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>

.dockerignore

@@ -0,0 +1,36 @@
+# Keep the image small and reproducible. The 11 GB test corpora, the local
+# venv, and the vendored Flipper firmware are NOT needed to serve the app.
+
+# Heavy / irrelevant
+data/rf_test_datasets/
+data/test_db/
+data/test_known_devices/
+data/test_rtl433_real/
+signatures/
+venv/
+logs/
+docs/
+tests/
+test_files/
+scripts/
+
+# Benched Phase 3B CNN binaries (not on the serving path)
+models/category_cnn.pt
+models/category_cnn.onnx
+
+# VCS / caches / editor
+.git/
+.gitignore
+**/__pycache__/
+**/*.pyc
+**/*.pyo
+.pytest_cache/
+.mypy_cache/
+*.egg-info/
+.DS_Store
+
+# Docs & planning (not needed at runtime)
+*.md
+CONTEXT.md
+FABLE.md
+PLAN_TO_PROD.md