110 lines
4.0 KiB
TOML
110 lines
4.0 KiB
TOML
[project]
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name = "tianmu"
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version = "0.2.0"
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description = "天目 · 魂镜本地 OCR 引擎 — PP-OCRv6（via RapidOCR）+ PaddleOCR-VL-1.6"
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authors = [{ name = "chenxin", email = "2685475410@qq.com" }]
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license = "MIT"
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requires-python = ">=3.12"
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dependencies = [
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"fastapi>=0.139.0",
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"httpx[http2]>=0.28.1",
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"loguru>=0.7.3",
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"pillow>=10.0.0",
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"pydantic>=2.13.4",
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"pydantic-settings>=2.14.2",
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"python-multipart>=0.0.32",
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"uvicorn[standard]>=0.51.0",
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]
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[project.optional-dependencies]
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# ═══════════════════════════════════════════════════════════
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# PP-OCRv6 引擎（RapidOCR + ONNX Runtime）
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# ═══════════════════════════════════════════════════════════
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# CPU 推理（所有平台通用，包括 M4 的 CoreML 实验性支持）
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rapidocr = [
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"rapidocr>=3.9.0",
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"onnxruntime>=1.20.0",
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]
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# GPU 推理（NVIDIA CUDA，仅 Linux/Windows）
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rapidocr-gpu = [
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"onnxruntime-gpu>=1.20.0",
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]
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# ═══════════════════════════════════════════════════════════
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# PaddleOCR-VL 引擎（HuggingFace Transformers）
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# ═══════════════════════════════════════════════════════════
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# CPU/MPS（macOS 自动获取 CPU/MPS 版本，Linux/Windows 自动走 CPU 版本）
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vl = [
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"torch>=2.0.0",
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"torchvision>=0.15.0",
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"transformers>=5.0.0",
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]
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# GPU（通过 [tool.uv.sources] 自动选择 CUDA 版本，无需重复声明）
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# 见下方 pytorch-cu128 源映射：Linux/Windows 自动使用 CUDA 12.8 版本
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# ═══════════════════════════════════════════════════════════
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all = ["tianmu[rapidocr,rapidocr-gpu,vl]"]
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dev = [
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"pre-commit>=4.6.0",
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"pytest>=9.1.1",
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"pytest-asyncio>=1.4.0",
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"ruff>=0.15.21",
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]
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lint = ["black>=26.5.1", "mypy>=2.3.0", "ruff>=0.15.21"]
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test = ["pytest>=9.1.1", "pytest-asyncio>=1.4.0", "pytest-cov>=7.1.0"]
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dev-all = ["tianmu[dev,lint,test]"]
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[project.scripts]
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tianmu = "src.main:main_cli"
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# =============================================
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# uv 配置 - 跨平台 PyTorch 自动适配
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# =============================================
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# 默认使用中科大镜像（所有平台通用）
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[[tool.uv.index]]
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name = "ustc"
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url = "https://pypi.mirrors.ustc.edu.cn/simple/"
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default = true
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# PyTorch CUDA 12.8 专用源（仅 Linux/Windows 使用）
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[[tool.uv.index]]
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name = "pytorch-cu128"
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url = "https://mirrors.nju.edu.cn/pytorch/whl/cu128/"
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explicit = true
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# === 包来源映射：根据操作系统自动选择 ===
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[tool.uv.sources]
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# Linux/Windows：使用 CUDA 12.8 版本
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torch = { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }
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torchvision = { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }
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torchaudio = { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }
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tianmu = { workspace = true }
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# macOS：从默认源（中科大）自动获取 CPU/MPS 版本
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# 不需要额外配置，会自动回退到 default = true 的源
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# 验证命令：
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# macOS: python -c "import torch; print(f'PyTorch: {torch.__version__}'); print(f'MPS available: {torch.backends.mps.is_available()}')"
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# Linux: python -c "import torch; print(f'PyTorch: {torch.__version__}'); print(f'CUDA available: {torch.cuda.is_available()}')"
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[build-system]
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requires = ["hatchling>=1.25.0"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["src"]
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[tool.ruff]
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line-length = 100
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target-version = "py312"
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[tool.ruff.lint]
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select = ["E", "F", "I", "N", "W"]
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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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