add main function

app.py

@@ -64,49 +64,54 @@ def generate_adversial_image(img, epsilon):
     return undo_preprocess(processed_img), undo_preprocess(adv_img), preds, adv_preds
 
 
+def main():
 
-with gr.Blocks() as demo:
-    
-    gr.Markdown('''## Generate Adversarial Image with Fast Gradient Sign Method
-                
-                Given an input image and a neural network, the adversarial image can be generated from 
-                
-                <code>adv_img = input_img + epsilon*input_img.grad</code>
-                
-                ''')
-                                             
-    with gr.Box():
-        input_image = gr.Image(type="pil", label="Input Image")
-        example_images = gr.Dataset(components=[input_image],
-                                    samples=[['coral.jpg'], ['goldfish.jpg'], ['otter.jpg'], ['panda.jpg']])
+    with gr.Blocks() as demo:
         
-        with gr.Row():
-            epsilon = gr.Slider(minimum=0, maximum=0.5, value=0.02, step=0.01, label="epsilon")
-            btn = gr.Button("Generate Adversarial Image")
-    
-    gr.Markdown('''### Original Image''')
-    with gr.Box():
-        with gr.Row():
-            img_before = gr.Image(label="Original Image")
-            label_before = gr.Label(label="Original Prediction")
-    
-    gr.Markdown('''### Adversarial Image''')
-    with gr.Box():
-        with gr.Row():
-            img_after = gr.Image(label="Adversarial Image")
-            label_after = gr.Label(label="New Prediction")
+        gr.Markdown('''## Generate Adversarial Image with Fast Gradient Sign Method
+                    
+                    Given an input image and a neural network, the adversarial image can be generated from 
+                    
+                    <code>adv_img = input_img + epsilon*input_img.grad</code>
+                    
+                    ''')
+                                                 
+        with gr.Box():
+            input_image = gr.Image(type="pil", label="Input Image")
+            example_images = gr.Dataset(components=[input_image],
+                                        samples=[['coral.jpg'], ['goldfish.jpg'], ['otter.jpg'], ['panda.jpg']])
+            
+            with gr.Row():
+                epsilon = gr.Slider(minimum=0, maximum=0.5, value=0.02, step=0.01, label="epsilon")
+                btn = gr.Button("Generate Adversarial Image")
         
-    gr.Markdown('''The prediction is done by ResNet18. Example images are from [Unsplash](https://unsplash.com).''')
-    
-    
-    # events
-    btn.click(fn=generate_adversial_image, 
-              inputs=[input_image, epsilon], 
-              outputs=[img_before, img_after, label_before, label_after])
-    
-    example_images.click(fn=set_example_image,
-                             inputs=example_images,
-                             outputs=example_images.components)
+        gr.Markdown('''### Original Image''')
+        with gr.Box():
+            with gr.Row():
+                img_before = gr.Image(label="Original Image")
+                label_before = gr.Label(label="Original Prediction")
+        
+        gr.Markdown('''### Adversarial Image''')
+        with gr.Box():
+            with gr.Row():
+                img_after = gr.Image(label="Adversarial Image")
+                label_after = gr.Label(label="New Prediction")
+            
+        gr.Markdown('''The prediction is done by ResNet18. Example images are from [Unsplash](https://unsplash.com).''')
+        
+        
+        # events
+        btn.click(fn=generate_adversial_image, 
+                  inputs=[input_image, epsilon], 
+                  outputs=[img_before, img_after, label_before, label_after])
+        
+        example_images.click(fn=set_example_image,
+                                 inputs=example_images,
+                                 outputs=example_images.components)
+
+    demo.launch()
+
 
 
-demo.launch()
+if __name__ == '__main__':
+    main()