Makefile

# GuichetOI ML — common dev shortcuts
#
# Usage:
#   make install      Install Python deps into ./.venv
#   make test         Run the pytest suite (171 tests, ~12 s)
#   make test-fast    Run only the cms_generator tests (no model load, <2 s)
#   make demo         Launch the Streamlit demo
#   make audit        Re-run the 11-demande audit
#   make lint         Run mypy on the business-logic modules
#   make clean        Remove caches, temp outputs, __pycache__
#
# On Windows install GNU make via:
#   winget install GnuWin32.Make
# Or invoke any target's commands directly in PowerShell.

PYTHON     ?= .venv/Scripts/python.exe
PIP        ?= .venv/Scripts/pip.exe
STREAMLIT  ?= .venv/Scripts/streamlit.exe
UVICORN    ?= .venv/Scripts/uvicorn.exe
PYTEST_ARGS = -q

.PHONY: help install test test-fast test-engine test-cms test-inference \
        demo api docker release lint typecheck ruff clean

help:
	@echo "GuichetOI ML — make targets"
	@echo "  install         pip install -e .[dev,ui] + requirements.txt pins"
	@echo "  test            full pytest suite (181 tests)"
	@echo "  test-fast       cms_generator tests only (no model load)"
	@echo "  test-engine     recommendation engine tests"
	@echo "  test-inference  inference post-process tests"
	@echo "  demo            streamlit run apps/streamlit_demo.py"
	@echo "  api             uvicorn guichetoi.api.main:app (port 8000)"
	@echo "  docker          docker build -t guichetoi-ml ."
	@echo "  release         build + push image to GHCR (for Render deploy)"
	@echo "  lint            ruff check + mypy on src/"
	@echo "  clean           remove __pycache__, .pytest_cache, outputs/, *.pyc"

install:
	$(PIP) install -e ".[dev,ui]"
	$(PIP) install -r requirements.txt

# ── Tests ────────────────────────────────────────────────────────────────
test:
	$(PYTHON) -m pytest $(PYTEST_ARGS)

test-fast:
	$(PYTHON) -m pytest tests/test_cms_generator.py $(PYTEST_ARGS)

test-engine:
	$(PYTHON) -m pytest tests/test_recommendation_engine.py $(PYTEST_ARGS)

test-inference:
	$(PYTHON) -m pytest tests/test_inference_postprocess.py $(PYTEST_ARGS)

# ── Run ──────────────────────────────────────────────────────────────────
demo:
	$(STREAMLIT) run apps/streamlit_demo.py

api:
	$(UVICORN) guichetoi.api.main:app --host 0.0.0.0 --port 8000 --reload

docker:
	docker build -t guichetoi-ml .

# ── Release (push image to GHCR for Render deploy) ───────────────────────
# Models are gitignored, so GitHub Actions can't build a complete image.
# Run this locally — your working tree DOES have models/ on disk.
#
# One-time prep:
#   1. Create a GitHub PAT with `write:packages` scope.
#   2. echo $GHCR_PAT | docker login ghcr.io -u medaziz012 --password-stdin
IMAGE ?= ghcr.io/medaziz012/guichetoi-ml
TAG   ?= $(shell git rev-parse --short HEAD)

release:
	@echo "Building $(IMAGE):$(TAG) and $(IMAGE):latest"
	docker build -t $(IMAGE):$(TAG) -t $(IMAGE):latest .
	docker push $(IMAGE):$(TAG)
	docker push $(IMAGE):latest
	@echo "Pushed $(IMAGE):$(TAG) — Render will redeploy if autoDeploy: true"

# ── Quality ──────────────────────────────────────────────────────────────
lint typecheck:
	$(PYTHON) -m ruff check src/ tests/
	$(PYTHON) -m mypy --config-file mypy.ini src/guichetoi/cms.py src/guichetoi/recommendation.py

ruff:
	$(PYTHON) -m ruff check src/ tests/

# ── Cleanup ──────────────────────────────────────────────────────────────
clean:
	-rm -rf __pycache__ tests/__pycache__ .pytest_cache .mypy_cache outputs/*.json outputs/*.xlsx
	-find . -name "*.pyc" -delete 2>/dev/null || true