Update app.py

app.py

@@ -34,6 +34,7 @@ sample_images = [
 ]
 
 with gr.Blocks() as app:
+
     '''
     Select feature interface
     '''
@@ -58,52 +59,59 @@ with gr.Blocks() as app:
             grad_cam_opacity = gr.Slider(0, 1, value=0.4, step=0.1, label="Choose opacity of the gradient")
 
             with gr.Column():
-                grad_cam_btn = gr.Button("Yes, Go Ahead")
+                grad_cam_btn = gr.Button("Yes, Go Ahead", variant='primary')
 
         with gr.Column(visible=False) as grad_cam_output:
             grad_cam_output_gallery = gr.Gallery(value=[], columns=3, label='Output')
             # prediction_title = gr.Label(value='')
 
+
     '''
     Options for Missclassfied images feature
     '''
     with gr.Row(visible=False) as missclassified_col:
         with gr.Row():
             missclassified_img_count = gr.Slider(1, 20, value=5, step=1, label="Choose image count",
-                                                 info="How man missclassified images you want to view?")
-            missclassified_btn = gr.Button("Click to Continue")
+                                                 info="How many missclassified images you want to view?")
+            missclassified_btn = gr.Button("Click to Continue", variant='primary')
     with gr.Row(visible=False) as missclassified_img_output:
         missclassified_img_output_gallery = gr.Gallery(value=[], columns=5, label='Output')
 
+
     '''
     Option for Top prediction classes
     '''
     with gr.Row(visible=True) as top_pred_cls_col:
         with gr.Column():
-            example_images = gr.Gallery(allow_preview=False, label='Select image ', info='',
-                                        value=[img[0] for img in sample_images], columns=3, rows=2,
-                                        object_fit='scale_down')
-
+            example_images = gr.Gallery(allow_preview=False, label='Select image ', info='', value=[img[0] for img in sample_images], columns=3, rows=2, object_fit='scale_down')
+        
         with gr.Column():
             with gr.Row():
                 top_pred_image = gr.Image(shape=(32, 32), label='Upload Image or Select from the gallery')
                 top_class_count = gr.Slider(1, 10, value=5, step=1, label="Number of classes to predict")
-                top_class_btn = gr.Button("Submit")
+                top_class_btn = gr.Button("Submit", variant='primary')
+                tc_clear_btn = gr.ClearButton()
 
         with gr.Row(visible=True) as top_class_output:
-            # top_class_output_img = gr.Image().style(width=256, height=256)
+            #top_class_output_img = gr.Image().style(width=256, height=256)
             top_class_output_labels = gr.Label(num_top_classes=top_class_count.value, label='Output')
-
-
+    
+            
+    def clear_data():
+        return {
+            top_pred_image: None,
+            top_class_output_labels: None
+        }
+    
+    tc_clear_btn.click(clear_data, None, [top_pred_image, top_class_output_labels])
+    
     def on_select(evt: gr.SelectData):
         return {
             top_pred_image: sample_images[evt.index][0]
         }
-
-
+    
     example_images.select(on_select, None, top_pred_image)
-
-
+        
     def top_class_img_upload(input_img, top_class_count):
         if input_img is not None:
             transform = transforms.ToTensor()
@@ -116,24 +124,25 @@ with gr.Blocks() as app:
             o = softmax(outputs.flatten())
             confidences = {get_dataset_labels()[i]: float(o[i]) for i in range(10)}
             top_class_output_labels.num_top_classes = top_class_count
+            #tc_clear_btn.add([top_pred_image, top_class_output_labels])
+        
             return {
                 top_class_output: gr.update(visible=True),
-                # top_class_output_img: org_img,
+                #top_class_output_img: org_img,
                 top_class_output_labels: confidences
             }
-
-
+        
     top_class_btn.click(
         top_class_img_upload,
         [top_pred_image, top_class_count],
         [top_class_output, top_class_output_labels]
     )
-
+    
+    
+    
     '''
     Missclassified Images feature
     '''
-
-
     def show_missclassified_images(img_count):
         imgs = []
         for i in range(img_count):
@@ -157,13 +166,13 @@ with gr.Blocks() as app:
         [missclassified_img_output_gallery, missclassified_img_output]
     )
 
+
     '''
     GradCAM Feature
     '''
-
-
+    
     def grad_cam_submit(img_count, layer_idx, grad_opacity):
-
+        
         target_layers = [model.get_layer(-1 * (layer_idx + 1))]
         cam = GradCAM(model=model, target_layers=target_layers)
 
@@ -204,7 +213,6 @@ with gr.Blocks() as app:
     Select Feature to showcase
     '''
 
-
     def select_feature(feature):
         if feature == 0:
             return {
@@ -238,8 +246,8 @@ with gr.Blocks() as app:
 
     radio_btn.change(select_feature,
                      [radio_btn],
-                     [grad_cam_col, grad_cam_output, missclassified_col, missclassified_img_output, top_pred_cls_col,
-                      top_class_output])
+                     [grad_cam_col, grad_cam_output, missclassified_col, missclassified_img_output, top_pred_cls_col, top_class_output])
+
 
 '''
 Launch the app