summarizer.get_model_info() - assert model_info['model_loaded'], "Model should be loaded" - assert model_info['tokenizer_loaded'], "Tokenizer should be loaded" - assert model_info['model_name'] == "t5-small", "Should use t5-small model" - + assert model_info["model_loaded"], "Model should be loaded" + assert model_info["tokenizer_loaded"], "Tokenizer should be loaded" + assert model_info["model_name"] == "t5-small", "Should use t5-small model" + print(f" Model: {model_info['model_name']}") print(f" Device: {model_info['device']}") print(f" Model loaded: {model_info['model_loaded']}") print(f" Tokenizer loaded: {model_info['tokenizer_loaded']}") - + return summarizer @@ -53,9 +54,9 @@ def test_single_summarization(summarizer): some of the techniques I learned. This has been one of the most productive days I've had in months. """ - + result = summarizer.generate_summary(test_text) - + # Assertions instead of conditionals assert result["success"], f"Summarization failed: {result.get('error', 'Unknown error')}" assert "summary" in result, "Result should contain summary" @@ -66,7 +67,7 @@ def test_single_summarization(summarizer): assert 0 < result["compression_ratio"] < 1, "Compression ratio should be between 0 and 1" assert "emotional_keywords" in result, "Result should contain emotional keywords" assert isinstance(result["emotional_keywords"], list), "Emotional keywords should be a list" - + print("โœ… Summarization successful!") print(f" Original length: {result['original_length']} words") print(f" Summary length: {result['summary_length']} words") @@ -82,47 +83,51 @@ def test_batch_processing(summarizer): test_texts = [ "I'm feeling really happy today because I accomplished my goals and I'm excited about the future possibilities that lie ahead.", "This has been a challenging week with many obstacles to overcome but I'm grateful for the lessons learned and the growth I've experienced.", - "I'm grateful for all the support I've received from my friends and family during this difficult time and I know I can count on them." + "I'm grateful for all the support I've received from my friends and family during this difficult time and I know I can count on them.", ] - + batch_results = summarizer.generate_batch_summaries(test_texts) - + # Assertions for batch processing assert len(batch_results) == len(test_texts), "Should return results for all inputs" - + successful_summaries = sum(r["success"] for r in batch_results) print(f"โœ… Batch processing: {successful_summaries}/{len(test_texts)} successful") - + # Assert each summary is non-empty and emotional keywords are extracted for idx, result in enumerate(batch_results): assert result["success"], f"Batch summary {idx} failed" - assert "summary" in result and isinstance(result["summary"], str) and result["summary"].strip(), f"Summary {idx} is empty" - assert "emotional_keywords" in result and isinstance(result["emotional_keywords"], list), f"Emotional keywords missing for input {idx}" + assert ( + "summary" in result and isinstance(result["summary"], str) and result["summary"].strip() + ), f"Summary {idx} is empty" + assert "emotional_keywords" in result and isinstance( + result["emotional_keywords"], list + ), f"Emotional keywords missing for input {idx}" def test_error_handling(summarizer): """Test error handling with individual test cases.""" print("\n5. Testing error handling...") - + # Test empty text result = summarizer.generate_summary("") assert not result["success"], "Empty text should fail" assert "error" in result, "Error should be reported" print(f" โœ… Empty text handled correctly: {result['error']}") - + # Test too short text result = summarizer.generate_summary("Short") assert not result["success"], "Short text should fail" assert "error" in result, "Error should be reported" print(f" โœ… Short text handled correctly: {result['error']}") - + # Test too long text long_text = "word " * 1000 # Create text with 1000 words result = summarizer.generate_summary(long_text) assert not result["success"], "Long text should fail" assert "error" in result, "Error should be reported" print(f" โœ… Long text handled correctly: {result['error']}") - + # Test wrong type result = summarizer.generate_summary(123) assert not result["success"], "Wrong type should fail" @@ -134,23 +139,25 @@ def test_samo_t5_summarizer(): """Test the SAMO T5 summarizer functionality.""" print("๐Ÿงช Testing SAMO T5 Summarization Model") print("=" * 50) - + try: # Run all test functions summarizer = test_summarizer_initialization() test_single_summarization(summarizer) test_batch_processing(summarizer) test_error_handling(summarizer) - + print("\n๐ŸŽ‰ All tests completed successfully!") return True - + except Exception as e: print(f"โŒ Test failed with error: {e}") import traceback + traceback.print_exc() return False + if __name__ == "__main__": success = test_samo_t5_summarizer() sys.exit(0 if success else 1) diff --git a/test_samo_whisper_standalone.py b/test_samo_whisper_standalone.py index 794d57efe..a1e87263b 100644 --- a/test_samo_whisper_standalone.py +++ b/test_samo_whisper_standalone.py @@ -6,33 +6,36 @@ to ensure it works correctly before integration. """ -import sys -import os import logging +import os +import sys from pathlib import Path + import numpy as np import soundfile as sf +from models.voice_processing.samo_whisper_transcriber import create_samo_whisper_transcriber + # Add src to path for standalone testing # Note: This is necessary for the standalone test script to import modules # In production, the project should be installed with pip install -e . sys.path.insert(0, str(Path(__file__).parent / "src")) -from models.voice_processing.samo_whisper_transcriber import create_samo_whisper_transcriber logger = logging.getLogger(__name__) + def test_audio_files(): """Test available audio files.""" # Note: These are hardcoded for standalone testing # In CI/CD, consider using synthetic audio or test fixtures test_audio_files = [ "american_sample.wav", - "french_sample.wav", + "french_sample.wav", "interview_audio.wav", - "test_audio.wav" + "test_audio.wav", ] - + available_audio = [] # Note: Loops and conditionals are acceptable in standalone integration tests # This is not a unit test but a comprehensive integration test script @@ -42,7 +45,7 @@ def test_audio_files(): print(f" โœ… Found: {audio_file}") else: print(f" โš ๏ธ Not found: {audio_file}") - + return available_audio @@ -51,9 +54,9 @@ def test_single_transcription(transcriber, audio_file, file_num, expected_langua print(f"\n Testing file {file_num}: {audio_file}") try: result = transcriber.transcribe(audio_file) - + print(" โœ… Transcription successful!") - text_preview = result.text[:100] + ('...' if len(result.text) > 100 else '') + text_preview = result.text[:100] + ("..." if len(result.text) > 100 else "") print(f" Text: {text_preview}") print(f" Language: {result.language}") print(f" Confidence: {result.confidence:.3f}") @@ -65,11 +68,11 @@ def test_single_transcription(transcriber, audio_file, file_num, expected_langua print(f" No speech probability: {result.no_speech_probability:.3f}") if expected_language is not None: - assert result.language == expected_language, ( - f"Detected language '{result.language}' does not match expected '{expected_language}'" - ) + assert ( + result.language == expected_language + ), f"Detected language '{result.language}' does not match expected '{expected_language}'" print(f" โœ… Language detection correct: {result.language}") - + except Exception as e: print(f" โŒ Transcription failed: {e}") @@ -81,15 +84,15 @@ def test_batch_transcription(transcriber, available_audio): results = transcriber.transcribe_batch(available_audio) successful = sum(bool(r.text.strip()) for r in results) print(f" โœ… Batch transcription complete: {successful}/{len(results)} successful") - + total_duration = sum(r.duration for r in results) total_processing = sum(r.processing_time for r in results) avg_confidence = sum(r.confidence for r in results) / len(results) - + print(f" Total audio: {total_duration:.1f}s") print(f" Total processing: {total_processing:.1f}s") print(f" Average confidence: {avg_confidence:.3f}") - + except Exception as e: print(f" โŒ Batch transcription failed: {e}") @@ -97,29 +100,29 @@ def test_batch_transcription(transcriber, available_audio): def test_silence_detection(transcriber): """Test silence detection with silent audio.""" print("\n6. Testing silence detection...") - - + # Generate 2 seconds of silence at 16kHz silent_wav_path = "silent_test.wav" sr = 16000 silence = np.zeros(sr * 2, dtype=np.float32) - + try: # Create silent audio file sf.write(silent_wav_path, silence, sr) print(f" Created silent audio file: {silent_wav_path}") - + # Test transcription result = transcriber.transcribe(silent_wav_path) print(f" Text: {result.text!r}") print(f" No speech probability: {result.no_speech_probability:.3f}") print(f" Audio quality: {result.audio_quality}") - - + # Assert high no speech probability for silence - assert result.no_speech_probability > 0.5, f"No speech probability should be high for silence, got {result.no_speech_probability:.3f}" + assert ( + result.no_speech_probability > 0.5 + ), f"No speech probability should be high for silence, got {result.no_speech_probability:.3f}" print(" โœ… Silence detection test passed") - + except Exception as e: print(f" โŒ Silence detection test failed: {e}") raise @@ -133,51 +136,57 @@ def test_silence_detection(transcriber): def test_multilingual_language_detection(transcriber): """Test multilingual audio samples for language detection accuracy.""" print("\n7. Testing multilingual language detection...") - + # Define multilingual audio samples and their expected languages multilingual_samples = [ {"audio_file": "american_sample.wav", "expected_language": "en"}, {"audio_file": "french_sample.wav", "expected_language": "fr"}, # Add more samples as they become available ] - + print("Testing multilingual audio samples for language detection accuracy:") successful_detections = 0 total_tests = 0 - + for idx, sample in enumerate(multilingual_samples, 1): audio_file = sample["audio_file"] expected_language = sample["expected_language"] - + if Path(audio_file).exists(): total_tests += 1 print(f"\n Testing file {idx}: {audio_file}") print(f" Expected language: {expected_language}") - + try: result = transcriber.transcribe(audio_file) detected_language = result.language confidence = result.confidence - + print(f" Detected language: {detected_language}") print(f" Confidence: {confidence:.3f}") - print(f" Text preview: {result.text[:100]}{'...' if len(result.text) > 100 else ''}") - + print( + f" Text preview: {result.text[:100]}{'...' if len(result.text) > 100 else ''}" + ) + if detected_language == expected_language: print(f" โœ… Language detection correct: {detected_language}") successful_detections += 1 else: - print(f" โŒ Language detection incorrect: expected {expected_language}, got {detected_language}") - + print( + f" โŒ Language detection incorrect: expected {expected_language}, got {detected_language}" + ) + except Exception as e: print(f" โŒ Transcription failed: {e}") else: print(f" โš ๏ธ Audio file not found: {audio_file}") - + if total_tests > 0: accuracy = (successful_detections / total_tests) * 100 - print(f"\n Language detection accuracy: {successful_detections}/{total_tests} ({accuracy:.1f}%)") - + print( + f"\n Language detection accuracy: {successful_detections}/{total_tests} ({accuracy:.1f}%)" + ) + if accuracy >= 90: print(" โœ… Language detection accuracy meets target (โ‰ฅ90%)") else: @@ -194,7 +203,7 @@ def test_samo_whisper_transcriber(): try: # Note: This is a comprehensive integration test script, not a unit test # The main function orchestrates multiple test phases for end-to-end validation - + # Initialize transcriber print("1. Initializing SAMO Whisper Transcriber...") transcriber = create_samo_whisper_transcriber("configs/samo_whisper_config.yaml") @@ -214,7 +223,7 @@ def test_samo_whisper_transcriber(): # Test audio preprocessing print("\n3. Testing audio preprocessing...") available_audio = test_audio_files() - + if not available_audio: print(" โš ๏ธ No test audio files found. Creating a simple test...") # Test with a simple audio validation @@ -227,7 +236,7 @@ def test_samo_whisper_transcriber(): else: # Test transcription with available audio print(f"\n4. Testing transcription with {len(available_audio)} audio file(s)...") - + for i, audio_file in enumerate(available_audio, 1): # Test all available files test_single_transcription(transcriber, audio_file, i) @@ -237,14 +246,14 @@ def test_samo_whisper_transcriber(): # Test silence detection test_silence_detection(transcriber) - + # Test multilingual language detection test_multilingual_language_detection(transcriber) print("\n" + "=" * 50) print("๐ŸŽ‰ SAMO Whisper Transcriber test completed successfully!") print("โœ… Model loaded and ready for production use") - + return True except Exception as e: @@ -252,6 +261,7 @@ def test_samo_whisper_transcriber(): print(f"\nโŒ Test failed: {e}") return False + if __name__ == "__main__": success = test_samo_whisper_transcriber() sys.exit(0 if success else 1) diff --git a/tests/conftest.py b/tests/conftest.py index 34621f56b..013600b72 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,17 +1,18 @@ - # Create a simple sine wave for testing -# Custom markers for test categorization -# Skip GPU tests if CUDA not available -from fastapi.testclient import TestClient +# Create a simple sine wave for testing +import os +import tempfile from pathlib import Path -from src.unified_ai_api import app from unittest.mock import Mock, patch + import numpy as np -import os import pytest -import tempfile import torch +# Custom markers for test categorization +# Skip GPU tests if CUDA not available +from fastapi.testclient import TestClient +from src.unified_ai_api import app """ SAMO Deep Learning - Pytest Configuration and Shared Fixtures @@ -103,11 +104,11 @@ def cpu_device(): def api_client(): """Provide FastAPI test client.""" client = TestClient(app) - + # Reset rate limiter state before each test - if hasattr(app.state, 'rate_limiter'): + if hasattr(app.state, "rate_limiter"): app.state.rate_limiter.reset_state() - + return client diff --git a/tests/e2e/__pycache__/__init__.cpython-38.pyc b/tests/e2e/__pycache__/__init__.cpython-38.pyc deleted file mode 100644 index 8360bab3a..000000000 Binary files a/tests/e2e/__pycache__/__init__.cpython-38.pyc and /dev/null differ diff --git a/tests/e2e/__pycache__/test_complete_workflows.cpython-38-pytest-8.3.5.pyc b/tests/e2e/__pycache__/test_complete_workflows.cpython-38-pytest-8.3.5.pyc deleted file mode 100644 index 2d542f772..000000000 Binary files a/tests/e2e/__pycache__/test_complete_workflows.cpython-38-pytest-8.3.5.pyc and /dev/null differ diff --git a/tests/e2e/test_complete_workflows.py b/tests/e2e/test_complete_workflows.py index 06f7c8dbb..21d8619a3 100644 --- a/tests/e2e/test_complete_workflows.py +++ b/tests/e2e/test_complete_workflows.py @@ -24,7 +24,8 @@ class TestCompleteWorkflows: """End-to-end tests for SAMO AI complete user workflows.""" - def test_text_journal_complete_workflow(self, api_client, sample_journal_entry): + @staticmethod + def test_text_journal_complete_workflow(api_client, sample_journal_entry): """Test complete text journal analysis workflow.""" start_time = time.time() @@ -67,10 +68,13 @@ def test_text_journal_complete_workflow(self, api_client, sample_journal_entry): assert isinstance(summary["key_emotions"], list) assert workflow_time < MAX_WORKFLOW_TIME # Complete workflow under 3 seconds - assert data["processing_time_ms"] < MAX_PROCESSING_TIME * 1000 # Processing time under 2 seconds + assert ( + data["processing_time_ms"] < MAX_PROCESSING_TIME * 1000 + ) # Processing time under 2 seconds @pytest.mark.slow - def test_voice_journal_complete_workflow(self, api_client, sample_audio_data): + @staticmethod + def test_voice_journal_complete_workflow(api_client, sample_audio_data): """Test complete voice journal analysis workflow.""" with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio: temp_audio.write(b"fake audio data for testing") @@ -107,7 +111,8 @@ def test_voice_journal_complete_workflow(self, api_client, sample_audio_data): # Clean up temporary file Path(temp_audio_path).unlink(missing_ok=True) - def test_error_recovery_workflow(self, api_client): + @staticmethod + def test_error_recovery_workflow(api_client): """Test error recovery and graceful degradation.""" # Test with invalid input response = api_client.post( @@ -135,10 +140,15 @@ def test_error_recovery_workflow(self, api_client): ) assert response.status_code == HTTP_OK - def test_high_volume_workflow(self, api_client): + @staticmethod + def test_high_volume_workflow(api_client): """Test high volume processing with multiple requests.""" requests_data = [ - {"text": f"Request {i}: I had a great day!", "generate_summary": True, "emotion_threshold": 0.5} + { + "text": f"Request {i}: I had a great day!", + "generate_summary": True, + "emotion_threshold": 0.5, + } for i in range(5) ] @@ -150,7 +160,8 @@ def test_high_volume_workflow(self, api_client): assert success_count >= 4 # At least 80% success rate - def test_data_consistency_workflow(self, api_client): + @staticmethod + def test_data_consistency_workflow(api_client): """Test data consistency across multiple requests.""" test_text = "I had a great day today!" responses = [] @@ -173,14 +184,15 @@ def test_data_consistency_workflow(self, api_client): # Check data consistency response_data = [r.json() for r in responses] - + # Basic structure should be consistent for data in response_data: assert "emotion_analysis" in data assert "summary" in data assert "processing_time_ms" in data - def test_configuration_workflow(self, api_client): + @staticmethod + def test_configuration_workflow(api_client): """Test different configuration options.""" test_text = "I had a great day today!" @@ -207,7 +219,8 @@ def test_configuration_workflow(self, api_client): assert response.status_code == HTTP_OK @pytest.mark.model - def test_model_integration_workflow(self, api_client): + @staticmethod + def test_model_integration_workflow(api_client): """Test integration between different AI models.""" test_text = "I had a great day today!" diff --git a/tests/integration/__pycache__/__init__.cpython-38.pyc b/tests/integration/__pycache__/__init__.cpython-38.pyc deleted file mode 100644 index 036ec3b5c..000000000 Binary files a/tests/integration/__pycache__/__init__.cpython-38.pyc and /dev/null differ diff --git a/tests/integration/__pycache__/test_api_endpoints.cpython-38-pytest-8.3.5.pyc b/tests/integration/__pycache__/test_api_endpoints.cpython-38-pytest-8.3.5.pyc deleted file mode 100644 index 210192f8b..000000000 Binary files a/tests/integration/__pycache__/test_api_endpoints.cpython-38-pytest-8.3.5.pyc and /dev/null differ diff --git a/tests/integration/test_api_endpoints.py b/tests/integration/test_api_endpoints.py index 9cb71e451..f320cf70a 100644 --- a/tests/integration/test_api_endpoints.py +++ b/tests/integration/test_api_endpoints.py @@ -1,43 +1,45 @@ - # Check field types are consistent - # CI environment should respond within 2 seconds - # Check all requests succeeded - # Check all responses have same structure - # Check emotion analysis structure - # Check expected models - # Check model status structure - # Check processing time in response - # Check required fields - # Check response structure - # Create multiple threads - # For JSON-based endpoints, form data might not be accepted - # Mock the emotion detection - # Note: Depending on FastAPI configuration, this might need adjustment - # Test JSON content type (primary) - # Test empty text - # Test form data (fallback) - # Test invalid endpoint - # Test malformed request - # Test missing required field - # Test very long text - # Wait for all threads to complete -from unittest.mock import patch -import pytest +# Check field types are consistent +# CI environment should respond within 2 seconds +# Check all requests succeeded +# Check all responses have same structure +# Check emotion analysis structure +# Check expected models +# Check model status structure +# Check processing time in response +# Check required fields +# Check response structure +# Create multiple threads +# For JSON-based endpoints, form data might not be accepted +# Mock the emotion detection +# Note: Depending on FastAPI configuration, this might need adjustment +# Test JSON content type (primary) +# Test empty text +# Test form data (fallback) +# Test invalid endpoint +# Test malformed request +# Test missing required field +# Test very long text +# Wait for all threads to complete import queue import threading import time +from unittest.mock import patch + +import pytest + + """ Integration tests for API endpoints. Tests API functionality, request/response handling, and error scenarios. """ - - @pytest.mark.integration class TestAPIEndpoints: """Integration tests for SAMO AI API endpoints.""" - def test_health_endpoint(self, api_client): + @staticmethod + def test_health_endpoint(api_client): """Test /health endpoint returns correct status.""" response = api_client.get("/health") @@ -53,7 +55,8 @@ def test_health_endpoint(self, api_client): assert "loaded" in model_status assert "status" in model_status - def test_root_endpoint(self, api_client): + @staticmethod + def test_root_endpoint(api_client): """Test root endpoint returns welcome message.""" response = api_client.get("/") @@ -65,7 +68,8 @@ def test_root_endpoint(self, api_client): assert "version" in data @patch("src.models.emotion_detection.bert_classifier.BERTEmotionClassifier") - def test_journal_analysis_endpoint(self, mock_bert, api_client): + @staticmethod + def test_journal_analysis_endpoint(mock_bert, api_client): """Test /analyze/journal endpoint with text input.""" mock_model = mock_bert.return_value mock_model.predict_emotions.return_value = [0, 13, 17] # joy, excitement, gratitude @@ -93,7 +97,8 @@ def test_journal_analysis_endpoint(self, mock_bert, api_client): assert "confidence" in emotion_analysis assert isinstance(emotion_analysis["emotions"], dict) - def test_journal_analysis_validation(self, api_client): + @staticmethod + def test_journal_analysis_validation(api_client): """Test journal analysis input validation.""" response = api_client.post("/analyze/journal", json={"text": ""}) assert response.status_code == 422 @@ -105,7 +110,8 @@ def test_journal_analysis_validation(self, api_client): response = api_client.post("/analyze/journal", json={}) assert response.status_code == 422 - def test_models_status_endpoint(self, api_client): + @staticmethod + def test_models_status_endpoint(api_client): """Test /models/status endpoint returns model information.""" response = api_client.get("/models/status") @@ -121,7 +127,8 @@ def test_models_status_endpoint(self, api_client): assert "capabilities" in data[model] @pytest.mark.slow - def test_performance_requirements(self, api_client): + @staticmethod + def test_performance_requirements(api_client): """Test API meets performance requirements.""" test_data = {"text": "I feel great today! This is a wonderful experience."} @@ -138,7 +145,8 @@ def test_performance_requirements(self, api_client): assert "processing_time_ms" in data assert data["processing_time_ms"] > 0 - def test_error_handling(self, api_client): + @staticmethod + def test_error_handling(api_client): """Test API error handling and response format.""" response = api_client.get("/invalid/endpoint") assert response.status_code == 404 @@ -150,7 +158,8 @@ def test_error_handling(self, api_client): ) assert response.status_code == 422 - def test_concurrent_requests(self, api_client): + @staticmethod + def test_concurrent_requests(api_client): """Test API handles concurrent requests.""" results = queue.Queue() test_data = {"text": "Testing concurrent request handling."} @@ -175,7 +184,8 @@ def make_request(): result = results.get() assert result == 200 - def test_content_type_handling(self, api_client): + @staticmethod + def test_content_type_handling(api_client): """Test API handles different content types correctly.""" test_data = {"text": "Testing content type handling."} @@ -184,7 +194,8 @@ def test_content_type_handling(self, api_client): response = api_client.post("/analyze/journal", data=test_data) - def test_response_consistency(self, api_client): + @staticmethod + def test_response_consistency(api_client): """Test API response format consistency across multiple calls.""" test_data = {"text": "Testing response consistency."} @@ -194,7 +205,13 @@ def test_response_consistency(self, api_client): assert response.status_code == 200 responses.append(response.json()) - required_fields = ["emotion_analysis", "summary", "processing_time_ms", "pipeline_status", "insights"] + required_fields = [ + "emotion_analysis", + "summary", + "processing_time_ms", + "pipeline_status", + "insights", + ] for response_data in responses: for field in required_fields: diff --git a/tests/integration/test_priority1_features.py b/tests/integration/test_priority1_features.py index 417f014cd..a14e89e1e 100644 --- a/tests/integration/test_priority1_features.py +++ b/tests/integration/test_priority1_features.py @@ -9,20 +9,18 @@ 5. Comprehensive Monitoring Dashboard """ -import asyncio -import json import os import tempfile -from pathlib import Path import time -from typing import Dict, Any +from pathlib import Path +from unittest.mock import patch + import pytest from fastapi.testclient import TestClient -from unittest.mock import Mock, patch -from src.unified_ai_api import app -from src.security.jwt_manager import JWTManager from src.monitoring.dashboard import MonitoringDashboard +from src.security.jwt_manager import JWTManager +from src.unified_ai_api import app # Test client with test user agent to bypass rate limiting client = TestClient(app, headers={"User-Agent": "pytest-testclient"}) @@ -53,163 +51,164 @@ def __exit__(self, exc_type, exc, tb): pass self._opened = [] + @pytest.fixture(autouse=True) def reset_state(): """Reset rate limiter and JWT manager state between tests.""" # Reset rate limiter state - if hasattr(app.state, 'rate_limiter'): + if hasattr(app.state, "rate_limiter"): app.state.rate_limiter.reset_state() - + # Reset JWT manager blacklist from src.unified_ai_api import jwt_manager + jwt_manager.blacklisted_tokens.clear() # Enable test-only permission injection path for batch endpoints os.environ["PYTEST_CURRENT_TEST"] = "1" os.environ["ENABLE_TEST_PERMISSION_INJECTION"] = "true" - + yield + class TestJWTAuthentication: """Test JWT-based authentication system.""" - - def test_user_registration(self): + + @staticmethod + def test_user_registration(): """Test user registration endpoint.""" user_data = { "username": "testuser@example.com", "email": "testuser@example.com", "password": "testpassword123", - "full_name": "Test User" + "full_name": "Test User", } - + response = client.post("/auth/register", json=user_data) assert response.status_code == 200 - + data = response.json() assert "access_token" in data assert "refresh_token" in data assert data["token_type"] == "bearer" assert data["expires_in"] > 0 - - def test_user_login(self): + + @staticmethod + def test_user_login(): """Test user login endpoint.""" - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } - + login_data = {"username": "testuser@example.com", "password": "testpassword123"} + response = client.post("/auth/login", json=login_data) assert response.status_code == 200 - + data = response.json() assert "access_token" in data assert "refresh_token" in data - - def test_token_refresh(self): + + @staticmethod + def test_token_refresh(): """Test token refresh endpoint.""" # First login to get tokens - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) refresh_token = login_response.json()["refresh_token"] - + # Test refresh with proper request body response = client.post("/auth/refresh", json={"refresh_token": refresh_token}) assert response.status_code == 200 - + data = response.json() assert "access_token" in data assert "refresh_token" in data - - def test_token_refresh_invalid_token(self): + + @staticmethod + def test_token_refresh_invalid_token(): """Test token refresh with invalid refresh token.""" response = client.post("/auth/refresh", json={"refresh_token": "invalid_token"}) assert response.status_code == 401 # Unauthorized - - def test_protected_endpoint_with_auth(self): + + @staticmethod + def test_protected_endpoint_with_auth(): """Test accessing protected endpoint with valid token.""" # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Test protected endpoint headers = {"Authorization": f"Bearer {access_token}"} response = client.get("/auth/profile", headers=headers) assert response.status_code == 200 - + data = response.json() assert "user_id" in data assert "username" in data assert "email" in data - - def test_protected_endpoint_without_auth(self): + + @staticmethod + def test_protected_endpoint_without_auth(): """Test accessing protected endpoint without authentication.""" response = client.get("/auth/profile") - assert response.status_code == 403 # Forbidden - FastAPI returns 403 for missing authentication - - def test_invalid_token(self): + assert ( + response.status_code == 403 + ) # Forbidden - FastAPI returns 403 for missing authentication + + @staticmethod + def test_invalid_token(): """Test accessing protected endpoint with invalid token.""" headers = {"Authorization": "Bearer invalid_token"} response = client.get("/auth/profile", headers=headers) assert response.status_code == 403 # Forbidden - FastAPI returns 403 for invalid tokens + class TestEnhancedVoiceTranscription: """Test enhanced voice transcription features.""" - - @patch('src.unified_ai_api.voice_transcriber') - def test_voice_transcription_endpoint(self, mock_transcriber): + + @patch("src.unified_ai_api.voice_transcriber") + @staticmethod + def test_voice_transcription_endpoint(mock_transcriber): """Test enhanced voice transcription endpoint.""" # Mock transcription result mock_transcriber.return_value.transcribe.return_value = { "text": "This is a test transcription", "language": "en", "confidence": 0.95, - "duration": 10.5 + "duration": 10.5, } - + # Removed duplicate early definitions; see patched versions below - - @patch('src.unified_ai_api.voice_transcriber') - def test_voice_transcription_missing_file(self, mock_transcriber): + + @patch("src.unified_ai_api.voice_transcriber") + @staticmethod + def test_voice_transcription_missing_file(mock_transcriber): """Test voice transcription with missing audio file.""" # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Test transcription endpoint without file headers = {"Authorization": f"Bearer {access_token}"} response = client.post("/transcribe/voice", headers=headers) - + assert response.status_code == 422 # Validation error - - @patch('src.unified_ai_api.voice_transcriber') - def test_voice_transcription_invalid_format(self, mock_transcriber): + + @patch("src.unified_ai_api.voice_transcriber") + @staticmethod + def test_voice_transcription_invalid_format(mock_transcriber): """Test voice transcription with invalid audio format.""" # Mock transcription to raise exception mock_transcriber.transcribe.side_effect = Exception("Invalid audio format") - + # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Create test file with invalid content with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as temp_file: temp_file.write(b"not audio data") temp_file_path = temp_file.name - + try: # Test transcription endpoint headers = {"Authorization": f"Bearer {access_token}"} @@ -217,25 +216,26 @@ def test_voice_transcription_invalid_format(self, mock_transcriber): files = {"audio_file": ("test.txt", audio_file, "text/plain")} data = {"language": "en", "model_size": "base"} response = client.post("/transcribe/voice", files=files, data=data, headers=headers) - + assert response.status_code == 500 # Internal server error - + finally: Path(temp_file_path).unlink(missing_ok=True) - - @patch('src.unified_ai_api.voice_transcriber') - def test_batch_transcription(self, mock_transcriber): + + @patch("src.unified_ai_api.voice_transcriber") + @staticmethod + def test_batch_transcription(mock_transcriber): """Test batch transcription endpoint.""" # Mock transcription result mock_transcriber.return_value.transcribe.return_value = { "text": "Batch transcription result", "language": "en", "confidence": 0.92, - "duration": 8.0 + "duration": 8.0, } - - # Removed duplicate early definition; deterministic version retained below - + + # Removed duplicate early definition; deterministic version retained below + # Create test audio files temp_files = [] try: @@ -244,30 +244,27 @@ def test_batch_transcription(self, mock_transcriber): temp_file.write(b"fake audio data") temp_file.close() temp_files.append(temp_file.name) - + # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Test batch transcription endpoint with proper permission headers = { "Authorization": f"Bearer {access_token}", - "X-User-Permissions": "batch_processing" + "X-User-Permissions": "batch_processing", } files = [] for i, temp_file_path in enumerate(temp_files): with open(temp_file_path, "rb") as audio_file: files.append(("audio_files", (f"test{i}.wav", audio_file, "audio/wav"))) - + data = {"language": "en"} response = client.post("/transcribe/batch", files=files, data=data, headers=headers) - + assert response.status_code == 200 - + data = response.json() assert "total_files" in data assert "successful_transcriptions" in data @@ -277,22 +274,27 @@ def test_batch_transcription(self, mock_transcriber): # Negative cases: missing and incorrect permissions missing_headers = {"Authorization": f"Bearer {access_token}"} - response_missing = client.post("/transcribe/batch", files=files, data=data, headers=missing_headers) + response_missing = client.post( + "/transcribe/batch", files=files, data=data, headers=missing_headers + ) assert response_missing.status_code == 403 wrong_headers = { "Authorization": f"Bearer {access_token}", - "X-User-Permissions": "wrong_permission" + "X-User-Permissions": "wrong_permission", } - response_wrong = client.post("/transcribe/batch", files=files, data=data, headers=wrong_headers) + response_wrong = client.post( + "/transcribe/batch", files=files, data=data, headers=wrong_headers + ) assert response_wrong.status_code == 403 - + finally: for temp_file_path in temp_files: Path(temp_file_path).unlink(missing_ok=True) - - @patch('src.unified_ai_api.voice_transcriber') - def test_batch_transcription_partial_failures(self, mock_transcriber): + + @patch("src.unified_ai_api.voice_transcriber") + @staticmethod + def test_batch_transcription_partial_failures(mock_transcriber): """Test batch transcription with partial failures.""" # Deterministic side effect (no conditionals): first success, then failure mock_transcriber.transcribe.side_effect = [ @@ -304,7 +306,7 @@ def test_batch_transcription_partial_failures(self, mock_transcriber): }, RuntimeError("Transcription failed"), ] - + # Create test audio files temp_files = [] try: @@ -314,21 +316,21 @@ def test_batch_transcription_partial_failures(self, mock_transcriber): temp_file.write(b"fake audio data") temp_file.close() temp_files.append(temp_file.name) - + # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Test batch transcription endpoint - headers = {"Authorization": f"Bearer {access_token}", "X-User-Permissions": "batch_processing"} + headers = { + "Authorization": f"Bearer {access_token}", + "X-User-Permissions": "batch_processing", + } data = {"language": "en"} with to_uploads(temp_files, "file") as files: response = client.post("/transcribe/batch", files=files, data=data, headers=headers) - + assert response.status_code == 200 data = response.json() assert data["total_files"] == 2 @@ -340,8 +342,10 @@ def test_batch_transcription_partial_failures(self, mock_transcriber): finally: for temp_file_path in temp_files: Path(temp_file_path).unlink(missing_ok=True) - @patch('src.unified_ai_api.voice_transcriber') - def test_batch_transcription_all_failures(self, mock_transcriber): + + @patch("src.unified_ai_api.voice_transcriber") + @staticmethod + def test_batch_transcription_all_failures(mock_transcriber): """Test batch transcription where all transcriptions fail.""" mock_transcriber.transcribe.side_effect = RuntimeError("Transcription failed") @@ -358,7 +362,10 @@ def test_batch_transcription_all_failures(self, mock_transcriber): login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - headers = {"Authorization": f"Bearer {access_token}", "X-User-Permissions": "batch_processing"} + headers = { + "Authorization": f"Bearer {access_token}", + "X-User-Permissions": "batch_processing", + } with to_uploads(temp_files, "f") as files: response = client.post("/transcribe/batch", files=files, headers=headers) @@ -372,11 +379,14 @@ def test_batch_transcription_all_failures(self, mock_transcriber): for temp_file_path in temp_files: Path(temp_file_path).unlink(missing_ok=True) - @patch('src.unified_ai_api.voice_transcriber') - def test_batch_transcription_all_success(self, mock_transcriber): + @patch("src.unified_ai_api.voice_transcriber") + @staticmethod + def test_batch_transcription_all_success(mock_transcriber): """Test batch transcription where all transcriptions succeed.""" + def ok_side_effect(file_path, language=None): return {"text": "ok", "language": "en", "confidence": 0.9, "duration": 1.0} + mock_transcriber.transcribe.side_effect = ok_side_effect temp_files = [] @@ -390,7 +400,10 @@ def ok_side_effect(file_path, language=None): login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - headers = {"Authorization": f"Bearer {access_token}", "X-User-Permissions": "batch_processing"} + headers = { + "Authorization": f"Bearer {access_token}", + "X-User-Permissions": "batch_processing", + } with to_uploads(temp_files, "f") as files: response = client.post("/transcribe/batch", files=files, headers=headers) @@ -405,209 +418,203 @@ def ok_side_effect(file_path, language=None): for temp_file_path in temp_files: Path(temp_file_path).unlink(missing_ok=True) + class TestEnhancedTextSummarization: """Test enhanced text summarization features.""" - - @patch('src.unified_ai_api.text_summarizer') - def test_text_summarization_endpoint(self, mock_summarizer): + + @patch("src.unified_ai_api.text_summarizer") + @staticmethod + def test_text_summarization_endpoint(mock_summarizer): """Test enhanced text summarization endpoint.""" # Mock summarization result mock_summarizer.return_value.summarize.return_value = { "summary": "This is a test summary of the input text.", "key_emotions": ["neutral"], - "compression_ratio": 0.75 + "compression_ratio": 0.75, } - + # Removed duplicate early summarization tests; consolidated versions follow - - @patch('src.unified_ai_api.text_summarizer') - def test_text_summarization_empty_input(self, mock_summarizer): + + @patch("src.unified_ai_api.text_summarizer") + @staticmethod + def test_text_summarization_empty_input(mock_summarizer): """Test summarization endpoint with empty input.""" # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Test with empty text headers = {"Authorization": f"Bearer {access_token}"} data = {"text": "", "model": "t5-small"} response = client.post("/summarize/text", data=data, headers=headers) - + assert response.status_code == 422 # Validation error - - @patch('src.unified_ai_api.text_summarizer') - def test_text_summarization_too_short_input(self, mock_summarizer): + + @patch("src.unified_ai_api.text_summarizer") + @staticmethod + def test_text_summarization_too_short_input(mock_summarizer): """Test summarization endpoint with too-short input.""" # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Test with too short text (less than min_length=10) headers = {"Authorization": f"Bearer {access_token}"} data = {"text": "Hi.", "model": "t5-small"} response = client.post("/summarize/text", data=data, headers=headers) - + assert response.status_code == 422 # Validation error - - @patch('src.unified_ai_api.text_summarizer') - def test_text_summarization_unsupported_model(self, mock_summarizer): + + @patch("src.unified_ai_api.text_summarizer") + @staticmethod + def test_text_summarization_unsupported_model(mock_summarizer): """Test summarization endpoint with unsupported model name.""" # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Test with unsupported model headers = {"Authorization": f"Bearer {access_token}"} - data = {"text": "This is a valid input text for summarization.", "model": "nonexistent-model"} + data = { + "text": "This is a valid input text for summarization.", + "model": "nonexistent-model", + } response = client.post("/summarize/text", data=data, headers=headers) - + # Should either return 400 or 422 depending on validation assert response.status_code in [400, 422] + class TestWebSocketAuthentication: """Test WebSocket authentication and real-time processing.""" - - def test_websocket_authentication_required(self): + + @staticmethod + def test_websocket_authentication_required(): """Test that WebSocket requires authentication.""" # This would require a WebSocket client test # For now, we'll test the authentication logic - pass - - def test_websocket_with_valid_token(self): + + @staticmethod + def test_websocket_with_valid_token(): """Test WebSocket connection with valid token.""" # This would require a WebSocket client test # For now, we'll test the authentication logic - pass + class TestAPIValidation: """Test API endpoint validation and error handling.""" - - def test_voice_transcription_file_size_validation(self): + + @staticmethod + def test_voice_transcription_file_size_validation(): """Test file size validation for voice transcription.""" # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Create a large file (simulate > 50MB) large_content = b"fake audio data" * (50 * 1024 * 1024 // 16 + 1) # > 50MB - + headers = {"Authorization": f"Bearer {access_token}"} files = {"audio_file": ("large.wav", large_content, "audio/wav")} data = {"language": "en", "model_size": "base"} - + response = client.post("/transcribe/voice", files=files, data=data, headers=headers) assert response.status_code == 400 assert "too large" in response.json()["detail"].lower() - - def test_text_summarization_length_validation(self): + + @staticmethod + def test_text_summarization_length_validation(): """Test text length validation for summarization.""" # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Test with text that's too short headers = {"Authorization": f"Bearer {access_token}"} data = {"text": "Hi", "model": "t5-small"} # Too short - + response = client.post("/summarize/text", data=data, headers=headers) assert response.status_code == 422 # Validation error - - def test_batch_processing_permission_validation(self): + + @staticmethod + def test_batch_processing_permission_validation(): """Test that batch processing requires proper permissions.""" # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Test batch endpoint without batch_processing permission headers = {"Authorization": f"Bearer {access_token}"} files = [("audio_files", ("test.wav", b"fake audio", "audio/wav"))] data = {"language": "en"} - + response = client.post("/transcribe/batch", files=files, data=data, headers=headers) # Should return 403 if user doesn't have batch_processing permission assert response.status_code == 403 + class TestCompleteWorkflow: """Test complete end-to-end workflow scenarios.""" - - @patch('src.unified_ai_api.voice_transcriber') - @patch('src.unified_ai_api.text_summarizer') - @patch('src.unified_ai_api.emotion_detector') - def test_complete_voice_journal_analysis(self, mock_emotion_detector, mock_summarizer, mock_transcriber): + + @patch("src.unified_ai_api.voice_transcriber") + @patch("src.unified_ai_api.text_summarizer") + @patch("src.unified_ai_api.emotion_detector") + @staticmethod + def test_complete_voice_journal_analysis( + mock_emotion_detector, mock_summarizer, mock_transcriber + ): """Test complete voice journal analysis workflow.""" # Mock all the AI components mock_transcriber.transcribe.return_value = { "text": "Today I received a promotion at work and I'm really excited about it.", "language": "en", "confidence": 0.95, - "duration": 15.4 + "duration": 15.4, } - + mock_emotion_detector.detect_emotions.return_value = { "emotions": {"joy": 0.85, "gratitude": 0.75}, "primary_emotion": "joy", "confidence": 0.85, - "emotional_intensity": "high" + "emotional_intensity": "high", } - + mock_summarizer.summarize.return_value = { "summary": "User expressed joy about their recent promotion.", "key_emotions": ["joy", "gratitude"], "compression_ratio": 0.8, - "emotional_tone": "positive" + "emotional_tone": "positive", } - + # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Create test audio file with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file: temp_file.write(b"fake audio data") temp_file_path = temp_file.name - + try: # Test complete voice journal analysis headers = {"Authorization": f"Bearer {access_token}"} with open(temp_file_path, "rb") as audio_file: files = {"audio_file": ("test.wav", audio_file, "audio/wav")} - data = { - "language": "en", - "generate_summary": True, - "emotion_threshold": 0.1 - } - response = client.post("/analyze/voice-journal", files=files, data=data, headers=headers) - + data = {"language": "en", "generate_summary": True, "emotion_threshold": 0.1} + response = client.post( + "/analyze/voice-journal", files=files, data=data, headers=headers + ) + assert response.status_code == 200 data = response.json() - + # Check all components are present assert "transcription" in data assert "emotion_analysis" in data @@ -615,82 +622,79 @@ def test_complete_voice_journal_analysis(self, mock_emotion_detector, mock_summa assert "processing_time_ms" in data assert "pipeline_status" in data assert "insights" in data - + # Check pipeline status assert data["pipeline_status"]["voice_processing"] is True assert data["pipeline_status"]["emotion_detection"] is True assert data["pipeline_status"]["text_summarization"] is True - + finally: Path(temp_file_path).unlink(missing_ok=True) - - def test_authentication_workflow(self): + + @staticmethod + def test_authentication_workflow(): """Test complete authentication workflow.""" # 1. Register new user user_data = { "username": "newuser@example.com", "email": "newuser@example.com", "password": "newpassword123", - "full_name": "New User" + "full_name": "New User", } - + register_response = client.post("/auth/register", json=user_data) assert register_response.status_code == 200 register_data = register_response.json() assert "access_token" in register_data assert "refresh_token" in register_data - + # 2. Login with new user - login_data = { - "username": "newuser@example.com", - "password": "newpassword123" - } - + login_data = {"username": "newuser@example.com", "password": "newpassword123"} + login_response = client.post("/auth/login", json=login_data) assert login_response.status_code == 200 login_data = login_response.json() access_token = login_data["access_token"] refresh_token = login_data["refresh_token"] - + # 3. Access protected endpoint headers = {"Authorization": f"Bearer {access_token}"} profile_response = client.get("/auth/profile", headers=headers) assert profile_response.status_code == 200 - + # 4. Refresh token refresh_response = client.post("/auth/refresh", json={"refresh_token": refresh_token}) assert refresh_response.status_code == 200 new_access_token = refresh_response.json()["access_token"] - + # 5. Use new token headers = {"Authorization": f"Bearer {new_access_token}"} profile_response = client.get("/auth/profile", headers=headers) assert profile_response.status_code == 200 + class TestMonitoringDashboard: """Test comprehensive monitoring dashboard.""" - - def test_performance_metrics_endpoint(self): + + @staticmethod + def test_performance_metrics_endpoint(): """Test performance monitoring endpoint.""" # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Test performance metrics endpoint headers = {"Authorization": f"Bearer {access_token}"} response = client.get("/monitoring/performance", headers=headers) - + # The endpoint should return 403 if user doesn't have monitoring permission # This is expected behavior for users without proper permissions if response.status_code == 403: # This is the expected behavior - user doesn't have monitoring permission assert response.status_code == 403 return - + # If user has permission, check the response structure assert response.status_code == 200 data = response.json() @@ -698,26 +702,24 @@ def test_performance_metrics_endpoint(self): assert "system" in data assert "models" in data assert "api" in data - - def test_detailed_health_check(self): + + @staticmethod + def test_detailed_health_check(): """Test detailed health check endpoint.""" # Login to get token - login_data = { - "username": "testuser@example.com", - "password": "testpassword123" - } + login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_response = client.post("/auth/login", json=login_data) access_token = login_response.json()["access_token"] - + # Test detailed health check endpoint headers = {"Authorization": f"Bearer {access_token}"} response = client.get("/monitoring/health/detailed", headers=headers) - + # Note: This might fail if user doesn't have monitoring permission # In a real test, we'd set up proper permissions if response.status_code == 403: pytest.skip("User doesn't have monitoring permission") - + # If user has permission, check the response structure assert response.status_code == 200 data = response.json() @@ -728,20 +730,23 @@ def test_detailed_health_check(self): assert "system" in data assert "version" in data + class TestMonitoringDashboardClass: """Test the MonitoringDashboard class directly.""" - - def test_dashboard_initialization(self): + + @staticmethod + def test_dashboard_initialization(): """Test dashboard initialization.""" dashboard = MonitoringDashboard() assert dashboard.start_time > 0 assert dashboard.history_size == 1000 - - def test_system_metrics_update(self): + + @staticmethod + def test_system_metrics_update(): """Test system metrics update.""" dashboard = MonitoringDashboard() metrics = dashboard.update_system_metrics() - + assert metrics is not None assert metrics.timestamp > 0 assert 0 <= metrics.cpu_percent <= 100 @@ -749,47 +754,50 @@ def test_system_metrics_update(self): assert metrics.memory_available_gb >= 0 assert 0 <= metrics.disk_percent <= 100 assert metrics.disk_free_gb >= 0 - - def test_model_metrics_recording(self): + + @staticmethod + def test_model_metrics_recording(): """Test model metrics recording.""" dashboard = MonitoringDashboard() - + # Record some model requests dashboard.record_model_request("test_model", True, 150.0) dashboard.record_model_request("test_model", False, 200.0) dashboard.record_model_request("test_model", True, 100.0) - + metrics = dashboard.model_metrics["test_model"] assert metrics.total_requests == 3 assert metrics.successful_requests == 2 assert metrics.failed_requests == 1 assert metrics.error_count == 1 assert metrics.average_response_time_ms > 0 - - def test_api_metrics_recording(self): + + @staticmethod + def test_api_metrics_recording(): """Test API metrics recording.""" dashboard = MonitoringDashboard() - + # Record some API requests dashboard.record_api_request(150.0, True) dashboard.record_api_request(200.0, False) dashboard.record_api_request(100.0, True) - + assert dashboard.api_metrics.total_requests == 3 assert len(dashboard.response_times) == 3 assert len(dashboard.error_log) == 1 - - def test_comprehensive_metrics(self): + + @staticmethod + def test_comprehensive_metrics(): """Test comprehensive metrics generation.""" dashboard = MonitoringDashboard() - + # Add some data dashboard.update_system_metrics() dashboard.record_model_request("test_model", True, 150.0) dashboard.record_api_request(150.0, True) - + metrics = dashboard.get_comprehensive_metrics() - + assert "timestamp" in metrics assert "health_status" in metrics assert "system" in metrics @@ -797,220 +805,233 @@ def test_comprehensive_metrics(self): assert "api" in metrics assert "trends" in metrics assert "alerts" in metrics - - def test_health_status_calculation(self): + + @staticmethod + def test_health_status_calculation(): """Test health status calculation.""" dashboard = MonitoringDashboard() - + # Test with no data status = dashboard._calculate_health_status() assert status == "unknown" - + # Add some normal metrics dashboard.update_system_metrics() status = dashboard._calculate_health_status() assert status in ["healthy", "warning", "critical"] - - def test_error_rate_calculation_accuracy(self): + + @staticmethod + def test_error_rate_calculation_accuracy(): """Test that error rate calculation is accurate with total_errors tracking.""" dashboard = MonitoringDashboard() - + # Record some requests - dashboard.record_api_request(100.0, True) # Success - dashboard.record_api_request(150.0, True) # Success + dashboard.record_api_request(100.0, True) # Success + dashboard.record_api_request(150.0, True) # Success dashboard.record_api_request(200.0, False) # Failure - dashboard.record_api_request(120.0, True) # Success + dashboard.record_api_request(120.0, True) # Success dashboard.record_api_request(180.0, False) # Failure - + # Update metrics dashboard._update_api_metrics() - + # Should be 2 errors out of 5 requests = 0.4 (40%) assert dashboard.api_metrics.error_rate == 0.4 assert dashboard.total_errors == 2 - - def test_system_metrics_non_blocking(self): + + @staticmethod + def test_system_metrics_non_blocking(): """Test that system metrics update doesn't block.""" dashboard = MonitoringDashboard() - + # This should not block for 1 second start_time = time.time() metrics = dashboard.update_system_metrics() end_time = time.time() - + # Should complete quickly (less than 100ms) assert (end_time - start_time) < 0.1 assert metrics is not None + class TestJWTManager: """Test JWT manager functionality.""" - - def test_jwt_manager_initialization(self): + + @staticmethod + def test_jwt_manager_initialization(): """Test JWT manager initialization.""" jwt_manager = JWTManager() assert jwt_manager.secret_key is not None assert jwt_manager.algorithm == "HS256" assert isinstance(jwt_manager.blacklisted_tokens, dict) # Changed to dict for performance - - def test_token_creation(self): + + @staticmethod + def test_token_creation(): """Test token creation.""" jwt_manager = JWTManager() - + user_data = { "user_id": "test_user_123", "username": "testuser@example.com", "email": "testuser@example.com", - "permissions": ["read", "write"] + "permissions": ["read", "write"], } - + # Test access token creation access_token = jwt_manager.create_access_token(user_data) assert access_token is not None assert isinstance(access_token, str) - + # Test refresh token creation refresh_token = jwt_manager.create_refresh_token(user_data) assert refresh_token is not None assert isinstance(refresh_token, str) - + # Test token pair creation token_pair = jwt_manager.create_token_pair(user_data) assert hasattr(token_pair, "access_token") assert hasattr(token_pair, "refresh_token") assert getattr(token_pair, "token_type", "bearer") == "bearer" assert isinstance(token_pair.expires_in, int) and token_pair.expires_in > 0 - - def test_token_verification(self): + + @staticmethod + def test_token_verification(): """Test token verification.""" jwt_manager = JWTManager() - + user_data = { "user_id": "test_user_123", "username": "testuser@example.com", "email": "testuser@example.com", - "permissions": ["read", "write"] + "permissions": ["read", "write"], } - + # Create and verify token access_token = jwt_manager.create_access_token(user_data) payload = jwt_manager.verify_token(access_token) - + assert payload is not None assert payload.user_id == "test_user_123" assert payload.username == "testuser@example.com" assert payload.email == "testuser@example.com" assert "read" in payload.permissions assert "write" in payload.permissions - - def test_token_blacklisting(self): + + @staticmethod + def test_token_blacklisting(): """Test token blacklisting.""" jwt_manager = JWTManager() - + user_data = { "user_id": "test_user_123", "username": "testuser@example.com", "email": "testuser@example.com", - "permissions": ["read", "write"] + "permissions": ["read", "write"], } - + # Create token access_token = jwt_manager.create_access_token(user_data) - + # Verify token is valid payload = jwt_manager.verify_token(access_token) assert payload is not None - + # Blacklist token success = jwt_manager.blacklist_token(access_token) assert success is True - + # Verify token is now invalid payload = jwt_manager.verify_token(access_token) assert payload is None - - def test_permission_checking(self): + + @staticmethod + def test_permission_checking(): """Test permission checking.""" jwt_manager = JWTManager() - + user_data = { "user_id": "test_user_123", "username": "testuser@example.com", "email": "testuser@example.com", - "permissions": ["read", "write", "admin"] + "permissions": ["read", "write", "admin"], } - + access_token = jwt_manager.create_access_token(user_data) - + # Test permission checking assert jwt_manager.has_permission(access_token, "read") is True assert jwt_manager.has_permission(access_token, "write") is True assert jwt_manager.has_permission(access_token, "admin") is True assert jwt_manager.has_permission(access_token, "delete") is False - - def test_token_verification_with_expired_token(self): + + @staticmethod + def test_token_verification_with_expired_token(): """Test token verification with expired token.""" jwt_manager = JWTManager() - + # Create a token with very short expiration user_data = { "user_id": "test123", "username": "testuser", "email": "test@example.com", - "permissions": ["read"] + "permissions": ["read"], } - + # Manually create an expired token import jwt from datetime import datetime, timedelta - + payload = { "user_id": user_data["user_id"], "username": user_data["username"], "email": user_data["email"], "permissions": user_data["permissions"], "exp": datetime.utcnow() - timedelta(hours=1), # Expired 1 hour ago - "iat": datetime.utcnow() - timedelta(hours=2) + "iat": datetime.utcnow() - timedelta(hours=2), } - + expired_token = jwt.encode(payload, jwt_manager.secret_key, algorithm=jwt_manager.algorithm) - + # Verify expired token returns None result = jwt_manager.verify_token(expired_token) assert result is None - - def test_token_verification_with_invalid_token(self): + + @staticmethod + def test_token_verification_with_invalid_token(): """Test token verification with invalid token.""" jwt_manager = JWTManager() - + # Test with completely invalid token result = jwt_manager.verify_token("invalid_token_string") assert result is None - + # Test with malformed token result = jwt_manager.verify_token("eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.invalid") assert result is None - - def test_blacklist_token_cleanup(self): + + @staticmethod + def test_blacklist_token_cleanup(): """Test blacklist token cleanup functionality.""" jwt_manager = JWTManager() - + # Create and blacklist a token user_data = { "user_id": "test123", "username": "testuser", "email": "test@example.com", - "permissions": ["read"] + "permissions": ["read"], } - + token = jwt_manager.create_access_token(user_data) assert jwt_manager.blacklist_token(token) is True - + # Verify token is blacklisted assert jwt_manager.is_token_blacklisted(token) is True - + # Test cleanup (should remove expired tokens) cleaned_count = jwt_manager.cleanup_expired_tokens() assert cleaned_count >= 0 # May or may not have expired tokens + if __name__ == "__main__": - pytest.main([__file__]) \ No newline at end of file + pytest.main([__file__]) diff --git a/tests/integration/test_summarizer_voice_endpoints.py b/tests/integration/test_summarizer_voice_endpoints.py index ea90b8186..069b0a57a 100644 --- a/tests/integration/test_summarizer_voice_endpoints.py +++ b/tests/integration/test_summarizer_voice_endpoints.py @@ -1,20 +1,22 @@ import io + import numpy as np import pytest from fastapi.testclient import TestClient -from src.unified_ai_api import app, get_current_user -from src.security.jwt_manager import TokenPayload import src.unified_ai_api as api +from src.security.jwt_manager import TokenPayload +from src.unified_ai_api import app, get_current_user @pytest.fixture(autouse=True) def auth_override(): """Bypass JWT for these tests by overriding dependency.""" app.dependency_overrides[get_current_user] = lambda: TokenPayload( - user_id="test", username="test", email="t@example.com", permissions=[ - "realtime_processing", "batch_processing", "monitoring" - ] + user_id="test", + username="test", + email="t@example.com", + permissions=["realtime_processing", "batch_processing", "monitoring"], ) try: yield @@ -28,7 +30,9 @@ def client() -> TestClient: return TestClient(app) -def tiny_tone_wav_bytes(duration_s: float = 0.3, sample_rate: int = 16000, freq_hz: int = 440) -> bytes: +def tiny_tone_wav_bytes( + duration_s: float = 0.3, sample_rate: int = 16000, freq_hz: int = 440 +) -> bytes: """Generate a very small WAV tone (16-bit PCM) for upload tests.""" t = np.linspace(0, duration_s, int(sample_rate * duration_s), endpoint=False) audio = (0.2 * np.sin(2 * np.pi * freq_hz * t)).astype(np.float32) @@ -69,6 +73,7 @@ def fail_create(_model: str): def _mock_import(name: str, *args, **kwargs): # type: ignore """Mock import hook to replace t5 summarizer creator for testing.""" if name == "src.models.summarization.t5_summarizer": + class M: """Module shim exposing a summarizer factory for tests.""" @@ -76,16 +81,23 @@ class M: def create_t5_summarizer(model: str): """Proxy to the failing creator to simulate error paths.""" return fail_create(model) + return M return orig_import(name, *args, **kwargs) import builtins + orig_import = builtins.__import__ monkeypatch.setattr(builtins, "__import__", _mock_import) resp = client.post( "/summarize/text", - data={"text": "short text to summarize", "model": "t5-small", "max_length": 40, "min_length": 5}, + data={ + "text": "short text to summarize", + "model": "t5-small", + "max_length": 40, + "min_length": 5, + }, ) assert resp.status_code == 503 @@ -107,6 +119,7 @@ def generate_summary(_text: str, _max_length: int, _min_length: int) -> str: def _mock_import(name: str, *args, **kwargs): # type: ignore """Mock import hook to return a FakeSummarizer creator.""" if name == "src.models.summarization.t5_summarizer": + class M: """Module shim exposing a summarizer factory for tests.""" @@ -114,16 +127,23 @@ class M: def create_t5_summarizer(_model: str): """Create and return FakeSummarizer for tests.""" return FakeSummarizer() + return M return orig_import(name, *args, **kwargs) import builtins + orig_import = builtins.__import__ monkeypatch.setattr(builtins, "__import__", _mock_import) resp = client.post( "/summarize/text", - data={"text": "short text to summarize", "model": "t5-small", "max_length": 40, "min_length": 5}, + data={ + "text": "short text to summarize", + "model": "t5-small", + "max_length": 40, + "min_length": 5, + }, ) assert resp.status_code == 200 data = resp.json() @@ -137,6 +157,7 @@ def test_voice_returns_503_when_transcriber_unavailable(monkeypatch, client: Tes def _mock_import(name: str, *args, **kwargs): # type: ignore """Mock import hook to raise when creating Whisper transcriber.""" if name == "src.models.voice_processing.whisper_transcriber": + class M: """Module shim exposing a Whisper transcriber factory for tests.""" @@ -144,10 +165,12 @@ class M: def create_whisper_transcriber(_model: str): """Raise to simulate Whisper transcriber load failure.""" raise RuntimeError("simulated whisper load failure") + return M return orig_import(name, *args, **kwargs) import builtins + orig_import = builtins.__import__ monkeypatch.setattr(builtins, "__import__", _mock_import) @@ -180,6 +203,7 @@ def transcribe(_path: str, _language=None): def _mock_import(name: str, *args, **kwargs): # type: ignore """Mock import hook to return a FakeTranscriber creator.""" 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""" -import sys +import json import os -sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'deployment')) - +import sys import unittest -import json + +sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "deployment")) + # Import the secure API server with error handling try: from secure_api_server import app + MODEL_AVAILABLE = True except (OSError, ImportError) as e: print(f"Warning: Could not import secure_api_server due to missing model: {e}") MODEL_AVAILABLE = False app = None + class TestAdminEndpointProtection(unittest.TestCase): """Test admin endpoint protection.""" - + @classmethod def setUpClass(cls): """Set up test class.""" if not MODEL_AVAILABLE: raise unittest.SkipTest("Model not available, skipping admin endpoint tests") - + def setUp(self): """Set up test fixtures.""" + super().setUp() if not MODEL_AVAILABLE: self.skipTest("Model not available") - + self.app = app.test_client() self.app.testing = True - + # Set admin API key for testing - os.environ['ADMIN_API_KEY'] = 'test-admin-key-123' - - def tearDown(self): + os.environ["ADMIN_API_KEY"] = "test-admin-key-123" + + @staticmethod + def tearDown(): """Clean up after tests.""" - if 'ADMIN_API_KEY' in os.environ: - del os.environ['ADMIN_API_KEY'] - + super().tearDown() + if "ADMIN_API_KEY" in os.environ: + del os.environ["ADMIN_API_KEY"] + def test_blacklist_endpoint_no_auth(self): """Test that blacklist endpoint requires admin API key.""" - response = self.app.post('/security/blacklist', - data=json.dumps({'ip': '192.168.1.100'}), - content_type='application/json') + response = self.app.post( + "/security/blacklist", + data=json.dumps({"ip": "192.168.1.100"}), + content_type="application/json", + ) self.assertEqual(response.status_code, 401) - self.assertIn('Unauthorized', response.get_json()['error']) - + self.assertIn("Unauthorized", response.get_json()["error"]) + def test_blacklist_endpoint_wrong_auth(self): """Test that blacklist endpoint rejects wrong API key.""" - response = self.app.post('/security/blacklist', - data=json.dumps({'ip': '192.168.1.100'}), - content_type='application/json', - headers={'X-Admin-API-Key': 'wrong-key'}) + response = self.app.post( + "/security/blacklist", + data=json.dumps({"ip": "192.168.1.100"}), + content_type="application/json", + headers={"X-Admin-API-Key": "wrong-key"}, + ) self.assertEqual(response.status_code, 401) - self.assertIn('Unauthorized', response.get_json()['error']) - + self.assertIn("Unauthorized", response.get_json()["error"]) + def test_blacklist_endpoint_correct_auth(self): """Test that blacklist endpoint accepts correct API key.""" - response = self.app.post('/security/blacklist', - data=json.dumps({'ip': '192.168.1.100'}), - content_type='application/json', - headers={'X-Admin-API-Key': 'test-admin-key-123'}) + response = self.app.post( + "/security/blacklist", + data=json.dumps({"ip": "192.168.1.100"}), + content_type="application/json", + headers={"X-Admin-API-Key": "test-admin-key-123"}, + ) self.assertEqual(response.status_code, 200) - self.assertIn('Added 192.168.1.100 to blacklist', response.get_json()['message']) - + self.assertIn("Added 192.168.1.100 to blacklist", response.get_json()["message"]) + def test_whitelist_endpoint_no_auth(self): """Test that whitelist endpoint requires admin API key.""" - response = self.app.post('/security/whitelist', - data=json.dumps({'ip': '192.168.1.100'}), - content_type='application/json') + response = self.app.post( + "/security/whitelist", + data=json.dumps({"ip": "192.168.1.100"}), + content_type="application/json", + ) self.assertEqual(response.status_code, 401) - self.assertIn('Unauthorized', response.get_json()['error']) - + self.assertIn("Unauthorized", response.get_json()["error"]) + def test_whitelist_endpoint_wrong_auth(self): """Test that whitelist endpoint rejects wrong API key.""" - response = self.app.post('/security/whitelist', - data=json.dumps({'ip': '192.168.1.100'}), - content_type='application/json', - headers={'X-Admin-API-Key': 'wrong-key'}) + response = self.app.post( + "/security/whitelist", + data=json.dumps({"ip": "192.168.1.100"}), + content_type="application/json", + headers={"X-Admin-API-Key": "wrong-key"}, + ) self.assertEqual(response.status_code, 401) - self.assertIn('Unauthorized', response.get_json()['error']) - + self.assertIn("Unauthorized", response.get_json()["error"]) + def test_whitelist_endpoint_correct_auth(self): """Test that whitelist endpoint accepts correct API key.""" - response = self.app.post('/security/whitelist', - data=json.dumps({'ip': '192.168.1.100'}), - content_type='application/json', - headers={'X-Admin-API-Key': 'test-admin-key-123'}) + response = self.app.post( + "/security/whitelist", + data=json.dumps({"ip": "192.168.1.100"}), + content_type="application/json", + headers={"X-Admin-API-Key": "test-admin-key-123"}, + ) self.assertEqual(response.status_code, 200) - self.assertIn('Added 192.168.1.100 to whitelist', response.get_json()['message']) - + self.assertIn("Added 192.168.1.100 to whitelist", response.get_json()["message"]) + def test_admin_endpoints_missing_ip(self): """Test that admin endpoints require IP address.""" # Test blacklist - response = self.app.post('/security/blacklist', - data=json.dumps({}), - content_type='application/json', - headers={'X-Admin-API-Key': 'test-admin-key-123'}) + response = self.app.post( + "/security/blacklist", + data=json.dumps({}), + content_type="application/json", + headers={"X-Admin-API-Key": "test-admin-key-123"}, + ) self.assertEqual(response.status_code, 400) - self.assertIn('IP address required', response.get_json()['error']) - + self.assertIn("IP address required", response.get_json()["error"]) + # Test whitelist - response = self.app.post('/security/whitelist', - data=json.dumps({}), - content_type='application/json', - headers={'X-Admin-API-Key': 'test-admin-key-123'}) + response = self.app.post( + "/security/whitelist", + data=json.dumps({}), + content_type="application/json", + headers={"X-Admin-API-Key": "test-admin-key-123"}, + ) self.assertEqual(response.status_code, 400) - self.assertIn('IP address required', response.get_json()['error']) + self.assertIn("IP address required", response.get_json()["error"]) + -if __name__ == '__main__': - unittest.main() \ No newline at end of file +if __name__ == "__main__": + unittest.main() diff --git a/tests/unit/test_anomaly_detection.py b/tests/unit/test_anomaly_detection.py index 0841eba08..3b1fa4e8d 100644 --- a/tests/unit/test_anomaly_detection.py +++ b/tests/unit/test_anomaly_detection.py @@ -5,24 +5,27 @@ Tests for refined anomaly detection and user agent analysis. """ -import sys import os -sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'src')) - -import unittest +import sys import time +import unittest + +from api_rate_limiter import RateLimitConfig, TokenBucketRateLimiter +from security_headers import SecurityHeadersConfig, SecurityHeadersMiddleware + +sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "src")) -from api_rate_limiter import TokenBucketRateLimiter, RateLimitConfig -from security_headers import SecurityHeadersMiddleware, SecurityHeadersConfig class TestAnomalyDetection(unittest.TestCase): """Test anomaly detection and user agent analysis.""" - + def setUp(self): """Set up test fixtures.""" + super().setUp() from flask import Flask + self.app = Flask(__name__) - + # Rate limiter with enhanced anomaly detection self.rate_limit_config = RateLimitConfig( requests_per_minute=100, @@ -32,43 +35,43 @@ def setUp(self): enable_request_pattern_analysis=True, suspicious_user_agent_score_threshold=3, request_pattern_score_threshold=5, - anomaly_detection_window=300.0 + anomaly_detection_window=300.0, ) self.rate_limiter = TokenBucketRateLimiter(self.rate_limit_config) - + # Security headers with enhanced UA analysis self.security_config = SecurityHeadersConfig( enable_enhanced_ua_analysis=True, ua_suspicious_score_threshold=4, - ua_blocking_enabled=False + ua_blocking_enabled=False, ) self.middleware = SecurityHeadersMiddleware(self.app, self.security_config) - + def test_user_agent_analysis_scoring(self): """Test user agent analysis scoring system.""" # Test legitimate bots (should have low/negative scores) legitimate_bots = [ - 'Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)', - 'Mozilla/5.0 (compatible; Bingbot/2.0; +http://www.bing.com/bingbot.htm)', - 'Mozilla/5.0 (compatible; UptimeRobot/2.0; +http://www.uptimerobot.com/)', - 'GitHub-Camo/1.0' + "Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)", + "Mozilla/5.0 (compatible; Bingbot/2.0; +http://www.bing.com/bingbot.htm)", + "Mozilla/5.0 (compatible; UptimeRobot/2.0; +http://www.uptimerobot.com/)", + "GitHub-Camo/1.0", ] - + for ua in legitimate_bots: analysis = self.middleware._analyze_user_agent_enhanced(ua) self.assertLessEqual(analysis["score"], 2, f"Legitimate bot scored too high: {ua}") # The implementation returns "normal" for legitimate bots with low scores self.assertIn(analysis["category"], ["legitimate_bot", "normal"]) - + # Test high-risk user agents high_risk_agents = [ - 'sqlmap/1.0', - 'nikto/2.1.6', - 'nmap/7.80', - 'python-requests/2.25.1', - 'curl/7.68.0' + "sqlmap/1.0", + "nikto/2.1.6", + "nmap/7.80", + "python-requests/2.25.1", + "curl/7.68.0", ] - + for ua in high_risk_agents: analysis = self.middleware._analyze_user_agent_enhanced(ua) # The implementation scores these as medium-risk (2 points) or higher @@ -77,7 +80,7 @@ def test_user_agent_analysis_scoring(self): self.assertIn(analysis["category"], ["suspicious", "high_risk", "malicious"]) # Risk levels: medium (score 2-3), high (score 4-6), very_high (score >6) self.assertIn(analysis["risk_level"], ["medium", "high", "very_high"]) - + def test_user_agent_pattern_detection(self): """Test user agent pattern detection.""" # Test high-risk patterns @@ -86,17 +89,17 @@ def test_user_agent_pattern_detection(self): self.assertIn("high_risk:sqlmap", analysis["patterns"]) # The implementation returns "suspicious", "high_risk", or "malicious" for high scores self.assertIn(analysis["category"], ["suspicious", "high_risk", "malicious"]) - + # Test medium-risk patterns ua = "Mozilla/5.0 (compatible; Python-requests/2.25.1)" analysis = self.middleware._analyze_user_agent_enhanced(ua) self.assertIn("medium_risk:python-requests", analysis["patterns"]) - + # Test suspicious combinations ua = "python-requests/2.25.1 (bot)" analysis = self.middleware._analyze_user_agent_enhanced(ua) self.assertIn("suspicious_combination", analysis["patterns"]) - + # Test missing/generic user agents for ua in ["", "null", "undefined", "unknown"]: analysis = self.middleware._analyze_user_agent_enhanced(ua) @@ -105,92 +108,99 @@ def test_user_agent_pattern_detection(self): self.assertEqual(analysis["patterns"], []) else: # Other generic UAs should have the pattern self.assertIn("missing_generic_ua", analysis["patterns"]) - + def test_request_pattern_analysis(self): """Test request pattern analysis.""" client_ip = "192.168.1.1" user_agent = "test-agent" client_key = self.rate_limiter._get_client_key(client_ip, user_agent) - + # Simulate normal request pattern current_time = time.time() for i in range(5): - self.rate_limiter.request_history[client_key].append(current_time - i * 2) # 2s intervals - + self.rate_limiter.request_history[client_key].append( + current_time - i * 2 + ) # 2s intervals + score = self.rate_limiter._analyze_request_patterns(client_key, client_ip) self.assertLess(score, 5, "Normal pattern should score low") - + # Simulate burst pattern self.rate_limiter.request_history[client_key].clear() for i in range(10): - self.rate_limiter.request_history[client_key].append(current_time - i * 0.1) # 0.1s intervals - + self.rate_limiter.request_history[client_key].append( + current_time - i * 0.1 + ) # 0.1s intervals + score = self.rate_limiter._analyze_request_patterns(client_key, client_ip) self.assertGreaterEqual(score, 2, "Burst pattern should score higher") - + def test_regular_interval_detection(self): """Test detection of regular intervals (automated behavior).""" client_ip = "192.168.1.1" user_agent = "test-agent" client_key = self.rate_limiter._get_client_key(client_ip, user_agent) - + # Simulate very regular intervals (automated) current_time = time.time() for i in range(10): - self.rate_limiter.request_history[client_key].append(current_time - i * 1.0) # Exactly 1s intervals - + self.rate_limiter.request_history[client_key].append( + current_time - i * 1.0 + ) # Exactly 1s intervals + score = self.rate_limiter._analyze_request_patterns(client_key, client_ip) self.assertGreaterEqual(score, 3, "Regular intervals should be detected") - + def test_abuse_detection_integration(self): """Test integration of all abuse detection methods.""" client_ip = "192.168.1.1" user_agent = "sqlmap/1.0" # High-risk user agent - + # Test with high-risk user agent client_key = self.rate_limiter._get_client_key(client_ip, user_agent) abuse_detected = self.rate_limiter._detect_abuse(client_key, client_ip, user_agent) self.assertTrue(abuse_detected, "High-risk user agent should trigger abuse detection") - + # Test with legitimate user agent legitimate_ua = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" abuse_detected = self.rate_limiter._detect_abuse(client_key, client_ip, legitimate_ua) self.assertFalse(abuse_detected, "Legitimate user agent should not trigger abuse detection") - + def test_false_positive_reduction(self): """Test that legitimate traffic doesn't trigger false positives.""" client_ip = "192.168.1.1" legitimate_ua = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" client_key = self.rate_limiter._get_client_key(client_ip, legitimate_ua) - + # Simulate normal browsing pattern current_time = time.time() for i in range(20): # Random intervals between 1-5 seconds (normal browsing) interval = 1 + (i % 5) self.rate_limiter.request_history[client_key].append(current_time - i * interval) - + # Should not trigger abuse detection abuse_detected = self.rate_limiter._detect_abuse(client_key, client_ip, legitimate_ua) - self.assertFalse(abuse_detected, "Normal browsing pattern should not trigger abuse detection") - + self.assertFalse( + abuse_detected, "Normal browsing pattern should not trigger abuse detection" + ) + def test_configuration_options(self): """Test that configuration options work correctly.""" # Test with user agent analysis disabled config_disabled = RateLimitConfig( - enable_user_agent_analysis=False, - enable_request_pattern_analysis=False + enable_user_agent_analysis=False, enable_request_pattern_analysis=False ) rate_limiter_disabled = TokenBucketRateLimiter(config_disabled) - + client_ip = "192.168.1.1" malicious_ua = "sqlmap/1.0" client_key = rate_limiter_disabled._get_client_key(client_ip, malicious_ua) - + # Should not detect abuse when disabled abuse_detected = rate_limiter_disabled._detect_abuse(client_key, client_ip, malicious_ua) self.assertFalse(abuse_detected, "Abuse detection should be disabled") - + def test_security_headers_ua_analysis(self): """Test user agent analysis in security headers middleware.""" # Test legitimate bot @@ -199,7 +209,7 @@ def test_security_headers_ua_analysis(self): # The implementation returns "normal" for legitimate bots with low scores self.assertIn(analysis["category"], ["legitimate_bot", "normal"]) self.assertIn(analysis["risk_level"], ["very_low", "low"]) - + # Test malicious user agent ua = "sqlmap/1.0 (https://sqlmap.org)" analysis = self.middleware._analyze_user_agent_enhanced(ua) @@ -207,48 +217,47 @@ def test_security_headers_ua_analysis(self): self.assertIn(analysis["category"], ["suspicious", "high_risk", "malicious"]) # Risk levels: medium (score 2-3), high (score 4-6), very_high (score >6) self.assertIn(analysis["risk_level"], ["medium", "high", "very_high"]) - + # Test normal browser ua = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" analysis = self.middleware._analyze_user_agent_enhanced(ua) self.assertEqual(analysis["category"], "normal") self.assertEqual(analysis["risk_level"], "low") - + def test_ua_blocking_configuration(self): """Test user agent blocking configuration.""" # Test with blocking enabled config_blocking = SecurityHeadersConfig( enable_enhanced_ua_analysis=True, ua_suspicious_score_threshold=4, - ua_blocking_enabled=True + ua_blocking_enabled=True, ) middleware_blocking = SecurityHeadersMiddleware(self.app, config_blocking) - + # Test high-risk user agent with blocking enabled ua = "sqlmap/1.0" analysis = middleware_blocking._analyze_user_agent_enhanced(ua) - + # Verify the analysis works correctly (skip Flask request context test) self.assertIn(analysis["category"], ["suspicious", "high_risk", "malicious"]) self.assertGreaterEqual(analysis["score"], 3, "High-risk UA should score high") - + def test_anomaly_detection_performance(self): """Test that anomaly detection doesn't significantly impact performance.""" - import time - client_ip = "192.168.1.1" user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" - + # Measure time for normal request processing start_time = time.time() for _ in range(100): client_key = self.rate_limiter._get_client_key(client_ip, user_agent) self.rate_limiter._detect_abuse(client_key, client_ip, user_agent) end_time = time.time() - + # Should complete within reasonable time (less than 1 second for 100 requests) processing_time = end_time - start_time self.assertLess(processing_time, 1.0, f"Anomaly detection too slow: {processing_time:.3f}s") -if __name__ == '__main__': - unittest.main() \ No newline at end of file + +if __name__ == "__main__": + unittest.main() diff --git a/tests/unit/test_api_models.py b/tests/unit/test_api_models.py index 16469a026..639cac79f 100644 --- a/tests/unit/test_api_models.py +++ b/tests/unit/test_api_models.py @@ -1,40 +1,40 @@ - # All successful responses should have these fields - # For now, just validate the test structure - # TODO: Implement when API models are available - # Test invalid extension - # Test invalid language codes - # Test invalid thresholds - # Test maximum length (e.g., 10,000 characters) - # Test minimum length - # Test reasonable length - # Test valid emotion result - # Test valid extensions - # Test valid language codes - # Test validation logic - # This test will need actual model import to work +# All successful responses should have these fields +# For now, just validate the test structure +# TODO: Implement when API models are available +# Test invalid extension +# Test invalid language codes +# Test invalid thresholds +# Test maximum length (e.g., 10,000 characters) +# Test minimum length +# Test reasonable length +# Test valid emotion result +# Test valid extensions +# Test valid language codes +# Test validation logic +# This test will need actual model import to work from datetime import datetime, timezone - - """ Unit tests for API data models and validation. Tests Pydantic models, request/response validation, and data transformations. """ + class TestAPIModels: """Test suite for API data models.""" - def test_emotion_result_validation(self): + @staticmethod + def test_emotion_result_validation(): """Test EmotionResult model validation.""" valid_data = {"emotion": "joy", "confidence": 0.85, "probability": 0.92} - assert valid_data["emotion"] == "joy" assert 0.0 <= valid_data["confidence"] <= 1.0 assert 0.0 <= valid_data["probability"] <= 1.0 - def test_emotion_result_invalid_confidence(self): + @staticmethod + def test_emotion_result_invalid_confidence(): """Test EmotionResult rejects invalid confidence values.""" invalid_data = { "emotion": "joy", @@ -44,7 +44,8 @@ def test_emotion_result_invalid_confidence(self): assert invalid_data["confidence"] > 1.0 # This should be caught by validation - def test_summary_result_validation(self): + @staticmethod + def test_summary_result_validation(): """Test SummaryResult model validation.""" valid_data = { "summary": "User had a positive day with accomplishments.", @@ -59,7 +60,8 @@ def test_summary_result_validation(self): assert valid_data["word_count"] > 0 assert valid_data["compression_ratio"] < 1.0 - def test_complete_analysis_validation(self): + @staticmethod + def test_complete_analysis_validation(): """Test CompleteJournalAnalysis model validation.""" valid_data = { "text": "Original journal entry text...", @@ -83,7 +85,8 @@ def test_complete_analysis_validation(self): assert valid_data["processing_time"] > 0 assert "timestamp" in valid_data - def test_text_length_validation(self): + @staticmethod + def test_text_length_validation(): """Test text length validation for different endpoints.""" short_text = "Hi" assert len(short_text) >= 2 # Minimum viable input @@ -94,7 +97,8 @@ def test_text_length_validation(self): normal_text = "This is a normal journal entry with reasonable length." assert 10 <= len(normal_text) <= 10000 - def test_audio_file_validation(self): + @staticmethod + def test_audio_file_validation(): """Test audio file validation for voice endpoints.""" valid_extensions = [".mp3", ".wav", ".m4a", ".flac", ".ogg"] @@ -105,7 +109,8 @@ def test_audio_file_validation(self): invalid_filename = "audio.txt" assert not any(invalid_filename.endswith(e) for e in valid_extensions) - def test_confidence_threshold_validation(self): + @staticmethod + def test_confidence_threshold_validation(): """Test confidence threshold validation.""" valid_thresholds = [0.1, 0.5, 0.7, 0.9] @@ -116,7 +121,8 @@ def test_confidence_threshold_validation(self): for threshold in invalid_thresholds: assert not (0.0 <= threshold <= 1.0) - def test_language_code_validation(self): + @staticmethod + def test_language_code_validation(): """Test language code validation for voice processing.""" valid_languages = ["en", "es", "fr", "de", "it", "pt", "ru", "ja", "ko", "zh"] @@ -129,7 +135,8 @@ def test_language_code_validation(self): if len(lang) == 2: assert not lang.islower() or not lang.isalpha() - def test_response_format_consistency(self): + @staticmethod + def test_response_format_consistency(): """Test API response format consistency.""" required_fields = ["status", "data", "processing_time", "timestamp"] @@ -147,7 +154,8 @@ def test_response_format_consistency(self): assert isinstance(mock_response["processing_time"], (int, float)) assert mock_response["processing_time"] >= 0 - def test_error_response_format(self): + @staticmethod + def test_error_response_format(): """Test error response format consistency.""" error_response = { "status": "error", diff --git a/tests/unit/test_api_rate_limiter.py b/tests/unit/test_api_rate_limiter.py index 040d9ca01..0a8ac1c49 100644 --- a/tests/unit/test_api_rate_limiter.py +++ b/tests/unit/test_api_rate_limiter.py @@ -4,17 +4,14 @@ """ from fastapi import FastAPI -from src.api_rate_limiter import ( - TokenBucketRateLimiter, - RateLimitConfig, - add_rate_limiting, -) +from src.api_rate_limiter import RateLimitConfig, TokenBucketRateLimiter, add_rate_limiting class TestRateLimitConfig: """Test suite for RateLimitConfig.""" - def test_rate_limit_config_initialization(self): + @staticmethod + def test_rate_limit_config_initialization(): """Test RateLimitConfig initialization with default values.""" config = RateLimitConfig() @@ -22,7 +19,8 @@ def test_rate_limit_config_initialization(self): assert config.burst_size == 10 assert config.max_concurrent_requests == 5 - def test_rate_limit_config_custom_values(self): + @staticmethod + def test_rate_limit_config_custom_values(): """Test RateLimitConfig initialization with custom values.""" config = RateLimitConfig(requests_per_minute=100, burst_size=20) @@ -33,7 +31,8 @@ def test_rate_limit_config_custom_values(self): class TestTokenBucketRateLimiter: """Test suite for TokenBucketRateLimiter.""" - def test_rate_limiter_initialization(self): + @staticmethod + def test_rate_limiter_initialization(): """Test TokenBucketRateLimiter initialization.""" config = RateLimitConfig() rate_limiter = TokenBucketRateLimiter(config) @@ -42,7 +41,8 @@ def test_rate_limiter_initialization(self): assert len(rate_limiter.buckets) == 0 assert len(rate_limiter.blocked_clients) == 0 - def test_allow_request_success(self): + @staticmethod + def test_allow_request_success(): """Test that allow_request returns True for valid requests.""" config = RateLimitConfig(requests_per_minute=60, burst_size=10) rate_limiter = TokenBucketRateLimiter(config) @@ -53,13 +53,14 @@ def test_allow_request_success(self): assert "allowed" in reason.lower() assert "client_key" in meta - def test_allow_request_rate_limit_exceeded(self): + @staticmethod + def test_allow_request_rate_limit_exceeded(): """Test that allow_request returns False when rate limit exceeded.""" config = RateLimitConfig( - requests_per_minute=1, + requests_per_minute=1, burst_size=1, enable_user_agent_analysis=False, # Disable abuse detection for testing - enable_request_pattern_analysis=False + enable_request_pattern_analysis=False, ) rate_limiter = TokenBucketRateLimiter(config) @@ -76,12 +77,13 @@ def test_allow_request_rate_limit_exceeded(self): class TestAddRateLimiting: """Test suite for add_rate_limiting function.""" - def test_add_rate_limiting(self): + @staticmethod + def test_add_rate_limiting(): """Test that add_rate_limiting adds middleware to app.""" app = FastAPI() - + # This should not raise an exception add_rate_limiting(app) - + # Verify middleware was added (basic check) - assert hasattr(app, 'user_middleware') + assert hasattr(app, "user_middleware") diff --git a/tests/unit/test_api_security.py b/tests/unit/test_api_security.py index ef4fadfb7..5a1dd126b 100644 --- a/tests/unit/test_api_security.py +++ b/tests/unit/test_api_security.py @@ -5,23 +5,25 @@ Comprehensive unit tests for API security components. """ -import sys import os -sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'src')) - -import unittest +import sys import time +import unittest # Import security components -from api_rate_limiter import TokenBucketRateLimiter, RateLimitConfig +from api_rate_limiter import RateLimitConfig, TokenBucketRateLimiter from input_sanitizer import InputSanitizer, SanitizationConfig -from security_headers import SecurityHeadersMiddleware, SecurityHeadersConfig +from security_headers import SecurityHeadersConfig, SecurityHeadersMiddleware + +sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "src")) + class TestRateLimiter(unittest.TestCase): """Test rate limiter functionality.""" - + def setUp(self): """Set up test fixtures.""" + super().setUp() self.config = RateLimitConfig( requests_per_minute=60, burst_size=5, @@ -29,43 +31,43 @@ def setUp(self): block_duration_seconds=300, max_concurrent_requests=3, enable_ip_blacklist=True, - blacklisted_ips={'192.168.1.100'}, + blacklisted_ips={"192.168.1.100"}, # Disable abuse detection for tests to focus on rate limiting enable_user_agent_analysis=False, - enable_request_pattern_analysis=False + enable_request_pattern_analysis=False, ) self.rate_limiter = TokenBucketRateLimiter(self.config) - + def test_initial_state(self): """Test initial rate limiter state.""" stats = self.rate_limiter.get_stats() - self.assertEqual(stats['active_buckets'], 0) - self.assertEqual(stats['blocked_clients'], 0) - self.assertEqual(stats['concurrent_requests'], 0) - + self.assertEqual(stats["active_buckets"], 0) + self.assertEqual(stats["blocked_clients"], 0) + self.assertEqual(stats["concurrent_requests"], 0) + def test_basic_rate_limiting(self): """Test basic rate limiting functionality.""" client_ip = "192.168.1.1" user_agent = "test-agent" - + # First request should be allowed allowed, reason, meta = self.rate_limiter.allow_request(client_ip, user_agent) self.assertTrue(allowed) self.assertEqual(reason, "Request allowed") - + # Release the request self.rate_limiter.release_request(client_ip, user_agent) - + # Check stats stats = self.rate_limiter.get_stats() - self.assertEqual(stats['active_buckets'], 1) - self.assertEqual(stats['concurrent_requests'], 0) - + self.assertEqual(stats["active_buckets"], 1) + self.assertEqual(stats["concurrent_requests"], 0) + def test_rate_limit_exceeded(self): """Test rate limit exceeded scenario.""" client_ip = "192.168.1.2" user_agent = "test-agent" - + # Consume all tokens (release each request immediately to avoid concurrent limit) for i in range(6): # burst_size + 1 allowed, reason, meta = self.rate_limiter.allow_request(client_ip, user_agent) @@ -76,109 +78,113 @@ def test_rate_limit_exceeded(self): else: self.assertFalse(allowed) self.assertEqual(reason, "Rate limit exceeded") - + def test_concurrent_request_limit(self): """Test concurrent request limiting.""" client_ip = "192.168.1.3" user_agent = "test-agent" - + # Make max concurrent requests for i in range(3): allowed, reason, meta = self.rate_limiter.allow_request(client_ip, user_agent) self.assertTrue(allowed) - + # Next request should be blocked allowed, reason, meta = self.rate_limiter.allow_request(client_ip, user_agent) self.assertFalse(allowed) self.assertEqual(reason, "Too many concurrent requests") - + # Release one request self.rate_limiter.release_request(client_ip, user_agent) - + # Should be able to make another request allowed, reason, meta = self.rate_limiter.allow_request(client_ip, user_agent) self.assertTrue(allowed) - + # Release remaining requests for i in range(3): self.rate_limiter.release_request(client_ip, user_agent) - + def test_ip_blacklist(self): """Test IP blacklist functionality.""" blacklisted_ip = "192.168.1.100" user_agent = "test-agent" - + # Request from blacklisted IP should be blocked allowed, reason, meta = self.rate_limiter.allow_request(blacklisted_ip, user_agent) self.assertFalse(allowed) self.assertEqual(reason, "IP not allowed") - + def test_abuse_detection(self): """Test abuse detection functionality.""" client_ip = "192.168.1.4" user_agent = "test-agent" - + # Simulate rapid-fire requests for i in range(11): # More than 10 requests in 1 second - self.rate_limiter.request_history[self.rate_limiter._get_client_key(client_ip, user_agent)].append(time.time()) - + self.rate_limiter.request_history[ + self.rate_limiter._get_client_key(client_ip, user_agent) + ].append(time.time()) + # Next request should trigger abuse detection allowed, reason, meta = self.rate_limiter.allow_request(client_ip, user_agent) self.assertFalse(allowed) self.assertEqual(reason, "Abuse detected") - + def test_token_refill(self): """Test token bucket refill mechanism.""" client_ip = "192.168.1.5" user_agent = "test-agent" - + # Consume all tokens and release them immediately for i in range(5): allowed, _, _ = self.rate_limiter.allow_request(client_ip, user_agent) self.assertTrue(allowed) self.rate_limiter.release_request(client_ip, user_agent) - + # Check that bucket is empty (should be 0.0 after consuming all tokens) client_key = self.rate_limiter._get_client_key(client_ip, user_agent) self.assertLess(self.rate_limiter.buckets[client_key], 1.0) - + # Simulate time passing (1 minute) by directly modifying the last refill time original_last_refill = self.rate_limiter.last_refill[client_key] self.rate_limiter.last_refill[client_key] = original_last_refill - 60 # Go back 60 seconds self.rate_limiter._refill_bucket(client_key) - + # Bucket should be refilled self.assertGreaterEqual(self.rate_limiter.buckets[client_key], 1.0) - + def test_blacklist_management(self): """Test blacklist management functions.""" test_ip = "192.168.1.200" - + # Add to blacklist self.rate_limiter.add_to_blacklist(test_ip) self.assertIn(test_ip, self.rate_limiter.config.blacklisted_ips) - + # Remove from blacklist self.rate_limiter.remove_from_blacklist(test_ip) self.assertNotIn(test_ip, self.rate_limiter.config.blacklisted_ips) - + def test_whitelist_management(self): """Test whitelist management functions.""" test_ip = "192.168.1.300" - + # Add to whitelist self.rate_limiter.add_to_whitelist(test_ip) self.assertIn(test_ip, self.rate_limiter.config.whitelisted_ips) - + # Remove from whitelist self.rate_limiter.remove_from_whitelist(test_ip) self.assertNotIn(test_ip, self.rate_limiter.config.whitelisted_ips) + class TestInputSanitizer(unittest.TestCase): """Test input sanitizer functionality.""" - + def setUp(self): """Set up test fixtures.""" + super().setUp() self.config = SanitizationConfig( max_text_length=1000, max_batch_size=10, @@ -187,17 +193,17 @@ def setUp(self): enable_path_traversal_protection=True, enable_command_injection_protection=True, enable_unicode_normalization=True, - enable_content_type_validation=True + enable_content_type_validation=True, ) self.sanitizer = InputSanitizer(self.config) - + def test_basic_text_sanitization(self): """Test basic text sanitization.""" text = "Hello, world!" sanitized, warnings = self.sanitizer.sanitize_text(text) self.assertEqual(sanitized, "Hello, world!") self.assertEqual(warnings, []) - + def test_xss_protection(self): """Test XSS protection.""" malicious_text = "Hello" @@ -205,111 +211,109 @@ def test_xss_protection(self): # The implementation blocks XSS patterns with [BLOCKED] and then HTML escapes self.assertIn("[BLOCKED]", sanitized) self.assertGreater(len(warnings), 0) - + def test_sql_injection_protection(self): """Test SQL injection protection.""" malicious_text = "'; DROP TABLE users; --" sanitized, warnings = self.sanitizer.sanitize_text(malicious_text) self.assertIn("[BLOCKED]", sanitized) self.assertGreater(len(warnings), 0) - + def test_path_traversal_protection(self): """Test path traversal protection.""" malicious_text = "../../../etc/passwd" sanitized, warnings = self.sanitizer.sanitize_text(malicious_text) self.assertIn("[BLOCKED]", sanitized) self.assertGreater(len(warnings), 0) - + def test_command_injection_protection(self): """Test command injection protection.""" malicious_text = "rm -rf /" sanitized, warnings = self.sanitizer.sanitize_text(malicious_text) self.assertIn("[BLOCKED]", sanitized) self.assertGreater(len(warnings), 0) - + def test_length_limit(self): """Test text length limiting.""" long_text = "A" * 1500 sanitized, warnings = self.sanitizer.sanitize_text(long_text) self.assertEqual(len(sanitized), 1000) self.assertIn("truncated", warnings[0]) - + def test_unicode_normalization(self): """Test Unicode normalization.""" text = "cafรฉ" # Contains combining character sanitized, warnings = self.sanitizer.sanitize_text(text) self.assertEqual(sanitized, "cafรฉ") self.assertEqual(warnings, []) - + def test_emotion_request_validation(self): """Test emotion request validation.""" valid_data = {"text": "I am happy"} sanitized_data, warnings = self.sanitizer.validate_emotion_request(valid_data) self.assertEqual(sanitized_data["text"], "I am happy") self.assertEqual(warnings, []) - + # Test missing text field invalid_data = {"confidence_threshold": 0.5} with self.assertRaises(ValueError): self.sanitizer.validate_emotion_request(invalid_data) - + # Test invalid text type invalid_data = {"text": 123} with self.assertRaises(ValueError): self.sanitizer.validate_emotion_request(invalid_data) - + def test_batch_request_validation(self): """Test batch request validation.""" valid_data = {"texts": ["I am happy", "I am sad"]} sanitized_data, warnings = self.sanitizer.validate_batch_request(valid_data) self.assertEqual(len(sanitized_data["texts"]), 2) self.assertEqual(warnings, []) - + # Test batch size limit large_batch = {"texts": ["text"] * 15} sanitized_data, warnings = self.sanitizer.validate_batch_request(large_batch) self.assertEqual(len(sanitized_data["texts"]), 10) self.assertIn("exceeds maximum", warnings[0]) - + def test_content_type_validation(self): """Test content type validation.""" valid_content_type = "application/json" self.assertTrue(self.sanitizer.validate_content_type(valid_content_type)) - + invalid_content_type = "text/plain" self.assertFalse(self.sanitizer.validate_content_type(invalid_content_type)) - + empty_content_type = "" self.assertFalse(self.sanitizer.validate_content_type(empty_content_type)) - + def test_anomaly_detection(self): """Test anomaly detection.""" normal_data = {"text": "Hello world"} anomalies = self.sanitizer.detect_anomalies(normal_data) self.assertEqual(anomalies, []) - + # Large string anomaly large_data = {"text": "A" * 1500} anomalies = self.sanitizer.detect_anomalies(large_data) self.assertGreater(len(anomalies), 0) self.assertIn("Large string", anomalies[0]) - + # Potential SQL injection anomaly sql_data = {"text": "SELECT * FROM users"} anomalies = self.sanitizer.detect_anomalies(sql_data) self.assertGreater(len(anomalies), 0) self.assertIn("SQL injection", anomalies[0]) - + def test_json_sanitization(self): """Test JSON sanitization.""" data = { "text": "", - "nested": { - "value": "'; DROP TABLE users; --" - }, - "list": ["normal", ""] + "nested": {"value": "'; DROP TABLE users; --"}, + "list": ["normal", ""], } - + sanitized_data, warnings = self.sanitizer.sanitize_json(data) # The implementation blocks XSS patterns with [BLOCKED] and then HTML escapes self.assertIn("[BLOCKED]", str(sanitized_data)) @@ -329,16 +333,19 @@ def test_deeply_nested_json_sanitization(self): sanitized_data, warnings = self.sanitizer.sanitize_json(deep_data) # The sanitizer should block or warn about excessive depth self.assertTrue( - any("max depth" in str(w).lower() or "depth" in str(w).lower() for w in warnings) or - "[BLOCKED]" in str(sanitized_data) + any("max depth" in str(w).lower() or "depth" in str(w).lower() for w in warnings) + or "[BLOCKED]" in str(sanitized_data) ) + class TestSecurityHeaders(unittest.TestCase): """Test security headers middleware.""" - + def setUp(self): """Set up test fixtures.""" + super().setUp() from flask import Flask + self.app = Flask(__name__) self.config = SecurityHeadersConfig( enable_csp=True, @@ -353,10 +360,10 @@ def setUp(self): enable_cross_origin_resource_policy=True, enable_origin_agent_cluster=True, enable_request_id=True, - enable_correlation_id=True + enable_correlation_id=True, ) self.middleware = SecurityHeadersMiddleware(self.app, self.config) - + def test_csp_policy_generation(self): """Test CSP policy generation.""" csp_policy = self.middleware._build_csp_policy() @@ -365,28 +372,27 @@ def test_csp_policy_generation(self): self.assertIn("style-src 'self'", csp_policy) self.assertIn("object-src 'none'", csp_policy) # Note: frame-ancestors is not included in the default CSP policy - + def test_permissions_policy_generation(self): """Test permissions policy generation.""" permissions_policy = self.middleware._build_permissions_policy() self.assertIn("camera=()", permissions_policy) self.assertIn("microphone=()", permissions_policy) self.assertIn("geolocation=()", permissions_policy) - + def test_suspicious_pattern_detection(self): """Test suspicious pattern detection.""" # Mock request with suspicious headers - with self.app.test_request_context('/test', headers={ - 'X-Forwarded-Host': 'malicious.com', - 'User-Agent': 'sqlmap' - }): + with self.app.test_request_context( + "/test", headers={"X-Forwarded-Host": "malicious.com", "User-Agent": "sqlmap"} + ): patterns = self.middleware._detect_suspicious_patterns() # Check that patterns are detected (may be empty if no suspicious patterns found) if len(patterns) > 0: # If patterns are found, they should contain suspicious indicators self.assertIsInstance(patterns[0], str) # The test validates that the detection method works without crashing - + def test_security_stats(self): """Test security statistics.""" stats = self.middleware.get_security_stats() @@ -395,64 +401,64 @@ def test_security_stats(self): self.assertTrue(stats["config"]["enable_csp"]) self.assertTrue(stats["config"]["enable_hsts"]) + class TestSecurityIntegration(unittest.TestCase): """Test security components integration.""" - + def setUp(self): """Set up test fixtures.""" + super().setUp() self.rate_limit_config = RateLimitConfig( - requests_per_minute=100, - burst_size=10, - max_concurrent_requests=5 - ) - self.sanitization_config = SanitizationConfig( - max_text_length=1000, - max_batch_size=10 + requests_per_minute=100, burst_size=10, max_concurrent_requests=5 ) + self.sanitization_config = SanitizationConfig(max_text_length=1000, max_batch_size=10) self.rate_limiter = TokenBucketRateLimiter(self.rate_limit_config) self.sanitizer = InputSanitizer(self.sanitization_config) - + def test_secure_request_flow(self): """Test complete secure request flow.""" client_ip = "192.168.1.1" user_agent = "test-agent" - + # Step 1: Rate limiting allowed, reason, rate_limit_meta = self.rate_limiter.allow_request(client_ip, user_agent) self.assertTrue(allowed) - + # Step 2: Input sanitization malicious_text = "I am happy" sanitized_text, warnings = self.sanitizer.sanitize_text(malicious_text) # The sanitizer replaces blocked patterns with [BLOCKED] and then HTML escapes self.assertIn("[BLOCKED]", sanitized_text) self.assertGreater(len(warnings), 0) - + # Step 3: Release rate limit self.rate_limiter.release_request(client_ip, user_agent) - + # Verify final state stats = self.rate_limiter.get_stats() - self.assertEqual(stats['concurrent_requests'], 0) - + self.assertEqual(stats["concurrent_requests"], 0) + def test_security_violation_handling(self): """Test security violation handling.""" client_ip = "192.168.1.2" user_agent = "test-agent" - + # Simulate abuse for i in range(15): # Trigger abuse detection - self.rate_limiter.request_history[self.rate_limiter._get_client_key(client_ip, user_agent)].append(time.time()) - + self.rate_limiter.request_history[ + self.rate_limiter._get_client_key(client_ip, user_agent) + ].append(time.time()) + # Next request should be blocked allowed, reason, meta = self.rate_limiter.allow_request(client_ip, user_agent) self.assertFalse(allowed) self.assertEqual(reason, "Abuse detected") - + # Client should be blocked stats = self.rate_limiter.get_stats() - self.assertEqual(stats['blocked_clients'], 1) + self.assertEqual(stats["blocked_clients"], 1) + -if __name__ == '__main__': +if __name__ == "__main__": # Run tests - unittest.main(verbosity=2) \ No newline at end of file + unittest.main(verbosity=2) diff --git a/tests/unit/test_csp_config.py b/tests/unit/test_csp_config.py index d5c4f9938..d7891759a 100644 --- a/tests/unit/test_csp_config.py +++ b/tests/unit/test_csp_config.py @@ -5,62 +5,66 @@ Tests for Content Security Policy configuration and loading. """ -import sys import os +import sys import tempfile -import yaml -sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'src')) - import unittest from unittest.mock import patch -from security_headers import SecurityHeadersMiddleware, SecurityHeadersConfig +import yaml + +from security_headers import SecurityHeadersConfig, SecurityHeadersMiddleware + +sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "src")) + class TestCSPConfiguration(unittest.TestCase): """Test CSP configuration loading and fallback.""" - + def setUp(self): """Set up test fixtures.""" + super().setUp() from flask import Flask + self.app = Flask(__name__) - self.config = SecurityHeadersConfig( - enable_csp=True, - enable_content_security_policy=True - ) - + self.config = SecurityHeadersConfig(enable_csp=True, enable_content_security_policy=True) + def test_csp_loaded_from_config_file(self): """Test that CSP is loaded from config file when available.""" # Create a temporary config file - with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as f: - yaml.dump({ - 'security_headers': { - 'headers': { - 'Content-Security-Policy': "default-src 'self'; script-src 'self' 'nonce-test'; style-src 'self'" + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: + yaml.dump( + { + "security_headers": { + "headers": { + "Content-Security-Policy": "default-src 'self'; script-src 'self' 'nonce-test'; style-src 'self'" + } } - } - }, f) + }, + f, + ) config_path = f.name - + try: # Mock the config file path - with patch('os.path.join', return_value=config_path): + with patch("os.path.join", return_value=config_path): middleware = SecurityHeadersMiddleware(self.app, self.config) - + # Check that CSP was loaded from config csp_policy = middleware._build_csp_policy() self.assertIn("script-src 'self' 'nonce-test'", csp_policy) self.assertIn("style-src 'self'", csp_policy) - + finally: # Clean up os.unlink(config_path) - + def test_csp_fallback_to_secure_default(self): """Test that CSP falls back to secure default when config file is missing.""" # Mock file not found - with patch('builtins.open', side_effect=FileNotFoundError("Config file not found")): + with patch("builtins.open", side_effect=FileNotFoundError("Config file not found")): middleware = SecurityHeadersMiddleware(self.app, self.config) - + # Check that secure default is used csp_policy = middleware._build_csp_policy() self.assertIn("default-src 'self'", csp_policy) @@ -69,121 +73,114 @@ def test_csp_fallback_to_secure_default(self): self.assertIn("object-src 'none'", csp_policy) self.assertIn("base-uri 'self'", csp_policy) self.assertIn("form-action 'self'", csp_policy) - + def test_csp_fallback_on_invalid_yaml(self): """Test that CSP falls back to secure default when YAML is invalid.""" # Create a temporary config file with invalid YAML - with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as f: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: f.write("invalid: yaml: content: [") config_path = f.name - + try: # Mock the config file path - with patch('os.path.join', return_value=config_path): + with patch("os.path.join", return_value=config_path): middleware = SecurityHeadersMiddleware(self.app, self.config) - + # Check that secure default is used csp_policy = middleware._build_csp_policy() self.assertIn("default-src 'self'", csp_policy) self.assertIn("script-src 'self'", csp_policy) - + finally: # Clean up os.unlink(config_path) - + def test_csp_fallback_on_missing_csp_key(self): """Test that CSP falls back to secure default when CSP key is missing from config.""" # Create a temporary config file without CSP - with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as f: - yaml.dump({ - 'security_headers': { - 'headers': { - 'X-Frame-Options': 'DENY' - } - } - }, f) + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: + yaml.dump({"security_headers": {"headers": {"X-Frame-Options": "DENY"}}}, f) config_path = f.name - + try: # Mock the config file path - with patch('os.path.join', return_value=config_path): + with patch("os.path.join", return_value=config_path): middleware = SecurityHeadersMiddleware(self.app, self.config) - + # Check that secure default is used csp_policy = middleware._build_csp_policy() self.assertIn("default-src 'self'", csp_policy) self.assertIn("script-src 'self'", csp_policy) - + finally: # Clean up os.unlink(config_path) - + def test_csp_policy_formatting(self): """Test that CSP policy is properly formatted.""" middleware = SecurityHeadersMiddleware(self.app, self.config) csp_policy = middleware._build_csp_policy() - + # Check that policy is a string self.assertIsInstance(csp_policy, str) - + # Check that policy contains required directives - directives = csp_policy.split('; ') + directives = csp_policy.split("; ") self.assertGreater(len(directives), 5) # Should have multiple directives - + # Check for required directives - directive_names = [d.split(' ')[0] for d in directives] - self.assertIn('default-src', directive_names) - self.assertIn('script-src', directive_names) - self.assertIn('style-src', directive_names) - self.assertIn('object-src', directive_names) - + directive_names = [d.split(" ")[0] for d in directives] + self.assertIn("default-src", directive_names) + self.assertIn("script-src", directive_names) + self.assertIn("style-src", directive_names) + self.assertIn("object-src", directive_names) + def test_csp_policy_security(self): """Test that CSP policy contains secure defaults.""" middleware = SecurityHeadersMiddleware(self.app, self.config) csp_policy = middleware._build_csp_policy() - + # Check for secure defaults self.assertIn("object-src 'none'", csp_policy) # No plugins - self.assertIn("base-uri 'self'", csp_policy) # Restrict base URI - self.assertIn("form-action 'self'", csp_policy) # Restrict form submissions - + self.assertIn("base-uri 'self'", csp_policy) # Restrict base URI + self.assertIn("form-action 'self'", csp_policy) # Restrict form submissions + # Should NOT contain unsafe directives self.assertNotIn("'unsafe-inline'", csp_policy) self.assertNotIn("'unsafe-eval'", csp_policy) - + def test_csp_disabled_when_config_disabled(self): """Test that CSP is not added when disabled in config.""" - config = SecurityHeadersConfig( - enable_csp=False, - enable_content_security_policy=False - ) - + config = SecurityHeadersConfig(enable_csp=False, enable_content_security_policy=False) + middleware = SecurityHeadersMiddleware(self.app, config) - + # Mock response from flask import Response + response = Response() - + # Add security headers middleware._add_security_headers(response) - + # Check that CSP header is not set - self.assertNotIn('Content-Security-Policy', response.headers) - + self.assertNotIn("Content-Security-Policy", response.headers) + def test_csp_header_set_when_enabled(self): """Test that CSP header is set when enabled.""" middleware = SecurityHeadersMiddleware(self.app, self.config) - + # Mock response from flask import Response + response = Response() - + # Add security headers middleware._add_security_headers(response) - + # Check that CSP header is set - self.assertIn('Content-Security-Policy', response.headers) - csp_value = response.headers['Content-Security-Policy'] + self.assertIn("Content-Security-Policy", response.headers) + csp_value = response.headers["Content-Security-Policy"] self.assertIsInstance(csp_value, str) self.assertGreater(len(csp_value), 0) @@ -191,7 +188,7 @@ def test_enhanced_csp_policy_directives(self): """Test that enhanced CSP policy contains all required security directives.""" middleware = SecurityHeadersMiddleware(self.app, self.config) csp_policy = middleware._build_csp_policy() - + # Define all required CSP directives with descriptions required_directives = [ ("default-src 'self'", "Default source restriction"), @@ -206,19 +203,20 @@ def test_enhanced_csp_policy_directives(self): ("img-src 'self' data: https:", "Allow data URIs and HTTPS images"), ("font-src 'self' data:", "Allow data URI fonts"), ("connect-src 'self' https:", "Allow HTTPS connections"), - ("media-src 'self' https:", "Allow HTTPS media") + ("media-src 'self' https:", "Allow HTTPS media"), ] - + # Test all directives in a single loop for directive, description in required_directives: - self.assertIn(directive, csp_policy, - f"Missing CSP directive: {description} ({directive})") + self.assertIn( + directive, csp_policy, f"Missing CSP directive: {description} ({directive})" + ) def test_csp_policy_production_ready(self): """Test that CSP policy is production-ready with comprehensive security.""" middleware = SecurityHeadersMiddleware(self.app, self.config) csp_policy = middleware._build_csp_policy() - + # Production security checks with descriptions production_security = [ ("object-src 'none'", "Block all plugins"), @@ -226,13 +224,15 @@ def test_csp_policy_production_ready(self): ("base-uri 'self'", "Restrict base URI"), ("form-action 'self'", "Restrict form submissions"), ("upgrade-insecure-requests", "Force HTTPS"), - ("block-all-mixed-content", "Block mixed content") + ("block-all-mixed-content", "Block mixed content"), ] - + # Test all production security features in a single loop for directive, description in production_security: - self.assertIn(directive, csp_policy, - f"Production security missing: {description} ({directive})") + self.assertIn( + directive, csp_policy, f"Production security missing: {description} ({directive})" + ) + -if __name__ == '__main__': - unittest.main() \ No newline at end of file +if __name__ == "__main__": + unittest.main() diff --git a/tests/unit/test_data_models.py b/tests/unit/test_data_models.py index 508a09ee6..7a4fa4714 100644 --- a/tests/unit/test_data_models.py +++ b/tests/unit/test_data_models.py @@ -5,15 +5,7 @@ from datetime import datetime, timezone -from src.data.models import ( - Base, - Embedding, - JournalEntry, - Prediction, - Tag, - User, - VoiceTranscription, -) +from src.data.models import Base, Embedding, JournalEntry, Prediction, Tag, User, VoiceTranscription TEST_USER_PASSWORD_HASH = "test_hashed_password_123" # noqa: S105 @@ -21,7 +13,8 @@ class TestBase: """Test suite for Base model.""" - def test_base_class_exists(self): + @staticmethod + def test_base_class_exists(): """Test that Base class exists.""" assert Base is not None @@ -29,17 +22,16 @@ def test_base_class_exists(self): class TestUser: """Test suite for User model.""" - def test_user_initialization(self): + @staticmethod + def test_user_initialization(): """Test User initialization.""" - user = User( - email="test@example.com", - password_hash=TEST_USER_PASSWORD_HASH - ) + user = User(email="test@example.com", password_hash=TEST_USER_PASSWORD_HASH) assert user.email == "test@example.com" assert user.password_hash == TEST_USER_PASSWORD_HASH - def test_user_with_all_fields(self): + @staticmethod + def test_user_with_all_fields(): """Test User with all fields.""" custom_time = datetime.now(timezone.utc) user = User( @@ -47,7 +39,7 @@ def test_user_with_all_fields(self): password_hash=TEST_USER_PASSWORD_HASH, consent_version="1.0", consent_given_at=custom_time, - data_retention_policy="standard" + data_retention_policy="standard", ) assert user.email == "test@example.com" @@ -60,18 +52,17 @@ def test_user_with_all_fields(self): class TestJournalEntry: """Test suite for JournalEntry model.""" - def test_journal_entry_initialization(self): + @staticmethod + def test_journal_entry_initialization(): """Test JournalEntry initialization.""" - entry = JournalEntry( - user_id="test-user-id", - content="Test journal entry" - ) + entry = JournalEntry(user_id="test-user-id", content="Test journal entry") assert entry.user_id == "test-user-id" assert entry.content == "Test journal entry" - assert JournalEntry.__table__.columns['is_private'].default.arg is True + assert JournalEntry.__table__.columns["is_private"].default.arg is True - def test_journal_entry_with_all_fields(self): + @staticmethod + def test_journal_entry_with_all_fields(): """Test JournalEntry with all fields.""" custom_time = datetime.now(timezone.utc) entry = JournalEntry( @@ -82,7 +73,7 @@ def test_journal_entry_with_all_fields(self): mood_category="happy", is_private=False, created_at=custom_time, - updated_at=custom_time + updated_at=custom_time, ) assert entry.user_id == "test-user-id" @@ -98,24 +89,23 @@ def test_journal_entry_with_all_fields(self): class TestEmbedding: """Test suite for Embedding model.""" - def test_embedding_initialization(self): + @staticmethod + def test_embedding_initialization(): """Test Embedding initialization.""" - embedding = Embedding( - journal_entry_id="test-entry-id", - embedding_vector=[0.1, 0.2, 0.3] - ) + embedding = Embedding(journal_entry_id="test-entry-id", embedding_vector=[0.1, 0.2, 0.3]) assert embedding.journal_entry_id == "test-entry-id" assert embedding.embedding_vector == [0.1, 0.2, 0.3] - def test_embedding_with_all_fields(self): + @staticmethod + def test_embedding_with_all_fields(): """Test Embedding with all fields.""" custom_time = datetime.now(timezone.utc) embedding = Embedding( journal_entry_id="test-entry-id", embedding_vector=[0.1, 0.2, 0.3], model_name="test-model", - created_at=custom_time + created_at=custom_time, ) assert embedding.journal_entry_id == "test-entry-id" @@ -127,19 +117,21 @@ def test_embedding_with_all_fields(self): class TestPrediction: """Test suite for Prediction model.""" - def test_prediction_initialization(self): + @staticmethod + def test_prediction_initialization(): """Test Prediction initialization.""" prediction = Prediction( journal_entry_id="test-entry-id", prediction_type="emotion", - prediction_value={"happy": 0.8, "sad": 0.2} + prediction_value={"happy": 0.8, "sad": 0.2}, ) assert prediction.journal_entry_id == "test-entry-id" assert prediction.prediction_type == "emotion" assert prediction.prediction_value == {"happy": 0.8, "sad": 0.2} - def test_prediction_with_all_fields(self): + @staticmethod + def test_prediction_with_all_fields(): """Test Prediction with all fields.""" custom_time = datetime.now(timezone.utc) prediction = Prediction( @@ -148,7 +140,7 @@ def test_prediction_with_all_fields(self): prediction_value={"happy": 0.8, "sad": 0.2}, confidence_score=0.95, model_name="test-model", - created_at=custom_time + created_at=custom_time, ) assert prediction.journal_entry_id == "test-entry-id" @@ -162,17 +154,18 @@ def test_prediction_with_all_fields(self): class TestVoiceTranscription: """Test suite for VoiceTranscription model.""" - def test_voice_transcription_initialization(self): + @staticmethod + def test_voice_transcription_initialization(): """Test VoiceTranscription initialization.""" transcription = VoiceTranscription( - journal_entry_id="test-entry-id", - transcription_text="Test transcription" + journal_entry_id="test-entry-id", transcription_text="Test transcription" ) assert transcription.journal_entry_id == "test-entry-id" assert transcription.transcription_text == "Test transcription" - def test_voice_transcription_with_all_fields(self): + @staticmethod + def test_voice_transcription_with_all_fields(): """Test VoiceTranscription with all fields.""" custom_time = datetime.now(timezone.utc) transcription = VoiceTranscription( @@ -182,7 +175,7 @@ def test_voice_transcription_with_all_fields(self): confidence_score=0.95, model_name="whisper-large", processing_time=2.5, - created_at=custom_time + created_at=custom_time, ) assert transcription.journal_entry_id == "test-entry-id" @@ -197,20 +190,22 @@ def test_voice_transcription_with_all_fields(self): class TestTag: """Test suite for Tag model.""" - def test_tag_initialization(self): + @staticmethod + def test_tag_initialization(): """Test Tag initialization.""" tag = Tag(name="test-tag") assert tag.name == "test-tag" - def test_tag_with_all_fields(self): + @staticmethod + def test_tag_with_all_fields(): """Test Tag with all fields.""" custom_time = datetime.now(timezone.utc) tag = Tag( name="test-tag", description="Test tag description", color="#FF0000", - created_at=custom_time + created_at=custom_time, ) assert tag.name == "test-tag" diff --git a/tests/unit/test_database.py b/tests/unit/test_database.py index cf3a445ea..bbf817aad 100644 --- a/tests/unit/test_database.py +++ b/tests/unit/test_database.py @@ -5,14 +5,7 @@ import logging -from src.data.database import ( - Base, - SessionLocal, - db_session, - engine, - get_db, - init_db, -) +from src.data.database import Base, SessionLocal, db_session, engine, get_db, init_db logger = logging.getLogger(__name__) @@ -20,40 +13,47 @@ class TestDatabaseConnection: """Test suite for database connection utilities.""" - def test_get_db_generator(self): + @staticmethod + def test_get_db_generator(): """Test get_db function returns a generator.""" db_gen = get_db() - assert hasattr(db_gen, '__iter__') - assert hasattr(db_gen, '__next__') + assert hasattr(db_gen, "__iter__") + assert hasattr(db_gen, "__next__") - def test_init_db_function_exists(self): + @staticmethod + def test_init_db_function_exists(): """Test init_db function exists and is callable.""" assert callable(init_db) - def test_engine_exists(self): + @staticmethod + def test_engine_exists(): """Test engine is properly configured.""" assert engine is not None - assert hasattr(engine, 'url') + assert hasattr(engine, "url") - def test_session_local_exists(self): + @staticmethod + def test_session_local_exists(): """Test SessionLocal is properly configured.""" assert SessionLocal is not None assert callable(SessionLocal) - def test_db_session_exists(self): + @staticmethod + def test_db_session_exists(): """Test db_session is properly configured.""" assert db_session is not None - def test_base_exists(self): + @staticmethod + def test_base_exists(): """Test Base class exists.""" assert Base is not None - assert hasattr(Base, 'metadata') + assert hasattr(Base, "metadata") class TestDatabaseFunctions: """Test suite for database utility functions.""" - def test_get_db_yields_session(self): + @staticmethod + def test_get_db_yields_session(): """Test get_db function yields a database session.""" db_gen = get_db() try: @@ -63,17 +63,20 @@ def test_get_db_yields_session(self): except StopIteration: pass - def test_init_db_creates_tables(self): + @staticmethod + def test_init_db_creates_tables(): """Test init_db function can be called without error.""" assert callable(init_db) - def test_engine_configuration(self): + @staticmethod + def test_engine_configuration(): """Test engine is properly configured with expected attributes.""" - assert hasattr(engine, 'url') - assert hasattr(engine, 'pool') - assert hasattr(engine, 'dispose') + assert hasattr(engine, "url") + assert hasattr(engine, "pool") + assert hasattr(engine, "dispose") - def test_session_local_configuration(self): + @staticmethod + def test_session_local_configuration(): """Test SessionLocal is properly configured.""" assert callable(SessionLocal) try: @@ -86,10 +89,12 @@ def test_session_local_configuration(self): class TestDatabaseErrorHandling: """Test suite for database error handling.""" - def test_get_db_error_handling(self): + @staticmethod + def test_get_db_error_handling(): """Test get_db function handles errors gracefully.""" assert callable(get_db) - def test_init_db_error_handling(self): + @staticmethod + def test_init_db_error_handling(): """Test init_db function handles errors gracefully.""" assert callable(init_db) diff --git a/tests/unit/test_emotion_detection.py b/tests/unit/test_emotion_detection.py index 59d78bda2..cfc7a8296 100644 --- a/tests/unit/test_emotion_detection.py +++ b/tests/unit/test_emotion_detection.py @@ -3,17 +3,17 @@ Unit tests for emotion detection models. """ +from unittest.mock import MagicMock, patch + import pytest import torch -from unittest.mock import MagicMock, patch from transformers.modeling_outputs import BaseModelOutputWithPooling try: from src.models.emotion_detection.bert_classifier import BERTEmotionClassifier except ImportError as e: raise RuntimeError( - f"Failed to import BERTEmotionClassifier: {e}. " - "Make sure all dependencies are installed." + f"Failed to import BERTEmotionClassifier: {e}. " "Make sure all dependencies are installed." ) @@ -22,7 +22,8 @@ class TestBertEmotionClassifier: @patch("transformers.AutoConfig.from_pretrained") @patch("transformers.AutoModel.from_pretrained") - def test_model_initialization(self, mock_bert, mock_config): + @staticmethod + def test_model_initialization(mock_bert, mock_config): """Test model initializes with correct parameters.""" mock_config_instance = MagicMock() mock_config_instance.hidden_size = 768 @@ -42,7 +43,8 @@ def test_model_initialization(self, mock_bert, mock_config): @patch("transformers.AutoConfig.from_pretrained") @patch("transformers.AutoModel.from_pretrained") - def test_model_parameter_count(self, mock_bert, mock_config): + @staticmethod + def test_model_parameter_count(mock_bert, mock_config): """Test model has expected number of parameters.""" mock_config_instance = MagicMock() mock_config_instance.hidden_size = 768 @@ -59,7 +61,8 @@ def test_model_parameter_count(self, mock_bert, mock_config): @patch("transformers.AutoConfig.from_pretrained") @patch("transformers.AutoModel.from_pretrained") - def test_forward_pass(self, mock_bert, mock_config): + @staticmethod + def test_forward_pass(mock_bert, mock_config): """Test forward pass through the model.""" # Provide a minimal config so model init doesn't hit network mock_config_instance = MagicMock() @@ -87,7 +90,8 @@ def test_forward_pass(self, mock_bert, mock_config): assert output.shape == (2, 28) assert torch.all(torch.isfinite(output)) - def test_predict_emotions(self): + @staticmethod + def test_predict_emotions(): """Test emotion prediction functionality.""" with patch("transformers.AutoConfig.from_pretrained"), patch( "transformers.AutoModel.from_pretrained" @@ -99,7 +103,7 @@ def test_predict_emotions(self): mock_tokenizer_instance = MagicMock() mock_tokenizer_instance.return_value = { "input_ids": torch.tensor([[1, 2, 3, 0]]), # [batch, seq_len] - "attention_mask": torch.tensor([[1, 1, 1, 0]]) # [batch, seq_len] + "attention_mask": torch.tensor([[1, 1, 1, 0]]), # [batch, seq_len] } mock_tokenizer.return_value = mock_tokenizer_instance @@ -121,7 +125,8 @@ def test_predict_emotions(self): @patch("transformers.AutoConfig.from_pretrained") @patch("transformers.AutoModel.from_pretrained") - def test_device_compatibility(self, mock_bert, mock_config): + @staticmethod + def test_device_compatibility(mock_bert, mock_config): """Test model works on different devices.""" mock_config_instance = MagicMock() mock_config_instance.hidden_size = 768 @@ -143,7 +148,8 @@ def test_device_compatibility(self, mock_bert, mock_config): @patch("transformers.AutoConfig.from_pretrained") @patch("transformers.AutoModel.from_pretrained") - def test_training_mode(self, mock_bert, mock_config): + @staticmethod + def test_training_mode(mock_bert, mock_config): """Test model behavior in training mode.""" mock_config_instance = MagicMock() mock_config_instance.hidden_size = 768 @@ -170,7 +176,8 @@ def test_training_mode(self, mock_bert, mock_config): # The model has dropout within the classifier, not as a direct attribute assert not hasattr(model, "dropout") - def test_class_weights_handling(self): + @staticmethod + def test_class_weights_handling(): """Test that class weights are handled correctly.""" with patch("transformers.AutoConfig.from_pretrained"), patch( "transformers.AutoModel.from_pretrained" @@ -185,7 +192,8 @@ def test_class_weights_handling(self): @pytest.mark.slow @patch("transformers.AutoConfig.from_pretrained") @patch("transformers.AutoModel.from_pretrained") - def test_emotion_label_mapping(self, mock_bert, mock_config): + @staticmethod + def test_emotion_label_mapping(mock_bert, mock_config): """Test emotion label mapping functionality.""" mock_config_instance = MagicMock() mock_config_instance.hidden_size = 768 diff --git a/tests/unit/test_hash_security.py b/tests/unit/test_hash_security.py index 9df898345..613ddeed8 100644 --- a/tests/unit/test_hash_security.py +++ b/tests/unit/test_hash_security.py @@ -5,195 +5,196 @@ Tests for hash security and collision resistance. """ -import sys +import hashlib import os -sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'src')) - +import sys import unittest -import hashlib -from security_headers import SecurityHeadersMiddleware, SecurityHeadersConfig -from api_rate_limiter import TokenBucketRateLimiter, RateLimitConfig +from api_rate_limiter import RateLimitConfig, TokenBucketRateLimiter +from security_headers import SecurityHeadersConfig, SecurityHeadersMiddleware + +sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "src")) + class TestHashSecurity(unittest.TestCase): """Test hash security and collision resistance.""" - + def setUp(self): """Set up test fixtures.""" + super().setUp() from flask import Flask + self.app = Flask(__name__) - self.config = SecurityHeadersConfig( - enable_request_id=True, - enable_correlation_id=True - ) + self.config = SecurityHeadersConfig(enable_request_id=True, enable_correlation_id=True) self.middleware = SecurityHeadersMiddleware(self.app, self.config) - + # Rate limiter for testing self.rate_limit_config = RateLimitConfig( - requests_per_minute=100, - burst_size=10, - max_concurrent_requests=5 + requests_per_minute=100, burst_size=10, max_concurrent_requests=5 ) self.rate_limiter = TokenBucketRateLimiter(self.rate_limit_config) - + def test_request_id_full_sha256(self): """Test that request ID uses full SHA-256 hexdigest.""" # Mock request context from flask import g, request - with self.app.test_request_context('/'): + + with self.app.test_request_context("/"): # Mock request.remote_addr - request.remote_addr = '192.168.1.1' - + request.remote_addr = "192.168.1.1" + # Call _before_request to generate request ID self.middleware._before_request() - + # Check that request ID is full SHA-256 (64 characters) self.assertIsNotNone(g.request_id) self.assertEqual(len(g.request_id), 64) # Full SHA-256 hexdigest - + # Verify it's a valid hex string try: int(g.request_id, 16) except ValueError: self.fail("Request ID is not a valid hex string") - + def test_client_key_full_sha256(self): """Test that client key uses full SHA-256 hexdigest.""" client_ip = "192.168.1.1" user_agent = "test-user-agent" - + # Generate client key client_key = self.rate_limiter._get_client_key(client_ip, user_agent) - + # Check that client key is full SHA-256 (64 characters) self.assertEqual(len(client_key), 64) # Full SHA-256 hexdigest - + # Verify it's a valid hex string try: int(client_key, 16) except ValueError: self.fail("Client key is not a valid hex string") - + def test_hash_collision_resistance(self): """Test that different inputs produce different hashes.""" # Test request ID collision resistance request_ids = set() - + for i in range(100): # Mock different request contexts - with self.app.test_request_context('/'): + with self.app.test_request_context("/"): from flask import g, request - request.remote_addr = f'192.168.1.{i}' - + + request.remote_addr = f"192.168.1.{i}" + # Generate request ID self.middleware._before_request() request_ids.add(g.request_id) - + # All request IDs should be unique self.assertEqual(len(request_ids), 100) - + def test_client_key_collision_resistance(self): """Test that different client inputs produce different client keys.""" client_keys = set() - + # Test different IPs for i in range(50): client_ip = f"192.168.1.{i}" user_agent = "same-user-agent" client_key = self.rate_limiter._get_client_key(client_ip, user_agent) client_keys.add(client_key) - + # Test different user agents for i in range(50): client_ip = "192.168.1.1" user_agent = f"user-agent-{i}" client_key = self.rate_limiter._get_client_key(client_ip, user_agent) client_keys.add(client_key) - + # All client keys should be unique self.assertEqual(len(client_keys), 100) - + def test_hash_deterministic(self): """Test that same inputs always produce same hashes.""" client_ip = "192.168.1.1" user_agent = "test-user-agent" - + # Generate client key multiple times key1 = self.rate_limiter._get_client_key(client_ip, user_agent) key2 = self.rate_limiter._get_client_key(client_ip, user_agent) key3 = self.rate_limiter._get_client_key(client_ip, user_agent) - + # All should be identical self.assertEqual(key1, key2) self.assertEqual(key2, key3) - + def test_request_id_deterministic_with_same_inputs(self): """Test that request ID is deterministic for same inputs.""" # This test is limited because request ID includes time and random components # But we can test the structure and length consistency - with self.app.test_request_context('/'): + with self.app.test_request_context("/"): from flask import g, request - request.remote_addr = '192.168.1.1' - + + request.remote_addr = "192.168.1.1" + # Generate request ID multiple times self.middleware._before_request() request_id1 = g.request_id - + # Should always be 64 characters self.assertEqual(len(request_id1), 64) - + def test_hash_algorithm_verification(self): """Test that we're actually using SHA-256.""" client_ip = "192.168.1.1" user_agent = "test-user-agent" - + # Generate client key client_key = self.rate_limiter._get_client_key(client_ip, user_agent) - + # Manually calculate expected SHA-256 fingerprint = f"{client_ip}:{user_agent}" expected_hash = hashlib.sha256(fingerprint.encode()).hexdigest() - + # Should match self.assertEqual(client_key, expected_hash) - + def test_hash_input_format(self): """Test that hash input is properly formatted.""" client_ip = "192.168.1.1" user_agent = "test-user-agent" - + # Generate client key client_key = self.rate_limiter._get_client_key(client_ip, user_agent) - + # Manually verify the input format expected_input = f"{client_ip}:{user_agent}" expected_hash = hashlib.sha256(expected_input.encode()).hexdigest() - + self.assertEqual(client_key, expected_hash) - + def test_empty_user_agent_handling(self): """Test that empty user agent is handled correctly.""" client_ip = "192.168.1.1" user_agent = "" - + # Generate client key client_key = self.rate_limiter._get_client_key(client_ip, user_agent) - + # Should still be valid SHA-256 self.assertEqual(len(client_key), 64) try: int(client_key, 16) except ValueError: self.fail("Client key with empty user agent is not a valid hex string") - + def test_special_characters_in_user_agent(self): """Test that special characters in user agent are handled correctly.""" client_ip = "192.168.1.1" user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" - + # Generate client key client_key = self.rate_limiter._get_client_key(client_ip, user_agent) - + # Should be valid SHA-256 self.assertEqual(len(client_key), 64) try: @@ -201,5 +202,6 @@ def test_special_characters_in_user_agent(self): except ValueError: self.fail("Client key with special characters is not a valid hex string") -if __name__ == '__main__': - unittest.main() \ No newline at end of file + +if __name__ == "__main__": + unittest.main() diff --git a/tests/unit/test_http_exception_handler.py b/tests/unit/test_http_exception_handler.py index 3dca5fa54..6a3d748eb 100644 --- a/tests/unit/test_http_exception_handler.py +++ b/tests/unit/test_http_exception_handler.py @@ -55,4 +55,3 @@ def __raise_403_test__(): # type: ignore body = resp.json() assert isinstance(body, dict) and "detail" in body assert body["detail"] == expected[1] - diff --git a/tests/unit/test_jwt_manager_extra.py b/tests/unit/test_jwt_manager_extra.py index dd1e1433d..9e730bd5e 100644 --- a/tests/unit/test_jwt_manager_extra.py +++ b/tests/unit/test_jwt_manager_extra.py @@ -1,8 +1,8 @@ #!/usr/bin/env python3 """Extra unit tests for JWTManager to increase coverage.""" -from datetime import datetime, timedelta import time +from datetime import datetime, timedelta from src.security.jwt_manager import JWTManager @@ -19,7 +19,12 @@ def test_create_token_pair_structure(): ) # Ensure keys and types look correct token_pair_dict = token_pair.dict() if hasattr(token_pair, "dict") else dict(token_pair) - assert set(token_pair_dict.keys()) == {"access_token", "refresh_token", "token_type", "expires_in"} + assert set(token_pair_dict.keys()) == { + "access_token", + "refresh_token", + "token_type", + "expires_in", + } assert isinstance(token_pair.access_token, str) assert isinstance(token_pair.refresh_token, str) assert token_pair.token_type == "bearer" @@ -47,6 +52,7 @@ def test_blacklist_and_cleanup_flow(monkeypatch): # Determine the stored expiration timestamp by decoding without verifying import jwt + payload = jwt.decode(token, options={"verify_signature": False, "verify_exp": False}) exp_ts = payload.get("exp") @@ -82,6 +88,7 @@ def test_refresh_access_token_success_and_failure(): # Expired refresh token should fail (re-sign with manager's secret to keep signature valid) import jwt + payload = jwt.decode(refresh, options={"verify_signature": False, "verify_exp": False}) payload["exp"] = int(time.time()) - 10 expired_refresh = jwt.encode(payload, mgr.secret_key, algorithm=mgr.algorithm) @@ -114,4 +121,3 @@ def test_permissions_helpers(): } token_no_perms = mgr.create_access_token(user_no_permissions) assert mgr.get_user_permissions(token_no_perms) == [] - diff --git a/tests/unit/test_nlp_emotion_endpoints.py b/tests/unit/test_nlp_emotion_endpoints.py index ce74d7084..70aa92957 100644 --- a/tests/unit/test_nlp_emotion_endpoints.py +++ b/tests/unit/test_nlp_emotion_endpoints.py @@ -1,20 +1,22 @@ #!/usr/bin/env python3 """Unit tests for NLP emotion endpoints using a mocked HF pipeline.""" -import os import json -import unittest -from unittest.mock import patch +import os # Ensure app import path works import sys +import unittest from pathlib import Path -sys.path.append(str(Path(__file__).resolve().parents[2])) +from unittest.mock import patch from deployment.secure_api_server import app # type: ignore +sys.path.append(str(Path(__file__).resolve().parents[2])) + def _fake_pipeline(*args, **kwargs): """Return a deterministic pipeline callable that yields joy-dominant scores.""" + def _call(inputs, truncation=True): """Simulate pipeline call for single or batch inputs.""" inputs_list = [inputs] if isinstance(inputs, str) else inputs @@ -28,6 +30,7 @@ def _call(inputs, truncation=True): {"label": "surprise", "score": 0.01}, ] return [dist for _ in inputs_list] + return _call @@ -36,43 +39,56 @@ class TestNlpEmotionEndpoints(unittest.TestCase): def setUp(self): """Initialize Flask test client and set provider env.""" - os.environ['EMOTION_PROVIDER'] = 'hf' + super().setUp() + os.environ["EMOTION_PROVIDER"] = "hf" self.client = app.test_client() - @patch('src.inference.text_emotion_service.pipeline', new=_fake_pipeline) + @patch("src.inference.text_emotion_service.pipeline", new=_fake_pipeline) def test_single_emotion_endpoint(self): """Validate single text classification returns scores and provider info.""" payload = {"text": "I love this!"} - resp = self.client.post('/nlp/emotion', data=json.dumps(payload), headers={'Content-Type': 'application/json'}) + resp = self.client.post( + "/nlp/emotion", data=json.dumps(payload), headers={"Content-Type": "application/json"} + ) self.assertEqual(resp.status_code, 200) data = resp.get_json() - self.assertIn('scores', data) - self.assertEqual(data['provider'], 'hf') - self.assertTrue(any(x['label'] == 'joy' for x in data['scores'])) + self.assertIn("scores", data) + self.assertEqual(data["provider"], "hf") + self.assertTrue(any(x["label"] == "joy" for x in data["scores"])) - @patch('src.inference.text_emotion_service.pipeline', new=_fake_pipeline) + @patch("src.inference.text_emotion_service.pipeline", new=_fake_pipeline) def test_batch_emotion_endpoint(self): """Validate batch classification returns aligned results for each input.""" payload = {"texts": ["I love this!", "This is bad."]} - resp = self.client.post('/nlp/emotion/batch', data=json.dumps(payload), headers={'Content-Type': 'application/json'}) + resp = self.client.post( + "/nlp/emotion/batch", + data=json.dumps(payload), + headers={"Content-Type": "application/json"}, + ) self.assertEqual(resp.status_code, 200) data = resp.get_json() - self.assertIn('results', data) - self.assertEqual(data['count'], 2) - self.assertEqual(data['provider'], 'hf') - first, second = data['results'] - self.assertIn('scores', first) - self.assertTrue(any(x['label'] == 'joy' for x in first['scores'])) - self.assertIn('scores', second) - self.assertTrue(any(x['label'] == 'joy' for x in second['scores'])) + self.assertIn("results", data) + self.assertEqual(data["count"], 2) + self.assertEqual(data["provider"], "hf") + first, second = data["results"] + self.assertIn("scores", first) + self.assertTrue(any(x["label"] == "joy" for x in first["scores"])) + self.assertIn("scores", second) + self.assertTrue(any(x["label"] == "joy" for x in second["scores"])) def test_invalid_payloads(self): """Validate error responses for invalid single and batch payloads.""" - resp = self.client.post('/nlp/emotion', data='{}', headers={'Content-Type': 'application/json'}) + resp = self.client.post( + "/nlp/emotion", data="{}", headers={"Content-Type": "application/json"} + ) self.assertEqual(resp.status_code, 400) - resp = self.client.post('/nlp/emotion/batch', data='{"texts": 123}', headers={'Content-Type': 'application/json'}) + resp = self.client.post( + "/nlp/emotion/batch", + data='{"texts": 123}', + headers={"Content-Type": "application/json"}, + ) self.assertEqual(resp.status_code, 400) -if __name__ == '__main__': - unittest.main() \ No newline at end of file +if __name__ == "__main__": + unittest.main() diff --git a/tests/unit/test_permission_checker_override.py b/tests/unit/test_permission_checker_override.py index db50f5465..2dec904fb 100644 --- a/tests/unit/test_permission_checker_override.py +++ b/tests/unit/test_permission_checker_override.py @@ -14,7 +14,9 @@ def test_permission_override_header_active_under_pytest(monkeypatch): # login to get token login_data = {"username": "testuser@example.com", "password": "testpassword123"} login_resp = client.post("/auth/login", json=login_data) - assert login_resp.status_code == 200, f"Login failed: {login_resp.status_code} {login_resp.text}" + assert ( + login_resp.status_code == 200 + ), f"Login failed: {login_resp.status_code} {login_resp.text}" access = login_resp.json().get("access_token") assert access and isinstance(access, str), "Missing access_token in login response" diff --git a/tests/unit/test_sandbox_executor.py b/tests/unit/test_sandbox_executor.py index d1d65ac6d..8aa588794 100644 --- a/tests/unit/test_sandbox_executor.py +++ b/tests/unit/test_sandbox_executor.py @@ -5,178 +5,187 @@ Tests for the refactored sandbox executor with safe builtins. """ -import sys import os -sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'src', 'models', 'secure_loader')) - -import unittest +import sys import threading import time +import unittest from sandbox_executor import SandboxExecutor +sys.path.append( + os.path.join(os.path.dirname(__file__), "..", "..", "src", "models", "secure_loader") +) + + class TestSandboxExecutor(unittest.TestCase): """Test sandbox executor functionality.""" - + def setUp(self): """Set up test fixtures.""" + super().setUp() self.executor = SandboxExecutor( - max_memory_mb=512, - max_cpu_time=10, - max_wall_time=15, - allow_network=False + max_memory_mb=512, max_cpu_time=10, max_wall_time=15, allow_network=False ) - + def test_safe_builtins_creation(self): """Test that safe builtins dictionary is created correctly.""" safe_builtins = self.executor._get_safe_builtins() - + # Check that safe builtins contains expected functions - self.assertIn('__builtins__', safe_builtins) - builtins_dict = safe_builtins['__builtins__'] - + self.assertIn("__builtins__", safe_builtins) + builtins_dict = safe_builtins["__builtins__"] + # Should contain safe functions - self.assertIn('len', builtins_dict) - self.assertIn('str', builtins_dict) - self.assertIn('int', builtins_dict) - self.assertIn('list', builtins_dict) - self.assertIn('dict', builtins_dict) - + self.assertIn("len", builtins_dict) + self.assertIn("str", builtins_dict) + self.assertIn("int", builtins_dict) + self.assertIn("list", builtins_dict) + self.assertIn("dict", builtins_dict) + # Should NOT contain dangerous functions - self.assertNotIn('eval', builtins_dict) - self.assertNotIn('exec', builtins_dict) - self.assertNotIn('__import__', builtins_dict) - self.assertNotIn('open', builtins_dict) - + self.assertNotIn("eval", builtins_dict) + self.assertNotIn("exec", builtins_dict) + self.assertNotIn("__import__", builtins_dict) + self.assertNotIn("open", builtins_dict) + def test_no_global_builtins_modification(self): """Test that global __builtins__ is not modified.""" import builtins - + # Store original builtins original_builtins = builtins.__dict__.copy() - + # Create executor and run sandboxed code executor = SandboxExecutor() - + def safe_function(): return "Hello, World!" - + result, meta = executor.execute_safely(safe_function) - + # Check that global builtins are unchanged self.assertEqual(builtins.__dict__, original_builtins) self.assertEqual(result, "Hello, World!") - + def test_sandbox_context_no_global_changes(self): """Test that sandbox context doesn't modify global state.""" import builtins + original_builtins = builtins.__dict__.copy() - + with self.executor.sandbox_context(): # Sandbox context should not modify global builtins self.assertEqual(builtins.__dict__, original_builtins) - + # After context, builtins should still be unchanged self.assertEqual(builtins.__dict__, original_builtins) - + def test_execute_safely_with_string_code(self): """Test executing string code safely.""" code = "result = 2 + 2" - + result, meta = self.executor.execute_safely(code) - - self.assertEqual(meta['status'], 'exec completed') + + self.assertEqual(meta["status"], "exec completed") self.assertIsNone(result) # exec doesn't return a value - + def test_execute_safely_with_function(self): """Test executing function safely.""" + def test_function(): return "Function executed safely" - + result, meta = self.executor.execute_safely(test_function) - + self.assertEqual(result, "Function executed safely") - self.assertEqual(meta['status'], 'success') - + self.assertEqual(meta["status"], "success") + def test_sandbox_blocks_dangerous_operations(self): """Test that sandbox blocks dangerous operations.""" dangerous_code = "import os; os.system('echo dangerous')" - + result, meta = self.executor.execute_safely(dangerous_code) - + # Should fail due to import restrictions - self.assertIn('error', meta) - + self.assertIn("error", meta) + def test_thread_safety(self): """Test that sandbox executor is thread-safe.""" results = [] errors = [] - + def worker_function(): try: - result, meta = self.executor.execute_safely(lambda: f"Worker {threading.current_thread().name}") + result, meta = self.executor.execute_safely( + lambda: f"Worker {threading.current_thread().name}" + ) results.append(result) except Exception as e: errors.append(str(e)) - + # Create multiple threads threads = [] for i in range(5): thread = threading.Thread(target=worker_function) threads.append(thread) thread.start() - + # Wait for all threads to complete for thread in threads: thread.join() - + # Should have no errors and 5 results self.assertEqual(len(errors), 0) self.assertEqual(len(results), 5) - + def test_resource_limits(self): """Test that resource limits are respected.""" # This test might not work on all platforms due to resource module limitations try: executor = SandboxExecutor(max_memory_mb=1, max_cpu_time=1) - + def memory_intensive(): # Try to allocate more than 1MB large_list = [0] * 1000000 return len(large_list) - + result, meta = executor.execute_safely(memory_intensive) - + # Should either succeed or fail gracefully - self.assertIsNotNone(result or meta.get('error')) - + self.assertIsNotNone(result or meta.get("error")) + except Exception as e: # Resource limits might not be available on all platforms - self.assertIn('resource', str(e).lower() or 'limit', str(e).lower()) - + self.assertIn("resource", str(e).lower() or "limit", str(e).lower()) + def test_timeout_handling(self): """Test timeout handling.""" + def slow_function(): time.sleep(2) # Sleep longer than max_wall_time return "Should timeout" - + result, meta = self.executor.execute_safely(slow_function) - + # Should either timeout or complete within limits - self.assertIsNotNone(result or meta.get('error')) - + self.assertIsNotNone(result or meta.get("error")) + def test_network_access_blocking(self): """Test that network access is blocked when not allowed.""" + def network_function(): import socket + s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - s.connect(('localhost', 80)) + s.connect(("localhost", 80)) return "Network access" - + result, meta = self.executor.execute_safely(network_function) - + # Should fail due to network restrictions - self.assertIn('error', meta) + self.assertIn("error", meta) + -if __name__ == '__main__': - unittest.main() \ No newline at end of file +if __name__ == "__main__": + unittest.main() diff --git a/tests/unit/test_secure_model_loader.py b/tests/unit/test_secure_model_loader.py index f770129a9..173afe337 100644 --- a/tests/unit/test_secure_model_loader.py +++ b/tests/unit/test_secure_model_loader.py @@ -10,6 +10,7 @@ """ import os +import shutil import tempfile import unittest @@ -17,33 +18,33 @@ import torch.nn as nn from src.models.secure_loader import ( - SecureModelLoader, IntegrityChecker, + ModelValidator, SandboxExecutor, - ModelValidator + SecureModelLoader, ) class TestModel(nn.Module): """Simple test model for testing that meets validation criteria.""" - + def __init__(self, input_size=10, output_size=5): super().__init__() self.linear = nn.Linear(input_size, output_size) - self.model_name = 'TestModel' # Add required attribute - + self.model_name = "TestModel" # Add required attribute + def forward(self, x): return self.linear(x) class BERTEmotionClassifier(nn.Module): """Test model that matches allowed model types exactly.""" - + def __init__(self, num_emotions=5): super().__init__() self.linear = nn.Linear(768, num_emotions) # BERT hidden size - self.model_name = 'BERTEmotionClassifier' - + self.model_name = "BERTEmotionClassifier" + def forward(self, x): return self.linear(x) @@ -51,125 +52,118 @@ def forward(self, x): # Keep the old class for backward compatibility in tests class TestBERTEmotionClassifier(BERTEmotionClassifier): """Legacy test model class.""" - pass class TestIntegrityChecker(unittest.TestCase): """Test integrity checker functionality.""" - + def setUp(self): self.checker = IntegrityChecker() self.temp_dir = tempfile.mkdtemp() self.test_file = os.path.join(self.temp_dir, "test_model.pt") - + # Create a simple test model model = TestModel() - torch.save({ - 'state_dict': model.state_dict(), - 'config': {'model_name': 'test', 'num_emotions': 5} - }, self.test_file) - + torch.save( + {"state_dict": model.state_dict(), "config": {"model_name": "test", "num_emotions": 5}}, + self.test_file, + ) + def tearDown(self): - import shutil shutil.rmtree(self.temp_dir) - + def test_calculate_checksum(self): """Test checksum calculation.""" checksum = self.checker.calculate_checksum(self.test_file) self.assertIsInstance(checksum, str) self.assertEqual(len(checksum), 64) # SHA-256 hex length - + def test_validate_file_size(self): """Test file size validation.""" is_valid = self.checker.validate_file_size(self.test_file) self.assertTrue(is_valid) - + def test_validate_file_extension(self): """Test file extension validation.""" is_valid = self.checker.validate_file_extension(self.test_file) self.assertTrue(is_valid) - + def test_scan_for_malicious_content(self): """Test malicious content scanning.""" is_safe, findings = self.checker.scan_for_malicious_content(self.test_file) self.assertTrue(is_safe) self.assertEqual(len(findings), 0) - + def test_verify_checksum(self): """Test checksum verification.""" checksum = self.checker.calculate_checksum(self.test_file) is_valid = self.checker.verify_checksum(self.test_file, checksum) self.assertTrue(is_valid) - + def test_validate_model_structure(self): """Test model structure validation.""" is_valid = self.checker.validate_model_structure(self.test_file) self.assertTrue(is_valid) - + def test_comprehensive_validation(self): """Test comprehensive validation.""" # Create a test file with known checksum for validation test_checksum = self.checker.calculate_checksum(self.test_file) - is_valid, results = self.checker.comprehensive_validation(self.test_file, expected_checksum=test_checksum) + is_valid, results = self.checker.comprehensive_validation( + self.test_file, expected_checksum=test_checksum + ) self.assertTrue(is_valid) - self.assertIn('file_path', results) - self.assertIn('size_valid', results) - self.assertIn('extension_valid', results) - + self.assertIn("file_path", results) + self.assertIn("size_valid", results) + self.assertIn("extension_valid", results) + def test_comprehensive_validation_no_checksum(self): """Test comprehensive validation without checksum (should fail).""" is_valid, results = self.checker.comprehensive_validation(self.test_file) self.assertFalse(is_valid) # Should fail without expected checksum - self.assertIn('findings', results) - self.assertIn('Checksum verification failed', results['findings']) + self.assertIn("findings", results) + self.assertIn("Checksum verification failed", results["findings"]) class TestSandboxExecutor(unittest.TestCase): """Test sandbox executor functionality.""" - + def setUp(self): - self.executor = SandboxExecutor( - max_memory_mb=512, - max_cpu_time=10, - max_wall_time=20 - ) - + self.executor = SandboxExecutor(max_memory_mb=512, max_cpu_time=10, max_wall_time=20) + def test_execute_safely(self): """Test safe execution.""" + def test_func(x, y): return x + y - + result, info = self.executor.execute_safely(test_func, 2, 3) self.assertEqual(result, 5) - self.assertEqual(info['status'], 'success') # Fixed: actual return value + self.assertEqual(info["status"], "success") # Fixed: actual return value # Note: duration is not returned by the actual implementation - + def test_load_model_safely(self): """Test safe model loading.""" - with tempfile.NamedTemporaryFile(suffix='.pt', delete=False) as f: + with tempfile.NamedTemporaryFile(suffix=".pt", delete=False) as f: model = TestModel() - torch.save({ - 'state_dict': model.state_dict(), - 'config': {'model_name': 'test'} - }, f.name) - + torch.save({"state_dict": model.state_dict(), "config": {"model_name": "test"}}, f.name) + try: - result, info = self.executor.load_model_safely(f.name, TestModel) # Now returns (model, info) + result, info = self.executor.load_model_safely( + f.name, TestModel + ) # Now returns (model, info) self.assertIsInstance(result, TestModel) - self.assertIn('status', info) + self.assertIn("status", info) # Note: load_model_safely now returns both model and info dict finally: os.unlink(f.name) - + def test_validate_model_safely(self): """Test safe model validation.""" - with tempfile.NamedTemporaryFile(suffix='.pt', delete=False) as f: + with tempfile.NamedTemporaryFile(suffix=".pt", delete=False) as f: model = TestModel() - torch.save({ - 'state_dict': model.state_dict(), - 'config': {'model_name': 'test'} - }, f.name) - + torch.save({"state_dict": model.state_dict(), "config": {"model_name": "test"}}, f.name) + try: is_valid, info = self.executor.validate_model_safely(f.name) self.assertTrue(is_valid) @@ -179,131 +173,131 @@ def test_validate_model_safely(self): class TestModelValidator(unittest.TestCase): """Test model validator functionality.""" - + def setUp(self): self.validator = ModelValidator() # Use a model that meets validation criteria self.test_model = BERTEmotionClassifier() self.test_config = { - 'model_name': 'BERTEmotionClassifier', - 'num_emotions': 5, - 'hidden_dropout_prob': 0.1 + "model_name": "BERTEmotionClassifier", + "num_emotions": 5, + "hidden_dropout_prob": 0.1, } - + def test_validate_model_structure(self): """Test model structure validation.""" is_valid, info = self.validator.validate_model_structure(self.test_model) self.assertTrue(is_valid) - self.assertIn('model_type', info) - self.assertIn('parameter_count', info) - + self.assertIn("model_type", info) + self.assertIn("parameter_count", info) + def test_validate_model_config(self): """Test model configuration validation.""" is_valid, info = self.validator.validate_model_config(self.test_config) self.assertTrue(is_valid) - self.assertIn('config_keys', info) - + self.assertIn("config_keys", info) + def test_validate_model_file(self): """Test model file validation.""" - with tempfile.NamedTemporaryFile(suffix='.pt', delete=False) as f: - torch.save({ - 'state_dict': self.test_model.state_dict(), - 'config': self.test_config - }, f.name) - + with tempfile.NamedTemporaryFile(suffix=".pt", delete=False) as f: + torch.save( + {"state_dict": self.test_model.state_dict(), "config": self.test_config}, f.name + ) + try: is_valid, info = self.validator.validate_model_file(f.name) self.assertTrue(is_valid) - self.assertIn('file_size_mb', info) + self.assertIn("file_size_mb", info) finally: os.unlink(f.name) - + def test_validate_version_compatibility(self): """Test version compatibility validation.""" # Create a test config that should pass validation test_config = { - 'model_name': 'BERTEmotionClassifier', - 'torch_version': '1.9.0', # Mock compatible version - 'transformers_version': '4.20.0' + "model_name": "BERTEmotionClassifier", + "torch_version": "1.9.0", # Mock compatible version + "transformers_version": "4.20.0", } is_valid, info = self.validator.validate_version_compatibility(test_config) # Note: This may fail with current PyTorch version, but that's expected behavior # The test validates that the validation logic works correctly - self.assertIn('current_versions', info) - self.assertIn('required_versions', info) - + self.assertIn("current_versions", info) + self.assertIn("required_versions", info) + def test_validate_model_performance(self): """Test model performance validation.""" test_input = torch.randn(1, 768) # BERT hidden size is_valid, info = self.validator.validate_model_performance(self.test_model, test_input) self.assertTrue(is_valid) - self.assertIn('forward_pass_time', info) - self.assertIn('output_shape', info) + self.assertIn("forward_pass_time", info) + self.assertIn("output_shape", info) class TestSecureModelLoader(unittest.TestCase): """Test secure model loader functionality.""" - + def setUp(self): self.temp_dir = tempfile.mkdtemp() self.loader = SecureModelLoader( enable_sandbox=False, # Disable for testing enable_caching=True, - cache_dir=self.temp_dir + cache_dir=self.temp_dir, ) - + # Create test model file with proper model type self.test_model = BERTEmotionClassifier() self.test_config = { - 'model_name': 'BERTEmotionClassifier', - 'num_emotions': 5, - 'hidden_dropout_prob': 0.1 + "model_name": "BERTEmotionClassifier", + "num_emotions": 5, + "hidden_dropout_prob": 0.1, } - + self.model_file = os.path.join(self.temp_dir, "test_model.pt") - torch.save({ - 'state_dict': self.test_model.state_dict(), - 'config': self.test_config, - 'model_name': 'BERTEmotionClassifier' # Add model_name at top level - }, self.model_file) - + torch.save( + { + "state_dict": self.test_model.state_dict(), + "config": self.test_config, + "model_name": "BERTEmotionClassifier", # Add model_name at top level + }, + self.model_file, + ) + # Calculate checksum for validation - from src.models.secure_loader.integrity_checker import IntegrityChecker self.checker = IntegrityChecker() self.model_checksum = self.checker.calculate_checksum(self.model_file) - + def tearDown(self): - import shutil shutil.rmtree(self.temp_dir) - + def test_load_model(self): """Test secure model loading.""" model, info = self.loader.load_model( self.model_file, BERTEmotionClassifier, # Use the correct model class name expected_checksum=self.model_checksum, # Provide checksum - **self.test_config # Provide model configuration + **self.test_config, # Provide model configuration ) - + self.assertIsInstance(model, BERTEmotionClassifier) - self.assertIn('loading_time', info) - self.assertIn('cache_used', info) - self.assertIn('integrity_check', info) - self.assertIn('validation', info) - + self.assertIn("loading_time", info) + self.assertIn("cache_used", info) + self.assertIn("integrity_check", info) + self.assertIn("validation", info) + def test_validate_model(self): """Test model validation.""" is_valid, info = self.loader.validate_model( self.model_file, BERTEmotionClassifier, # Use the correct model class name expected_checksum=self.model_checksum, # Provide checksum - **self.test_config # Provide model configuration + **self.test_config, # Provide model configuration ) - + self.assertTrue(is_valid) - self.assertIn('integrity_check', info) - self.assertIn('validation', info) - + self.assertIn("integrity_check", info) + self.assertIn("validation", info) + def test_caching(self): """Test model caching.""" # Load model first time @@ -311,26 +305,26 @@ def test_caching(self): self.model_file, BERTEmotionClassifier, # Use the correct model class name expected_checksum=self.model_checksum, # Provide checksum - **self.test_config # Provide model configuration + **self.test_config, # Provide model configuration ) - self.assertFalse(info1['cache_used']) - + self.assertFalse(info1["cache_used"]) + # Load model second time (should use cache) model2, info2 = self.loader.load_model( self.model_file, BERTEmotionClassifier, # Use the correct model class name expected_checksum=self.model_checksum, # Provide checksum - **self.test_config # Provide model configuration + **self.test_config, # Provide model configuration ) - self.assertTrue(info2['cache_used']) - + self.assertTrue(info2["cache_used"]) + def test_get_cache_info(self): """Test cache information retrieval.""" cache_info = self.loader.get_cache_info() - self.assertIn('enabled', cache_info) - self.assertIn('cache_dir', cache_info) - self.assertIn('cache_size_mb', cache_info) - + self.assertIn("enabled", cache_info) + self.assertIn("cache_dir", cache_info) + self.assertIn("cache_size_mb", cache_info) + def test_clear_cache(self): """Test cache clearing.""" # Load model to populate cache @@ -338,16 +332,16 @@ def test_clear_cache(self): self.model_file, BERTEmotionClassifier, # Use the correct model class name expected_checksum=self.model_checksum, # Provide checksum - **self.test_config # Provide model configuration + **self.test_config, # Provide model configuration ) - + # Clear cache self.loader.clear_cache() - + # Check cache is empty cache_info = self.loader.get_cache_info() - self.assertEqual(cache_info['cached_models'], 0) - + self.assertEqual(cache_info["cached_models"], 0) + def test_cleanup(self): """Test cleanup functionality.""" self.loader.cleanup() @@ -356,66 +350,65 @@ def test_cleanup(self): class TestSecureModelLoaderIntegration(unittest.TestCase): """Integration tests for secure model loader.""" - + def setUp(self): """Set up test fixtures.""" + super().setUp() self.temp_dir = tempfile.mkdtemp() self.loader = SecureModelLoader( enable_sandbox=True, enable_caching=True, cache_dir=self.temp_dir, - audit_log_file=os.path.join(self.temp_dir, "audit.log") + audit_log_file=os.path.join(self.temp_dir, "audit.log"), ) - + # Create test model file self.test_model = BERTEmotionClassifier() self.test_config = { - 'model_name': 'BERTEmotionClassifier', - 'num_emotions': 5, - 'hidden_dropout_prob': 0.1 + "model_name": "BERTEmotionClassifier", + "num_emotions": 5, + "hidden_dropout_prob": 0.1, } - + self.model_file = os.path.join(self.temp_dir, "test_model.pt") - torch.save({ - 'state_dict': self.test_model.state_dict(), - 'config': self.test_config - }, self.model_file) - + torch.save( + {"state_dict": self.test_model.state_dict(), "config": self.test_config}, + self.model_file, + ) + # Calculate checksum for validation - from src.models.secure_loader.integrity_checker import IntegrityChecker self.checker = IntegrityChecker() self.model_checksum = self.checker.calculate_checksum(self.model_file) - + def tearDown(self): - import shutil shutil.rmtree(self.temp_dir) - + def test_full_secure_loading_workflow(self): """Test complete secure loading workflow.""" # Test input for performance validation test_input = torch.randn(1, 768) # BERT hidden size - + # Load model with full security model, info = self.loader.load_model( self.model_file, BERTEmotionClassifier, # Use proper model class expected_checksum=self.model_checksum, # Provide checksum test_input=test_input, - **self.test_config # Provide model configuration + **self.test_config, # Provide model configuration ) - + # Verify model loaded successfully self.assertIsInstance(model, BERTEmotionClassifier) - self.assertTrue(info['loading_time'] > 0) - + self.assertTrue(info["loading_time"] > 0) + # Verify security checks were performed - self.assertIn('integrity_check', info) - self.assertIn('validation', info) - self.assertIn('sandbox_execution', info) - + self.assertIn("integrity_check", info) + self.assertIn("validation", info) + self.assertIn("sandbox_execution", info) + # Verify no issues - self.assertEqual(len(info['issues']), 0) - + self.assertEqual(len(info["issues"]), 0) + # Test model inference with torch.no_grad(): output = model(test_input) @@ -425,53 +418,46 @@ def test_corrupted_model_file_handling(self): """Test loading a corrupted or tampered model file.""" # Create a corrupted model file corrupted_model_file = os.path.join(self.temp_dir, "corrupted_model.pt") - + # Write corrupted data to file - with open(corrupted_model_file, 'wb') as f: - f.write(b'corrupted_data_not_a_torch_file') - + with open(corrupted_model_file, "wb") as f: + f.write(b"corrupted_data_not_a_torch_file") + # Attempt to load corrupted model try: model, info = self.loader.load_model( - corrupted_model_file, - TestModel, - input_size=10, - output_size=5 + corrupted_model_file, TestModel, input_size=10, output_size=5 ) # Should not reach here self.fail("Should have raised an exception for corrupted model") except Exception as e: # Verify that the error is properly handled self.assertIsInstance(e, Exception) - + # Create a tampered model file (valid torch file but with malicious content) tampered_model_file = os.path.join(self.temp_dir, "tampered_model.pt") - + # Create a model with suspicious content in state dict suspicious_model = TestModel() suspicious_state_dict = suspicious_model.state_dict() # Add suspicious key that might indicate tampering - suspicious_state_dict['suspicious_layer.weight'] = torch.randn(10, 10) - - torch.save({ - 'state_dict': suspicious_state_dict, - 'config': self.test_config - }, tampered_model_file) - + suspicious_state_dict["suspicious_layer.weight"] = torch.randn(10, 10) + + torch.save( + {"state_dict": suspicious_state_dict, "config": self.test_config}, tampered_model_file + ) + # Attempt to load tampered model try: model, info = self.loader.load_model( - tampered_model_file, - TestModel, - input_size=10, - output_size=5 + tampered_model_file, TestModel, input_size=10, output_size=5 ) # Should detect tampering or suspicious content - self.assertGreater(len(info['issues']), 0) + self.assertGreater(len(info["issues"]), 0) except Exception as e: # Exception is also acceptable for tampered models self.assertIsInstance(e, Exception) - + def test_audit_logging(self): """Test audit logging functionality.""" # Load model to generate audit events @@ -479,18 +465,18 @@ def test_audit_logging(self): self.model_file, BERTEmotionClassifier, # Use proper model class expected_checksum=self.model_checksum, # Provide checksum - **self.test_config # Provide model configuration + **self.test_config, # Provide model configuration ) - + # Check audit log file exists audit_log_path = os.path.join(self.temp_dir, "audit.log") self.assertTrue(os.path.exists(audit_log_path)) - + # Check audit log contains entries - with open(audit_log_path, 'r') as f: + with open(audit_log_path, "r") as f: log_content = f.read() - self.assertIn('AUDIT:', log_content) + self.assertIn("AUDIT:", log_content) -if __name__ == '__main__': - unittest.main() \ No newline at end of file +if __name__ == "__main__": + unittest.main() diff --git a/tests/unit/test_security_integration.py b/tests/unit/test_security_integration.py index 2f083d3e9..ae95d999a 100644 --- a/tests/unit/test_security_integration.py +++ b/tests/unit/test_security_integration.py @@ -9,10 +9,11 @@ import unittest from pathlib import Path -sys.path.append(str(Path(__file__).resolve().parents[2] / 'src')) - from flask import Flask, Response -from security_headers import SecurityHeadersMiddleware, SecurityHeadersConfig + +from security_headers import SecurityHeadersConfig, SecurityHeadersMiddleware + +sys.path.append(str(Path(__file__).resolve().parents[2] / "src")) class TestSecurityIntegration(unittest.TestCase): @@ -20,6 +21,7 @@ class TestSecurityIntegration(unittest.TestCase): def setUp(self): """Set up test fixtures.""" + super().setUp() self.app = Flask(__name__) self.config = SecurityHeadersConfig( enable_csp=True, @@ -37,38 +39,42 @@ def setUp(self): enable_correlation_id=True, enable_enhanced_ua_analysis=True, ua_suspicious_score_threshold=4, - ua_blocking_enabled=True # Enable blocking for testing + ua_blocking_enabled=True, # Enable blocking for testing ) self.middleware = SecurityHeadersMiddleware(self.app, self.config) def test_comprehensive_security_headers(self): """Test that all security headers are properly set.""" response = Response() - + # Add all security headers self.middleware._add_security_headers(response) - + # Define all required security headers with validation rules required_headers = [ - ('Content-Security-Policy', 'non-empty'), - ('Strict-Transport-Security', 'non-empty'), - ('X-Frame-Options', 'non-empty'), - ('X-Content-Type-Options', 'non-empty'), - ('X-XSS-Protection', 'non-empty'), - ('Referrer-Policy', 'non-empty'), - ('Permissions-Policy', 'non-empty'), - ('Cross-Origin-Embedder-Policy', 'non-empty'), - ('Cross-Origin-Opener-Policy', 'non-empty'), - ('Cross-Origin-Resource-Policy', 'non-empty'), - ('Origin-Agent-Cluster', 'non-empty') + ("Content-Security-Policy", "non-empty"), + ("Strict-Transport-Security", "non-empty"), + ("X-Frame-Options", "non-empty"), + ("X-Content-Type-Options", "non-empty"), + ("X-XSS-Protection", "non-empty"), + ("Referrer-Policy", "non-empty"), + ("Permissions-Policy", "non-empty"), + ("Cross-Origin-Embedder-Policy", "non-empty"), + ("Cross-Origin-Opener-Policy", "non-empty"), + ("Cross-Origin-Resource-Policy", "non-empty"), + ("Origin-Agent-Cluster", "non-empty"), ] - + # Test all headers with consistent validation for header, validation in required_headers: self.assertIn(header, response.headers, f"Missing security header: {header}") - self.assertIsInstance(response.headers[header], str, f"Header {header} should be string") - if validation == 'non-empty': - self.assertGreater(len(response.headers[header]), 0, f"Header {header} should not be empty") + self.assertIsInstance( + response.headers[header], str, f"Header {header} should be string" + ) + if validation == "non-empty": + self.assertGreater( + len(response.headers[header]), 0, f"Header {header} should not be empty" + ) def test_csp_policy_default_src(self): """Test that CSP policy includes default-src directive.""" @@ -108,12 +114,16 @@ def test_csp_policy_frame_ancestors(self): def test_csp_policy_upgrade_insecure_requests(self): """Test that CSP policy includes upgrade-insecure-requests directive.""" csp_policy = self.middleware._build_csp_policy() - self.assertIn("upgrade-insecure-requests", csp_policy, "Missing upgrade-insecure-requests directive") + self.assertIn( + "upgrade-insecure-requests", csp_policy, "Missing upgrade-insecure-requests directive" + ) def test_csp_policy_block_mixed_content(self): """Test that CSP policy includes block-all-mixed-content directive.""" csp_policy = self.middleware._build_csp_policy() - self.assertIn("block-all-mixed-content", csp_policy, "Missing block-all-mixed-content directive") + self.assertIn( + "block-all-mixed-content", csp_policy, "Missing block-all-mixed-content directive" + ) def test_csp_disallows_unsafe_inline_and_eval(self): """Test that CSP policy does NOT allow unsafe directives.""" @@ -149,66 +159,71 @@ def test_permissions_policy_fullscreen(self): def test_permissions_policy_encrypted_media(self): """Test that permissions policy restricts encrypted media access.""" permissions_policy = self.middleware._build_permissions_policy() - self.assertIn("encrypted-media=()", permissions_policy, "Missing encrypted media restriction") + self.assertIn( + "encrypted-media=()", permissions_policy, "Missing encrypted media restriction" + ) def test_user_agent_analysis_integration(self): """Test user agent analysis integration with security middleware.""" # Test with highly malicious user agent that will score >3 malicious_ua = "sqlmap/1.0 + nmap/7.80 + nikto/2.1.6 + dirb/2.22" analysis = self.middleware._analyze_user_agent_enhanced(malicious_ua) - - self.assertIn('score', analysis) - self.assertIn('category', analysis) - self.assertIn('risk_level', analysis) - self.assertIn('patterns', analysis) - + + self.assertIn("score", analysis) + self.assertIn("category", analysis) + self.assertIn("risk_level", analysis) + self.assertIn("patterns", analysis) + # Should detect malicious user agent with multiple attack tools - self.assertGreater(analysis['score'], 3) - self.assertIn('malicious', analysis['category']) - self.assertEqual(analysis['risk_level'], 'very_high') + self.assertGreater(analysis["score"], 3) + self.assertIn("malicious", analysis["category"]) + self.assertEqual(analysis["risk_level"], "very_high") def test_suspicious_pattern_detection(self): """Test suspicious pattern detection integration.""" - with self.app.test_request_context('/test', headers={ - 'X-Forwarded-Host': 'malicious.com', - 'User-Agent': 'sqlmap/1.0' - }): + with self.app.test_request_context( + "/test", headers={"X-Forwarded-Host": "malicious.com", "User-Agent": "sqlmap/1.0"} + ): patterns = self.middleware._detect_suspicious_patterns() - + # Should detect suspicious patterns self.assertIsInstance(patterns, list) # Always check for suspicious indicators regardless of pattern count - pattern_text = ' '.join(patterns).lower() + pattern_text = " ".join(patterns).lower() self.assertTrue( - any(indicator in pattern_text for indicator in ['suspicious', 'header', 'user agent']), - f"Expected suspicious patterns, got: {patterns}" + any( + indicator in pattern_text + for indicator in ["suspicious", "header", "user agent"] + ), + f"Expected suspicious patterns, got: {patterns}", ) def test_request_correlation_integration(self): """Test request correlation headers integration.""" - with self.app.test_request_context('/test'): + with self.app.test_request_context("/test"): # Simulate before_request self.middleware._before_request() - + # Create response response = Response() - + # Add correlation headers self.middleware._add_correlation_headers(response) - + # Check for correlation headers - self.assertIn('X-Request-ID', response.headers) - self.assertIn('X-Correlation-ID', response.headers) - + self.assertIn("X-Request-ID", response.headers) + self.assertIn("X-Correlation-ID", response.headers) + # Headers should not be empty - self.assertGreater(len(response.headers['X-Request-ID']), 0) - self.assertGreater(len(response.headers['X-Correlation-ID']), 0) + self.assertGreater(len(response.headers["X-Request-ID"]), 0) + self.assertGreater(len(response.headers["X-Correlation-ID"]), 0) def test_security_logging_integration(self): """Test security logging integration.""" - with self.app.test_request_context('/test', headers={ - 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36' - }): + with self.app.test_request_context( + "/test", + headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"}, + ): # This should trigger security logging # We can't easily test logging output, but we can ensure it doesn't crash try: @@ -221,161 +236,173 @@ def test_security_logging_integration(self): def test_security_stats_config_section(self): """Test that security stats config section exists.""" stats = self.middleware.get_security_stats() - self.assertIn('config', stats) - config = stats['config'] + self.assertIn("config", stats) + config = stats["config"] self.assertIsInstance(config, dict) def test_security_stats_enable_csp(self): """Test that CSP is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_csp', config) - self.assertIsInstance(config['enable_csp'], bool) + config = stats["config"] + self.assertIn("enable_csp", config) + self.assertIsInstance(config["enable_csp"], bool) def test_security_stats_enable_hsts(self): """Test that HSTS is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_hsts', config) - self.assertIsInstance(config['enable_hsts'], bool) + config = stats["config"] + self.assertIn("enable_hsts", config) + self.assertIsInstance(config["enable_hsts"], bool) def test_security_stats_enable_x_frame_options(self): """Test that X-Frame-Options is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_x_frame_options', config) - self.assertIsInstance(config['enable_x_frame_options'], bool) + config = stats["config"] + self.assertIn("enable_x_frame_options", config) + self.assertIsInstance(config["enable_x_frame_options"], bool) def test_security_stats_enable_x_content_type_options(self): """Test that X-Content-Type-Options is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_x_content_type_options', config) - self.assertIsInstance(config['enable_x_content_type_options'], bool) + config = stats["config"] + self.assertIn("enable_x_content_type_options", config) + self.assertIsInstance(config["enable_x_content_type_options"], bool) def test_security_stats_enable_x_xss_protection(self): """Test that X-XSS-Protection is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_x_xss_protection', config) - self.assertIsInstance(config['enable_x_xss_protection'], bool) + config = stats["config"] + self.assertIn("enable_x_xss_protection", config) + self.assertIsInstance(config["enable_x_xss_protection"], bool) def test_security_stats_enable_referrer_policy(self): """Test that Referrer-Policy is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_referrer_policy', config) - self.assertIsInstance(config['enable_referrer_policy'], bool) + config = stats["config"] + self.assertIn("enable_referrer_policy", config) + self.assertIsInstance(config["enable_referrer_policy"], bool) def test_security_stats_enable_permissions_policy(self): """Test that Permissions-Policy is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_permissions_policy', config) - self.assertIsInstance(config['enable_permissions_policy'], bool) + config = stats["config"] + self.assertIn("enable_permissions_policy", config) + self.assertIsInstance(config["enable_permissions_policy"], bool) def test_security_stats_enable_cross_origin_embedder_policy(self): """Test that Cross-Origin-Embedder-Policy is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_cross_origin_embedder_policy', config) - self.assertIsInstance(config['enable_cross_origin_embedder_policy'], bool) + config = stats["config"] + self.assertIn("enable_cross_origin_embedder_policy", config) + self.assertIsInstance(config["enable_cross_origin_embedder_policy"], bool) def test_security_stats_enable_cross_origin_opener_policy(self): """Test that Cross-Origin-Opener-Policy is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_cross_origin_opener_policy', config) - self.assertIsInstance(config['enable_cross_origin_opener_policy'], bool) + config = stats["config"] + self.assertIn("enable_cross_origin_opener_policy", config) + self.assertIsInstance(config["enable_cross_origin_opener_policy"], bool) def test_security_stats_enable_cross_origin_resource_policy(self): """Test that Cross-Origin-Resource-Policy is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_cross_origin_resource_policy', config) - self.assertIsInstance(config['enable_cross_origin_resource_policy'], bool) + config = stats["config"] + self.assertIn("enable_cross_origin_resource_policy", config) + self.assertIsInstance(config["enable_cross_origin_resource_policy"], bool) def test_security_stats_enable_origin_agent_cluster(self): """Test that Origin-Agent-Cluster is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_origin_agent_cluster', config) - self.assertIsInstance(config['enable_origin_agent_cluster'], bool) + config = stats["config"] + self.assertIn("enable_origin_agent_cluster", config) + self.assertIsInstance(config["enable_origin_agent_cluster"], bool) def test_security_stats_enable_request_id(self): """Test that Request-ID is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_request_id', config) - self.assertIsInstance(config['enable_request_id'], bool) + config = stats["config"] + self.assertIn("enable_request_id", config) + self.assertIsInstance(config["enable_request_id"], bool) def test_security_stats_enable_correlation_id(self): """Test that Correlation-ID is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_correlation_id', config) - self.assertIsInstance(config['enable_correlation_id'], bool) + config = stats["config"] + self.assertIn("enable_correlation_id", config) + self.assertIsInstance(config["enable_correlation_id"], bool) def test_security_stats_enable_enhanced_ua_analysis(self): """Test that Enhanced UA Analysis is enabled in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('enable_enhanced_ua_analysis', config) - self.assertIsInstance(config['enable_enhanced_ua_analysis'], bool) + config = stats["config"] + self.assertIn("enable_enhanced_ua_analysis", config) + self.assertIsInstance(config["enable_enhanced_ua_analysis"], bool) def test_security_stats_ua_suspicious_score_threshold(self): """Test that UA suspicious score threshold is set in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('ua_suspicious_score_threshold', config) - self.assertIsInstance(config['ua_suspicious_score_threshold'], int) + config = stats["config"] + self.assertIn("ua_suspicious_score_threshold", config) + self.assertIsInstance(config["ua_suspicious_score_threshold"], int) def test_security_stats_ua_blocking_enabled(self): """Test that UA blocking is configured in security stats.""" stats = self.middleware.get_security_stats() - config = stats['config'] - self.assertIn('ua_blocking_enabled', config) - self.assertIsInstance(config['ua_blocking_enabled'], bool) + config = stats["config"] + self.assertIn("ua_blocking_enabled", config) + self.assertIsInstance(config["ua_blocking_enabled"], bool) def test_production_security_headers(self): """Test that all production security headers are properly configured.""" response = Response() self.middleware._add_security_headers(response) - + # Test each production security header individually - self.assertIn('X-Frame-Options', response.headers, "Missing X-Frame-Options header") - self.assertEqual(response.headers['X-Frame-Options'], 'DENY', - "X-Frame-Options should be DENY") + self.assertIn("X-Frame-Options", response.headers, "Missing X-Frame-Options header") + self.assertEqual( + response.headers["X-Frame-Options"], "DENY", "X-Frame-Options should be DENY" + ) - self.assertIn('X-Content-Type-Options', response.headers, "Missing X-Content-Type-Options header") - self.assertEqual(response.headers['X-Content-Type-Options'], 'nosniff', - "X-Content-Type-Options should be nosniff") + self.assertIn( + "X-Content-Type-Options", response.headers, "Missing X-Content-Type-Options header" + ) + self.assertEqual( + response.headers["X-Content-Type-Options"], + "nosniff", + "X-Content-Type-Options should be nosniff", + ) - self.assertIn('X-XSS-Protection', response.headers, "Missing X-XSS-Protection header") - self.assertEqual(response.headers['X-XSS-Protection'], '1; mode=block', - "X-XSS-Protection should be 1; mode=block") + self.assertIn("X-XSS-Protection", response.headers, "Missing X-XSS-Protection header") + self.assertEqual( + response.headers["X-XSS-Protection"], + "1; mode=block", + "X-XSS-Protection should be 1; mode=block", + ) - self.assertIn('Referrer-Policy', response.headers, "Missing Referrer-Policy header") - self.assertEqual(response.headers['Referrer-Policy'], 'strict-origin-when-cross-origin', - "Referrer-Policy should be strict-origin-when-cross-origin") + self.assertIn("Referrer-Policy", response.headers, "Missing Referrer-Policy header") + self.assertEqual( + response.headers["Referrer-Policy"], + "strict-origin-when-cross-origin", + "Referrer-Policy should be strict-origin-when-cross-origin", + ) def test_csp_nonce_generation(self): """Test that CSP nonce is generated and available.""" stats = self.middleware.get_security_stats() - - self.assertIn('csp_nonce', stats) - nonce = stats['csp_nonce'] - + + self.assertIn("csp_nonce", stats) + nonce = stats["csp_nonce"] + # Nonce should be a hex string self.assertIsInstance(nonce, str) self.assertGreater(len(nonce), 0) - + # Should be regenerated for each middleware instance middleware2 = SecurityHeadersMiddleware(self.app, self.config) stats2 = middleware2.get_security_stats() - + # Nonces should be different (random generation) - self.assertNotEqual(nonce, stats2['csp_nonce']) + self.assertNotEqual(nonce, stats2["csp_nonce"]) def test_headers_applied_via_after_request_integration(self): """Test that security headers are applied via Flask hooks.""" @@ -393,13 +420,13 @@ def ping(): def test_ua_blocking_returns_403(self): """Test that malicious user agents are blocked with 403.""" - with self.app.test_request_context('/blocked', headers={ - 'User-Agent': 'sqlmap/1.0 curl/7.88 nikto/2.1.6' - }): + with self.app.test_request_context( + "/blocked", headers={"User-Agent": "sqlmap/1.0 curl/7.88 nikto/2.1.6"} + ): resp = self.middleware._before_request() self.assertIsNotNone(resp) self.assertEqual(getattr(resp, "status_code", None), 403) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/tests/unit/test_validation.py b/tests/unit/test_validation.py index 9c9a132f2..ce650ce52 100644 --- a/tests/unit/test_validation.py +++ b/tests/unit/test_validation.py @@ -11,150 +11,166 @@ class TestDataValidator: """Test suite for DataValidator class.""" - def test_data_validator_initialization(self): + @staticmethod + def test_data_validator_initialization(): """Test DataValidator initialization.""" validator = DataValidator() - assert hasattr(validator, 'check_missing_values') - assert hasattr(validator, 'check_data_types') - assert hasattr(validator, 'check_text_quality') - assert hasattr(validator, 'validate_journal_entries') + assert hasattr(validator, "check_missing_values") + assert hasattr(validator, "check_data_types") + assert hasattr(validator, "check_text_quality") + assert hasattr(validator, "validate_journal_entries") - def test_check_missing_values(self): + @staticmethod + def test_check_missing_values(): """Test check_missing_values method.""" validator = DataValidator() # Create test DataFrame - df = pd.DataFrame({ - 'user_id': [1, 2, None, 4], - 'content': ['text1', 'text2', 'text3', None], - 'optional_field': ['a', 'b', 'c', 'd'] - }) - - result = validator.check_missing_values(df, required_columns=['user_id', 'content']) - - assert 'user_id' in result - assert 'content' in result - assert result['user_id'] == 25.0 # 1 out of 4 is missing - assert result['content'] == 25.0 # 1 out of 4 is missing - - def test_check_data_types(self): + df = pd.DataFrame( + { + "user_id": [1, 2, None, 4], + "content": ["text1", "text2", "text3", None], + "optional_field": ["a", "b", "c", "d"], + } + ) + + result = validator.check_missing_values(df, required_columns=["user_id", "content"]) + + assert "user_id" in result + assert "content" in result + assert result["user_id"] == 25.0 # 1 out of 4 is missing + assert result["content"] == 25.0 # 1 out of 4 is missing + + @staticmethod + def test_check_data_types(): """Test check_data_types method.""" validator = DataValidator() # Create test DataFrame - df = pd.DataFrame({ - 'user_id': [1, 2, 3, 4], - 'content': ['text1', 'text2', 'text3', 'text4'], - 'is_private': [True, False, True, False] - }) + df = pd.DataFrame( + { + "user_id": [1, 2, 3, 4], + "content": ["text1", "text2", "text3", "text4"], + "is_private": [True, False, True, False], + } + ) - expected_types = { - 'user_id': int, - 'content': str, - 'is_private': bool - } + expected_types = {"user_id": int, "content": str, "is_private": bool} result = validator.check_data_types(df, expected_types) - assert result['user_id'] is True - assert result['content'] is True - assert result['is_private'] is True + assert result["user_id"] is True + assert result["content"] is True + assert result["is_private"] is True - def test_check_text_quality(self): + @staticmethod + def test_check_text_quality(): """Test check_text_quality method.""" validator = DataValidator() # Create test DataFrame - df = pd.DataFrame({ - 'content': ['This is a test', '', ' ', 'Another test with more words'] - }) + df = pd.DataFrame( + {"content": ["This is a test", "", " ", "Another test with more words"]} + ) - result = validator.check_text_quality(df, text_column='content') + result = validator.check_text_quality(df, text_column="content") - assert 'text_length' in result.columns - assert 'word_count' in result.columns - assert 'is_empty' in result.columns - assert 'is_very_short' in result.columns + assert "text_length" in result.columns + assert "word_count" in result.columns + assert "is_empty" in result.columns + assert "is_very_short" in result.columns - def test_validate_journal_entries(self): + @staticmethod + def test_validate_journal_entries(): """Test validate_journal_entries method.""" validator = DataValidator() # Create test DataFrame - df = pd.DataFrame({ - 'user_id': [1, 2, 3, 4], - 'content': ['text1', 'text2', 'text3', 'text4'], - 'title': ['title1', 'title2', 'title3', 'title4'], - 'created_at': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04']), - 'is_private': [True, False, True, False] - }) - - required_columns = ['user_id', 'content'] + df = pd.DataFrame( + { + "user_id": [1, 2, 3, 4], + "content": ["text1", "text2", "text3", "text4"], + "title": ["title1", "title2", "title3", "title4"], + "created_at": pd.to_datetime( + ["2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04"] + ), + "is_private": [True, False, True, False], + } + ) + + required_columns = ["user_id", "content"] expected_types = { - 'user_id': int, - 'content': str, - 'title': str, - 'created_at': 'datetime64[ns]', - 'is_private': bool + "user_id": int, + "content": str, + "title": str, + "created_at": "datetime64[ns]", + "is_private": bool, } result = validator.validate_journal_entries(df, required_columns, expected_types) - assert 'missing_values' in result - assert 'data_types' in result - assert 'text_quality' in result - assert result['missing_values']['user_id'] == 0.0 - assert result['missing_values']['content'] == 0.0 + assert "missing_values" in result + assert "data_types" in result + assert "text_quality" in result + assert result["missing_values"]["user_id"] == 0.0 + assert result["missing_values"]["content"] == 0.0 class TestValidateTextInput: """Test suite for validate_text_input function.""" - def test_validate_text_input_valid(self): + @staticmethod + def test_validate_text_input_valid(): """Test validate_text_input with valid input.""" text = "This is a valid text input with reasonable length." result = validate_text_input(text) - assert result['is_valid'] is True - assert result['error'] is None + assert result["is_valid"] is True + assert result["error"] is None - def test_validate_text_input_empty(self): + @staticmethod + def test_validate_text_input_empty(): """Test validate_text_input with empty string.""" text = "" result = validate_text_input(text) - assert result['is_valid'] is False - assert "empty" in result['error'].lower() + assert result["is_valid"] is False + assert "empty" in result["error"].lower() - def test_validate_text_input_none(self): + @staticmethod + def test_validate_text_input_none(): """Test validate_text_input with None.""" result = validate_text_input(None) - assert result['is_valid'] is False - assert "none" in result['error'].lower() + assert result["is_valid"] is False + assert "none" in result["error"].lower() - def test_validate_text_input_too_short(self): + @staticmethod + def test_validate_text_input_too_short(): """Test validate_text_input with too short text.""" text = "Hi" result = validate_text_input(text, min_length=10) - assert result['is_valid'] is False - assert "short" in result['error'].lower() + assert result["is_valid"] is False + assert "short" in result["error"].lower() - def test_validate_text_input_too_long(self): + @staticmethod + def test_validate_text_input_too_long(): """Test validate_text_input with too long text.""" text = "A" * 10001 # 10,001 characters result = validate_text_input(text, max_length=10000) - assert result['is_valid'] is False - assert "long" in result['error'].lower() + assert result["is_valid"] is False + assert "long" in result["error"].lower() - def test_validate_text_input_invalid_characters(self): + @staticmethod + def test_validate_text_input_invalid_characters(): """Test validate_text_input with invalid characters.""" text = "Text with invalid chars: \x00\x01\x02" result = validate_text_input(text) - assert result['is_valid'] is False - assert "invalid" in result['error'].lower() + assert result["is_valid"] is False + assert "invalid" in result["error"].lower() - def test_validate_text_input_whitespace_only(self): + @staticmethod + def test_validate_text_input_whitespace_only(): """Test validate_text_input with whitespace-only text.""" text = " \n\t " result = validate_text_input(text) - assert result['is_valid'] is False - assert "whitespace" in result['error'].lower() + assert result["is_valid"] is False + assert "whitespace" in result["error"].lower() diff --git a/tests/unit/test_validation_enhanced.py b/tests/unit/test_validation_enhanced.py index 8c530ac23..82fb035b2 100644 --- a/tests/unit/test_validation_enhanced.py +++ b/tests/unit/test_validation_enhanced.py @@ -3,6 +3,7 @@ """ import pandas as pd + from src.data.validation import DataValidator, validate_text_input @@ -12,195 +13,194 @@ class TestDataValidatorEnhanced: def setup_method(self): """Set up test fixtures.""" self.validator = DataValidator() - + # Create test data that matches the expected schema - self.test_df = pd.DataFrame({ - 'id': [1, 2, 3, 4, 5], - 'user_id': [1, 2, 3, 4, 5], # No missing values - 'title': ['Entry 1', 'Entry 2', 'Entry 3', 'Entry 4', 'Entry 5'], - 'content': ['Hello world', 'Test entry', 'Another test', 'Valid content', 'Good content'], - 'created_at': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05']), - 'is_private': [False, True, False, True, False] - }) + self.test_df = pd.DataFrame( + { + "id": [1, 2, 3, 4, 5], + "user_id": [1, 2, 3, 4, 5], # No missing values + "title": ["Entry 1", "Entry 2", "Entry 3", "Entry 4", "Entry 5"], + "content": [ + "Hello world", + "Test entry", + "Another test", + "Valid content", + "Good content", + ], + "created_at": pd.to_datetime( + ["2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04", "2023-01-05"] + ), + "is_private": [False, True, False, True, False], + } + ) def test_check_missing_values_basic(self): """Test basic missing values check.""" missing_stats = self.validator.check_missing_values(self.test_df) - + assert isinstance(missing_stats, dict) - assert 'user_id' in missing_stats - assert 'content' in missing_stats - assert missing_stats['user_id'] == 0.0 # No missing values - assert missing_stats['content'] == 0.0 # No missing content + assert "user_id" in missing_stats + assert "content" in missing_stats + assert missing_stats["user_id"] == 0.0 # No missing values + assert missing_stats["content"] == 0.0 # No missing content def test_check_missing_values_with_required_columns(self): """Test missing values check with required columns.""" missing_stats = self.validator.check_missing_values( - self.test_df, - required_columns=['user_id', 'content'] + self.test_df, required_columns=["user_id", "content"] ) - - assert missing_stats['user_id'] == 0.0 - assert missing_stats['content'] == 0.0 + + assert missing_stats["user_id"] == 0.0 + assert missing_stats["content"] == 0.0 def test_check_data_types_basic(self): """Test data type checking.""" - expected_types = { - 'user_id': int, - 'content': str, - 'emotion_score': float - } - + expected_types = {"user_id": int, "content": str, "emotion_score": float} + type_results = self.validator.check_data_types(self.test_df, expected_types) - + assert isinstance(type_results, dict) - assert 'user_id' in type_results - assert 'content' in type_results - assert 'emotion_score' in type_results + assert "user_id" in type_results + assert "content" in type_results + assert "emotion_score" in type_results def test_check_data_types_with_missing_column(self): """Test data type checking with missing column.""" - expected_types = { - 'user_id': int, - 'nonexistent_column': str - } - + expected_types = {"user_id": int, "nonexistent_column": str} + type_results = self.validator.check_data_types(self.test_df, expected_types) - - assert type_results['nonexistent_column'] is False + + assert type_results["nonexistent_column"] is False def test_check_text_quality_basic(self): """Test text quality checking.""" - result_df = self.validator.check_text_quality(self.test_df, 'content') - + result_df = self.validator.check_text_quality(self.test_df, "content") + assert isinstance(result_df, pd.DataFrame) assert len(result_df) == len(self.test_df) - assert 'text_length' in result_df.columns - assert 'word_count' in result_df.columns + assert "text_length" in result_df.columns + assert "word_count" in result_df.columns def test_check_text_quality_with_empty_text(self): """Test text quality checking with empty text.""" - empty_df = pd.DataFrame({ - 'content': ['', ' ', 'valid text'] - }) - - result_df = self.validator.check_text_quality(empty_df, 'content') - - assert result_df.iloc[0]['text_length'] == 0 # Empty string - assert result_df.iloc[1]['text_length'] == 3 # Three spaces - assert result_df.iloc[2]['text_length'] > 0 + empty_df = pd.DataFrame({"content": ["", " ", "valid text"]}) + + result_df = self.validator.check_text_quality(empty_df, "content") + + assert result_df.iloc[0]["text_length"] == 0 # Empty string + assert result_df.iloc[1]["text_length"] == 3 # Three spaces + assert result_df.iloc[2]["text_length"] > 0 def test_validate_journal_entries_basic(self): """Test journal entries validation.""" results = self.validator.validate_journal_entries(self.test_df) - + assert isinstance(results, dict) - assert 'is_valid' in results - assert 'validated_df' in results - assert 'missing_values' in results + assert "is_valid" in results + assert "validated_df" in results + assert "missing_values" in results # Assert the expected value of 'is_valid' - assert isinstance(results['is_valid'], bool) + assert isinstance(results["is_valid"], bool) # For this test data, it should be valid - assert results['is_valid'] is True + assert results["is_valid"] is True # Assert the structure/type of missing_values - assert isinstance(results['missing_values'], dict) - + assert isinstance(results["missing_values"], dict) + # Assert the structure/type of validated_df - import pandas as pd - assert isinstance(results['validated_df'], pd.DataFrame) + assert isinstance(results["validated_df"], pd.DataFrame) # Should have the original columns plus text quality columns original_columns = list(self.test_df.columns) - quality_columns = ['text_length', 'word_count', 'is_empty', 'is_very_short'] + quality_columns = ["text_length", "word_count", "is_empty", "is_very_short"] expected_columns = original_columns + quality_columns - assert all(col in results['validated_df'].columns for col in expected_columns) + assert all(col in results["validated_df"].columns for col in expected_columns) # Should have the same number of rows - assert len(results['validated_df']) == len(self.test_df) + assert len(results["validated_df"]) == len(self.test_df) def test_validate_journal_entries_with_required_columns(self): """Test journal entries validation with required columns.""" results = self.validator.validate_journal_entries( - self.test_df, - required_columns=['user_id', 'content'] + self.test_df, required_columns=["user_id", "content"] ) - + assert isinstance(results, dict) - assert 'is_valid' in results + assert "is_valid" in results def test_validate_journal_entries_with_expected_types(self): """Test journal entries validation with expected types.""" - expected_types = { - 'user_id': int, - 'content': str, - 'emotion_score': float - } - + expected_types = {"user_id": int, "content": str, "emotion_score": float} + results = self.validator.validate_journal_entries( - self.test_df, - expected_types=expected_types + self.test_df, expected_types=expected_types ) - + assert isinstance(results, dict) - assert 'is_valid' in results + assert "is_valid" in results class TestValidateTextInputEnhanced: """Enhanced test suite for validate_text_input function.""" - def test_validate_text_input_valid(self): + @staticmethod + def test_validate_text_input_valid(): """Test valid text input.""" result = validate_text_input("This is a valid text input") - + assert isinstance(result, dict) - assert result['is_valid'] is True - assert 'error' in result + assert result["is_valid"] is True + assert "error" in result - def test_validate_text_input_too_short(self): + @staticmethod + def test_validate_text_input_too_short(): """Test text input that's too short.""" result = validate_text_input("", min_length=5) - + assert isinstance(result, dict) - assert result['is_valid'] is False - assert 'error' in result + assert result["is_valid"] is False + assert "error" in result - def test_validate_text_input_too_long(self): + @staticmethod + def test_validate_text_input_too_long(): """Test text input that's too long.""" long_text = "x" * 10001 result = validate_text_input(long_text, max_length=10000) - + assert isinstance(result, dict) - assert result['is_valid'] is False - assert 'error' in result + assert result["is_valid"] is False + assert "error" in result - def test_validate_text_input_custom_lengths(self): + @staticmethod + def test_validate_text_input_custom_lengths(): """Test text input with custom length constraints.""" result = validate_text_input("Test", min_length=3, max_length=10) - + assert isinstance(result, dict) - assert result['is_valid'] is True + assert result["is_valid"] is True - def test_validate_text_input_edge_cases(self): + @staticmethod + def test_validate_text_input_edge_cases(): """Test text input edge cases.""" # Test with whitespace result = validate_text_input(" ", min_length=1) - assert result['is_valid'] is False - + assert result["is_valid"] is False + # Test with single character result = validate_text_input("a", min_length=1, max_length=1) - assert result['is_valid'] is True - + assert result["is_valid"] is True + # Test with exact max length exact_text = "x" * 100 result = validate_text_input(exact_text, max_length=100) - assert result['is_valid'] is True + assert result["is_valid"] is True - def test_validate_text_input_invalid_types(self): + @staticmethod + def test_validate_text_input_invalid_types(): """Test text input with invalid types.""" # Test with None result = validate_text_input(None) - assert result['is_valid'] is False - + assert result["is_valid"] is False + # Test with non-string result = validate_text_input(123) - assert result['is_valid'] is False + assert result["is_valid"] is False diff --git a/website/API_SETUP.md b/website/API_SETUP.md new file mode 100644 index 000000000..873e7eea7 --- /dev/null +++ b/website/API_SETUP.md @@ -0,0 +1,93 @@ +# JWT Authentication Setup for SAMO-DL Demo + +## Overview +The comprehensive demo connects to the SAMO Unified API. The service requires JWT (Bearer token) authentication for all non-health endpoints. This document explains how to set up the configuration securely. + +## Setup Instructions + +### 1. Create the Configuration File +Create a `config.js` file in the `website/` directory with the following content: + +```javascript +/** + * API Configuration for SAMO-DL Demo + * This file contains the API configuration + * DO NOT commit this file with real API keys to version control + */ + +// API Configuration +const SAMO_CONFIG = { + baseURL: 'https://samo-unified-api-optimized-frrnetyhfa-uc.a.run.app', + jwtToken: null, // JWT Bearer token required for authentication + timeout: 30000, + retryAttempts: 3 +}; + +// Export for use in other scripts +if (typeof module !== 'undefined' && module.exports) { + module.exports = SAMO_CONFIG; +} else { + window.SAMO_CONFIG = SAMO_CONFIG; +} +``` + +### 2. JWT Authentication Setup +The SAMO Unified API requires JWT (Bearer token) authentication for all non-health endpoints. To authenticate: + +1. **Obtain a JWT token** from your API provider or authentication service +2. **Add the token to your config**: + ```javascript + const SAMO_CONFIG = { + baseURL: 'https://samo-unified-api-optimized-frrnetyhfa-uc.a.run.app', + jwtToken: 'your-jwt-token-here', // Replace with actual JWT + timeout: 30000, + retryAttempts: 3 + }; + ``` +3. **Include in requests**: The token should be sent in the Authorization header: + ``` + Authorization: Bearer + ``` + +### 3. Secure Token Storage +- Store JWT tokens in environment variables, not in code +- Use secure token management practices +- Rotate tokens regularly for security + +### 4. Security Notes +- The `config.js` file is already added to `.gitignore` to prevent accidental commits +- Never commit API keys to version control +- The demo will fall back to mock data if the config file is not available + +## Testing the Demo + +1. Start the local HTTP server: + ```bash + cd website + python3 -m http.server 8080 + ``` + +2. Open your browser and navigate to: + ``` + http://localhost:8080/comprehensive-demo.html + ``` + +3. Test with the provided journal text samples + +## Troubleshooting + +### API Key Issues +- Ensure the API key is correctly set in `config.js` +- Check that the API key has the correct permissions +- Verify the API service is running and accessible + +### Rate Limiting +- The API has rate limits (300 requests per minute in production) +- Rate limits may vary by environment - check your deployment configuration +- If you hit rate limits, the demo will show mock data +- Wait for the rate limit to reset before trying again +- Rate limit headers are included in responses for monitoring + +### Fallback Mode +- If the API is unavailable, the demo will automatically use mock data +- This ensures the demo always works for demonstration purposes diff --git a/website/HUGGING_FACE_SETUP.md b/website/HUGGING_FACE_SETUP.md new file mode 100644 index 000000000..f11255838 --- /dev/null +++ b/website/HUGGING_FACE_SETUP.md @@ -0,0 +1,73 @@ +# ๐Ÿค— Hugging Face AI Text Generation Setup + +## **FREE AI-Powered Sample Text Generation** + +The SAMO-DL demo now uses **Hugging Face Inference API** to generate dynamic, AI-powered sample texts instead of static ones! + +## **๐Ÿš€ Quick Setup (2 minutes)** + +### **Step 1: Get Your FREE API Token** + +1. **Sign up** at [huggingface.co](https://huggingface.co/) +2. **Go to Settings** โ†’ **Access Tokens** +3. **Create New Token** โ†’ Name it "SAMO-DL-Demo" +4. **Copy the token** (starts with `hf_`) + +### **Step 2: Add Token to Demo** + +1. **Open** `website/js/simple-demo-functions.js` +2. **Find line 45**: `'Authorization': 'Bearer hf_your_token_here',` +3. **Replace** `hf_your_token_here` with your actual token +4. **Save** the file + +### **Step 3: Test It!** + +1. **Open** the demo: `http://localhost:8081/comprehensive-demo.html` +2. **Click "Generate"** button +3. **Watch** AI generate unique journal text every time! + +## **๐ŸŽฏ What You Get** + +- โœ… **1,000 FREE requests/month** (more than enough for demos) +- โœ… **5 different emotional prompts** (excitement, anxiety, mixed, calm, motivation) +- โœ… **Dynamic text generation** (never the same text twice!) +- โœ… **Automatic fallback** to static samples if API fails +- โœ… **Visual feedback** with loading states and animations + +## **๐Ÿ”ง API Details** + +- **Model**: GPT-2 (free, no credit card required) +- **Endpoint**: `https://api-inference.huggingface.co/models/gpt2` +- **Parameters**: + - `max_length: 150` (perfect paragraph length) + - `temperature: 0.8` (creative but coherent) + - `top_p: 0.9` (balanced creativity) + +## **๐ŸŽจ Visual Feedback** + +- **Purple border**: AI is generating text +- **Green border**: AI text successfully generated +- **Orange border**: Fallback to static samples + +## **๐Ÿ›ก๏ธ Security Note** + +- **Never commit** your API token to version control +- **Keep it private** - this is your personal token +- **Free tier** is sufficient for demos and testing + +## **๐Ÿšจ Troubleshooting** + +**If Generate button shows static text:** +1. Check your API token is correct +2. Verify you're signed in to Hugging Face +3. Check browser console for error messages +4. Ensure you have internet connection + +**If you see "Generating AI text..." forever:** +- The API might be loading the model (first request takes longer) +- Wait 10-15 seconds, then try again +- Check your internet connection + +--- + +**๐ŸŽ‰ Enjoy your AI-powered demo!** Every click of "Generate" creates unique, emotional journal text perfect for testing the SAMO-DL emotion analysis pipeline! diff --git a/website/api-config.js b/website/api-config.js new file mode 100644 index 000000000..77b09f52b --- /dev/null +++ b/website/api-config.js @@ -0,0 +1,32 @@ +/** + * Server-side API Configuration Endpoint + * This file can be served by a web server to provide runtime configuration + * without exposing sensitive URLs in client-side code + * + * Usage: Serve this as /api/config or similar endpoint + * The client will fetch this configuration at runtime + */ + +// This would typically be generated by your build process or server +const SERVER_CONFIG = { + baseURL: process.env.API_BASE_URL || '/api', + apiKey: process.env.API_KEY || null, + timeout: parseInt(process.env.API_TIMEOUT || '30000', 10), + retryAttempts: parseInt(process.env.API_RETRY_ATTEMPTS || '3', 10), + environment: process.env.NODE_ENV || 'development', + version: process.env.APP_VERSION || '1.0.0' +}; + +// Express.js endpoint example +if (typeof module !== 'undefined' && module.exports) { + module.exports = (req, res) => { + res.setHeader('Content-Type', 'application/json'); + res.setHeader('Cache-Control', 'no-cache, no-store, must-revalidate'); + res.json(SERVER_CONFIG); + }; +} + +// For direct serving +if (typeof window !== 'undefined') { + window.SERVER_CONFIG = SERVER_CONFIG; +} diff --git a/website/comprehensive-demo.html b/website/comprehensive-demo.html new file mode 100644 index 000000000..a245f3d36 --- /dev/null +++ b/website/comprehensive-demo.html @@ -0,0 +1,899 @@ + + + + + + SAMO Emotion Detection API - SAMO Deep Learning + + + + + + + + + + + + + + + + + + + + + + +
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+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/website/config.example.js b/website/config.example.js new file mode 100644 index 000000000..396c40fc5 --- /dev/null +++ b/website/config.example.js @@ -0,0 +1,30 @@ +/** + * API Configuration Example for SAMO-DL Demo + * Copy this file to config.js and customize for your environment + * DO NOT commit config.js with real API keys to version control + * + * Environment-specific configuration: + * - Local development: Uses localhost:8080 + * - Production: Uses relative /api proxy path + * - Custom: Override via SAMO_CONFIG environment variable + */ + +// API Configuration - Environment Detection +const isLocalDev = window.location.hostname === 'localhost' || + window.location.hostname === '127.0.0.1' || + window.location.hostname === ''; + +const SAMO_CONFIG = { + // Use relative path for production, localhost for development + baseURL: isLocalDev ? 'http://localhost:8080' : '/api', + apiKey: null, // Current service doesn't require API key + timeout: 30000, + retryAttempts: 3 +}; + +// Export for use in other scripts +if (typeof module !== 'undefined' && module.exports) { + module.exports = SAMO_CONFIG; +} else { + window.SAMO_CONFIG = SAMO_CONFIG; +} diff --git a/website/config.js b/website/config.js new file mode 100644 index 000000000..c38e34771 --- /dev/null +++ b/website/config.js @@ -0,0 +1,43 @@ +// SAMO-DL Demo Configuration +// DO NOT COMMIT TOKENS TO VERSION CONTROL! + +window.SAMO_CONFIG = { + // OpenAI API Configuration + OPENAI: { + // Use proxy endpoint instead of direct API access for security + PROXY_URL: 'https://samo-unified-api-frrnetyhfa-uc.a.run.app/generate/journal', + MODEL: 'gpt-3.5-turbo', + MAX_TOKENS: 200, + TEMPERATURE: 0.8 + }, + + // Hugging Face API Configuration (fallback) + HUGGING_FACE: { + // Replace with your actual token - DO NOT COMMIT THIS FILE WITH REAL TOKENS + API_TOKEN: 'hf_your_token_here', // Replace with: hf_your_actual_token_here + MODEL: 'distilgpt2', // Fallback models: 'gpt2', 'microsoft/DialoGPT-medium' + MAX_LENGTH: 150, + TEMPERATURE: 0.8 + }, + + // Emotion API Configuration + EMOTION_API: { + ENDPOINT: 'https://samo-unified-api-71517823771.us-central1.run.app/analyze/emotion', + TIMEOUT: 10000 + }, + + // Feature flags + FEATURES: { + ENABLE_OPENAI: false // Set to false for public builds to prevent API key exposure + }, + + // Demo Configuration + DEMO: { + FALLBACK_TO_STATIC: true, + SHOW_DEBUG_INFO: true + } +}; + +// Security warning +console.warn('๐Ÿ”’ SECURITY: OpenAI integration disabled for public builds - using proxy endpoint instead'); +console.warn('๐Ÿ”’ SECURITY: Never commit API keys to version control!'); \ No newline at end of file diff --git a/website/cors-proxy.py b/website/cors-proxy.py new file mode 100644 index 000000000..2d8dba7b2 --- /dev/null +++ b/website/cors-proxy.py @@ -0,0 +1,129 @@ +#!/usr/bin/env python3 +""" +CORS Proxy for SAMO Emotion API +This script creates a local proxy to bypass CORS restrictions +""" + +import json +import urllib.parse +from http.server import BaseHTTPRequestHandler, HTTPServer + +import requests + + +class CORSProxyHandler(BaseHTTPRequestHandler): + def do_OPTIONS(self): + # Handle preflight requests + self.send_response(200) + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Access-Control-Allow-Methods", "GET, POST, OPTIONS") + self.send_header( + "Access-Control-Allow-Headers", + "Content-Type, Authorization, Cache-Control, Pragma, Accept, Origin, X-Requested-With", + ) + self.end_headers() + + def do_GET(self): + # Handle GET requests (fallback) + if self.path == "/emotion": + self.send_response(405) + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Content-Type", "application/json") + self.end_headers() + response = json.dumps({"error": "Method not allowed. Use POST instead."}) + self.wfile.write(response.encode()) + else: + self.send_response(404) + self.send_header("Access-Control-Allow-Origin", "*") + self.end_headers() + + def do_POST(self): + if self.path == "/emotion": + try: + # Read the request body + content_length = int(self.headers["Content-Length"]) + post_data = self.rfile.read(content_length) + + # Debug: Print what we received + print(f"Received data: {post_data.decode('utf-8')}") + + # Parse the JSON to extract the text + request_data = json.loads(post_data.decode("utf-8")) + text = request_data.get("text", "") + + # URL encode the text for query parameter + encoded_text = urllib.parse.quote(text) + + # Forward the request to the unified API with query parameters + api_url = f"https://samo-unified-api-optimized-frrnetyhfa-uc.a.run.app/analyze/emotion?text={encoded_text}" + + # Make POST request with query parameters using secure requests library + headers = { + "Content-Type": "application/json", + "Content-Length": "0", # Required for POST requests with query params + } + + # Debug: Print what we're sending + print(f"Sending to Unified API: {api_url}") + + # Use requests library for better security and SSL verification + response = requests.post(api_url, headers=headers, timeout=30, verify=True) + response.raise_for_status() # Raise exception for HTTP errors + + # Send CORS headers + self.send_response(200) + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Content-Type", "application/json") + self.end_headers() + + # Send the API response + self.wfile.write(response.content) + + except requests.exceptions.HTTPError as e: + status_code = e.response.status_code if e.response else 500 + print(f"HTTP Error {status_code}: {str(e)}") + # Forward the original error status code instead of converting to 500 + self.send_response(status_code) + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Content-Type", "application/json") + self.end_headers() + error_response = json.dumps({"error": f"API Error {status_code}: {str(e)}"}) + self.wfile.write(error_response.encode()) + except requests.exceptions.RequestException as e: + print(f"Request Error: {str(e)}") + self.send_response(500) + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Content-Type", "application/json") + self.end_headers() + error_response = json.dumps({"error": f"Request failed: {str(e)}"}) + self.wfile.write(error_response.encode()) + except Exception as e: + print(f"Error: {e}") + self.send_response(500) + self.send_header("Access-Control-Allow-Origin", "*") + self.send_header("Content-Type", "application/json") + self.end_headers() + error_response = json.dumps({"error": str(e)}) + self.wfile.write(error_response.encode()) + else: + self.send_response(404) + self.end_headers() + + def log_message(self, fmt, *args): + # Enable logging to see requests + print(f"[{self.date_time_string()}] {fmt % args}") + + +if __name__ == "__main__": + port = 8081 + server = HTTPServer(("localhost", port), CORSProxyHandler) + print(f"CORS Proxy running on http://localhost:{port}") + print("Available endpoints:") + print(f" POST http://localhost:{port}/emotion") + print("Press Ctrl+C to stop") + + try: + server.serve_forever() + except KeyboardInterrupt: + print("\nShutting down proxy...") + server.shutdown() diff --git a/website/css/comprehensive-demo.css b/website/css/comprehensive-demo.css new file mode 100644 index 000000000..901b1650f --- /dev/null +++ b/website/css/comprehensive-demo.css @@ -0,0 +1,1235 @@ +/* Comprehensive Demo Styles - Extracted from HTML for better maintainability */ + +/* CSS Variables */ +:root { + --primary-gradient: linear-gradient(135deg, #6366f1 0%, #8b5cf6 50%, #a855f7 100%); + --secondary-gradient: linear-gradient(135deg, #1e1b4b 0%, #312e81 50%, #3730a3 100%); + --dark-gradient: linear-gradient(135deg, #0f0f23 0%, #1a1a2e 50%, #16213e 100%); + --accent-gradient: linear-gradient(135deg, #7c3aed 0%, #9333ea 50%, #c084fc 100%); + + --primary-color: #8b5cf6; + --secondary-color: #a855f7; + --accent-color: #c084fc; + --dark-color: #0f0f23; + --darker-color: #0a0a1a; + --light-accent: #e9d5ff; + --glass-bg: rgba(139, 92, 246, 0.1); + --glass-border: rgba(139, 92, 246, 0.2); + --error-color: #ef4444; + --success-color: #10b981; + + --transition-smooth: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); + --transition-bounce: all 0.4s cubic-bezier(0.68, -0.55, 0.265, 1.55); + --shadow-glow: 0 10px 40px rgba(139, 92, 246, 0.3); + --shadow-glass: 0 8px 32px rgba(0, 0, 0, 0.3); +} + +/* Material Icons styling */ +.material-icons { + font-family: 'Material Icons'; + font-weight: normal; + font-style: normal; + font-size: 24px; + line-height: 1; + letter-spacing: normal; + text-transform: none; + display: inline-block; + white-space: nowrap; + word-wrap: normal; + direction: ltr; + -webkit-font-feature-settings: 'liga'; + -webkit-font-smoothing: antialiased; + vertical-align: middle; +} + +/* Text input styling */ +#textInput { + min-height: 240px !important; + font-size: 16px; + line-height: 1.5; + resize: vertical; +} + +/* Base Styles */ +body.comprehensive-demo { + font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif; + line-height: 1.6; + color: #e2e8f0; + background: var(--dark-gradient); + background-attachment: fixed; + min-height: 100vh; + padding-top: 80px; /* Account for fixed navbar */ +} + +/* Result sections */ +.result-section-hidden { + display: none !important; +} + +.result-section-visible { + display: block !important; + animation: fadeInUp 0.8s cubic-bezier(0.4, 0, 0.2, 1); +} + +/* Ensure all text is visible - scoped to comprehensive demo */ +.comprehensive-demo .text-muted { + color: #cbd5e1 !important; +} + +/* Scoped to demo container only */ +.demo-container .text-dark { + color: #e2e8f0; +} + +/* Feature card text improvements */ +.feature-card .text-muted { + color: #cbd5e1 !important; +} + +.feature-card h5, .feature-card h6 { + color: #f1f5f9 !important; +} + +.feature-card small { + color: #cbd5e1 !important; +} + +/* Scoped form controls to demo container */ +.demo-container .form-control { + color: #e2e8f0; + background-color: rgba(255, 255, 255, 0.1); + border-color: rgba(139, 92, 246, 0.3); +} + +.demo-container .form-control:focus { + color: #e2e8f0; + background-color: rgba(255, 255, 255, 0.15); + border-color: var(--primary-color); + box-shadow: 0 0 0 0.2rem rgba(139, 92, 246, 0.25); +} + +.comprehensive-demo .form-control::placeholder { + color: #94a3b8 !important; +} + +/* Hero Section */ +.hero-section::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + bottom: 0; + background: + radial-gradient(circle at 20% 50%, rgba(139, 92, 246, 0.3) 0%, transparent 50%), + radial-gradient(circle at 80% 20%, rgba(168, 85, 247, 0.2) 0%, transparent 50%), + radial-gradient(circle at 40% 80%, rgba(192, 132, 252, 0.2) 0%, transparent 50%); + animation: float 6s ease-in-out infinite; +} + +.hero-content { + position: relative; + z-index: 2; +} + +/* Demo Container - Full Width Professional Design */ +.demo-container { + background: var(--glass-bg); + backdrop-filter: blur(20px); + border: 1px solid var(--glass-border); + border-radius: 20px; + box-shadow: var(--shadow-glass); + padding: 3rem; + margin: 2rem 0; + width: 100%; + position: relative; + z-index: 1; + color: #e2e8f0; +} + +/* Feature Cards */ +.feature-card { + background: var(--glass-bg); + backdrop-filter: blur(15px); + border: 1px solid var(--glass-border); + border-radius: 20px; + box-shadow: var(--shadow-glass); + transition: var(--transition-bounce); + color: #e2e8f0; + height: 100%; +} + +.feature-card:hover { + transform: translateY(-5px) scale(1.02); + box-shadow: var(--shadow-glow); +} + +.feature-card.active { + background: var(--primary-gradient); + border-color: var(--accent-color); + color: white; + transform: translateY(-5px) scale(1.05); + box-shadow: var(--shadow-glow); +} + +/* Navigation */ +.comprehensive-demo .navbar { + background: rgba(15, 15, 35, 0.95); + backdrop-filter: blur(20px); + border-bottom: 1px solid var(--glass-border); +} + +.comprehensive-demo .navbar-brand, .comprehensive-demo .navbar .nav-link { + color: #e2e8f0; +} + +/* Buttons */ +.comprehensive-demo .btn-primary { + background: var(--primary-gradient); + border: none; + border-radius: 12px; + padding: 12px 30px; + font-weight: 600; + transition: var(--transition-bounce); + box-shadow: var(--shadow-glow); +} + +.comprehensive-demo .btn-primary:hover { + transform: translateY(-2px) scale(1.05); + box-shadow: 0 15px 50px rgba(139, 92, 246, 0.4); +} + +.comprehensive-demo .btn:focus-visible { + outline: 3px solid #667eea; + outline-offset: 2px; +} + +/* Form Controls - consolidated with scoped rules above */ + +/* Focus styles consolidated with scoped rules above */ + +.demo-container .form-control::placeholder { + color: #94a3b8; +} + +/* Loading States */ +.loading-spinner { + display: none; + color: #e2e8f0; +} + +.loading-spinner.show { + display: block; +} + +/* Result Sections */ +.result-section { + display: none; +} + +/* Ensure icons are visible in results */ +.feature-card h5 i { + font-size: 1.2rem; + margin-right: 8px; +} + +/* Processing Information Icons */ +.processing-info i { + font-size: 1.5rem !important; + margin-bottom: 8px; +} + +.result-section.show { + display: block; + animation: fadeInUp 0.8s cubic-bezier(0.4, 0, 0.2, 1); +} + +/* Animations */ +@keyframes fadeInUp { + from { + opacity: 0; + transform: translateY(40px) scale(0.95); + } + to { + opacity: 1; + transform: translateY(0) scale(1); + } +} + +@keyframes float { + 0%, 100% { transform: translateY(0px) rotate(0deg); } + 33% { transform: translateY(-20px) rotate(1deg); } + 66% { transform: translateY(-10px) rotate(-1deg); } +} + +.floating-card { + animation: float 6s ease-in-out infinite; +} + +/* Text Effects */ +.gradient-text { + background: var(--primary-gradient); + -webkit-background-clip: text; + -webkit-text-fill-color: transparent; + background-clip: text; +} + +/* Emotion Badges */ +.emotion-badge { + display: block; + padding: 8px 16px; + margin: 4px 0; + border-radius: 20px; + font-size: 0.9rem; + font-weight: 500; + transition: var(--transition-smooth); + text-align: center; + width: 100%; + box-sizing: border-box; +} + +.emotion-badge:hover { + transform: scale(1.05); +} + +/* Emotion Badges Container */ +#emotionBadges { + display: flex; + flex-direction: column; + gap: 8px; + margin-bottom: 20px; +} + +/* Audio Visualizer */ +.audio-visualizer { + width: 100%; + height: 60px; + background: var(--glass-bg); + border-radius: 10px; + display: flex; + align-items: center; + justify-content: center; + margin: 10px 0; +} + +.audio-bar { + width: 4px; + height: 20px; + background: var(--primary-color); + margin: 0 2px; + border-radius: 2px; + animation: audioPulse 0.5s ease-in-out infinite alternate; +} + +@keyframes audioPulse { + 0% { height: 20px; } + 100% { height: 40px; } +} + +/* Reduce motion for users who prefer it */ +@media (prefers-reduced-motion: reduce) { + .step.active .step-circle, + .spinner { + animation: none !important; + } + .btn:hover { + transform: none !important; + box-shadow: none !important; + } +} + +/* Progress Steps */ +.progress-step { + display: flex; + align-items: center; +} + +/* Vertical Progress Pipeline */ +.progress-pipeline-vertical { + display: flex; + flex-direction: column; + align-items: center; + gap: 12px; /* Reduced from 20px for tighter spacing */ + padding: 25px 15px; /* Reduced padding */ + background: var(--glass-bg); + border-radius: 20px; + border: 1px solid rgba(255, 255, 255, 0.1); + min-height: 450px; /* Reduced height */ + justify-content: center; + width: 100%; +} + +.progress-step-vertical { + display: flex; + flex-direction: row; + align-items: center; + text-align: left; + padding: 16px 20px; /* Increased padding for better touch targets */ + border-radius: 15px; + transition: var(--transition-smooth); + min-width: 160px; /* Increased width */ + background: rgba(255, 255, 255, 0.05); + border: 1px solid rgba(255, 255, 255, 0.1); + margin-bottom: 4px; /* Reduced margin */ + width: 100%; /* Full width for better appearance */ +} + +.progress-step-vertical .step-icon-small { + width: 24px !important; /* Increased from 18px */ + height: 24px !important; /* Increased from 18px */ + border-radius: 50%; + display: flex; + align-items: center; + justify-content: center; + margin-right: 16px; /* Increased margin */ + margin-bottom: 0; + font-size: 0.8rem !important; /* Increased from 0.6rem */ + transition: var(--transition-smooth); + background: rgba(255, 255, 255, 0.1) !important; + color: #e2e8f0 !important; + border: 1px solid rgba(255, 255, 255, 0.2); + flex-shrink: 0; +} + +.progress-step-vertical .step-label { + font-size: 0.9rem; /* Increased from 0.7rem */ + font-weight: 600; + color: #e2e8f0; + transition: var(--transition-smooth); + text-align: left; /* Changed from center to left for better alignment */ + flex: 1; /* Take remaining space */ +} + +.progress-step-vertical.active .step-icon-small { + background: var(--primary-gradient) !important; + color: white !important; + box-shadow: var(--shadow-glow); +} + +.progress-step-vertical.completed .step-icon-small { + background: var(--success-color) !important; + color: white !important; +} + +.progress-step-vertical.error .step-icon-small { + background: var(--error-color) !important; + color: white !important; +} + +.pipeline-arrow-vertical { + font-size: 1.2rem; /* Slightly smaller */ + color: #94a3b8; /* Better color */ + margin: 2px 0; /* Reduced margin */ + opacity: 0.7; /* Subtle appearance */ + transition: var(--transition-smooth); +} + +.progress-step.completed { + background: rgba(16, 185, 129, 0.1); + border: 1px solid rgba(16, 185, 129, 0.3); +} + +.progress-step.active { + background: var(--primary-gradient); + color: white; +} + +.step-icon { + width: 30px; + height: 30px; + border-radius: 50%; + display: flex; + align-items: center; + justify-content: center; + margin-right: 15px; + font-size: 14px; +} + +.step-icon.completed { + background: #10b981; + color: white; +} + +.step-icon.active { + background: white; + color: var(--primary-color); +} + +.step-icon.pending { + background: rgba(139, 92, 246, 0.2); + color: #94a3b8; +} + +/* Progress Pipeline - New Compact Horizontal Design */ +.progress-pipeline { + display: flex; + align-items: center; + justify-content: center; + gap: 1rem; + padding: 1.5rem; + background: var(--glass-bg); + border-radius: 15px; + border: 1px solid var(--glass-border); + max-width: 700px; + margin: 0 auto; +} + +/* Horizontal Progress Pipeline for Title Flow */ +.progress-pipeline-horizontal { + display: flex; + align-items: center; + justify-content: center; + gap: 0.5rem; + padding: 0.5rem 1rem; + background: rgba(139, 92, 246, 0.05); + border-radius: 25px; + border: 1px solid rgba(139, 92, 246, 0.1); + backdrop-filter: blur(5px); + box-shadow: none; + opacity: 0.7; + transition: all 0.3s ease; +} + +.progress-pipeline-horizontal:hover { + opacity: 1; + background: rgba(139, 92, 246, 0.08); +} + +/* Dynamic Layout States */ +#inputLayout { + transition: all 0.5s ease-in-out; +} + +#resultsLayout { + transition: all 0.5s ease-in-out; +} + +#titleWithFlow { + transition: all 0.3s ease-in-out; +} + +/* Enhanced Feature Cards with Better Background Prominence */ +.feature-card { + background: linear-gradient(135deg, + rgba(139, 92, 246, 0.15) 0%, + rgba(168, 85, 247, 0.12) 50%, + rgba(192, 132, 252, 0.1) 100%); + backdrop-filter: blur(15px); + border: 1px solid rgba(139, 92, 246, 0.25); + border-radius: 20px; + box-shadow: var(--shadow-glass); + transition: var(--transition-bounce); + color: #e2e8f0; + height: 100%; + padding: 2rem; /* Increased padding for better background-to-text ratio */ + min-height: 140px; /* Ensure minimum height for prominence */ + position: relative; +} + +/* Add subtle glow effect to feature cards */ +.feature-card::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + bottom: 0; + background: linear-gradient(135deg, + rgba(139, 92, 246, 0.05), + rgba(168, 85, 247, 0.03), + rgba(192, 132, 252, 0.02)); + border-radius: 20px; + z-index: -1; + opacity: 0; + transition: opacity 0.3s ease; +} + +.feature-card:hover::before { + opacity: 1; +} + +/* Enhanced demo container background */ +.demo-container { + background: linear-gradient(135deg, + rgba(139, 92, 246, 0.12) 0%, + rgba(15, 15, 35, 0.95) 50%, + rgba(168, 85, 247, 0.08) 100%); + backdrop-filter: blur(20px); + border: 1px solid rgba(139, 92, 246, 0.2); + border-radius: 20px; + box-shadow: + var(--shadow-glass), + 0 0 40px rgba(139, 92, 246, 0.1); + padding: 3rem; + margin: 2rem 0; + width: 100%; + position: relative; + z-index: 1; + color: #e2e8f0; +} + +/* Enhanced Loading Step Styles */ +.process-step { + background: rgba(255, 255, 255, 0.05); + border: 1px solid rgba(255, 255, 255, 0.1); + border-radius: 12px; + padding: 15px; + transition: all 0.3s ease; + text-align: center; +} + +.process-step.active { + background: rgba(139, 92, 246, 0.15); + border-color: rgba(139, 92, 246, 0.4); + transform: scale(1.02); +} + +.process-step.completed { + background: rgba(16, 185, 129, 0.15); + border-color: rgba(16, 185, 129, 0.4); +} + +.step-text { + font-weight: 600; + color: #e2e8f0; + font-size: 0.9rem; + margin-bottom: 8px; +} + +.step-status { + font-size: 0.8rem; + color: #94a3b8; + font-weight: 500; +} + +.process-step.active .step-status { + color: #a855f7; +} + +.process-step.completed .step-status { + color: #10b981; +} + +/* Processing Information Compact Styles */ +#processingInfoSidebar .feature-card { + min-height: auto; +} + +#processingInfoSidebar .bg-dark { + transition: all 0.2s ease; +} + +#processingInfoSidebar .bg-dark:hover { + background-color: rgba(0, 0, 0, 0.4) !important; + transform: translateX(2px); +} + +/* Button Consistency and Improved Spacing */ +.btn-lg { + padding: 0.75rem 1.5rem; + font-weight: 600; + border-radius: 12px; + transition: all 0.3s ease; + min-width: 120px; /* Ensure consistent button widths */ +} + +/* Debug Test Section Toggleable */ +#debugTestSection.hidden { + display: none !important; +} + +#debugTestSection .btn-sm { + padding: 0.5rem 1rem; + font-size: 0.875rem; + border-radius: 8px; + transition: all 0.2s ease; +} + +/* Enhanced Input Area Background */ +#textInput { + min-height: 240px !important; + font-size: 16px; + line-height: 1.5; + resize: vertical; + padding: 1.5rem; /* Increased padding for better appearance */ + background-color: rgba(255, 255, 255, 0.12) !important; /* More prominent background */ + border: 2px solid rgba(139, 92, 246, 0.3) !important; /* Thicker border */ +} + +#textInput:focus { + background-color: rgba(255, 255, 255, 0.18) !important; + border-color: var(--primary-color) !important; + box-shadow: 0 0 0 0.3rem rgba(139, 92, 246, 0.25) !important; + transform: scale(1.01); /* Subtle scale on focus */ +} + +.progress-step-horizontal { + display: flex; + flex-direction: column; + align-items: center; + text-align: center; + min-width: 60px; + transition: var(--transition-smooth); +} + +.progress-step-horizontal .step-icon-small { + width: 18px !important; + height: 18px !important; + border-radius: 50%; + display: flex; + align-items: center; + justify-content: center; + margin-bottom: 0.25rem; + font-size: 0.7rem !important; + transition: var(--transition-smooth); + background: rgba(255, 255, 255, 0.1) !important; + color: #cbd5e1 !important; + border: 1px solid rgba(255, 255, 255, 0.15); + flex-shrink: 0; +} + +.progress-step-horizontal .step-label { + font-size: 0.65rem; + font-weight: 500; + color: #94a3b8; + white-space: nowrap; + transition: var(--transition-smooth); +} + +.progress-step-horizontal.active .step-icon-small { + background: var(--primary-gradient) !important; + color: white !important; + transform: scale(1.1); +} + +.progress-step-horizontal.active .step-label { + color: var(--primary-color); + font-weight: 600; +} + +.progress-step-horizontal.completed .step-icon-small { + background: #10b981 !important; + color: white !important; +} + +.progress-step-horizontal.completed .step-label { + color: #10b981; +} + +.pipeline-arrow { + font-size: 0.8rem; + color: #64748b; + font-weight: normal; + user-select: none; + opacity: 0.6; +} + + +/* Responsive Design for Progress Pipeline */ +@media (max-width: 768px) { + .progress-pipeline { + flex-direction: column; + gap: 1.5rem; + padding: 2rem 1rem; + } + + .pipeline-arrow { + transform: rotate(90deg); + font-size: 1.5rem; + } + + .progress-step-horizontal { + min-width: 100px; + } + + .step-label { + font-size: 0.8rem; + } +} + +/* Error Messages */ +.error-message { + color: #ef4444; + font-size: 0.875rem; + margin-top: 8px; + padding: 8px 12px; + background: rgba(239, 68, 68, 0.1); + border: 1px solid rgba(239, 68, 68, 0.2); + border-radius: 8px; + display: none; +} + +.error-message.show { + display: block; + animation: fadeInUp 0.3s ease-out; +} + +/* Success Messages */ +.success-message { + color: #10b981; + font-size: 0.875rem; + margin-top: 8px; + padding: 8px 12px; + background: rgba(16, 185, 129, 0.1); + border: 1px solid rgba(16, 185, 129, 0.2); + border-radius: 8px; + display: none; +} + +.success-message.show { + display: block; + animation: fadeInUp 0.3s ease-out; +} + +/* Model Detail Cards */ +.model-detail-card { + background: rgba(255, 255, 255, 0.05); + border: 1px solid rgba(255, 255, 255, 0.1); + border-radius: 8px; + padding: 12px; + margin-bottom: 10px; + transition: all 0.3s ease; +} + +.model-detail-card:hover { + background: rgba(255, 255, 255, 0.08); + transform: translateY(-1px); +} + +.model-detail-label { + font-size: 0.75rem; + color: #94a3b8; + text-transform: uppercase; + letter-spacing: 0.5px; + margin-bottom: 4px; + font-weight: 600; +} + +.model-detail-value { + font-size: 1.1rem; + color: #f1f5f9; + font-weight: 700; +} + +.model-detail-text { + font-size: 0.9rem; + color: #cbd5e1; + line-height: 1.4; +} + +/* Pure HTML/CSS Chart styling - BEAUTIFUL DESIGN */ +.emotion-chart-container, .summary-chart-container { + background: linear-gradient(135deg, rgba(255, 255, 255, 0.1), rgba(255, 255, 255, 0.05)); + border-radius: 16px; + padding: 25px; + margin: 20px 0; + border: 1px solid rgba(255, 255, 255, 0.2); + box-shadow: 0 8px 32px rgba(0, 0, 0, 0.3); + backdrop-filter: blur(10px); + position: relative; + overflow: hidden; +} + +.emotion-chart-container::before, .summary-chart-container::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + height: 3px; + background: linear-gradient(90deg, #8b5cf6, #a855f7, #c084fc); +} + +.chart-header { + text-align: center; + margin-bottom: 25px; +} + +.chart-title { + color: #fbbf24; + font-weight: 700; + margin-bottom: 8px; + font-size: 1.25rem; + text-shadow: 0 2px 4px rgba(0, 0, 0, 0.3); +} + +.chart-subtitle { + color: #cbd5e1; + font-size: 0.9rem; + opacity: 0.8; +} + +.emotion-bars { + display: flex; + flex-direction: column; + gap: 20px; +} + +.emotion-bar { + margin-bottom: 20px; + animation: slideInLeft 0.8s ease-out forwards; + opacity: 0; + transform: translateX(-30px); + background: rgba(255, 255, 255, 0.03); + border-radius: 12px; + padding: 15px; + border: 1px solid rgba(255, 255, 255, 0.1); + transition: all 0.3s ease; + display: block !important; + visibility: visible !important; +} + +.emotion-bar:hover { + background: rgba(255, 255, 255, 0.08); + transform: translateY(-2px); + box-shadow: 0 4px 12px rgba(0, 0, 0, 0.2); +} + +@keyframes slideInLeft { + to { + opacity: 1; + transform: translateX(0); + } +} + +.emotion-label { + display: flex; + justify-content: space-between; + margin-bottom: 12px; + align-items: center; +} + +.emotion-name { + font-weight: 700; + color: #f1f5f9; + text-transform: capitalize; + font-size: 1rem; + letter-spacing: 0.5px; +} + +.emotion-percentage { + color: #a855f7; + font-weight: 700; + font-size: 1.1rem; + background: rgba(168, 85, 247, 0.1); + padding: 4px 12px; + border-radius: 20px; + border: 1px solid rgba(168, 85, 247, 0.3); +} + +.emotion-bar-bg { + background: rgba(0, 0, 0, 0.3); + border-radius: 15px; + height: 16px; + overflow: hidden; + position: relative; + box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.3); +} + +.emotion-bar-fill { + height: 100%; + border-radius: 15px; + transition: width 1.2s cubic-bezier(0.4, 0, 0.2, 1); + position: relative; + overflow: hidden; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.3); +} + +.emotion-bar-fill::after { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + bottom: 0; + background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.4), transparent); + animation: shimmer 3s infinite; +} + +@keyframes shimmer { + 0% { transform: translateX(-100%); } + 100% { transform: translateX(100%); } +} + +.summary-stats { + display: flex; + justify-content: space-around; + margin-bottom: 25px; + gap: 20px; +} + +.stat-item { + text-align: center; + flex: 1; + background: rgba(255, 255, 255, 0.05); + border-radius: 12px; + padding: 15px; + border: 1px solid rgba(255, 255, 255, 0.1); + transition: all 0.3s ease; +} + +.stat-item:hover { + background: rgba(255, 255, 255, 0.08); + transform: translateY(-2px); +} + +.stat-item.highlight { + background: linear-gradient(135deg, rgba(139, 92, 246, 0.2), rgba(168, 85, 247, 0.1)); + border: 2px solid rgba(139, 92, 246, 0.4); + box-shadow: 0 4px 16px rgba(139, 92, 246, 0.2); +} + +.stat-value { + font-size: 1.8rem; + font-weight: 800; + color: #fbbf24; + margin-bottom: 8px; + text-shadow: 0 2px 4px rgba(0, 0, 0, 0.3); +} + +.stat-item.highlight .stat-value { + color: #c084fc; + font-size: 2rem; +} + +.stat-label { + font-size: 0.8rem; + color: #cbd5e1; + text-transform: uppercase; + letter-spacing: 1px; + font-weight: 600; +} + +.summary-bars { + display: flex; + flex-direction: column; + gap: 20px; +} + +.summary-bar { + margin-bottom: 20px; + background: rgba(255, 255, 255, 0.03); + border-radius: 12px; + padding: 15px; + border: 1px solid rgba(255, 255, 255, 0.1); + transition: all 0.3s ease; +} + +.summary-bar:hover { + background: rgba(255, 255, 255, 0.08); + transform: translateY(-1px); +} + +.bar-label { + font-weight: 700; + color: #f1f5f9; + margin-bottom: 12px; + font-size: 1rem; +} + +.bar-bg { + background: rgba(0, 0, 0, 0.3); + border-radius: 12px; + height: 24px; + overflow: hidden; + position: relative; + box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.3); +} + +.bar-fill { + height: 100%; + border-radius: 12px; + transition: width 1.5s cubic-bezier(0.4, 0, 0.2, 1); + position: relative; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.3); +} + +.bar-fill.original { + background: linear-gradient(90deg, #3b82f6, #60a5fa, #93c5fd); +} + +.bar-fill.summary { + background: linear-gradient(90deg, #10b981, #34d399, #6ee7b7); +} + +.bar-value { + text-align: right; + font-size: 0.9rem; + color: #cbd5e1; + margin-top: 8px; + font-weight: 600; +} + +.chart-footer { + text-align: center; + margin-top: 20px; + padding-top: 20px; + border-top: 1px solid rgba(255, 255, 255, 0.1); + color: #94a3b8; + font-size: 0.85rem; +} + + +/* Keyboard Focus Indicators */ +.comprehensive-demo .btn-primary:focus-visible { + outline: 3px solid #c084fc; + outline-offset: 2px; + box-shadow: 0 0 0 3px rgba(139, 92, 246, 0.45); +} + +.demo-container .form-control:focus-visible { + outline: 2px solid var(--primary-color); + outline-offset: 2px; +} + +/* Reduced Motion Preferences */ +@media (prefers-reduced-motion: reduce) { + * { + animation: none !important; + transition: none !important; + } + .hero-section::before { + animation: none !important; + } + .floating-card { + animation: none !important; + } + .audio-bar { + animation: none !important; + } +} + +/* Responsive Design */ +@media (max-width: 1200px) { + .progress-pipeline-horizontal { + gap: 0.5rem; + padding: 0.8rem 1rem; + } + + .progress-pipeline-horizontal .step-label { + font-size: 0.7rem; + } + + #processingInfoSidebar { + margin-top: 2rem; + } +} + +@media (max-width: 768px) { + .demo-container { + padding: 30px 20px; + margin: -50px 15px 30px 15px; + } + + .hero-section { + padding: 80px 0; + } + + /* Stack title and flow vertically on mobile */ + #titleWithFlow .d-flex { + flex-direction: column; + gap: 0.75rem; + text-align: center; + } + + .progress-pipeline-horizontal { + gap: 0.25rem; + padding: 0.4rem 0.6rem; + justify-content: center; + } + + .progress-step-horizontal { + min-width: 40px; + } + + .progress-step-horizontal .step-icon-small { + width: 16px !important; + height: 16px !important; + font-size: 0.6rem !important; + } + + .progress-step-horizontal .step-label { + font-size: 0.5rem; + } + + .pipeline-arrow { + font-size: 0.7rem; + } + + /* Stack buttons vertically on mobile */ + .d-flex.gap-3.justify-content-center { + flex-direction: column; + align-items: center; + gap: 0.75rem !important; + } + + .btn-lg { + min-width: 200px; + width: 100%; + max-width: 300px; + } + + /* Full width input on mobile */ + #textInput { + min-height: 180px !important; + padding: 1rem; + } + + /* Compact processing info on mobile */ + #processingInfoSidebar .bg-dark { + padding: 0.5rem !important; + } + + #processingInfoSidebar .feature-card { + padding: 1rem; + } + + /* Results layout adjustments */ + #resultsLayout .col-lg-8, + #resultsLayout .col-lg-4 { + flex: 0 0 100%; + max-width: 100%; + } +} + +@media (max-width: 576px) { + .demo-container { + padding: 20px 15px; + margin: -50px 10px 20px 10px; + } + + .progress-pipeline-horizontal { + padding: 0.5rem; + } + + .btn-lg { + padding: 0.6rem 1rem; + font-size: 0.9rem; + } + + .feature-card { + padding: 1rem !important; + margin-bottom: 1rem; + } + + #textInput { + min-height: 160px !important; + font-size: 14px; + } +} + +/* Debug section responsive behavior */ +@media (max-width: 992px) { + #debugTestSection { + margin-top: 1rem; + } +} + +/* Enhanced transitions for responsive changes */ +@media (prefers-reduced-motion: no-preference) { + #titleWithFlow, + #inputLayout, + #resultsLayout { + transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); + } +} \ No newline at end of file diff --git a/website/demo.html b/website/demo.html deleted file mode 100644 index c9f48b5b9..000000000 --- a/website/demo.html +++ /dev/null @@ -1,1029 +0,0 @@ - - - - - - Live Emotion Detection Demo - SAMO Deep Learning - - - - - - - - - - - - - - - - -
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Why Choose SAMO-DL?

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- ยฉ 2025 SAMO-DL. All rights reserved. -

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- - - - - - - - \ No newline at end of file diff --git a/website/favicon.ico b/website/favicon.ico new file mode 100644 index 000000000..1e66dbf7c Binary files /dev/null and b/website/favicon.ico differ diff --git a/website/http-server-with-csp.py b/website/http-server-with-csp.py new file mode 100644 index 000000000..c2899f45f --- /dev/null +++ b/website/http-server-with-csp.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 +"""HTTP Server with proper CSP headers""" + +from http.server import HTTPServer, SimpleHTTPRequestHandler + + +class CSPHTTPRequestHandler(SimpleHTTPRequestHandler): + def end_headers(self): + # Add CSP header with frame-ancestors + self.send_header( + "Content-Security-Policy", + "default-src 'self'; " + "script-src 'self' 'unsafe-inline' https://cdn.jsdelivr.net https://cdnjs.cloudflare.com; " + "style-src 'self' 'unsafe-inline' https://cdn.jsdelivr.net https://cdnjs.cloudflare.com https://fonts.googleapis.com; " + "font-src 'self' https://fonts.gstatic.com https://cdnjs.cloudflare.com https://cdn.jsdelivr.net; " + "img-src 'self' data: https:; " + "media-src 'self' blob:; " + "worker-src 'self' blob:; " + "object-src 'none'; " + "connect-src 'self' http://localhost:8081 https://samo-unified-api-optimized-frrnetyhfa-uc.a.run.app https://api.samo-dl.com https://api.openai.com https://cdn.jsdelivr.net; " + "frame-ancestors 'none'; " + "base-uri 'self'; " + "form-action 'self'", + ) + super().end_headers() + + +if __name__ == "__main__": + port = 8082 + server = HTTPServer(("localhost", port), CSPHTTPRequestHandler) + print(f"HTTP Server with CSP running on http://localhost:{port}") + print("Press Ctrl+C to stop") + + try: + server.serve_forever() + except KeyboardInterrupt: + print("\nShutting down server...") + server.shutdown() diff --git a/website/index.html b/website/index.html index af02fd77b..78c8fddce 100644 --- a/website/index.html +++ b/website/index.html @@ -3,8 +3,11 @@ - SAMO Deep Learning - Production Emotion Detection API - + SAMO-DL - AI Emotion Detection API + + + + @@ -13,7 +16,7 @@ @@ -376,19 +273,10 @@ Features
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    - ๐Ÿš€ Complete AI Integration Platform + SAMO: Emotion Detection

    - 100% Priority 1 Features Complete! Enterprise-grade AI platform with JWT authentication, - voice transcription, text summarization, real-time processing, and comprehensive monitoring. - Production-ready with >90% F1 score and 2.3x performance optimization. + Production-ready end-to-end emotion detection pipeline with + 2.3x performance optimization. Enterprise-grade security and reliability.

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    Comprehensive Testing
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    ๐Ÿš€ Enterprise-Grade AI Platform

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    - Complete AI integration platform with authentication, voice processing, text analysis, and real-time monitoring +

    ๐ŸŽฏ Core Features

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    + Everything you need for emotion detection in production

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    ๐Ÿš€ Enterprise-Grade AI Platform

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    Enterprise Security
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    - Integration guides for backend, frontend, UX, and data science teams. + Simple REST API with comprehensive documentation and examples for all frameworks.

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    - - +
    -

    ๐Ÿš€ Try Our Complete AI Platform

    -

    - Test our comprehensive AI platform with emotion detection, voice transcription, and text summarization +

    ๐Ÿงช Try Our Simulated Demo

    +

    + Test a simulated emotion detection client-side demo using your text

    @@ -739,7 +449,7 @@

    ๐Ÿš€ Try Our Complete AI Platform

    - +
    +
    + + + Results are simulated and not from the production API + +
    - -
    -
    -
    -
    -

    ๐Ÿค Complete Team Integration

    -

    - Comprehensive integration guides for Backend, Frontend, Data Science, and UX teams with live API endpoints -

    -
    -
    -
    -
    -
    -
    - - Backend Integration -
    -
    -
    import requests
-from typing import BinaryIO, IO
-
-class SAMO_API_Client:
-    def __init__(self, base_url="https://api.example.com"):
-        # Replace 'https://api.example.com' with your actual deployment URL
-        self.base_url = base_url
-        self.session = requests.Session()
-
-    def analyze_emotion(self, text: str) -> dict:
-        try:
-            response = self.session.post(
-                f"{self.base_url}/predict",
-                json={"text": text},
-                headers={"Content-Type": "application/json"},
-                timeout=10
-            )
-            response.raise_for_status()
-            return response.json()
-        except requests.exceptions.RequestException as e:
-            return {"error": str(e)}
-
-    def transcribe_voice(self, audio_file: BinaryIO | IO[bytes]) -> dict:
-        if not hasattr(audio_file, "read"):
-            return {"error": "audio_file must be a binary file-like object (supports .read())"}
-        files = {"audio_file": audio_file}
-        try:
-            response = self.session.post(
-                f"{self.base_url}/transcribe/voice",
-                files=files,
-                timeout=30
-            )
-            response.raise_for_status()
-            return response.json()
-        except requests.exceptions.RequestException as e:
-            return {"error": str(e)}
-
-# Example usage
-client = SAMO_API_Client()
-emotions = client.analyze_emotion("I'm excited!")
-# Returns: [{"emotion": "excitement", "confidence": 0.92}]
    -
    -
    -
    -
    -
    -
    - - Frontend Integration -
    -
    -
    async function analyzeEmotion(text) {
-  const response = await fetch(
-    'https://samo-unified-api-frrnetyhfa-uc.a.run.app/analyze/journal',
-    {
-      method: 'POST',
-      headers: { 'Content-Type': 'application/json' },
-      body: JSON.stringify({ text, generate_summary: false })
-    }
-  );
-  return await response.json();
-}
-
-// Example usage
-const analysis = await analyzeEmotion("This is amazing!");
-console.log(analysis.emotion_analysis?.emotions);
    -
    -
    -
    -
    -
    -
    - - Data Science Integration -
    -
    -
    import pandas as pd
-import requests
-
-def analyze_dataset(texts: list) -> pd.DataFrame:
-    results = []
-    for text in texts:
-        analysis = requests.post(
-            "https://samo-unified-api-frrnetyhfa-uc.a.run.app/analyze/journal",
-            json={"text": text, "generate_summary": False}
-        ).json()
-        results.append({
-            'text': text,
-            'emotions': analysis.get('emotion_analysis', {}).get('emotions', {})
-        })
-    return pd.DataFrame(results)
    -
    -
    -
    -
    -
    -
    - - Mobile Integration -
    -
    -
    // React Native / Flutter
-const analyzeUserFeedback = async (feedback) => {
-  try {
-    const response = await fetch(
-      'https://samo-unified-api-frrnetyhfa-uc.a.run.app/analyze/journal',
-      {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({ text: feedback, generate_summary: false })
-      }
-    );
-    const analysis = await response.json();
-    return analysis;
-  } catch (error) {
-    console.error('Error:', error);
-  }
-};
    -
    -
    -
    -
    -
    -
    - - -
    -
    -
    -
    -

    ๐ŸŸข Live API Endpoints

    -

    - All endpoints are live and ready for production integration -

    -
    -
    -
    -
    -
    -
    -
    -
    LIVE
    -
    Base URL
    -
    - -
    -
    -
    -
    -
    -
    -
    ๐Ÿ” Authentication Endpoints
    -
      -
    • POST /auth/register - User registration
      • -
    • POST /auth/login - User login
      • -
    • POST /auth/refresh - Token refresh
      • -
    • - POST /auth/logout - User logout - Requires Auth -
      • -
    • - GET /auth/profile - User profile - Requires Auth -
      • -
    -
    -
    -
    -
    -
    -
    -
    ๐ŸŽค Voice Processing
    -
      -
    • POST /transcribe/voice - Single audio
      • -
    • POST /transcribe/batch - Batch processing
      • -
    • WS /ws/transcribe - Real-time streaming
      • -
    -
    -
    -
    -
    -
    -
    -
    ๐Ÿ“ Text Analysis
    -
      -
    • POST /predict - Emotion detection
      • -
    • POST /summarize/text - Text summarization
      • -
    • POST /predict_batch - Batch emotions
      • -
    -
    -
    -
    -
    -
    -
    -
    ๐Ÿ“Š Monitoring
    -
      -
    • GET /health - Health check
      • -
    • GET /metrics - System metrics
      • -
    • GET /monitoring/dashboard - Dashboard
      • -
    -
    -
    -
    -
    -
    -
    -
    ๐Ÿ”ง System
    -
      -
    • GET /docs - API documentation
      • -
    • GET /openapi.json - OpenAPI spec
      • -
    • GET /version - API version
      • -
    -
    -
    -
    -
    -
    -
    - - -
    + +
    -
    +
    -

    Documentation & Resources

    -

    - Complete guides and resources for successful integration +

    Ready to Get Started?

    +

    + Explore our comprehensive demo or integrate our production API into your application

    -
    -
    -
    -
    -
    -
    - -
    API Documentation
    -

    Complete API reference with examples

    - View Docs -
    -
    -
    -
    -
    -
    - -
    Deployment Guide
    -

    Step-by-step deployment instructions

    - Deploy Now -
    -
    -
    -
    -
    -
    - -
    Team Guides
    -

    Integration guides for all teams

    - Learn More -
    -
    -
    -
    -
    -
    - -
    Source Code
    -

    Open source project on GitHub

    - View Code -
    +
    + + + Full Demo + + + + View Code +
    @@ -1060,70 +505,18 @@
    Source Code
    -