julian
6933406ae3
Skeleton of the host-side vision service per the packaging decision: a Python/FastAPI app at apps/vision/, uv-managed, wired into the Turbo graph via a thin package.json shim (dev/lint/test/build → uv/uvicorn/ruff/pytest). A per-package turbo.json sets build outputs [] so the no-op build is warning-free. Endpoints: GET /health (readiness + model version) and POST /analyze (raw octet-stream body, so Node POSTs Snapshot.bytes directly; empty→400, oversize→413, recognizer-not-ready→503). The recognizer is a Protocol with a StubRecognizer (no models, boots/tests offline — the dev/CI default) and a FastAlprRecognizer (the real MIT YOLOv9+CCT/ONNX stack, lazily imported; missing models ⇒ ready=False, not a crash) — the device-adapter pattern applied to the model. fast-alpr + onnxruntime are an optional `alpr` extra, so `uv sync` needs no model download. Verified: turbo run lint|test|build includes @parking/vision and stays green; uv run mypy strict-clean; uvicorn boots and serves /health + /analyze live; pnpm workspace 6→7. Not built yet: the Node VisionClient adapter, a Dockerfile + model fetch, and Job 2 (vehicle verification). Updates the packaging decision (As-scaffolded) + log. Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
16 lines
737 B
JSON
16 lines
737 B
JSON
{
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"name": "@parking/vision",
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"version": "0.0.0",
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"private": true,
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"//": "Thin shim so this Python service is a first-class node in the Turbo task graph (it is NOT a JS package — deps are managed by uv/pyproject.toml). Each script shells to Python tooling. See wiki/decisions/vision-service-packaging.md.",
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"scripts": {
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"dev": "uv run uvicorn vision_service.app:app --reload --host 0.0.0.0 --port 8089",
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"start": "uv run uvicorn vision_service.app:app --host 0.0.0.0 --port 8089",
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"lint": "uv run ruff check .",
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"format": "uv run ruff format .",
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"typecheck": "uv run mypy vision_service",
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"test": "uv run pytest -q",
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"build": "echo 'no build step (Python service; models fetched at deploy)'"
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}
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}
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