Delete app.py

app.py

@@ -1,27 +0,0 @@
-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()