Basar-Bashir/dr_crop

# Dr. Crop — AI Crop Disease Detection A mobile-first Progressive Web App that identifies crop diseases from leaf photos and provides AI-powered treatment recommendations. **Stack:** Next.js (App Router) · FastAPI · PyTorch · Exa AI · OpenAI · Apify --- ## Quick Start ### Prerequisites - Node.js 18+ - Python 3.10+ - pip / venv ### 1. Clone & configure ```bash cp .env.example .env # Edit .env and add your API keys (optional — app works with fallback mocks) ``` ### 2. Backend ```bash cd backend python -m venv .venv && source .venv/bin/activate pip install -r requirements.txt cp .env.example .env uvicorn app.main:app --reload --port 8000 ``` Backend runs at **http://localhost:8000** — try localhost for interactive API docs. ### 3. Frontend ```bash cd frontend npm install cp .env.example .env.local npm run dev ``` Frontend runs at **http://localhost:3000**. ### 4. (Optional) Run both at once ```bash ./scripts/dev.sh ``` --- ## Project Structure ``` dr-crop/ ├── frontend/ # Next.js PWA │ ├── src/ │ │ ├── app/ # App Router pages │ │ ├── components/ # React components │ │ ├── lib/ # Types, SW registration │ │ └── services/ # API client │ └── public/ # manifest.json, sw.js, icons │ ├── backend/ # FastAPI server │ ├── app/ │ │ ├── main.py # App entrypoint │ │ ├── config.py # Pydantic settings │ │ ├── routes/ # /predict, /recommend │ │ ├── services/ # Exa, LLM, Apify integrations │ │ └── models/ # Pydantic schemas │ └── ml/ │ ├── model_loader.py # Load PyTorch model │ └── inference.py # Preprocessing + prediction │ ├── scripts/ # Dev helpers ├── .env.example └── README.md ``` --- ## API Endpoints | Method | Path | Description | | ------ | ------------ | ---------------------------------------- | | POST …