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setup.py
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from typing import List
from setuptools import setup, find_packages
import os
PATH_ROOT = os.path.dirname(__file__)
with open("README.md", "r") as fh:
long_description = fh.read()
def load_requirements(
path_dir: str = PATH_ROOT, comment_char: str = "#"
) -> List:
with open(os.path.join(path_dir, "core_requirements.txt"), "r") as file:
lines = [ln.strip() for ln in file.readlines()]
reqs = []
for ln in lines:
# filer all comments
if comment_char in ln:
ln = ln[: ln.index(comment_char)]
if ln: # if requirement is not empty
reqs.append(ln)
return reqs
install_requires = load_requirements()
setup(
name="box_embeddings",
version="0.1.0",
author="Dhruvesh Patel, Shib Sankar Dasgupta, Michael Boratko, Purujit Goyal, Tejas Chheda, Trang Tran, Xiang (Lorraine) Li",
author_email="1793dnp@gmail.com",
description="Pytorch and Tensorflow implemention of box embedding models",
long_description=long_description,
long_description_content_type="text/markdown",
url="http://www.iesl.cs.umass.edu/box-embeddings/",
project_urls={
"Documentation": "http://www.iesl.cs.umass.edu/box-embeddings",
"Source Code": "https://github.com/iesl/box-embeddings",
},
packages=find_packages(
exclude=["*.tests", "*.tests.*", "tests.*", "tests", "examples"]
),
package_data={"box_embeddings": ["py.typed"]},
install_requires=install_requires,
keywords=[
"pytorch",
"tensorflow",
"AI",
"ML",
"Machine Learning",
"Deep Learning",
],
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Intended Audience :: Education",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
],
python_requires=">=3.5",
)