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import time |
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import gradio as gr |
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import atexit |
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import pathlib |
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log_file = pathlib.Path(__file__).parent / "cancel_events_output_log.txt" |
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def fake_diffusion(steps): |
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log_file.write_text("") |
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for i in range(steps): |
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print(f"Current step: {i}") |
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with log_file.open("a") as f: |
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f.write(f"Current step: {i}\n") |
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time.sleep(0.2) |
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yield str(i) |
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def long_prediction(*args, **kwargs): |
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time.sleep(10) |
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return 42 |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(): |
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n = gr.Slider(1, 10, value=9, step=1, label="Number Steps") |
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run = gr.Button(value="Start Iterating") |
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output = gr.Textbox(label="Iterative Output") |
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stop = gr.Button(value="Stop Iterating") |
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with gr.Column(): |
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textbox = gr.Textbox(label="Prompt") |
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prediction = gr.Number(label="Expensive Calculation") |
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run_pred = gr.Button(value="Run Expensive Calculation") |
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with gr.Column(): |
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cancel_on_change = gr.Textbox( |
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label="Cancel Iteration and Expensive Calculation on Change" |
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) |
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cancel_on_submit = gr.Textbox( |
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label="Cancel Iteration and Expensive Calculation on Submit" |
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) |
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echo = gr.Textbox(label="Echo") |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image( |
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sources=["webcam"], label="Cancel on clear", interactive=True |
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) |
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with gr.Column(): |
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video = gr.Video( |
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sources=["webcam"], label="Cancel on start recording", interactive=True |
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) |
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click_event = run.click(fake_diffusion, n, output) |
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stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event]) |
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pred_event = run_pred.click( |
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fn=long_prediction, inputs=[textbox], outputs=prediction |
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) |
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cancel_on_change.change(None, None, None, cancels=[click_event, pred_event]) |
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cancel_on_submit.submit( |
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lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event] |
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) |
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image.clear(None, None, None, cancels=[click_event, pred_event]) |
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video.start_recording(None, None, None, cancels=[click_event, pred_event]) |
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demo.queue(max_size=20) |
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atexit.register(lambda: log_file.unlink()) |
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if __name__ == "__main__": |
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demo.launch() |
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