Update app.py

app.py

@@ -8,9 +8,9 @@ from scipy.cluster.vq import kmeans
 
 # ... (previous functions remain the same)
 
-def infer(image_input, mask_input, dst_height: str, dst_width: str):
-    image_in = image_input
-    mask_in = mask_input
+def infer(image_input, dst_height: str, dst_width: str):
+    image_in = image_input["image"]
+    mask_in = image_input["mask"]
     torch_img = K.utils.image_to_tensor(image_in).float() / 255.0
     
     centroids = get_coordinates_from_mask(mask_in)
@@ -55,14 +55,14 @@ description = """In this space you can warp an image using perspective transform
 
 example_mask = np.zeros((327, 600, 4), dtype=np.uint8)
 example_mask[:, :, 3] = 255
+example_image_dict = {"image": "bruce.png", "mask": example_mask}
 
 with gr.Blocks() as demo:
     gr.Markdown("# Homography Warping")
     gr.Markdown(description)
     
     with gr.Row():
-        image_input = gr.Image(label="Input Image", type="numpy")
-        mask_input = gr.Image(label="Mask", source="canvas", shape=(600, 327), tool="sketch", interactive=True)
+        image_input = gr.Image(type="numpy", label="Input Image", tool="sketch")
         output_plot = gr.Plot(label="Output")
     
     with gr.Row():
@@ -72,13 +72,13 @@ with gr.Blocks() as demo:
     submit_button = gr.Button("Submit")
     submit_button.click(
         fn=infer,
-        inputs=[image_input, mask_input, dst_height, dst_width],
+        inputs=[image_input, dst_height, dst_width],
         outputs=output_plot
     )
     
     gr.Examples(
-        examples=[["bruce.png", example_mask, "64", "128"]],
-        inputs=[image_input, mask_input, dst_height, dst_width],
+        examples=[[example_image_dict, "64", "128"]],
+        inputs=[image_input, dst_height, dst_width],
         outputs=output_plot,
         fn=infer,
         cache_examples=True