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import pickle |
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import streamlit as st |
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import requests |
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import numpy as np |
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import pandas as pd |
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def fetch_poster(movie_id): |
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url = "https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US".format(movie_id) |
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data = requests.get(url) |
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data = data.json() |
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poster_path = data['poster_path'] |
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full_path = "https://image.tmdb.org/t/p/w500/" + poster_path |
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return full_path |
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def recommend(movie): |
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index = movies[movies['title'] == movie].index[0] |
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distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1]) |
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recommended_movie_names = [] |
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recommended_movie_posters = [] |
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for i in distances[1:6]: |
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movie_id = movies.iloc[i[0]].movie_id |
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recommended_movie_posters.append(fetch_poster(movie_id)) |
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recommended_movie_names.append(movies.iloc[i[0]].title) |
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return recommended_movie_names,recommended_movie_posters |
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st.header('Movie Recommender System') |
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movies = pd.read_pickle('movie_list.pkl') |
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similarity = pd.read_pickle('similarity.pkl') |
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movie_list = movies['title'].values |
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selected_movie = st.selectbox( |
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"Type or select a movie from the dropdown", |
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movie_list |
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) |
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if st.button('Show Recommendation'): |
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recommended_movie_names,recommended_movie_posters = recommend(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(recommended_movie_names[0]) |
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st.image(recommended_movie_posters[0]) |
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with col2: |
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st.text(recommended_movie_names[1]) |
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st.image(recommended_movie_posters[1]) |
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with col3: |
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st.text(recommended_movie_names[2]) |
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st.image(recommended_movie_posters[2]) |
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with col4: |
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st.text(recommended_movie_names[3]) |
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st.image(recommended_movie_posters[3]) |
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with col5: |
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st.text(recommended_movie_names[4]) |
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st.image(recommended_movie_posters[4]) |
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