Update src/streamlit_app.py

src/streamlit_app.py

@@ -7,7 +7,7 @@ import os
 import zipfile
 import tempfile
 
-# Paths
+# Define static file paths
 BASE_DIR = os.path.dirname(__file__)
 ZIP_MODEL_PATH = os.path.join(BASE_DIR, "recommender_model.zip")
 MOVIES_PATH = os.path.join(BASE_DIR, "movies.csv")
@@ -16,16 +16,19 @@ ENCODINGS_PATH = os.path.join(BASE_DIR, "encodings.pkl")
 @st.cache_resource
 def load_model():
     try:
-        extract_dir = os.path.join(tempfile.gettempdir(), "recommender_model")
+        # Define extraction directory in a writable temp location
+        extract_dir = os.path.join(tempfile.gettempdir(), "recommender_model_extracted")
 
-        # Extract only if not already extracted
+        # Only extract if not already done
         if not os.path.exists(extract_dir):
             with zipfile.ZipFile(ZIP_MODEL_PATH, "r") as zip_ref:
                 zip_ref.extractall(extract_dir)
 
+        # Load model from extracted directory
         return tf.keras.models.load_model(extract_dir)
+
     except Exception as e:
-        st.error(f"Failed to load model: {e}")
+        st.error(f"❌ Failed to load model:\n\n{e}")
         st.stop()
 
 @st.cache_data
@@ -33,13 +36,13 @@ def load_assets():
     try:
         df_movies = pd.read_csv(MOVIES_PATH)
     except FileNotFoundError:
-        st.error("❌ Error: movies.csv not found.")
+        st.error("❌ movies.csv not found.")
         st.stop()
 
     try:
         user_map, movie_map = joblib.load(ENCODINGS_PATH)
     except FileNotFoundError:
-        st.error("❌ Error: encodings.pkl not found.")
+        st.error("❌ encodings.pkl not found.")
         st.stop()
 
     return df_movies, user_map, movie_map
@@ -49,20 +52,23 @@ model = load_model()
 movies_df, user2idx, movie2idx = load_assets()
 reverse_movie_map = {v: k for k, v in movie2idx.items()}
 
-# UI
+# App UI
 st.title("🎬 TensorFlow Movie Recommender")
 st.write("Select some movies you've liked to get personalized recommendations:")
 
+# Movie selection UI
 movie_titles = movies_df.set_index("movieId")["title"].to_dict()
-movie_choices = [movie_titles[mid] for mid in movie2idx.keys() if mid in movie_titles]
+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 = {}
 for title in selected_titles:
     movie_id = next((k for k, v in movie_titles.items() if v == title), None)
     if movie_id:
         user_ratings[movie_id] = 5.0
 
+# Generate recommendations
 if st.button("🎯 Get Recommendations"):
     if not user_ratings:
         st.warning("Please select at least one movie.")
@@ -72,12 +78,14 @@ 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]
 
+        # Top N recommendations excluding already-liked
         recommended = []
         for idx in top_indices:
             mid = reverse_movie_map.get(idx)
@@ -86,6 +94,7 @@ if st.button("🎯 Get Recommendations"):
             if len(recommended) >= 10:
                 break
 
+        # Display recommendations
         st.subheader("🍿 Top 10 Recommendations")
         for title, score in recommended:
             st.write(f"**{title}** — Score: `{score:.3f}`")