🌊 Float Chat -ARGO OCEAN INTELLIGENCE SYSTEM

AI-Powered Conversational Interface for ARGO Oceanographic Data

Float Chat is an intelligent chatbot that lets you explore ocean data from ARGO floats using natural language. Ask questions in plain English and get instant visualizations, charts, and insights about ocean temperature, salinity, and float trajectories.

• 🤖 Natural Language Queries - Ask questions like "Show floats in Indian Ocean" or "Compare temperature of floats 1902669 and 1902670"
• 🗺️ Interactive Maps - Visualize float trajectories and locations with Folium
• 📊 Dynamic Charts - Compare temperature, salinity, pressure, and depth profiles with Plotly
• 💾 Automated Data Pipeline - Download and ingest ARGO NetCDF files automatically
• 🧠 3-Layer AI System - Intelligent query routing with Google Gemini 2.5 Flash
• 🎯 Smart Query Understanding - Handles complex multi-step queries with AI orchestration
• 📈 Real-time Analytics - SQL-based aggregations for statistical queries

• Python 3.11+
• PostgreSQL database
• Google Gemini API key
• Supabase account (optional, for vector search)

1. Clone the repository

   git clone https://github.com/Achalnawal2745/floatchat.git
   cd floatchat

2. Install dependencies

   pip install -r requirements.txt

3. Configure environment

   Create a .env file in the project root:

   DATABASE_URL=postgresql://user:password@localhost:5432/argo_db
   GEMINI_API_KEY=your_gemini_api_key_here
   SUPABASE_URL=your_supabase_url
   SUPABASE_KEY=your_supabase_key

4. Start the application

   Windows:

   start.bat

   Linux/Mac:

   # Terminal 1 - Backend
   python backend16.py
   
   # Terminal 2 - Frontend
   streamlit run app.py

5. Open your browser

   Navigate to http://localhost:8501

Try these natural language queries:

• "Show all floats in the Indian Ocean"
• "What's the temperature profile for float 2900565?"
• "Compare salinity between floats 1902669 and 1902670"
• "Show me the path of float 2900565"
• "How many floats are in the Arabian Sea?"
• "Average temperature at 100m depth for float 2900565"

Via Web Interface:

1. Open the sidebar in the Streamlit app
2. Navigate to "Admin Panel"
3. Enter the float ID (e.g., 2900565)
4. Click "Download & Ingest"

Via API:

# Download float data
curl -X POST http://127.0.0.1:8000/admin/download-float \
  -H "Content-Type: application/json" \
  -d '{"float_id": "2900565"}'

# Ingest into database
curl -X POST http://127.0.0.1:8000/admin/ingest-float \
  -H "Content-Type: application/json" \
  -d '{"float_id": "2900565"}'

Float Chat uses a sophisticated 3-layer AI system:

┌─────────────────────────────────────────────────────────┐
│                    Streamlit UI                         │
│            (Chat Interface + Visualizations)            │
└────────────────────┬────────────────────────────────────┘
                     │
                     ↓
┌─────────────────────────────────────────────────────────┐
│                  FastAPI Backend                        │
│                                                         │
│  ┌──────────┐  ┌──────────┐  ┌──────────────────┐       │
│  │ Layer 1  │→ │ Layer 2  │→ │    Layer 3       │       │
│  │Direct    │  │Complex   │  │SQL Generation    │       │
│  │Tool Call │  │Orchestr. │  │& Fallback        │       │
│  └──────────┘  └──────────┘  └──────────────────┘       │
└────────────────────┬────────────────────────────────────┘
                     │
        ┌────────────┴────────────┐
        ↓                         ↓
┌──────────────┐          ┌──────────────┐
│  PostgreSQL  │          │ Gemini AI    │
│  Database    │          │ (2.5 Flash)  │
└──────────────┘          └──────────────┘

• Layer 1: Direct tool execution for simple queries (e.g., "show float 2900565")
• Layer 2: AI orchestration for complex multi-step queries (e.g., "temperature of all floats in Indian Ocean")
• Layer 3: SQL generation for analytical queries (e.g., "average temperature at 100m depth")

float-chat/
├── app.py                 # Streamlit frontend
├── backend16.py           # FastAPI backend with 3-layer AI system
├── argo_ingestion.py      # Data ingestion module
├── download_floats.py     # Float download utility
├── ingest_floats.py       # Batch ingestion script
├── requirements.txt       # Python dependencies
├── start.bat             # Windows startup script
├── .env                  # Environment configuration
├── README.md             # This file
└── QUICKSTART.md         # Quick start guide

POST /query
Content-Type: application/json

{
  "query": "show floats in indian ocean",
  "session_id": "optional-session-id"
}

POST /admin/download-float
POST /admin/ingest-float
GET /health
GET /floats

See API Documentation for full details.

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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

• ARGO float data provided by the ARGO Program
• AI powered by Google Gemini
• Built with FastAPI and Streamlit

For questions, issues, or feature requests, please open an issue on GitHub.

Made with ❤️ for ocean data exploration