[project]
name = "tianmu"
version = "0.2.0"
description = "天目 · 魂镜本地 OCR 引擎 — PP-OCRv6（via RapidOCR）+ PaddleOCR-VL-1.6"
authors = [{ name = "chenxin", email = "2685475410@qq.com" }]
license = "MIT"
requires-python = ">=3.12"
dependencies = [
    "fastapi>=0.139.0",
    "httpx[http2]>=0.28.1",
    "loguru>=0.7.3",
    "pillow>=10.0.0",
    "pydantic>=2.13.4",
    "pydantic-settings>=2.14.2",
    "python-multipart>=0.0.32",
    "uvicorn[standard]>=0.51.0",
]

[project.optional-dependencies]
# ═══════════════════════════════════════════════════════════
# PP-OCRv6 引擎（RapidOCR + ONNX Runtime）
# ═══════════════════════════════════════════════════════════
# CPU 推理（所有平台通用，包括 M4 的 CoreML 实验性支持）
rapidocr = [
    "rapidocr>=3.9.0",
    "onnxruntime>=1.20.0",
]

# GPU 推理（NVIDIA CUDA，仅 Linux/Windows）
rapidocr-gpu = [
    "onnxruntime-gpu>=1.20.0",
]

# ═══════════════════════════════════════════════════════════
# PaddleOCR-VL 引擎（HuggingFace Transformers）
# ═══════════════════════════════════════════════════════════
# CPU/MPS（macOS 自动获取 CPU/MPS 版本，Linux/Windows 自动走 CPU 版本）
vl = [
    "torch>=2.0.0",
    "torchvision>=0.15.0",
    "transformers>=5.0.0",
]

# GPU（通过 [tool.uv.sources] 自动选择 CUDA 版本，无需重复声明）
# 见下方 pytorch-cu128 源映射：Linux/Windows 自动使用 CUDA 12.8 版本
# ═══════════════════════════════════════════════════════════

all = ["tianmu[rapidocr,rapidocr-gpu,vl]"]
dev = [
    "pre-commit>=4.6.0",
    "pytest>=9.1.1",
    "pytest-asyncio>=1.4.0",
    "ruff>=0.15.21",
]
lint = ["black>=26.5.1", "mypy>=2.3.0", "ruff>=0.15.21"]
test = ["pytest>=9.1.1", "pytest-asyncio>=1.4.0", "pytest-cov>=7.1.0"]
dev-all = ["tianmu[dev,lint,test]"]

[project.scripts]
tianmu = "src.main:main_cli"

# =============================================
# uv 配置 - 跨平台 PyTorch 自动适配
# =============================================

# 默认使用中科大镜像（所有平台通用）
[[tool.uv.index]]
name = "ustc"
url = "https://pypi.mirrors.ustc.edu.cn/simple/"
default = true

# PyTorch CUDA 12.8 专用源（仅 Linux/Windows 使用）
[[tool.uv.index]]
name = "pytorch-cu128"
url = "https://mirrors.nju.edu.cn/pytorch/whl/cu128/"
explicit = true

# === 包来源映射：根据操作系统自动选择 ===
[tool.uv.sources]
# Linux/Windows：使用 CUDA 12.8 版本
torch = { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }
torchvision = { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }
torchaudio = { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }
tianmu = { workspace = true }

# macOS：从默认源（中科大）自动获取 CPU/MPS 版本
# 不需要额外配置，会自动回退到 default = true 的源

# 验证命令：
# macOS: python -c "import torch; print(f'PyTorch: {torch.__version__}'); print(f'MPS available: {torch.backends.mps.is_available()}')"
# Linux:  python -c "import torch; print(f'PyTorch: {torch.__version__}'); print(f'CUDA available: {torch.cuda.is_available()}')"

[build-system]
requires = ["hatchling>=1.25.0"]
build-backend = "hatchling.build"

[tool.hatch.build.targets.wheel]
packages = ["src"]

[tool.ruff]
line-length = 100
target-version = "py312"

[tool.ruff.lint]
select = ["E", "F", "I", "N", "W"]

[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]