Update app.py

app.py

@@ -1,67 +1,12 @@
 import time
 import gradio as gr
-import atexit
-import pathlib
 
-log_file = pathlib.Path(__file__).parent / "cancel_events_output_log.txt"
+def slow_echo(message, history):
+    for i in range(len(message)):
+        time.sleep(0.05)
+        yield "You typed: " + message[: i + 1]
 
-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())
+demo = gr.ChatInterface(slow_echo, type="messages")
 
 if __name__ == "__main__":
     demo.launch()