Update app.py

app.py

@@ -60,13 +60,13 @@ def predict_rating(user_id, movie_id):
         if user_id not in ratings_matrix.index or movie_id not in ratings_matrix.columns:
             return "Error: User ID or Movie ID not found in dataset"
         
-        # Scale the data
-        scaler = StandardScaler()
-        ratings_scaled = scaler.fit_transform(ratings_matrix)
+        # Instead of StandardScaler, we'll normalize the data to [0,1] range
+        ratings_array = ratings_matrix.values
+        ratings_normalized = (ratings_array - ratings_array.min()) / (ratings_array.max() - ratings_array.min())
         
         # Apply NMF
-        nmf = NMF(n_components=50, random_state=42)
-        user_features = nmf.fit_transform(ratings_scaled)
+        nmf = NMF(n_components=50, init='random', random_state=42)
+        user_features = nmf.fit_transform(ratings_normalized)
         movie_features = nmf.components_
         
         # Get user and movie indices
@@ -76,8 +76,8 @@ def predict_rating(user_id, movie_id):
         # Predict rating
         prediction = np.dot(user_features[user_idx], movie_features[:, movie_idx])
         
-        # Rescale prediction
-        prediction = scaler.inverse_transform([[prediction]])[0][0]
+        # Scale prediction back to original range [0.5, 5]
+        prediction = (prediction * (5 - 0.5)) + 0.5
         prediction = max(0.5, min(5, prediction))  # Clip between 0.5 and 5
         
         return f"🎬 Predicted Rating: {prediction:.2f} / 5.0"