Practical reference implementation that combines a FastAPI backend, React+Vite frontend, MongoDB Atlas, and lightweight ML services powered by PyTorch and scikit-learn. Focus is on getting a compelling hackathon demo live quickly: crop disease detection, career guidance, entrepreneurship builder, SOS + live tracking, mentorship, and schemes navigator.

• Python 3.10+ with pip
• Node.js 18+ / npm
• MongoDB Atlas cluster (or local Mongo on mongodb://localhost:27017)
• Optional: CUDA GPU for training the crop model

Copy .env.example to .env and adjust credentials (MongoDB, Twilio, model paths).

cd backend
python -m venv .venv
. .venv/Scripts/activate  # PowerShell: .\\.venv\\Scripts\\Activate.ps1
pip install -r app/requirements.txt
uvicorn app.main:app --reload --port 8000

Key endpoints:

• POST /ml/detect – crop disease inference (fallback if model missing)
• POST /career/recommend – ML/rule-based career paths
• POST /business/generate – entrepreneurship plan builder
• POST /schemes/match – filters seeded schemes
• GET /mentors/ + POST /mentors/request – mentorship flow
• WS /safety/ws/sos – WebSocket real-time SOS relay

Training helpers live in backend/app/ml/. Run them after arranging a small dataset to refresh models.

cd frontend/agriher-frontend
npm install
npm run dev -- --host

Routes/pages include:

• Landing with feature highlights
• Crop detection uploader
• Career navigator & learning journey
• Entrepreneurship builder + printable plan
• SOS dashboard with live WebSocket feed
• Mentor directory and schemes explorer

TailwindCSS is configured for rapid UI tweaks.

Backend & frontend ship with individual Dockerfiles for quick deployment. Example:

cd backend
docker build -t agriher-api .
docker run -p 8000:8000 --env-file ../.env agriher-api

1. Career form ➜ recommendation & roadmap
2. Business plan generator ➜ share PDF/screenshot
3. Crop detection ➜ upload demo leaf, show response
4. SOS button ➜ observe WebSocket alert stream
5. Mentor directory + scheme matches ➜ highlight data
6. Wrap with architecture slide + deployment mention

• Backend: add FastAPI tests via pytest (not included yet); start with health + route smoke tests.
• Frontend: run npm run lint to ensure JSX consistency.

• Swap fallback models with fully trained PyTorch checkpoints
• Integrate Whisper for speech UX, LangChain for scheme Q&A
• Wire push notifications (Firebase) + production SMS/voice via Twilio
• Deploy on Render/Railway (backend) and Vercel/Netlify (frontend)

Happy hacking! Contributions welcome.