gradio/demo/image_editor/run.ipynb

{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_editor"]}, {"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": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_editor/cheetah.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "def sleep(im):\n", "    time.sleep(5)\n", "    return [im[\"background\"], im[\"layers\"][0], im[\"layers\"][1], im[\"composite\"]]\n", "\n", "def predict(im):\n", "    return im[\"composite\"]\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        im = gr.ImageEditor(\n", "            type=\"numpy\",\n", "            crop_size=\"1:1\",\n", "        )\n", "        im_preview = gr.Image()\n", "    n_upload = gr.Number(0, label=\"Number of upload events\", step=1)\n", "    n_change = gr.Number(0, label=\"Number of change events\", step=1)\n", "    n_input = gr.Number(0, label=\"Number of input events\", step=1)\n", "\n", "    im.upload(lambda x: x + 1, outputs=n_upload, inputs=n_upload)\n", "    im.change(lambda x: x + 1, outputs=n_change, inputs=n_change)\n", "    im.input(lambda x: x + 1, outputs=n_input, inputs=n_input)\n", "    im.change(predict, outputs=im_preview, inputs=im, show_progress=\"hidden\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}