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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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