webgym/setup.py at main · microsoft/webgym · GitHub

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import setuptools

# Omniboxes server dependencies
omnibox_requires = [
    "fastapi[standard]",
    "httpx",
    "Pillow==11.2.1",
    "playwright",
    "redis",
    "Requests==2.32.3",
    "psutil==7.0.0",
]

# Training / webgym pipeline dependencies
train_requires = [
    "aiohttp==3.11.13",
    "boto3",
    "botocore",
    "click==8.1.7",
    "fitz==0.0.1.dev2",
    "gradio==5.29.0",
    "gym==0.26.2",
    "hydra-core==1.3.2",
    "nltk==3.9.1",
    "omegaconf==2.3.0",
    "paramiko==3.5.1",
    "peft==0.14.0",
    "Pillow==11.2.1",
    "psutil==7.0.0",
    "Requests==2.32.3",
    "setuptools==78.1.0",
    "tqdm==4.67.1",
    "webdriver_manager==4.0.2",
    "tenacity",
    "ipython",
    "ipywidgets",
    "wandb",
    "fastparquet",
    "matplotlib",
    "antlr4-python3-runtime==4.9.3",
    "flask",
    "httpx",
    "ipykernel",
    "torch",
    "transformers==4.57.1",
    "vllm",
    "openai",
    "pandas",
    "numpy",
]

setuptools.setup(
    name="webgym",
    version='1.0.0',
    author=("Hao Bai"),
    description="A scalable reinforcement learning framework for training web automation agents using vision-language models.",
    long_description=open("README.md", "r", encoding='utf-8').read(),
    long_description_content_type="text/markdown",
    keywords='webgym',
    license='MIT',
    packages=setuptools.find_packages(),
    install_requires=[],
    extras_require={
        "omnibox": omnibox_requires,
        "train": train_requires,
        "all": omnibox_requires + train_requires,
    },
    include_package_data=True,
    python_requires='>=3.10',
    classifiers=[
        'Intended Audience :: Science/Research',
        'License :: OSI Approved :: MIT License',
        'Programming Language :: Python :: 3',
        'Topic :: Scientific/Engineering :: Artificial Intelligence',
    ],
)