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.gitignore

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+venv

app.py

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+import pandas as pd
+import streamlit as st
+import requests
+
+def fetch_poster(movie_id):
+    url = "https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US".format(
+        movie_id)
+    data = requests.get(url)
+    data = data.json()
+    poster_path = data['poster_path']
+    full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
+    return full_path
+
+
+import pickle
+
+
+similarity=pickle.load(open('similarity.pkl','rb'))
+movies_dict= pickle.load(open('movies_dict.pkl','rb'))
+movies=pd.DataFrame(movies_dict)
+st.title('Movie Recommender System ')
+
+def add_bg_from_url():
+    st.markdown(
+         f"""
+         <style>
+         .stApp {{
+             background-image: url("https://images.pexels.com/photos/2397414/pexels-photo-2397414.jpeg");
+             background-attachment: fixed;
+             background-size: cover
+         }}
+         </style>
+         """,
+         unsafe_allow_html=True
+     )
+
+add_bg_from_url()
+def recommend(movie):
+    index = movies[movies['title'] == movie].index[0]
+    distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
+    recommended_movie_names = []
+    recommended_movie_posters = []
+    for i in distances[1:6]:
+        # fetch the movie poster
+
+        movie_id = movies.iloc[i[0]].movie_id
+        recommended_movie_posters.append(fetch_poster(movie_id))
+        recommended_movie_names.append(movies.iloc[i[0]].title)
+
+    return recommended_movie_names,recommended_movie_posters
+
+
+option = st.selectbox('How would you like to be continued?', movies['title'].values)
+
+
+if st.button('Recommend') :
+    recommended_movie_names,recommended_movie_posters= recommend(option)
+
+    col1, col2, col3, col4, col5 = st.columns(5)
+    with col1:
+        st.text(recommended_movie_names[0])
+        st.image(recommended_movie_posters[0])
+    with col2:
+        st.text(recommended_movie_names[1])
+        st.image(recommended_movie_posters[1])
+
+    with col3:
+        st.text(recommended_movie_names[2])
+        st.image(recommended_movie_posters[2])
+    with col4:
+        st.text(recommended_movie_names[3])
+        st.image(recommended_movie_posters[3])
+    with col5:
+        st.text(recommended_movie_names[4])
+        st.image(recommended_movie_posters[4])

movies_dict.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4524ee20e02083a5d39d5886066222f0acb9d1c8e9f12cf8f0d3016114e8f070
+size 2216682

procfile

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

requirements.txt

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+altair==4.2.2
+attrs==23.1.0
+blinker==1.6.2
+cachetools==5.3.0
+certifi==2023.5.7
+charset-normalizer==3.1.0
+click==8.1.3
+colorama==0.4.6
+decorator==5.1.1
+entrypoints==0.4
+gitdb==4.0.10
+GitPython==3.1.31
+idna==3.4
+importlib-metadata==6.6.0
+Jinja2==3.1.2
+jsonschema==4.17.3
+markdown-it-py==2.2.0
+MarkupSafe==2.1.2
+mdurl==0.1.2
+numpy==1.24.3
+packaging==23.1
+pandas==2.0.1
+Pillow==9.5.0
+protobuf==3.20.3
+pyarrow==12.0.0
+pydeck==0.8.1b0
+Pygments==2.15.1
+Pympler==1.0.1
+pyrsistent==0.19.3
+python-dateutil==2.8.2
+pytz==2023.3
+pytz-deprecation-shim==0.1.0.post0
+requests==2.30.0
+rich==13.3.5
+six==1.16.0
+smmap==5.0.0
+streamlit==1.22.0
+tenacity==8.2.2
+toml==0.10.2
+toolz==0.12.0
+tornado==6.3.1
+typing_extensions==4.5.0
+tzdata==2023.3
+tzlocal==4.3
+urllib3==2.0.2
+validators==0.20.0
+watchdog==3.0.0
+zipp==3.15.0

setup.sh

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+mkdir -p ~/.streamlit/
+
+echo"\
+[server]\n\
+port=$PORT\n\
+enableCORS=false\n\
+headLess=true\n\
+\n\"
+"> ~/.streamlit/config.toml

similarity.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:afe491e2d67528352bb0998b9626f0d67313108f2641028b9a5dd44d47fdb0c7
+size 184781251