InsightFlow

Business Insights Tracker is an automated data analysis platform that transforms raw business CSV files into actionable financial and inventory insights. The project features a React-based interactive dashboard and a Flask-powered Python backend for robust data processing.

Deployed URL: https://deft-kulfi-a1904d.netlify.app

Features

• Dynamic File Upload: Support for drag-and-drop or manual selection of business data files (e.g., sales.csv, products.csv) which are automatically saved to the backend for processing.
• Real-time Analytics Dashboard:
  • Revenue Tracking: Visualizes total daily revenue based on processed sales.
  • Quantity Sold: Monitors the total volume of products sold.
  • Category breakdown: Provides a detailed list of quantities sold per product category.
  • Average Order Value (AOV): Automatically calculates and displays the average value of transactions.
  • Recent Sales Log: Lists the latest transactions including date, product name, quantity, and price.
• Automated Data Processing: Uses Python and Pandas to merge disparate datasets and maintain historical records in the processedData directory.

Tech Stack

Frontend

• Core: React 19, Vite
• Styling: CSS3 with custom components for data cards and upload zones

Backend

• Server: Flask (Python)
• Security: Flask-CORS for cross-origin resource sharing
• Analysis Engine: Pandas for CSV manipulation and data merging

API Endpoints

• POST /upload: Receives and saves business data files to the uploads/ folder.
• GET /getRev: Returns the latest total revenue entry.
• GET /getQuan: Returns the latest total quantity sold entry.
• GET /getProdTot: Returns quantities sold summarized by category.
• GET /getAOV: Returns the calculated Average Order Value.
• GET /getLatProd: Returns the 40 most recent transaction records.

Project Structure

• Data-Processing/: Contains Python scripts (DataProcessing.py, RecieveData.py) and processed CSV data stores.
• src/: React source files including FileUpload.jsx for data input and DisplayCards.jsx for the metrics dashboard.

Setup and Installation

Backend

1. Navigate to the Data-Processing folder.
2. Install dependencies:

   pip install flask flask-cors pandas