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{"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} |