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

apps.py

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+import streamlit as st
+import pickle
+import requests
+import pandas as pd
+
+
+
+st.set_page_config(page_title=None, page_icon=None, layout="wide", initial_sidebar_state="auto", menu_items=None)
+
+movies = pickle.load(open("model/movies_list.pkl",'rb'))
+similarity = pickle.load(open("model/similarity.pkl",'rb'))
+
+
+def fetch_poster(movie_id):
+    url = "https://api.themoviedb.org/3/movie/{}?api_key=8735d1698444eb906584226ecb77307b&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
+
+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
+
+
+st.header('Movie Recommendation System (Content Based)')
+
+st.text("A movie recommendation system, or a movie recommender system, is an ML-based approach to filtering or predicting the users' film preferences ")
+
+
+
+movie_list = movies['title'].values
+selected_movie = st.selectbox(
+    "Type or select a movie from the dropdown",
+    movie_list
+)
+
+if st.button('Show Recommendation'):
+    recommended_movie_names,recommended_movie_posters = recommend(selected_movie)
+    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_list.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bf95869ff7334761fcd41790aa16cd06e5f8de07c33aeedaec0b14e46fc78a9b
+size 2614026

requirements.txt

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+altair==4.2.0
+attrs==22.1.0
+blinker==1.5
+cachetools==5.2.0
+certifi==2022.9.24
+charset-normalizer==2.1.1
+click==8.1.3
+colorama==0.4.6
+commonmark==0.9.1
+decorator==5.1.1
+entrypoints==0.4
+flask==2.2.2
+gitdb==4.0.10
+GitPython==3.1.29
+idna==3.4
+importlib-metadata==5.1.0
+itsdangerous==2.1.2
+Jinja2==3.1.2
+jsonschema==4.17.3
+MarkupSafe==2.1.1
+numpy==1.23.5
+packaging==21.3
+pandas==1.5.2
+Pillow==9.3.0
+protobuf==3.20.3
+pyarrow==10.0.1
+pydeck==0.8.0
+Pygments==2.13.0
+Pympler==1.0.1
+pyparsing==3.0.9
+pyrsistent==0.19.2
+python-dateutil==2.8.2
+pytz==2022.6
+pytz-deprecation-shim==0.1.0.post0
+requests==2.28.1
+rich==12.6.0
+semver==2.13.0
+six==1.16.0
+sklearn==0.0.post1
+smmap==5.0.0
+streamlit==1.15.2
+toml==0.10.2
+toolz==0.12.0
+tornado==6.2
+typing_extensions==4.4.0
+tzdata==2022.7
+tzlocal==4.2
+urllib3==1.26.13
+validators==0.20.0
+watchdog==2.1.9
+Werkzeug==2.2.2
+zipp==3.11.0

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