README.md

metadata

title: CropIntel
emoji: 🌾
colorFrom: green
colorTo: blue
sdk: docker
app_port: 3050
pinned: false

CropIntel

Crop leaf-disease classifier for 5 crops (corn, soybean, wheat, rice, tomato), EfficientNetB0 → TensorFlow Lite, served behind a Next.js UI. One Docker container runs the web app and a persistent Python inference service together.

Quick start (run the whole thing)

You do not need Kaggle, training, or any model files — the trained models (~38 MB) are fetched automatically from the GitHub Release on first start.

git clone https://github.com/rakshithj09/CropIntel.git
cd CropIntel
docker compose -f docker-compose.prod.yml up -d --build
curl -fsS http://localhost:3050/api/health    # {"web":"ok","inference":{"ready":true,...}}

Open http://localhost:3050. That's it.

Optional environment (drop a .env next to the compose file):

NEXT_PUBLIC_GOOGLE_MAPS_API_KEY=...               # only for the outbreak map
CROPINTEL_ADMIN_TOKEN=$(openssl rand -hex 16)     # only to guard POST /admin/reload
CROPINTEL_MODELS_URL=...                           # override the default v1 model bundle

For a real domain + TLS, monitoring, and model promotion/rollback, see docs/DEPLOYMENT.md.

Local development (no Docker)

The web app forwards predictions to the inference service, so run both:

# 1) fetch models once (into ml/models/, gitignored)
pip install -r ml/requirements-inference.txt
export CROPINTEL_MODELS_URL='https://github.com/rakshithj09/CropIntel/releases/download/v1/cropintel-models-mobile.zip'
python3 -m ml.scripts.fetch_models

# 2) start the inference service (terminal A)
python3 -m uvicorn ml.serve.inference_app:app --host 127.0.0.1 --port 8000

# 3) start the web app (terminal B)
npm install && npm run dev

Open http://localhost:3050. The UI calls /api/predict, which forwards to the inference service at INFERENCE_URL (default http://127.0.0.1:8000).

Train it yourself (needs Kaggle data)

See ml/README.md for the Kaggle API setup and training scripts (pip install -r ml/requirements.txt). Models are gated on an external (out-of-distribution) eval before promotion — see ml/scripts/test_external.py and ml/scripts/promote_model.py.

Maintainer: ship updated models

After training/promoting, repackage and replace the release bundle:

python3 -m ml.scripts.package_models --tflite-only -o cropintel-models-mobile.zip
gh release upload v1 cropintel-models-mobile.zip -R rakshithj09/CropIntel --clobber
# on a running server: rm ml/models/.cropintel-fetch-ok && docker compose -f docker-compose.prod.yml restart

Project layout

• app/ — Next.js UI + /api/predict (forwards to the inference service) + /api/health
• ml/serve/inference_app.py — FastAPI inference service (loads every crop model once)
• ml/ — training (training/), predictors (inference/), config, scripts
• docker-compose.prod.yml, docker/, docs/DEPLOYMENT.md — production deploy
• tests/ — pytest suite (.github/workflows/ci.yml runs web + Python checks)

License

See repository.