Update src/streamlit_app.py

src/streamlit_app.py

@@ -5,7 +5,7 @@ import tensorflow as tf
 import joblib
 import os
 
-# Define file paths
+# Define file paths (assuming all files are in the same folder as this script)
 BASE_DIR = os.path.dirname(__file__)
 KERAS_MODEL_PATH = os.path.join(BASE_DIR, "recommender_model.keras")
 MOVIES_PATH = os.path.join(BASE_DIR, "movies.csv")
@@ -13,6 +13,9 @@ ENCODINGS_PATH = os.path.join(BASE_DIR, "encodings.pkl")
 
 @st.cache_resource
 def load_model():
+    if not os.path.exists(KERAS_MODEL_PATH):
+        st.error(f"❌ Model file not found at: {KERAS_MODEL_PATH}")
+        st.stop()
     try:
         return tf.keras.models.load_model(KERAS_MODEL_PATH)
     except Exception as e:
@@ -21,35 +24,35 @@ def load_model():
 
 @st.cache_data
 def load_assets():
-    try:
-        df_movies = pd.read_csv(MOVIES_PATH)
-    except FileNotFoundError:
+    if not os.path.exists(MOVIES_PATH):
         st.error("❌ movies.csv not found.")
         st.stop()
-
+    if not os.path.exists(ENCODINGS_PATH):
+        st.error("❌ encodings.pkl not found.")
+        st.stop()
     try:
+        df_movies = pd.read_csv(MOVIES_PATH)
         user_map, movie_map = joblib.load(ENCODINGS_PATH)
-    except FileNotFoundError:
-        st.error("❌ encodings.pkl not found.")
+        return df_movies, user_map, movie_map
+    except Exception as e:
+        st.error(f"❌ Failed to load assets:\n\n{e}")
         st.stop()
 
-    return df_movies, user_map, movie_map
-
 # Load model and assets
 model = load_model()
 movies_df, user2idx, movie2idx = load_assets()
 reverse_movie_map = {v: k for k, v in movie2idx.items()}
 
-# App UI
+# UI
 st.title("🎬 TensorFlow Movie Recommender")
 st.write("Select some movies you've liked to get personalized recommendations:")
 
-# Movie selection UI
+# Movie title selection
 movie_titles = movies_df.set_index("movieId")["title"].to_dict()
 movie_choices = [movie_titles[mid] for mid in movie2idx if mid in movie_titles]
 selected_titles = st.multiselect("🎞️ Liked movies", sorted(movie_choices))
 
-# Create ratings dictionary
+# User ratings dict
 user_ratings = {}
 for title in selected_titles:
     movie_id = next((k for k, v in movie_titles.items() if v == title), None)
@@ -66,23 +69,25 @@ if st.button("🎯 Get Recommendations"):
             st.error("⚠️ No valid movie encodings found.")
             st.stop()
 
-        # Get embedding averages and scores
-        avg_embedding = tf.reduce_mean(model.layers[2](tf.constant(liked_indices)), axis=0, keepdims=True)
-        all_movie_indices = tf.range(len(movie2idx))
-        movie_embeddings = model.layers[3](all_movie_indices)
-        scores = tf.reduce_sum(avg_embedding * movie_embeddings, axis=1).numpy()
-        top_indices = np.argsort(scores)[::-1]
+        try:
+            # Calculate average embedding and similarity scores
+            avg_embedding = tf.reduce_mean(model.layers[2](tf.constant(liked_indices)), axis=0, keepdims=True)
+            all_movie_indices = tf.range(len(movie2idx))
+            movie_embeddings = model.layers[3](all_movie_indices)
+            scores = tf.reduce_sum(avg_embedding * movie_embeddings, axis=1).numpy()
+            top_indices = np.argsort(scores)[::-1]
 
-        # Top N recommendations excluding already-liked
-        recommended = []
-        for idx in top_indices:
-            mid = reverse_movie_map.get(idx)
-            if mid not in user_ratings and mid in movie_titles:
-                recommended.append((movie_titles[mid], scores[idx]))
-            if len(recommended) >= 10:
-                break
+            # Top 10 recommendations excluding already liked
+            recommended = []
+            for idx in top_indices:
+                mid = reverse_movie_map.get(idx)
+                if mid not in user_ratings and mid in movie_titles:
+                    recommended.append((movie_titles[mid], scores[idx]))
+                if len(recommended) >= 10:
+                    break
 
-        # Display recommendations
-        st.subheader("🍿 Top 10 Recommendations")
-        for title, score in recommended:
-            st.write(f"**{title}** — Score: `{score:.3f}`")
+            st.subheader("🍿 Top 10 Recommendations")
+            for title, score in recommended:
+                st.write(f"**{title}** — Score: `{score:.3f}`")
+        except Exception as e:
+            st.error(f"❌ Error generating recommendations:\n\n{e}")