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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=fe34a557846e9a676a98fd362f059b28&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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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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movie_index = movies[movies['title'] == movie].index[0] |
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distances = similarity[movie_index] |
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movies_list = sorted(list(enumerate(distances)), 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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st.title('Movie Recommender System') |
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st.text('👨🏻💻 by Vividh Pandey') |
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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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movie_list = movies['title'].values |
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selected_movie_name = st.selectbox( |
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"Type or select a movie from the dropdown", |
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movies['title'].values |
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) |
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if st.button('Show Recommendation'): |
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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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