Update app.py

app.py

@@ -74,7 +74,25 @@ model = load_model()
 #     image = cv2.imdecode(file_bytes, 1)
 
 #     st.image(image, caption='Uploaded Image.', use_column_width=True)
-    
+
+
+@st.cache_resource
+def load_gradcam_model():
+    model = keras.models.load_model('./model_renamed.h5', compile=False)
+    model.compile(
+        loss={
+            "bbox": "mse",
+            "class": "sparse_categorical_crossentropy"
+        },
+        optimizer=tf.keras.optimizers.Adam(),
+        metrics={
+            "bbox": ['mse'],
+            "class": ['accuracy']
+        }
+    )
+    return model
+
+model_gradcam = load_gradcam_model()
 
 # Utility Functions
 
@@ -244,7 +262,7 @@ def compute_gradcam(model_gradcam, img_path, layer_name='bn'):
     # Load the original model
 
     # Now use this modified model in your application
-    model_gradcam = keras.models.load_model('./model_renamed.h5')
+    model_gradcam = load_gradcam_model()
     
     preprocessed_input = load_image(img_path)
     predictions = model_gradcam.predict(preprocessed_input)
@@ -496,7 +514,7 @@ if uploaded_file is not None:
     with col3:
         if st.button('Generate Grad-CAM'):
             st.write("Loading model...")
-            model_gradcam = keras.models.load_model('./model_renamed.h5')
+            model_gradcam = load_gradcam_model()
             # Compute and show Grad-CAM
             st.write("Generating Grad-CAM visualizations")
             try: