import gradio as gr
import time


def sleep(im):
    time.sleep(5)
    return [im["background"], im["layers"][0], im["layers"][1], im["composite"]]


support_im_masks = [None for _ in range(1000)]
def predict(im):
  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():
      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()