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import streamlit as st |
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import pandas as pd |
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import requests |
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import pickle |
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with open('movie_data.pkl', 'rb') as file: |
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movies, cosine_sim = pickle.load(file) |
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def get_recommendations(title, cosine_sim=cosine_sim): |
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idx = movies[movies['title'] == title].index[0] |
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sim_scores = list(enumerate(cosine_sim[idx])) |
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sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True) |
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sim_scores = sim_scores[1:11] |
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movie_indices = [i[0] for i in sim_scores] |
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return movies[['title', 'movie_id']].iloc[movie_indices] |
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def fetch_poster(movie_id): |
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api_key = "d52d86933103ae578bbf057ec39d012e" |
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url = f'https://api.themoviedb.org/3/movie/{movie_id}?api_key={api_key}' |
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response = requests.get(url) |
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if response.status_code != 200: |
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return "https://via.placeholder.com/500x750?text=No+Image+Available" |
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data = response.json() |
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poster_path = data.get('poster_path') |
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if poster_path: |
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return f"https://image.tmdb.org/t/p/w500{poster_path}" |
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return "https://via.placeholder.com/500x750?text=No+Image+Available" |
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st.title("Movie Recommendation System") |
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selected_movie = st.selectbox("Select a movie:", movies['title'].values) |
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if st.button('Recommend'): |
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recommendations = get_recommendations(selected_movie) |
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st.write("Top 10 recommended movies:") |
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for i in range(0, 10, 5): |
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cols = st.columns(5) |
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for col, j in zip(cols, range(i, i+5)): |
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if j < len(recommendations): |
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movie_title = recommendations.iloc[j]['title'] |
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movie_id = recommendations.iloc[j]['movie_id'] |
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poster_url = fetch_poster(movie_id) |
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with col: |
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st.image(poster_url, width=130) |
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st.write(movie_title) |
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