557fcee803
- Refine effective candidate consumables and classifier labels - Adjust vision algorithm, TSV logging, and video session wiring - Refresh client surgery HTTP contract doc and staging/video docs - Update settings, docker-compose prod, tests, and uv.lock Made-with: Cursor
54 lines
1.3 KiB
TOML
54 lines
1.3 KiB
TOML
[project]
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name = "operation-room-monitor-server"
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version = "0.1.0"
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description = "Operation room monitor API server"
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requires-python = ">=3.13"
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dependencies = [
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"asyncpg>=0.31.0",
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"greenlet>=3.1.0",
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"minio>=7.2.15",
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"baidu-aip>=4.16.13",
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"chardet>=7.4.3",
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"fastapi>=0.136.0",
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"loguru>=0.7.3",
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"pillow>=12.2.0",
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"pydantic-settings>=2.13.1",
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"python-multipart>=0.0.26",
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"sqlalchemy>=2.0.49",
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"ultralytics>=8.4.40",
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"uvicorn[standard]>=0.44.0",
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"rich>=15.0.0",
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"pyyaml>=6.0.3",
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]
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[project.scripts]
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operation-room-monitor-server = "main:main"
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# Use PyTorch CPU wheels from the official index so:
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# - Linux Docker builds (incl. Docker Desktop on Mac) do not install NVIDIA CUDA pip bundles.
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# - Native macOS still resolves to the correct macosx_* wheels from the same index.
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# For NVIDIA servers, use a separate CUDA torch install or override in a dedicated prod Dockerfile.
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[tool.uv]
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index-strategy = "unsafe-best-match"
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[[tool.uv.index]]
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name = "pytorch-cpu"
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url = "https://download.pytorch.org/whl/cpu"
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[tool.uv.sources]
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torch = { index = "pytorch-cpu" }
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torchvision = { index = "pytorch-cpu" }
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[dependency-groups]
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dev = [
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"httpx>=0.28.0",
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"pytest>=8.3.0",
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"pytest-asyncio>=0.25.0",
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"aiosqlite>=0.21.0",
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"alembic>=1.14.0",
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]
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[tool.pytest.ini_options]
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asyncio_mode = "auto"
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testpaths = ["tests"]
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