deploy: containerize the live simple-mode API (Docker)

Ships the production serving path (FastAPI upload/map UI + statistical
device-category identification) as a lean, reproducible container:

- Dockerfile: python:3.10-slim, non-root user, /health HEALTHCHECK, serves
  uvicorn src.api.main_simple:app. Bakes in the joblib category model and the
  static rtl_433 protocol table; excludes the 11 GB test corpora and the
  benched Phase 3B CNN binaries. Built image is 417 MB.
- requirements-prod.txt: pins matched to the versions that actually train/serve
  the model (scikit-learn 1.6.1 / numpy 2.2.6) so joblib.load() stays valid.
  torch/onnx intentionally absent — the CNN is benched, not on the serving path.
- docker-compose.yml: single service, named volume seeds from baked data/ on
  first run then persists captures_simple.json writes across restarts.
- .dockerignore: keeps data/, venv/, vendored firmware, docs out of the image.

Validated: image builds, container reports healthy, and the statistical
classifier loads + predicts inside the container.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

docker-compose.yml

@@ -0,0 +1,22 @@
+# GigLez — single-service deployment of the live simple-mode API.
+#   docker compose up --build   → http://localhost:8000
+services:
+  giglez:
+    build: .
+    image: giglez:latest
+    ports:
+      - "8000:8000"
+    volumes:
+      # Persist the JSON capture store across restarts. Seeded from the
+      # baked-in data/ on first run (see Dockerfile), then app writes survive.
+      - giglez_data:/app/data
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "python", "-c", "import urllib.request,sys; sys.exit(0 if urllib.request.urlopen('http://127.0.0.1:8000/health').status==200 else 1)"]
+      interval: 30s
+      timeout: 3s
+      start_period: 15s
+      retries: 3
+
+volumes:
+  giglez_data: