Commit 1: Add 50 file(s)

demos/cancel_events/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", "import atexit\n", "import pathlib\n", "\n", "log_file = pathlib.Path(__file__).parent / \"cancel_events_output_log.txt\"\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", "def long_prediction(*args, **kwargs):\n", "    time.sleep(10)\n", "    return 42\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(\n", "                label=\"Cancel Iteration and Expensive Calculation on Change\"\n", "            )\n", "            cancel_on_submit = gr.Textbox(\n", "                label=\"Cancel Iteration and Expensive Calculation on Submit\"\n", "            )\n", "            echo = gr.Textbox(label=\"Echo\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            image = gr.Image(\n", "                sources=[\"webcam\"], label=\"Cancel on clear\", interactive=True\n", "            )\n", "        with gr.Column():\n", "            video = gr.Video(\n", "                sources=[\"webcam\"], label=\"Cancel on start recording\", interactive=True\n", "            )\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(\n", "        fn=long_prediction, inputs=[textbox], outputs=prediction\n", "    )\n", "\n", "    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])\n", "    cancel_on_submit.submit(\n", "        lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event]\n", "    )\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}
+{"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\"\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", "def long_prediction(*args, **kwargs):\n", "    time.sleep(4)\n", "    return 42, 42\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", "            loading_box = gr.Textbox(label=\"Loading indicator for expensive calculation\")\n", "            loading_box2 = gr.Textbox(label=\"Loading indicator for expensive calculation\")\n", "            prediction = gr.Number(label=\"Expensive Calculation\")\n", "            prediction2 = gr.Number(label=\"Expensive Calculation\")\n", "            run_pred = gr.Button(value=\"Run Expensive Calculation\")\n", "        with gr.Column():\n", "            cancel_on_change = gr.Textbox(\n", "                label=\"Cancel Iteration and Expensive Calculation on Change\"\n", "            )\n", "            cancel_on_submit = gr.Textbox(\n", "                label=\"Cancel Iteration and Expensive Calculation on Submit\"\n", "            )\n", "            echo = gr.Textbox(label=\"Echo\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            image = gr.Image(\n", "                sources=[\"webcam\"], label=\"Cancel on clear\", interactive=True\n", "            )\n", "        with gr.Column():\n", "            video = gr.Video(\n", "                sources=[\"webcam\"], label=\"Cancel on start recording\", interactive=True\n", "            )\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(\n", "        fn=long_prediction, inputs=[textbox], outputs=[prediction, prediction2], show_progress_on=[loading_box, loading_box2]\n", "    )\n", "\n", "    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])\n", "    cancel_on_submit.submit(\n", "        lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event]\n", "    )\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}

demos/cancel_events/run.py

@@ -15,8 +15,8 @@ def fake_diffusion(steps):
         yield str(i)
 
 def long_prediction(*args, **kwargs):
-    time.sleep(10)
-    return 42
+    time.sleep(4)
+    return 42, 42
 
 with gr.Blocks() as demo:
     with gr.Row():
@@ -27,7 +27,10 @@ with gr.Blocks() as demo:
             stop = gr.Button(value="Stop Iterating")
         with gr.Column():
             textbox = gr.Textbox(label="Prompt")
+            loading_box = gr.Textbox(label="Loading indicator for expensive calculation")
+            loading_box2 = gr.Textbox(label="Loading indicator for expensive calculation")
             prediction = gr.Number(label="Expensive Calculation")
+            prediction2 = gr.Number(label="Expensive Calculation")
             run_pred = gr.Button(value="Run Expensive Calculation")
         with gr.Column():
             cancel_on_change = gr.Textbox(
@@ -50,7 +53,7 @@ with gr.Blocks() as demo:
     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
+        fn=long_prediction, inputs=[textbox], outputs=[prediction, prediction2], show_progress_on=[loading_box, loading_box2]
     )
 
     cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])