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import pandas as pd |
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import streamlit as st |
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import pickle |
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import requests |
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def fetch_image(movie_id): |
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response = requests.get('https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US'.format(movie_id)) |
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data = response.json() |
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return "https://image.tmdb.org/t/p/w500/" + data['poster_path'] |
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def recommand(movie): |
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movie_index = movies[movies['title'] == movie].index[0] |
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distences = similarity[movie_index] |
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movies_list = sorted(list(enumerate(distences)),reverse=True,key = lambda x : x[1])[1:6] |
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recommanded_movie = [] |
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recommaned_poster=[] |
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for i in movies_list: |
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movie_id = movies.iloc[i[0]].movie_id |
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recommanded_movie.append(movies.iloc[i[0]].title) |
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recommaned_poster.append(fetch_image(movie_id)) |
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return recommanded_movie,recommaned_poster |
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movies_dict = pickle.load(open('movie_dict.pkl','rb')) |
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movies = pd.DataFrame(movies_dict) |
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similarity = pickle.load(open('similarity.pkl','rb')) |
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st.title('Movie Recommender System') |
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selected_movie = st.selectbox( |
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'What is your taste in movie ? ', |
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movies['title'].values) |
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st.write('You selected:', selected_movie) |
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if st.button('Recommend movie'): |
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name,poster =recommand(selected_movie) |
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col1, col2, col3, col4, col5 = st.columns(5) |
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with col1: |
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st.text(name[0]) |
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st.image(poster[0]) |
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with col2: |
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st.text(name[1]) |
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st.image(poster[1]) |
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with col3: |
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st.text(name[2]) |
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st.image(poster[2]) |
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with col4: |
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st.text(name[3]) |
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st.image(poster[3]) |
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with col5: |
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st.text(name[4]) |
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st.image(poster[4]) |
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