🤖 DataVision Assistant - AI-Powered Data Analysis Platform

Transform your CSV data into insights using natural language queries.

✨ Features

• 📊 Smart CSV Analysis - Upload and get instant insights from your datasets
• 🤖 AI-Powered Queries - Ask questions in plain English and get intelligent responses
• 📈 Interactive Visualizations - Plotly charts generated automatically from your queries
• 💬 Conversational Interface - ChatGPT-like experience for data exploration
• 🔒 Secure Execution - Sandboxed code execution with safety restrictions
• 💾 Session Management - Persistent analysis sessions with conversation history
• 🌐 Modern Web Interface - Responsive design with drag-and-drop file uploads

🏗️ Architecture

pydatabackend/
├── app/
│   ├── core/
│   │   └── config.py              # Application settings
│   ├── api/
│   │   └── v1/
│   │       ├── endpoints/
│   │       │   └── session.py     # API endpoints
│   │       └── schemas/
│   │           └── session.py     # Pydantic models
│   ├── services/
│   │   ├── session_manager.py     # Session lifecycle management
│   │   └── data_analysis.py       # LLM integration & code execution
│   └── main.py                    # FastAPI application
├── frontend/
│   ├── index.html                 # Main UI
│   ├── styles.css                 # Styling
│   └── script.js                  # JavaScript functionality
├── cache/                         # Session data storage
├── requirements.txt               # Python dependencies
├── .env.example                   # Environment variables template
└── README.md

🚀 Quick Start

1. Prerequisites

• Python 3.8 or higher
• Google Gemini API key (free from Google AI Studio)

2. Installation

# Clone or navigate to the project directory
cd pydatabackend

# Create virtual environment (recommended)
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

3. Configuration

# Copy environment template
copy .env.example .env

# Edit .env file and add your Gemini API key
# Get your API key from: https://makersuite.google.com/app/apikey

Required Environment Variables:

GEMINI_API_KEY=your_gemini_api_key_here
DEBUG=True

4. Run the Application

python app/main.py

The application will start on http://localhost:8000

Available URLs:

• Main App: http://localhost:8000/
• API Documentation: http://localhost:8000/docs
• Health Check: http://localhost:8000/health

🎯 Usage Guide

1. Upload Your Data

• Drag and drop a CSV file (max 16MB) or click to browse
• Supported format: CSV files with UTF-8 encoding
• The system will automatically analyze your data structure

2. Explore Your Data

• Sample Data Tab: View the first 5 rows
• Statistics Tab: See numeric statistics and missing value analysis
• Data Info Tab: Check column types and categorical information

3. Ask Questions

Use natural language to analyze your data:

Example Queries:

• "Show me a summary of the data"
• "Create a histogram of the age column"
• "What's the correlation between price and sales?"
• "Are there any missing values?"
• "Show sales by region as a bar chart"
• "Create a scatter plot of height vs weight"
• "What are the top 10 customers by revenue?"

🔧 API Endpoints

Session Management

• POST /api/v1/session/start - Upload CSV and start session
• POST /api/v1/session/query - Submit analysis query
• GET /api/v1/sessions - List active sessions
• GET /api/v1/session/{session_id} - Get session info
• DELETE /api/v1/session/{session_id} - Delete session

Example API Usage

# Start a session
curl -X POST "http://localhost:8000/api/v1/session/start" \
  -F "file=@your_data.csv"

# Query the data
curl -X POST "http://localhost:8000/api/v1/session/query" \
  -H "Content-Type: application/json" \
  -d '{
    "session_id": "your-session-id",
    "query": "Show me sales by region"
  }'

🛡️ Security Features

• Restricted Imports: Only safe libraries are allowed (pandas, numpy, plotly, etc.)
• Code Sandboxing: Generated code runs in a controlled environment
• Input Validation: File type and size validation
• Session Isolation: Each session operates independently

🎨 Frontend Features

• Modern UI: Clean, responsive design with gradient backgrounds
• Drag & Drop: Intuitive file upload experience
• Real-time Chat: Interactive conversation interface
• Data Visualization: Integrated Plotly charts
• Error Handling: User-friendly error messages and loading states
• Mobile Responsive: Works on desktop and mobile devices

📊 Supported Analysis Types

Data Exploration

• Dataset overview and statistics
• Missing value analysis
• Data type information
• Sample data preview

Visualizations

• Bar charts, line charts, scatter plots
• Histograms and distribution plots
• Correlation matrices
• Custom Plotly visualizations

Statistical Analysis

• Descriptive statistics
• Correlation analysis
• Grouping and aggregation
• Trend analysis

🔧 Development

Project Structure

app/
├── core/           # Core configuration and settings
├── api/            # API routes and schemas
├── services/       # Business logic services
└── main.py         # Application entry point

Key Components

• FastAPI: Modern Python web framework
• Google Gemini: Advanced AI for code generation
• Pandas: Data manipulation and analysis
• Plotly: Interactive visualizations
• Pydantic: Data validation and settings

Environment Variables

# Required
GEMINI_API_KEY=your_key_here

# Optional (with defaults)
DEBUG=True
MAX_FILE_SIZE=16777216
CACHE_DIR=cache
SESSION_TIMEOUT=86400
LLM_MODEL=gemini-1.5-flash

🐛 Troubleshooting

Common Issues

1. "GEMINI_API_KEY is required" error

   • Ensure you've set your API key in the .env file
   • Get a free API key from Google AI Studio

2. Module import errors

   • Activate your virtual environment
   • Run pip install -r requirements.txt

3. File upload fails

   • Check file format (must be CSV)
   • Verify file size (max 16MB)
   • Ensure UTF-8 encoding

4. Charts not displaying

   • Check browser console for JavaScript errors
   • Ensure internet connection for Plotly.js CDN

Performance Tips

• Use smaller datasets for faster processing
• Complex visualizations may take longer to generate
• Sessions are cached for 24 hours by default

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

---

Ready to explore your data with AI? Upload a CSV file and start asking questions! 🚀