affectlens/pyproject.toml at main · JoshW-dev/affectlens · GitHub

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[build-system]
requires = ["setuptools>=68", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "affectlens"
version = "0.1.0"
description = "Extract time-varying visual, audio, and semantic features from video/audio stimuli and relate them to human ratings or recorded signals."
readme = "README.md"
requires-python = ">=3.10"
license = { text = "MIT" }
authors = [{ name = "Joshua Wade" }]
keywords = [
    "feature-extraction",
    "video",
    "audio",
    "affective-computing",
    "stimuli",
    "encoding-models",
    "neuroimaging",
    "psychophysics",
]
classifiers = [
    "Development Status :: 3 - Alpha",
    "Intended Audience :: Science/Research",
    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Topic :: Scientific/Engineering",
    "Topic :: Multimedia :: Video",
    "Topic :: Multimedia :: Sound/Audio :: Analysis",
]
dependencies = [
    "numpy>=1.24",
    "pandas>=2.0",
    "scikit-learn>=1.3",
    "opencv-python-headless>=4.8",
    "imageio>=2.31",
    "imageio-ffmpeg>=0.4.9",
    "openpyxl>=3.1",
]

[project.optional-dependencies]
# Real high-level semantic path (offline hashing fallback needs none of these).
semantic = ["sentence-transformers>=2.2"]
asr = ["faster-whisper>=1.0"]
# Run the guided notebook (examples/getting_started.ipynb) and regenerate figures.
notebook = ["matplotlib>=3.7", "jupyterlab>=4.0"]
# Point-and-click web UI (webui/app.py): `streamlit run webui/app.py`.
webui = ["streamlit>=1.30"]
dev = ["pytest>=7.0"]

[project.urls]
Homepage = "https://github.com/JoshW-dev/affectlens"
Repository = "https://github.com/JoshW-dev/affectlens"
Issues = "https://github.com/JoshW-dev/affectlens/issues"

[project.scripts]
affectlens = "affectlens.cli:main"

[tool.setuptools.packages.find]
where = ["src"]

[tool.setuptools.package-data]
# Bundle the YuNet face-detection model (see src/affectlens/models/NOTICE).
affectlens = ["models/*.onnx", "models/NOTICE"]

[tool.pytest.ini_options]
testpaths = ["tests"]
pythonpath = ["src"]