import time
import gradio as gr


def fake_diffusion(steps):
    for i in range(steps):
        print(f"Current step: {i}")
        time.sleep(0.5)
        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)

if __name__ == "__main__":
    demo.launch()