Commit 2: Add 50 file(s)

demos/image_editor_events/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_editor_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 gradio as gr\n", "\n", "def predict(im):\n", "    return im[\"composite\"]\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Group():\n", "        with gr.Row():\n", "            im = gr.ImageEditor(\n", "                type=\"numpy\",\n", "                crop_size=\"1:1\",\n", "                elem_id=\"image_editor\",\n", "            )\n", "            im_preview = gr.Image()\n", "    with gr.Group():\n", "        with gr.Row():\n", "\n", "            n_upload = gr.Label(\n", "                0,\n", "                label=\"upload\",\n", "                elem_id=\"upload\",\n", "            )\n", "            n_change = gr.Label(\n", "                0,\n", "                label=\"change\",\n", "                elem_id=\"change\",\n", "            )\n", "            n_input = gr.Label(\n", "                0,\n", "                label=\"input\",\n", "                elem_id=\"input\",\n", "            )\n", "            n_apply = gr.Label(\n", "                0,\n", "                label=\"apply\",\n", "                elem_id=\"apply\",\n", "            )\n", "    clear_btn = gr.Button(\"Clear\", elem_id=\"clear\")\n", "\n", "    im.upload(\n", "        lambda x: int(x) + 1, outputs=n_upload, inputs=n_upload, show_progress=\"hidden\"\n", "    )\n", "    im.change(\n", "        lambda x: int(x) + 1, outputs=n_change, inputs=n_change, show_progress=\"hidden\"\n", "    )\n", "    im.input(\n", "        lambda x: int(x) + 1, outputs=n_input, inputs=n_input, show_progress=\"hidden\"\n", "    )\n", "    im.apply(\n", "        lambda x: int(x) + 1, outputs=n_apply, inputs=n_apply, show_progress=\"hidden\"\n", "    )\n", "    im.change(predict, outputs=im_preview, inputs=im, show_progress=\"hidden\")\n", "    clear_btn.click(\n", "        lambda: None,\n", "        None,\n", "        im,\n", "    )\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: image_editor_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 gradio as gr\n", "import numpy as np\n", "\n", "def predict(im):\n", "    return im[\"composite\"]\n", "\n", "def verify_clear(im):\n", "    return int(not np.any(im['composite'])), im[\"composite\"]\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Group():\n", "        with gr.Row():\n", "            im = gr.ImageEditor(\n", "                type=\"numpy\",\n", "                crop_size=\"1:1\",\n", "                elem_id=\"image_editor\",\n", "            )\n", "            im_preview = gr.Image()\n", "    with gr.Group():\n", "        with gr.Row():\n", "\n", "            n_upload = gr.Label(\n", "                0,\n", "                label=\"upload\",\n", "                elem_id=\"upload\",\n", "            )\n", "            n_change = gr.Label(\n", "                0,\n", "                label=\"change\",\n", "                elem_id=\"change\",\n", "            )\n", "            n_input = gr.Label(\n", "                0,\n", "                label=\"input\",\n", "                elem_id=\"input\",\n", "            )\n", "            n_apply = gr.Label(\n", "                0,\n", "                label=\"apply\",\n", "                elem_id=\"apply\",\n", "            )\n", "            cleared_properly = gr.Number(label=\"cleared properly\")\n", "    clear_btn = gr.Button(\"Clear Button\", elem_id=\"clear\")\n", "\n", "    im.upload(\n", "        lambda x: int(x) + 1, outputs=n_upload, inputs=n_upload, show_progress=\"hidden\"\n", "    )\n", "    im.change(\n", "        lambda x: int(x) + 1, outputs=n_change, inputs=n_change, show_progress=\"hidden\"\n", "    )\n", "    im.input(\n", "        lambda x: int(x) + 1, outputs=n_input, inputs=n_input, show_progress=\"hidden\"\n", "    )\n", "    im.apply(\n", "        lambda x: int(x) + 1, outputs=n_apply, inputs=n_apply, show_progress=\"hidden\"\n", "    )\n", "    im.change(predict, outputs=im_preview, inputs=im, show_progress=\"hidden\")\n", "    clear_btn.click(\n", "        lambda: None,\n", "        None,\n", "        im,\n", "    ).then(verify_clear,\n", "           inputs=im,\n", "           outputs=[cleared_properly, im])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/image_editor_events/run.py

@@ -1,8 +1,12 @@
 import gradio as gr
+import numpy as np
 
 def predict(im):
     return im["composite"]
 
+def verify_clear(im):
+    return int(not np.any(im['composite'])), im["composite"]
+
 with gr.Blocks() as demo:
     with gr.Group():
         with gr.Row():
@@ -35,7 +39,8 @@ with gr.Blocks() as demo:
                 label="apply",
                 elem_id="apply",
             )
-    clear_btn = gr.Button("Clear", elem_id="clear")
+            cleared_properly = gr.Number(label="cleared properly")
+    clear_btn = gr.Button("Clear Button", elem_id="clear")
 
     im.upload(
         lambda x: int(x) + 1, outputs=n_upload, inputs=n_upload, show_progress="hidden"
@@ -54,7 +59,9 @@ with gr.Blocks() as demo:
         lambda: None,
         None,
         im,
-    )
+    ).then(verify_clear,
+           inputs=im,
+           outputs=[cleared_properly, im])
 
 if __name__ == "__main__":
     demo.launch()