Update src/streamlit_app.py

src/streamlit_app.py

@@ -23,14 +23,15 @@ from recommendation_utils import (
 @st.cache_resource(show_spinner=False)
 def download_models_once():
     DOWNLOAD_DIR = "/tmp"
-    
+
     files = {
         "encodings.pkl": "1EzpdpaopfUp-Tfc7YjxPVYQUwnU_BX5-",
-        "recommender_model.keras": "1OwdH3RxlQfAX9UbUB7RwlS13i9uDZuG-",
+        "config.json": "1ZcTrVR0QtS-5EL4amsTR_y9xITDA6zlJ",
+        "model.weights.h5": "1VjjUx_7ulIVM-W1lqH-nDHI7HWfpcMQP",
         "svd_model.pkl": "1fN2biQruVjJHHv2vX1g1hLuMeJoPqyFX",
         "trainset.pkl": "1IDVVAQ57Xvf3HCAikbOSQgAigdHP7Ik7"
     }
-    
+
     def gdrive_download(file_id, destination):
         URL = "https://drive.google.com/uc?export=download"
         session = requests.Session()
@@ -45,19 +46,18 @@ def download_models_once():
             for chunk in response.iter_content(32768):
                 if chunk:
                     f.write(chunk)
-    
+
     for filename, file_id in files.items():
         full_path = os.path.join(DOWNLOAD_DIR, filename)
         if not os.path.exists(full_path):
             with st.spinner(f"Downloading {filename}..."):
                 gdrive_download(file_id, full_path)
 
-
 download_models_once()
 
 @st.cache_resource
 def load_models():
-    nn_model = load_nn_model("/tmp/recommender_model.keras")
+    nn_model = load_nn_model("/tmp/config.json", "/tmp/model.weights.h5")
     svd_model = load_svd_model("/tmp/svd_model.pkl")
     trainset = load_trainset("/tmp/trainset.pkl")
     return nn_model, svd_model, trainset
@@ -553,7 +553,7 @@ else:
         if user_ratings_dict:
             if st.session_state["model_selection"] == "Neural Network":
                 available_movies = movie_df["movieId"].tolist()
-                recommendations = recommend_with_nn(user_ratings, nn_model, encodings, top_n=10)
+                recommendations = recommend_with_nn(user_ratings_dict, nn_model, encodings, top_n=10)
 
             else:
                 ratings_full = pd.DataFrame(all_ratings_data)