Update app.py

app.py

@@ -201,12 +201,6 @@ def load_image(img_path, preprocess=True, height=320, width=320):
         x = np.expand_dims(x, axis=0)
     return x
 
-def rename_layers(model):
-    for layer in model.layers:
-        if '/' in layer.name:
-            layer._name = layer.name.replace('/', '_')
-    return model
-
 def grad_cam(input_model, img_array, cls, layer_name):
     grad_model = tf.keras.models.Model(
         [input_model.inputs],
@@ -248,16 +242,9 @@ def compute_gradcam(model_gradcam, img_path, layer_name='bn'):
     #           loss='sparse_categorical_crossentropy')
     # model.load_weights('./pretrained_model.h5')
     # Load the original model
-    original_model = keras.models.load_model('./gradcam_model.h5')
-    
-    # Rename the layers
-    modified_model = rename_layers(original_model)
-    
-    # Save the modified model
-    modified_model.save('./modified_gradcam_model.h5')
-    
+
     # Now use this modified model in your application
-    model_gradcam = keras.models.load_model('./modified_gradcam_model.h5')
+    model_gradcam = keras.models.load_model('./model_renamed.h5')
     
     preprocessed_input = load_image(img_path)
     predictions = model_gradcam.predict(preprocessed_input)
@@ -509,7 +496,7 @@ if uploaded_file is not None:
     with col3:
         if st.button('Generate Grad-CAM'):
             st.write("Loading model...")
-            model_gradcam = keras.models.load_model('./modified_gradcam_model.h5')
+            model_gradcam = keras.models.load_model('./model_renamed.h5')
             # Compute and show Grad-CAM
             st.write("Generating Grad-CAM visualizations")
             try: