An intelligent agricultural assistant that helps farmers make informed decisions about crops, market prices, pest management, and government schemes. Built with Google Gemini 2.5 Pro for comprehensive agricultural knowledge.
now live at: https://kisangpt-idgi.onrender.com/
- AI-Powered Q&A: Advanced agricultural knowledge and recommendations
- Market Intelligence: Real-time market prices and trends from government data sources
- Image Analysis: Pest and disease identification from crop photos
- Crop Recommendations: Region-specific suggestions with profitability analysis
- Government Schemes: Information about agricultural subsidies and programs
- Weather Advisory: Weather-based farming recommendations
- Profit Analysis: Market price categorization and optimization insights
- Multi-modal interface supporting both text and image inputs
- Session management with conversation history
- Optimized performance with smart query detection
- Clean, responsive web interface with drag-and-drop support
Demos:
- Backend: FastAPI with Python 3.8+
- AI Integration: Google Gemini 2.5 Pro
- Data Sources: data.gov.in APIs for agricultural market data
- Frontend: HTML5, CSS3, JavaScript
- Image Processing: Pillow library
- Clone the repository:
git clone https://github.com/kanishkez/KisanGPT.git
cd KisanGPT- Create and activate virtual environment:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate- Install required packages:
pip install -r requirements.txt- Configure environment variables:
cp env.example .env
# Edit .env file with your API keys- Start the application:
# Method 1: Using uvicorn directly
uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
# Method 2: Using Python module (recommended)
PYTHONPATH=. python3 app/main.pyAccess the application at http://localhost:8000
Create a .env file in the root directory:
GEMINI_API_KEY=your_gemini_api_key
DATAGOV_API_KEY=your_datagov_api_key
APP_NAME=KisanGPT
APP_VERSION=1.0.0
GET /- Main application interfacePOST /api/chat- Process text-based agricultural queriesPOST /api/chat-with-image- Analyze images for pest/disease identificationGET /api/config- Retrieve application configuration
Text Queries:
- "What crops should I grow in Punjab during winter season?"
- "Current market prices for wheat in Maharashtra"
- "Information about PM-KISAN scheme eligibility criteria"
- "Best farming practices for increasing yield in rice"
- "Weather-based farming advice for this season"
Image Analysis:
- Upload crop photos for pest identification (supports JPG, PNG, GIF, WebP)
- Disease diagnosis with treatment recommendations
- Nutrient deficiency detection and solutions
- Drag & drop or paste images directly into the chat interface
KisanGPT/
├── app/
│ ├── config.py # Application configuration
│ ├── main.py # FastAPI application entry point
│ ├── models/
│ │ └── schemas.py # Data models and schemas
│ ├── services/
│ │ ├── agricultural_data.py # Market data service
│ │ ├── langchain_agent.py # AI agent implementation
│ │ └── ...
│ ├── utils/
│ │ └── helpers.py # Utility functions
│ └── web/
│ └── static/ # Frontend assets
├── data/ # Data storage directory
├── requirements.txt # Python dependencies
├── .env.example # Environment variables template
└── README.md # Project documentation
python -m pytest tests/# Format code
black app/
# Check linting
flake8 app/- Fork the repository
- Create a feature branch:
git checkout -b feature-name - Make your changes and commit:
git commit -m 'Add feature' - Push to branch:
git push origin feature-name - Submit a pull request
This project is licensed under the MIT License. See LICENSE file for details.
- Google Gemini AI for language processing capabilities
- Data.gov.in for providing agricultural market data APIs
- FastAPI framework for the backend infrastructure
- The agricultural community for valuable feedback and requirements