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app.py

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+import streamlit as st
+from surprise import SVD
+import pandas as pd
+import pickle
+
+# Load data back from the file
+with open('svd_model.pkl', 'rb') as file:
+    svd_model, merged_data, movies = pickle.load(file)
+
+# Title for the app
+st.title("Movie Recommendations")
+
+# User input for user ID
+user_id = st.number_input("Enter User ID", min_value=1, step=1)
+
+# Get rated and unrated movies for the given user
+rated_user_movies = merged_data[merged_data['userId'] == user_id]['title'].values
+unrated_movies = movies[~movies['title'].isin(rated_user_movies)]['title']
+
+# Make predictions on unrated movies
+pred_ratings = [svd_model.predict(user_id, movie_id) for movie_id in unrated_movies]
+
+# Sort predictions by estimated rating in descending order
+sorted_predictions = sorted(pred_ratings, key=lambda x: x.est, reverse=True)
+
+# Get top 10 movie recommendations
+top_recommendations = sorted_predictions[:10]
+
+# Display recommendations
+st.write(f"\nTop 10 movie recommendations for User {user_id}:")
+for recommendation in top_recommendations:
+    movie_title = movies[movies['title'] == recommendation.iid]['title'].values[0]
+    st.write(f"{movie_title} (Estimated Rating: {recommendation.est})")

requirements.txt

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+pandas
+scikit-learn
+pickle
+streamlit
+surprise

svd_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6632a316ac38946f937a76160c523dee16af7b740debd9716d830ed96849f3b4
+size 14463353