import time
import gradio as gr

def fake_diffusion(steps):
    for i in range(steps):
        time.sleep(1)
        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()
            output = gr.Textbox(label="Iterative Output")
            stop = gr.Button(value="Stop Iterating")
        with gr.Column():
            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")

    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=None, outputs=prediction)

    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])


demo.queue(concurrency_count=1, max_size=20).launch()

import gradio as gr
import time

def iteration(steps):
    for i in range(steps):
       time.sleep(0.5)
       yield i

gr.Interface(iteration,
             inputs=gr.Slider(minimum=1, maximum=10, step=1, value=5),
             outputs=gr.Number()).queue().launch()