Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,25 +1,43 @@
-
 import streamlit as st
 import pandas as pd
 import numpy as np
 import tensorflow as tf
 import joblib
+import os
+
+# Define paths
+BASE_DIR = os.path.dirname(__file__)
+MODEL_PATH = os.path.join(BASE_DIR, "recommender_model.keras")
+MOVIES_PATH = os.path.join(BASE_DIR, "movies.csv")
+ENCODINGS_PATH = os.path.join(BASE_DIR, "encodings.pkl")
 
 @st.cache_resource
 def load_model():
-    return tf.keras.models.load_model("recommender_model.keras")
+    try:
+        return tf.keras.models.load_model(MODEL_PATH)
+    except Exception as e:
+        st.error(f"Failed to load model: {e}")
+        st.stop()
 
 @st.cache_data
 def load_assets():
-    df_movies = pd.read_csv("movies.csv")
-    user_map, movie_map = joblib.load("encodings.pkl")
+    try:
+        df_movies = pd.read_csv(MOVIES_PATH)
+    except FileNotFoundError:
+        st.error("movies.csv not found.")
+        st.stop()
+    try:
+        user_map, movie_map = joblib.load(ENCODINGS_PATH)
+    except FileNotFoundError:
+        st.error("encodings.pkl not found.")
+        st.stop()
     return df_movies, user_map, movie_map
 
 model = load_model()
 movies_df, user2idx, movie2idx = load_assets()
 reverse_movie_map = {v: k for k, v in movie2idx.items()}
 
-st.title("TensorFlow Movie Recommender")
+st.title("🎬 TensorFlow Movie Recommender")
 st.write("Select some movies you've liked to get recommendations:")
 
 movie_titles = movies_df.set_index("movieId")["title"].to_dict()
@@ -28,14 +46,19 @@ selected_titles = st.multiselect("Liked movies", sorted(movie_choices))
 
 user_ratings = {}
 for title in selected_titles:
-    movie_id = [k for k, v in movie_titles.items() if v == title][0]
-    user_ratings[movie_id] = 5.0
+    movie_id = next((k for k, v in movie_titles.items() if v == title), None)
+    if movie_id:
+        user_ratings[movie_id] = 5.0
 
 if st.button("Get Recommendations"):
     if not user_ratings:
         st.warning("Please select at least one movie.")
     else:
         liked_indices = [movie2idx[m] for m in user_ratings if m in movie2idx]
+        if not liked_indices:
+            st.error("No valid movie encodings found.")
+            st.stop()
+
         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)
@@ -44,12 +67,12 @@ if st.button("Get Recommendations"):
 
         recommended = []
         for idx in top_indices:
-            mid = reverse_movie_map[idx]
+            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
 
-        st.subheader("Top Recommendations")
+        st.subheader("🎯 Top Recommendations")
         for title, score in recommended:
             st.write(f"{title} — Score: {score:.3f}")