pyproject.toml

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

[project]
name = "tinyfold"
version = "0.1.0"
description = "AlphaFold3-style binary PPI predictor"
readme = "README.md"
license = "MIT"
requires-python = ">=3.10"
dependencies = [
    "torch>=2.5,<2.7",
    "gemmi>=0.6",
    "pyarrow>=14.0",
    "pandas>=2.0",
    "numpy>=1.24",
    "scipy>=1.10",
    "dill>=0.3",
    "DockQ>=2.1",
    "tqdm",
    "requests",
    "matplotlib>=3.5",
    "py3Dmol>=2.0",
    "jinja2>=3.0",
    "transformers>=4.40",
]

[project.scripts]
tinyfold-predict = "tinyfold.cli.predict:main"
tinyfold-embed-esm = "tinyfold.cli.prepare_esm2:main"
tinyfold-prepare-data = "tinyfold.cli.prepare_data:main"

[project.optional-dependencies]
dev = [
    "pytest>=7.0",
    "pytest-timeout",
    "ruff>=0.8",
]

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

# --- CUDA torch via uv --------------------------------------------------------
# PyPI only ships CPU torch on Windows; CUDA builds live on the PyTorch index.
# Pull the cu124 wheel on win32/linux (both have cu124 builds); fall back to
# PyPI elsewhere (macOS -> mps/cpu). This makes `uv sync` produce a GPU env.
[[tool.uv.index]]
name = "pytorch-cu124"
url = "https://download.pytorch.org/whl/cu124"
explicit = true

[tool.uv.sources]
torch = [
    { index = "pytorch-cu124", marker = "sys_platform == 'win32' or sys_platform == 'linux'" },
]

[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py"]
addopts = "-v --tb=short"
timeout = 120
markers = [
    "slow: marks tests as slow (deselect with '-m \"not slow\"')",
    "integration: marks tests as integration tests that load real data",
    "network: marks tests that make real network requests (deselect with '-m \"not network\"')",
]
filterwarnings = [
    "error",
    # DockQ's backend leaves its temporary PDB files unclosed during scoring.
    "ignore::ResourceWarning",
]