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requirements.txt

@@ -1,2 +1,2 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@72661e3391a432ccd77fb3100e69fbdf95093931#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/72661e3391a432ccd77fb3100e69fbdf95093931/gradio-4.23.0-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@ee804b2d567b4381ef90b1f035d42fe6bb2506bc#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/ee804b2d567b4381ef90b1f035d42fe6bb2506bc/gradio-4.23.0-py3-none-any.whl

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: cancel_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "\n", "\n", "def fake_diffusion(steps):\n", "    for i in range(steps):\n", "        print(f\"Current step: {i}\")\n", "        time.sleep(0.5)\n", "        yield str(i)\n", "\n", "\n", "def long_prediction(*args, **kwargs):\n", "    time.sleep(10)\n", "    return 42\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            n = gr.Slider(1, 10, value=9, step=1, label=\"Number Steps\")\n", "            run = gr.Button(value=\"Start Iterating\")\n", "            output = gr.Textbox(label=\"Iterative Output\")\n", "            stop = gr.Button(value=\"Stop Iterating\")\n", "        with gr.Column():\n", "            textbox = gr.Textbox(label=\"Prompt\")\n", "            prediction = gr.Number(label=\"Expensive Calculation\")\n", "            run_pred = gr.Button(value=\"Run Expensive Calculation\")\n", "        with gr.Column():\n", "            cancel_on_change = gr.Textbox(label=\"Cancel Iteration and Expensive Calculation on Change\")\n", "            cancel_on_submit = gr.Textbox(label=\"Cancel Iteration and Expensive Calculation on Submit\")\n", "            echo = gr.Textbox(label=\"Echo\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            image = gr.Image(sources=[\"webcam\"], label=\"Cancel on clear\", interactive=True)\n", "        with gr.Column():\n", "            video = gr.Video(sources=[\"webcam\"], label=\"Cancel on start recording\", interactive=True)\n", "\n", "    click_event = run.click(fake_diffusion, n, output)\n", "    stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])\n", "    pred_event = run_pred.click(fn=long_prediction, inputs=[textbox], outputs=prediction)\n", "\n", "    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])\n", "    cancel_on_submit.submit(lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event])\n", "    image.clear(None, None, None, cancels=[click_event, pred_event])\n", "    video.start_recording(None, None, None, cancels=[click_event, pred_event])\n", "\n", "    demo.queue(max_size=20)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: cancel_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "import atexit\n", "import pathlib\n", "\n", "log_file = (pathlib.Path(__file__).parent / \"cancel_events_output_log.txt\").resolve()\n", "\n", "def fake_diffusion(steps):\n", "    log_file.write_text(\"\")\n", "    for i in range(steps):\n", "        print(f\"Current step: {i}\")\n", "        with log_file.open(\"a\") as f:\n", "            f.write(f\"Current step: {i}\\n\")\n", "        time.sleep(0.2)\n", "        yield str(i)\n", "\n", "\n", "def long_prediction(*args, **kwargs):\n", "    time.sleep(10)\n", "    return 42\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            n = gr.Slider(1, 10, value=9, step=1, label=\"Number Steps\")\n", "            run = gr.Button(value=\"Start Iterating\")\n", "            output = gr.Textbox(label=\"Iterative Output\")\n", "            stop = gr.Button(value=\"Stop Iterating\")\n", "        with gr.Column():\n", "            textbox = gr.Textbox(label=\"Prompt\")\n", "            prediction = gr.Number(label=\"Expensive Calculation\")\n", "            run_pred = gr.Button(value=\"Run Expensive Calculation\")\n", "        with gr.Column():\n", "            cancel_on_change = gr.Textbox(label=\"Cancel Iteration and Expensive Calculation on Change\")\n", "            cancel_on_submit = gr.Textbox(label=\"Cancel Iteration and Expensive Calculation on Submit\")\n", "            echo = gr.Textbox(label=\"Echo\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            image = gr.Image(sources=[\"webcam\"], label=\"Cancel on clear\", interactive=True)\n", "        with gr.Column():\n", "            video = gr.Video(sources=[\"webcam\"], label=\"Cancel on start recording\", interactive=True)\n", "\n", "    click_event = run.click(fake_diffusion, n, output)\n", "    stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])\n", "    pred_event = run_pred.click(fn=long_prediction, inputs=[textbox], outputs=prediction)\n", "\n", "    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])\n", "    cancel_on_submit.submit(lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event])\n", "    image.clear(None, None, None, cancels=[click_event, pred_event])\n", "    video.start_recording(None, None, None, cancels=[click_event, pred_event])\n", "\n", "    demo.queue(max_size=20)\n", "    atexit.register(lambda: log_file.unlink())\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -1,11 +1,17 @@
 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}")
-        time.sleep(0.5)
+        with log_file.open("a") as f:
+            f.write(f"Current step: {i}\n")
+        time.sleep(0.2)
         yield str(i)
 
 
@@ -45,6 +51,7 @@ with gr.Blocks() as demo:
     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()