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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# if using pycharm
+.idea
+
+# if using VScode
+.vscode
+
+# add secret keys or API keys here
+configs/secrets.yaml
+
+# add your env folder here if its there
+

Procfile

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+web: sh setup.sh && streamlit run app.py

README.md

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----
-title: Book Recommendation
-emoji: 📊
-colorFrom: red
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import pickle
+import streamlit as st
+import numpy as np
+
+st.header("Movie Recommender System using machine learning system")
+model = pickle.load(open('artifacts/model.pkl','rb'))
+books_name = pickle.load(open('artifacts/books_names.pkl','rb'))
+final_rating = pickle.load(open('artifacts/final_rating.pkl','rb'))
+book_pivot = pickle.load(open('artifacts/book_pivot.pkl','rb'))
+
+selected_books = st.selectbox(
+    "Type or select a book",
+    books_name
+)
+def fetch_poster(suggestion):
+    books_name = []
+    ids_index = []
+    poster_url = []
+
+    for book_id in suggestion:
+        books_name.append(book_pivot.index[book_id])
+
+    for i in books_name[0]:
+        ids = np.where(final_rating['title']==i)[0][0]
+        ids_index.append(ids)
+
+    for idx in ids_index:
+        url = final_rating.iloc[idx]['img_url']
+        poster_url.append(url)
+    return poster_url
+
+
+
+def recommend_book(book_name):
+    book_list = []
+    book_id = np.where(book_pivot.index == book_name)[0][0]
+    distance, suggestion = model.kneighbors(book_pivot.iloc[book_id,:].values.reshape(1,-1), n_neighbors=6 )
+
+    poster_url = fetch_poster(suggestion)
+
+
+    for i in range(len(suggestion)):
+        books = book_pivot.index[suggestion[i]]
+        for j in books:
+            book_list.append(j)
+    return book_list, poster_url
+
+if st.button("Show Recommendation "):
+    recommended_books,poster_url = recommend_book(selected_books)
+    col1, col2, col3,col4, col5 = st.columns(5)
+
+    with col1:
+        st.text(recommended_books[1])
+        st.image(poster_url[1])
+
+    with col2:
+        st.text(recommended_books[2])
+        st.image(poster_url[2])
+
+    with col3:
+        st.text(recommended_books[3])
+        st.image(poster_url[3])
+    with col4:
+        st.text(recommended_books[4])
+        st.image(poster_url[4])
+    with col5:
+        st.text(recommended_books[5])
+        st.image(poster_url[5])
+

requirements.txt

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+streamlit
+numpy
+scikit-learn
+
+-e .

setup.py

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+from setuptools import setup
+
+with open("README.md", "r", encoding="utf-8") as f:
+    long_description = f.read()
+
+## edit below variables as per your requirements -
+REPO_NAME = "Books-Recommender-System-Using-Machine-Learning"
+AUTHOR_USER_NAME = "entbappy"
+SRC_REPO = "src"
+LIST_OF_REQUIREMENTS = ['streamlit', 'numpy']
+
+
+setup(
+    name=SRC_REPO,
+    version="0.0.1",
+    author=AUTHOR_USER_NAME,
+    description="A small package for Movie Recommender System",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url=f"https://github.com/{AUTHOR_USER_NAME}/{REPO_NAME}",
+    author_email="entbappy73@gmail.com",
+    packages=[SRC_REPO],
+    license="MIT",
+    python_requires=">=3.7",
+    install_requires=LIST_OF_REQUIREMENTS
+)