Polaris Data Whisperer - LLM-Based Data Analysis

A modern web application for AI-powered data analysis using CSV files and natural language queries.

Features

• CSV Upload & Analysis: Upload CSV files and ask questions in natural language
• AI-Powered Insights: Uses Google Gemini API for SQL generation and data insights
• Modern UI: Built with React, TypeScript, and Tailwind CSS
• Real-time Processing: Fast in-memory data processing with SQLite

Quick Start

Prerequisites

• Node.js (v16 or higher)
• Python (v3.8 or higher)
• npm or yarn

Installation

1. Clone the repository

   git clone <YOUR_GIT_URL>
   cd polaris-data-whisperer-main

2. Install frontend dependencies

   npm install

3. Install Python backend dependencies

   pip install -r requirements.txt

Running the Application

Option 1: Run both frontend and backend together

npm run dev:python

Option 2: Run separately

Backend (Python FastAPI)

# Using npm script
npm run backend

# Or directly
python app.py

# Or using the provided scripts
./run-backend.bat    # Windows
./run-backend.ps1    # PowerShell

Frontend (React/Vite)

npm run dev

Access Points

• Frontend: http://localhost:5173
• Backend API: http://localhost:8000
• API Documentation: http://localhost:8000/docs

How to Use

1. Upload a CSV file by dragging and dropping or clicking the upload area
2. Ask a question about your data in natural language (e.g., "What is the average sales by region?")
3. View results including:
   • AI-generated insights
   • Generated SQL query
   • Query results in a table format

Technology Stack

Frontend

• React 18
• TypeScript
• Vite
• Tailwind CSS
• shadcn/ui components
• React Router

Backend

• FastAPI (Python)
• Pandas for data processing
• SQLite for in-memory database
• Google Gemini API for AI analysis

API Endpoints

• POST /analyze - Upload CSV and analyze with a question
  • Parameters: file (CSV file), question (string)
  • Returns: Analysis results with insights, SQL query, and data

Development

Project Structure

src/
├── components/     # React components
├── pages/         # Page components
├── lib/           # API services and utilities
└── ui/            # shadcn/ui components

Available Scripts

• npm run dev - Start frontend development server
• npm run backend - Start Python FastAPI backend
• npm run dev:python - Start both frontend and backend
• npm run build - Build for production
• npm run lint - Run ESLint

Environment Variables

The backend uses a Google Gemini API key. Make sure to set up your API key in the app.py file or use environment variables for production.

Deployment

Frontend

The frontend can be deployed to any static hosting service (Vercel, Netlify, etc.)

Backend

The FastAPI backend can be deployed to:

• Heroku
• Railway
• DigitalOcean App Platform
• AWS/GCP/Azure

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

This project is licensed under the MIT License.