Update app.py

app.py

@@ -10,42 +10,29 @@ from streamlit_option_menu import option_menu
 
 st.set_page_config(page_title="Facial Analysis", page_icon="👤", layout="wide")
 
-# --- Path Setup & Model Loading ---
 try:
-    # Use a simpler path for deployment
     from src.cnnClassifier.pipeline.prediction import PredictionPipeline
 except ImportError:
-    # Fallback for local development
     src_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'src'))
     if src_path not in sys.path: sys.path.append(src_path)
     from cnnClassifier.pipeline.prediction import PredictionPipeline
 
-# --- TF Config (for MTCNN in Image/Video modes) ---
-try:
-    gpus = tf.config.list_physical_devices('GPU')
-    if gpus:
-        for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True)
-except Exception: pass
-
 @st.cache_resource
 def load_pipeline():
-    # Use the model from the Hub, not a local folder
-    return PredictionPipeline(repo_id="ALYYAN/Facial-Age-Det")
-
+    return PredictionPipeline()
 pipeline = load_pipeline()
 
 if 'webcam_running' not in st.session_state: st.session_state.webcam_running = False
 def start_webcam(): st.session_state.webcam_running = True
 def stop_webcam(): st.session_state.webcam_running = False
 
-# --- UI ---
 with st.sidebar:
     st.markdown("## ⚙️ Controls")
     app_mode = option_menu(None, ["Image", "Video", "Live Feed"], 
         icons=['image', 'film', 'camera-video'], menu_icon="cast", default_index=0)
 
 if not pipeline:
-    st.error("AI Pipeline failed to load. Please check the terminal for errors.")
+    st.error("AI Pipeline failed to load. Check terminal logs.")
 else:
     st.title("👤 Facial Demographics Analysis")
     st.header(f"Mode: {app_mode}")
@@ -95,7 +82,7 @@ else:
                     st.download_button("Download Processed Video", f, "output.mp4", "video/mp4", use_container_width=True)
 
     elif app_mode == "Live Feed":
-        st.info("Live feed uses a lightweight face detector for higher FPS.")
+        st.info("Live feed uses a lightweight face detector for performance.")
         col1, col2 = st.columns(2)
         with col1: st.button("Start Feed", on_click=start_webcam, use_container_width=True, type="primary")
         with col2: st.button("Stop Feed", on_click=stop_webcam, use_container_width=True)
@@ -108,14 +95,23 @@ else:
             while st.session_state.webcam_running:
                 start_time = time.time()
                 ret, frame = cap.read()
-                if not ret: break
+                if not ret: 
+                    st.warning("Could not read frame from webcam. Stopping.")
+                    stop_webcam()
+                    break
                 frame = cv2.flip(frame, 1)
-                # --- THE FIX: Call the LQ method ---
-                annotated_frame, _ = pipeline.predict_lq(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+                annotated_frame, _ = pipeline.predict(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
                 FRAME_WINDOW.image(annotated_frame, channels="RGB")
                 fps = 1.0 / (time.time() - start_time) if (time.time() - start_time) > 0 else 0
                 fps_display.markdown(f"<p style='text-align: center;'><b>FPS: {fps:.2f}</b></p>", unsafe_allow_html=True)
+            
             cap.release()
-            cv2.destroyAllWindows()
-            st.session_state.webcam_running = False
-            st.rerun()
+            # --- THE FIX ---
+            # cv2.destroyAllWindows() # This line is removed
+            # --- END FIX ---
+            
+            if st.session_state.webcam_running:
+                st.session_state.webcam_running = False
+                st.rerun()
+
+