Swati-13/Crop-Doctor

# Crop Doctor Welcome to **Crop Doctor**, an AI-powered crop health detection system. ## About This Project Crop Doctor leverages advanced deep learning technologies to identify crop diseases from leaf images. Simply upload a photo of a crop leaf, and our sophisticated model will analyze it in seconds to detect common diseases, helping you keep your plants healthy. ## Features - **AI-Powered Analysis**: Utilizes cutting-edge machine learning models to detect plant diseases with high accuracy. - **Rich Diagnosis & Care Tips**: Integrates with robust generative AI (Gemini) to provide tailored diagnosis, symptoms, and care recommendations. - **Modern User Interface**: A stunning, responsive UI built with Next.js, featuring smooth animations and detailed result breakdowns. - **Scalable Architecture**: Flexible backend powered by FastAPI that handles image validation, AI integrations, and background tasks for auto-saving newly discovered crop diseases. ## Getting Started ### Prerequisites - Python 3.12+ - Node.js 18+ ### Backend Setup 1. Navigate to the `api` directory: `cd api` 2. Create and activate a virtual environment: `python -m venv venv && source venv/bin/activate` 3. Install dependencies: `pip install -r requirements.txt` (Ensure necessary packages like `fastapi`, `uvicorn`, `google-generativeai`, etc., are installed). 4. Run the API: `uvicorn main:app --host 0.0.0.0 --port 8000 --reload` ### Frontend Setup 1. Navigate to the `frontend` directory: `cd frontend` 2. Install dependencies: `npm install` 3. Configure environment variables (e.g., `NEXT_PUBLIC_API_BASE_URL=localhost`) 4. Start the development server: `npm run dev` 5. Open your browser and go to `localhost` to start using Crop Doctor! ## How It Works 1. Upload a clear, well-lit image of a crop leaf. 2. The AI model checks if the image is valid and detects the specific crop. 3. If valid, the image is passed to the diagnostic models to determine if it's healthy or showing s …