Inventory Management using GenAI

This project demonstrates an innovative approach to inventory management leveraging Generative AI. It integrates a Streamlit web application, a SQLite database, and Google's Gemini Pro AI model for an intuitive, AI-powered inventory management interface.

Features

• Natural Language Queries: Ask questions about your inventory using plain English.
• AI-Powered SQL Generation: Converts natural language queries into SQL using Google's Gemini Pro.
• Interactive Dashboard: Visualizes inventory data with dynamic charts and graphs.
• Inventory Modification: Add, remove, or update products via natural language commands or Excel file uploads.
• Bulk Data Processing: Upload Excel files for batch inventory updates.
• Automated Insights: Generates insights based on sales and trends.
• Stock Prediction: Predicts product demand using AI.
• Report Generation: Creates comprehensive reports based on the database.
• Customizable Plotting: Allows users to generate plots based on their specified parameters, enabling non-technical users to perform basic data analysis.

Files in the Repository

1. app.py: The main Streamlit application handling user interface and AI interactions.
2. database.py: Initializes and populates the SQLite database with sample product data.
3. analytics.py: Contains functions for AI-powered inventory analysis (insights, predictions, categorization, reporting).
4. excel_processing.py: Handles processing of uploaded Excel files for database updates.
5. config.py: Loads environment variables and configures API keys.
6. prompt.py: (Assumed file - not present in provided code) Contains functions related to prompt engineering for the AI models.
7. utils.py: (Assumed file - not present in provided code) Contains utility functions used throughout the application.

Setup and Installation

1. Clone the repository:

   git clone <repository_url>
   cd inventory-management-using-GenAI

2. Install dependencies:

   python3.11 -m venv .venv
   source .venv/bin/activate
   pip install .

   The dependency set is verified on Python 3.11 and is managed through pyproject.toml. sqlite3 is included with Python's standard library and does not need to be installed separately.

   Developers/contributors: use pip install -e ".[dev]" instead to get an editable install with pytest, ruff, and pip-audit.

3. Set up API keys:

   • Create a .env file in the project root.
   • Add your Google Cloud API key: GOOGLE_API_KEY=your_api_key_here
   • Obtain a PandasAI API key from www.pandabi.com and add it: PANDASAI_API_KEY=your_api_key_here
   • If either key is missing, the app will still start and show a warning, but the related AI features will remain unavailable until configured.

4. Initialize the database:

   python database.py

5. Run the Streamlit app:

   streamlit run app.py

Usage

The application provides these main functionalities:

1. Ask Questions: Input natural language queries to retrieve inventory information.
2. View Dashboard: Access interactive dashboards displaying key inventory metrics.
3. Modify Inventory: Add, remove, or modify products using natural language or Excel file uploads.
4. Generate Insights/Predictions/Reports: Utilize AI-powered functions to gain deeper insights into your inventory.
5. Plot Parameters: Create custom plots to visualize your data.

Dependencies

• streamlit
• pandas
• python-dotenv
• google-generativeai
• pandasai
• plotly
• faker

sqlite3 is included with Python's standard library and does not need to be installed with pip.

Contributing

Contributions are welcome! Follow these steps:

1. Fork the repository.
2. Create a new branch for your feature.
3. Commit your changes.
4. Push to the branch.
5. Create a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.