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