leetcrypt
7535feb445
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>
37 lines
632 B
Plaintext
37 lines
632 B
Plaintext
# Keep the image small and reproducible. The 11 GB test corpora, the local
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# venv, and the vendored Flipper firmware are NOT needed to serve the app.
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# Heavy / irrelevant
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data/rf_test_datasets/
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data/test_db/
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data/test_known_devices/
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data/test_rtl433_real/
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signatures/
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venv/
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logs/
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docs/
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tests/
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test_files/
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scripts/
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# Benched Phase 3B CNN binaries (not on the serving path)
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models/category_cnn.pt
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models/category_cnn.onnx
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# VCS / caches / editor
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.git/
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.gitignore
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**/__pycache__/
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**/*.pyc
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**/*.pyo
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.pytest_cache/
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.mypy_cache/
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*.egg-info/
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.DS_Store
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# Docs & planning (not needed at runtime)
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*.md
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CONTEXT.md
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FABLE.md
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PLAN_TO_PROD.md
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