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mrrandom123
commited on
Commit
•
1bef7aa
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Parent(s):
268ce0a
Upload 8 files
Browse files- .gitignore +141 -0
- Books recommendation .ipynb +0 -0
- Procfile +1 -0
- README.md +0 -12
- app.py +69 -0
- requirements.txt +5 -0
- setup.py +26 -0
- setup.sh +0 -0
.gitignore
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# if using pycharm
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.idea
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# if using VScode
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.vscode
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# add secret keys or API keys here
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configs/secrets.yaml
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# add your env folder here if its there
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Books recommendation .ipynb
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The diff for this file is too large to render.
See raw diff
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Procfile
ADDED
@@ -0,0 +1 @@
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web: sh setup.sh && streamlit run app.py
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README.md
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@@ -1,12 +0,0 @@
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-
---
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title: Book Recommendation
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emoji: 📊
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colorFrom: red
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colorTo: purple
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sdk: streamlit
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sdk_version: 1.15.2
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
ADDED
@@ -0,0 +1,69 @@
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import pickle
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import streamlit as st
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import numpy as np
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st.header("Movie Recommender System using machine learning system")
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model = pickle.load(open('artifacts/model.pkl','rb'))
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books_name = pickle.load(open('artifacts/books_names.pkl','rb'))
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final_rating = pickle.load(open('artifacts/final_rating.pkl','rb'))
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book_pivot = pickle.load(open('artifacts/book_pivot.pkl','rb'))
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selected_books = st.selectbox(
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"Type or select a book",
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books_name
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)
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def fetch_poster(suggestion):
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books_name = []
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ids_index = []
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poster_url = []
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for book_id in suggestion:
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books_name.append(book_pivot.index[book_id])
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for i in books_name[0]:
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ids = np.where(final_rating['title']==i)[0][0]
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ids_index.append(ids)
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for idx in ids_index:
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url = final_rating.iloc[idx]['img_url']
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poster_url.append(url)
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return poster_url
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def recommend_book(book_name):
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book_list = []
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book_id = np.where(book_pivot.index == book_name)[0][0]
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distance, suggestion = model.kneighbors(book_pivot.iloc[book_id,:].values.reshape(1,-1), n_neighbors=6 )
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poster_url = fetch_poster(suggestion)
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for i in range(len(suggestion)):
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books = book_pivot.index[suggestion[i]]
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for j in books:
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book_list.append(j)
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return book_list, poster_url
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if st.button("Show Recommendation "):
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recommended_books,poster_url = recommend_book(selected_books)
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col1, col2, col3,col4, col5 = st.columns(5)
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with col1:
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st.text(recommended_books[1])
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st.image(poster_url[1])
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with col2:
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st.text(recommended_books[2])
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st.image(poster_url[2])
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with col3:
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st.text(recommended_books[3])
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st.image(poster_url[3])
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with col4:
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st.text(recommended_books[4])
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st.image(poster_url[4])
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with col5:
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st.text(recommended_books[5])
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st.image(poster_url[5])
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requirements.txt
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streamlit
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numpy
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scikit-learn
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-e .
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setup.py
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from setuptools import setup
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with open("README.md", "r", encoding="utf-8") as f:
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long_description = f.read()
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## edit below variables as per your requirements -
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REPO_NAME = "Books-Recommender-System-Using-Machine-Learning"
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AUTHOR_USER_NAME = "entbappy"
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SRC_REPO = "src"
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LIST_OF_REQUIREMENTS = ['streamlit', 'numpy']
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setup(
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name=SRC_REPO,
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version="0.0.1",
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author=AUTHOR_USER_NAME,
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description="A small package for Movie Recommender System",
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long_description=long_description,
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long_description_content_type="text/markdown",
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url=f"https://github.com/{AUTHOR_USER_NAME}/{REPO_NAME}",
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author_email="entbappy73@gmail.com",
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packages=[SRC_REPO],
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license="MIT",
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python_requires=">=3.7",
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install_requires=LIST_OF_REQUIREMENTS
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)
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setup.sh
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File without changes
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