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import streamlit as st
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import pickle
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import pandas as pd
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import requests
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def fetch_poster(movie_id):
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url = "https://api.themoviedb.org/3/movie/{}?api_key=c8c3756e312fabeb5d1802af7f1f2510&language=en-US".format(
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movie_id)
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response = requests.get(url)
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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 recommend(movie):
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movie_index = movies[movies["title"] == movie].index[0]
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distances = similarity[movie_index]
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movies_list = sorted(enumerate(distances),
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reverse=True, key=lambda x: x[1])[1:6]
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recommended_movies = []
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recommended_movies_posters = []
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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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recommended_movies.append(movies.iloc[i[0]].title)
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recommended_movies_posters.append(fetch_poster(movie_id))
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return recommended_movies, recommended_movies_posters
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movies = pickle.load(open("movies.pkl", "rb"))
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similarity = pickle.load(open("similarity.pkl", "rb"))
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movies_list = movies["title"].values
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st.title("Movie Recommender System")
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selected_movie_name = st.selectbox(
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"Select a movie",
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movies_list
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)
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if st.button("Recommend Movies"):
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names, posters = recommend(selected_movie_name)
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col1, col2, col3, col4, col5 = st.columns(5)
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with col1:
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st.text(names[0])
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st.image(posters[0])
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with col2:
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st.text(names[1])
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st.image(posters[1])
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with col3:
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st.text(names[2])
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st.image(posters[2])
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with col4:
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st.text(names[3])
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st.image(posters[3])
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with col5:
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st.text(names[4])
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st.image(posters[4])
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