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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 4.32.1
+sdk_version: 4.36.0
 app_file: run.py
 pinned: false
 hf_oauth: true

requirements.txt

@@ -1,2 +1,2 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@bc1eff12c1e9ca93bc44348913f06466fb888f9a#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/bc1eff12c1e9ca93bc44348913f06466fb888f9a/gradio-4.32.1-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@a2cd8a9838864596318a686510f15e7998a27880#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/a2cd8a9838864596318a686510f15e7998a27880/gradio-4.36.0-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", "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}
+{"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", "\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(\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}

run.py

@@ -3,7 +3,8 @@ import gradio as gr
 import atexit
 import pathlib
 
-log_file = (pathlib.Path(__file__).parent / "cancel_events_output_log.txt").resolve()
+log_file = pathlib.Path(__file__).parent / "cancel_events_output_log.txt"
+
 
 def fake_diffusion(steps):
     log_file.write_text("")
@@ -32,21 +33,33 @@ with gr.Blocks() as demo:
             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")
+            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)
+            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)
+            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)
+    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])
+    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])