🌾 ClimaSense AI - Climate Intelligence Platform

AI-powered climate intelligence platform for smart agriculture in Maharashtra, India. Features real-time weather forecasting, satellite imagery analysis, and multilingual support (English, Hindi, Marathi).

Website - Link -> https://clima-sense-ai-based-wheather-forec.vercel.app

🎯 New Features

🔐 Secure Authentication with Clerk

• Professional authentication system powered by Clerk
• Email/password and social login (Google, GitHub)
• Secure session management
• User profile management

🌟 Professional Landing Page

• Modern, engaging design with animations
• Clear value proposition and feature showcase
• Responsive design for all devices
• Smooth transitions and climate-themed visuals

✨ Features

🤖 AI-Powered Analysis

• AgriBERT: Agricultural text classification and recommendations
• GraphCast: 10-day weather forecasting with agricultural metrics
• AgriSense MCP: Model Context Protocol server for AI agents

🌍 Real-Time Data

• CHIRPS: Satellite rainfall data
• OpenWeather: Live weather conditions
• Google Earth Engine: NDVI vegetation health index
• NASA POWER: Historical climate data

🗺️ Interactive Visualization

• 3D Globe with climate overlays
• Real-time satellite imagery
• Temperature, rainfall, and vegetation maps
• Maharashtra-focused regional data

💬 AI Chat Assistant

• Natural language queries
• Weather forecasts for all Indian states
• Crop recommendations
• Pest control advice
• Responds in English, Hindi, or Marathi

🌐 Multilingual Support

• English - Default
• हिंदी (Hindi) - Full translation
• मराठी (Marathi) - Full translation
• Instant language switching
• AI responses in selected language

🚀 Quick Start

Prerequisites

• Node.js 18+ and npm
• Python 3.11+
• Git

One-Click Startup (Windows)

start-climasense.bat

This starts all services:

• ✅ AI Backend (FastAPI) - Port 8000
• ✅ AgriSense MCP Server - Port 9090
• ✅ GEE Server - Port 3001
• ✅ AI Forecast Server - Port 3002
• ✅ React Frontend - Port 5173

First Time Setup:

1. Visit http://localhost:5173 (landing page)
2. Click "Get Started" to create an account
3. Sign up with email or social login
4. Access the dashboard and all features

Manual Setup

1. Clone Repository

git clone https://github.com/Ontiomacer/Clima-Sense-AI---Based-Wheather-Forecasts.git
cd Clima-Sense-AI---Based-Wheather-Forecasts

2. Install Dependencies

Frontend:

npm install

AI Backend:

cd ai-backend
pip install -r requirements.txt
cd ..

AgriSense MCP:

cd agrisense-mcp
npm install
npm run build
cd ..

3. Environment Setup

Create .env file in root:

# Clerk Authentication (Required)
VITE_CLERK_PUBLISHABLE_KEY=pk_test_your_clerk_publishable_key

# Supabase (for database)
VITE_SUPABASE_URL=your_supabase_url
VITE_SUPABASE_ANON_KEY=your_supabase_key

# Google Maps
VITE_GOOGLE_MAPS_API_KEY=your_google_maps_key

Get Clerk Keys:

1. Sign up at clerk.com
2. Create a new application
3. Copy the publishable key from the dashboard
4. See CLERK_SETUP.md for detailed instructions

4. Start Services

Terminal 1 - AI Backend:

python ai-backend/main.py

Terminal 2 - AgriSense MCP:

cd agrisense-mcp
npm start

Terminal 3 - Frontend:

npm run dev

Terminal 4 - GEE Server:

npm run gee-server

Terminal 5 - AI Forecast:

npm run ai-forecast

Access the Application

• Landing Page: http://localhost:5173 (public)
• Dashboard: http://localhost:5173/dashboard (requires sign-in)
• AI Backend: http://localhost:8000
• AgriSense MCP: http://localhost:9090
• MCP Dashboard: http://localhost:9090/dashboard

Authentication:

• Visit the landing page and click "Get Started" or "Sign In"
• Create an account or sign in with Google/GitHub
• Access all features after authentication

📚 Documentation

Deployment Guides

• Deployment Documentation Index - Complete guide to all deployment docs
• Complete Deployment Guide - Comprehensive deployment instructions
• Vercel Deployment - Step-by-step Vercel setup
• Clerk Setup Guide - Complete Clerk authentication setup
• Environment Variables Setup - Detailed Clerk & Vercel configuration
• Quick Clerk Setup - Fast Clerk configuration

Development Guides

• Quick Start Guide - Get started in 5 minutes
• Changelog - Version history and updates
• Landing Page Documentation - Complete landing page guide
• Screenshots Guide - How to capture and manage screenshots
• Architecture Overview
• Setup Instructions
• AgriSense MCP Guide
• Language Support
• GraphCast Integration

🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                    Frontend (React)                      │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐             │
│  │Dashboard │  │   Map    │  │ AI Chat  │             │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘             │
└───────┼─────────────┼─────────────┼───────────────────┘
        │             │             │
        └─────────────┼─────────────┘
                      │
        ┌─────────────┴─────────────┐
        │                           │
        ▼                           ▼
┌──────────────┐          ┌──────────────────┐
│  AI Backend  │          │  AgriSense MCP   │
│  (FastAPI)   │          │  (Node.js)       │
│              │          │                  │
│  • AgriBERT  │          │  • HTTP API      │
│  • GraphCast │          │  • MCP Protocol  │
│  • ERA5 Data │          │  • Claude Desktop│
└──────────────┘          └──────────────────┘

🛠️ Tech Stack

Frontend

• React 18 with TypeScript
• Vite for build tooling
• TailwindCSS for styling
• Shadcn/ui component library
• React Router for navigation
• Tanstack Query for data fetching

Backend

• FastAPI (Python) for AI services
• Node.js/Express for MCP server
• PyTorch for ML models
• JAX for GraphCast
• Transformers for AgriBERT

AI Models

• AgriBERT: GautamR/agri_bert_classifier
• GraphCast: DeepMind weather forecasting
• Custom: Agricultural metrics calculator

Data Sources

• CHIRPS: Climate Hazards Group
• OpenWeather: Weather API
• Google Earth Engine: Satellite data
• NASA POWER: Climate data

📱 Features by Page

Landing Page (Public)

• Professional hero section with animated gradients
• Feature showcase with 6 key capabilities
• "How It Works" process explanation
• Statistics and metrics display
• Call-to-action sections
• Responsive design for all devices

Dashboard (Protected)

• Real-time climate metrics
• AI Climate Risk Index
• Key insights and recommendations
• Temperature, rainfall, wind speed, NDVI

Agriculture

• Crop health analysis
• Soil moisture monitoring
• AI farming advisory
• Yield predictions

Weather Forecast

• 10-day GraphCast forecasts
• Agricultural risk scores
• Temperature extremes
• Rainfall predictions

Interactive Map

• Satellite imagery layers
• CHIRPS rainfall visualization
• MODIS NDVI vegetation health
• ECMWF temperature anomalies

AI Chat

• Natural language queries
• Weather forecasts for Indian states
• Crop recommendations
• Multilingual responses

🌐 Language Support

Switch between languages using the globe icon (🌐) in the navigation bar.

Supported Languages:

• 🇬🇧 English (Default)
• 🇮🇳 हिंदी (Hindi)
• 🇮🇳 मराठी (Marathi)

What's Translated:

• ✅ All UI elements
• ✅ Navigation menus
• ✅ Form labels
• ✅ Dashboard metrics
• ✅ AI Chat responses
• ✅ Error messages

🤖 AgriSense MCP Server

Model Context Protocol server for AI agents (Claude Desktop, GPT, etc.)

Features:

• Analyze crop data with AI models
• Calculate risk scores
• Generate farming recommendations
• Request logging and dashboard

Usage with Claude Desktop:

Add to %APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "agrisense": {
      "command": "node",
      "args": ["path/to/agrisense-mcp/dist/mcp-server.js"],
      "env": {
        "PORT": "9090",
        "AI_BACKEND_URL": "http://localhost:8000"
      }
    }
  }
}

See CONNECT_TO_CLAUDE.md for details.

📊 API Endpoints

AI Backend (Port 8000)

POST /api/agri_analysis
POST /api/graphcast_forecast
POST /api/analyze-farm
GET  /api/health

AgriSense MCP (Port 9090)

POST /analyze
GET  /dashboard
GET  /health

🔧 Configuration

Frontend Environment Variables

# Clerk Authentication (Required)
VITE_CLERK_PUBLISHABLE_KEY=pk_test_your_clerk_publishable_key

# Supabase (for database)
VITE_SUPABASE_URL=your_supabase_url
VITE_SUPABASE_ANON_KEY=your_supabase_key

# Google Maps
VITE_GOOGLE_MAPS_API_KEY=your_google_maps_key

Important: Clerk authentication is required for the application to work. See CLERK_SETUP.md for setup instructions.

Backend Configuration

Edit ai-backend/graphcast/config.py for:

• Model paths
• Cache settings
• Region boundaries
• Agricultural thresholds

🧪 Testing

# Frontend tests
npm test

# Backend tests
cd ai-backend
pytest

# MCP server tests
cd agrisense-mcp
npm test

📦 Building for Production

Frontend

npm run build

Output in dist/ directory.

AI Backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r ai-backend/requirements.txt

# Run with gunicorn
gunicorn -w 4 -k uvicorn.workers.UvicornWorker ai-backend.main:app

AgriSense MCP

cd agrisense-mcp
npm run build
npm start

🚢 Deployment

Quick Deployment Guides

• 📖 Complete Deployment Guide - Comprehensive deployment instructions
• 🚀 Vercel Deployment - Step-by-step Vercel setup
• 🔐 Environment Variables Setup - Detailed guide for Clerk & Vercel
• ⚡ Quick Clerk Setup - Fast Clerk configuration

Frontend (Vercel) - Recommended

1. Connect Repository

   • Go to vercel.com
   • Import your GitHub repository
   • Framework: Vite

2. Configure Environment Variables

   # Required for authentication
   VITE_CLERK_PUBLISHABLE_KEY=pk_test_your_clerk_key
   
   # Database
   VITE_SUPABASE_URL=your_supabase_url
   VITE_SUPABASE_ANON_KEY=your_supabase_key
   
   # Maps
   VITE_GOOGLE_MAPS_API_KEY=your_maps_key

   📖 See CLERK_SETUP.md for Clerk setup 📖 See VERCEL_ENV_SETUP.md for detailed instructions

3. Deploy

   • Click "Deploy"
   • Automatic deployments on every push

4. Configure Clerk

   • Add production domain to Clerk Dashboard
   • Set redirect URLs
   • Test authentication

Backend (Railway/Render)

1. Create new service
2. Connect repository
3. Set Python buildpack
4. Configure environment variables
5. Deploy

Docker (Optional)

# Build images
docker-compose build

# Start services
docker-compose up -d

🤝 Contributing

Contributions are welcome! Please follow these steps:

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

📄 License

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

👥 Authors

• Ontiomacer - Initial work - GitHub

🙏 Acknowledgments

• DeepMind for GraphCast model
• Hugging Face for AgriBERT model
• Google Earth Engine for satellite data
• CHIRPS for rainfall data
• OpenWeather for weather API
• NASA POWER for climate data

📞 Support

• Documentation: See /docs folder
• Issues: GitHub Issues
• Discussions: GitHub Discussions

🗺️ Roadmap

Completed ✅

• Professional landing page
• Clerk authentication integration
• Protected routes
• Social login (Google, GitHub)
• User profile management

Upcoming 🚀

• Mobile app (React Native)
• More Indian languages (Gujarati, Tamil, Telugu)
• Offline mode
• SMS alerts
• WhatsApp integration
• Farmer community features
• Marketplace integration

📈 Status

• Version: 1.0.0
• Status: Production Ready
• Last Updated: November 2025

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Made with ❤️ for Indian farmers

🌾 Empowering agriculture through AI and climate intelligence