saap1tech/Smart-Agri-Advisor

This is a Smart Agricultural Yield Advice in Algeria (just experiments) # Smart Agri-Advisor 🌱 A hybrid crop recommendation system combining classical ML, quantum-inspired algorithms, and RAG-powered LLM insights. ## Features ✨ - **Hybrid Prediction System** - Classical Machine Learning models - Quantum-inspired algorithms - RAG (Retrieval Augmented Generation) powered by FAISS and Grover's Algorithms - Gemini LLM integration for recommendations - **Multiple Analysis Modes** - Model-only predictions - Historical context analysis (RAG) - Combined model+RAG analysis - **Dual Interface** - REST API (FastAPI) - Rich CLI interface ## Installation 💻 ### Prerequisites - Python 3.10+ - Gemini API key (optional) ```bash # Clone repository git clone github.com cd Smart-Agri-Advisor # Create virtual environment python -m venv venv source venv/bin/activate # Linux/Mac venv\Scripts\activate # Windows # Install dependencies pip install -r requirements.txt # Set up environment variables cp .env.example .env # Edit .env with your Gemini API key (optional) ``` ## Usage 🚀 ### CLI Interface ```bash # Run prediction workflow python cli.py predict # Example output: [bold cyan]Initializing resources...[/bold cyan] - RAG components loaded - Preprocessor created - ML models loaded - Gemini configured [bold blue]Enter Agricultural Data[/bold blue] Temperature (°C): 25.5 Humidity (%): 75 Soil pH: 6.2 Rainfall (mm): 200 Analysis type [model_only/rag_only/model_and_rag]: model_and_rag ``` ### API Service ```bash # Start the API server uvicorn app.main:app --reload # API Endpoints: - GET / : Web interface (actually, it is not working) - POST /predict : Prediction endpoint ``` ### Example API Request ```bash curl -X POST "localhost" \ -H "Content-Type: application/json" \ -d '{ "soil_type": "Loamy", "rainfall_mm": 750.5, "temperature_celsius": 25.0, "soil_ph": 6.5, "soil_health": 7.8, "fertilizer_used": true, "irrigation_used": false, "analysis_type": "model_and_rag" }' ``` ## Conf …