Update app.py

app.py

@@ -87,6 +87,8 @@ random_image = PIL.Image.open('examples/TomatoYellowCurlVirus6.JPG').convert('RG
 def plot_attention(image, layer_num):
     """Given an input image, plot the average attention weight given to each image patch by each attention head."""
     input_data = data_transforms(image)
+    print(input_data.shape)
+    print(layer_num)
     with nopdb.capture_call(vision_transformer.blocks[int(layer_num)-1].attn.forward) as attn_call:
         predict_tensor(img_transformed)
     attn = attn_call.locals['attn'][0]
@@ -130,7 +132,7 @@ article_attention = """From the dropdown menu, choose the Encoder layer whose at
 
 classify_interface = gr.Interface(
     fn=predict_disease,
-    inputs=gr.Image(type="pil", label="Image", value="examples/TomatoYellowCurlVirus6.JPG"),
+    inputs=gr.Image(type="pil", label="Image"),
     outputs=[gr.Label(num_top_classes=3, label="Predictions"),
               gr.Number(label="Prediction time (secs)")],
     examples=example_list,
@@ -142,8 +144,9 @@ classify_interface = gr.Interface(
 
 attention_interface = gr.Interface(
     fn=plot_attention,
-    inputs=[gr.Image(type="pil", label="Image", value="examples/TomatoYellowCurlVirus6.JPG"),
-            gr.Dropdown(choices=["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"], label="Attention Layer", value="6")],
+    inputs=[gr.Image(type="pil", label="Image"),
+            gr.Dropdown(choices=["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"], 
+                        value="6", label="Attention Layer")],
     outputs=gr.Gallery(value=attention_maps, label="Attention Maps").style(grid=(3, 4)),
     examples=example_list,
     title=title_attention,