Upload 4 files

app.py

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+import gradio as gr
+import joblib
+import pandas as pd
+import heapq
+
+# Load saved artifacts
+svd = joblib.load('svd_model.pkl')
+movies = joblib.load('movies.pkl')
+ratings = joblib.load('ratings.pkl')
+
+def recommend_movies(user_id, N=10):
+    anti_testset = [(u, i, r) for (u, i, r) in ratings[['userId', 'movieId', 'rating']].itertuples(index=False) if u != user_id]
+    user_anti_testset = [t for t in anti_testset if t[0] == user_id][:1000]  # Limit for speed
+    predictions = svd.test(user_anti_testset)
+    top_n = heapq.nlargest(N, predictions, key=lambda x: x.est)
+    top_movie_ids = [pred.iid for pred in top_n]
+    top_titles = movies[movies['movieId'].isin(top_movie_ids)]['title'].values
+    return "\n".join(top_titles)
+
+demo = gr.Interface(
+    fn=recommend_movies,
+    inputs=[gr.Number(label="User ID", value=1), gr.Number(label="N", value=10)],
+    outputs="text",
+    title="Movie Recommendation System",
+    description="Enter a User ID and N to get top movie recommendations."
+)
+demo.launch()

movies.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:69bcdaea1773bf66d71f461370d1baf4ddef14d87df15d5629bb11626d60e261
+size 434013

ratings.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:37751a8e2746e600c16ee257a65e93e1c5b165469a7edb863c605a88dee891ef
+size 4841441

svd_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e3f2ab55cbc8ce82c3cda5456929c5378579219515f4dccad76a3459d7ed1d21
+size 6266419