dasarinandini08-blip/agrimitra-ai-app

AI-powered smart farming assistant with weather, mandi prices, crop advice, and photo-based pest/disease diagnosis # 🌾 AgriMitra — AI Agent for Smart Farming Advice A RAG-ready, multi-language Streamlit application that gives farmers grounded, conversational advice on weather, market (mandi) prices, and crop health. This is a **demo/scaffold** build: all data services (weather, market prices, knowledge retrieval) are simulated with realistic mock logic so the app runs instantly with zero API keys. Clear placeholder functions are provided so you can plug in real APIs and an LLM in minutes. --- ## ✨ Features - **Multi-language UI**: English, Hindi, Spanish, Swahili (easy to extend) - **Farmer profile sidebar**: Location, Soil Type, Current Crop - **Live dashboard tabs**: Weather forecast, Mandi/market prices, Knowledge base - **AI chat interface**: `st.chat_input` / `st.chat_message`, with session-persisted history - **RAG-ready architecture**: simulated vector retrieval + LLM orchestration function, structured for easy integration with OpenAI, Google Gemini, or LangChain - **Trusted sources footer**: builds farmer confidence in the data --- ## 📁 Project Structure ``` agrimitra_project/ ├── app.py # Main Streamlit application ├── requirements.txt # Python dependencies ├── .env.example # Template for environment-variable secrets ├── .gitignore # Keeps secrets & venvs out of git ├── .streamlit/ │ ├── config.toml # App theme (greens/earth tones) │ └── secrets.toml.example # Template for Streamlit-native secrets └── README.md # This file ``` --- ## 🚀 Quick Start ```bash # 1. Create and activate a virtual environment (recommended) python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate # 2. Install dependencies pip install -r requirements.txt # 3. Run the app streamlit run app.py ``` The app opens at `localhost`. No API keys are required to explore the demo — everything works out of the box with simulated data. …