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import gradio as gr
import time
def sleep(im):
time.sleep(5)
return [im["background"], im["layers"][0], im["layers"][1], im["composite"]]
def predict(im):
return im["composite"]
with gr.Blocks() as demo:
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")
if __name__ == "__main__":
demo.launch()
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