3

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+mport gradio as gr
 import time
 
 
@@ -7,25 +7,40 @@ def sleep(im):
     return [im["background"], im["layers"][0], im["layers"][1], im["composite"]]
 
 
+support_im_masks = [None for _ in range(1000)]
 def predict(im):
-    return im["composite"]
-
+  for i in range(len(support_im_masks)):
+    if support_im_masks[i] is None:
+      break
+    support_im_mask = support_im_masks[i]
+    gr.Button(support_im_mask)
+    pass
 
 with gr.Blocks() as demo:
+    b = gr.Button("Add Textbox")
+    b2 = gr.Button("Generate Masks")
+    b2.click(predict)
+    print('hi')
+  
+    num = gr.State(0)
+    
+    b.click(lambda x:x+1, num, num)
+
     with gr.Row():
-        im = gr.ImageEditor(
-            type="numpy",
-            crop_size="1:1",
-        )
-        im_preview = gr.Image()
-    n_upload = gr.Number(0, label="Number of upload events", step=1)
-    n_change = gr.Number(0, label="Number of change events", step=1)
-    n_input = gr.Number(0, label="Number of input events", step=1)
-
-    im.upload(lambda x: x + 1, outputs=n_upload, inputs=n_upload)
-    im.change(lambda x: x + 1, outputs=n_change, inputs=n_change)
-    im.input(lambda x: x + 1, outputs=n_input, inputs=n_input)
-    im.change(predict, outputs=im_preview, inputs=im, show_progress="hidden")
+      query_im = gr.Image(label='query image')
+      
+      @gr.render(inputs=num)
+      def show_support_imgs(n):
+          with gr.Column():
+              for i in range(n):
+                  support_im = gr.ImageEditor(
+                      label="support image {}".format(i),
+                      type="numpy",
+                      crop_size="1:1",
+                  )
+                  support_im_masks[i] = support_im
+
 
 if __name__ == "__main__":
     demo.launch()
+