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requirements.txt

@@ -1,2 +1,2 @@
 
-https://gradio-main-build.s3.amazonaws.com/2883a12c1fe643ff0b6c9077786548529f7a9abb/gradio-3.36.0-py3-none-any.whl
+https://gradio-main-build.s3.amazonaws.com/b4173886bab24d47bef9936a96801ce6ac6aba0e/gradio-3.36.0-py3-none-any.whl

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: annotatedimage_component"]}, {"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 pathlib\n", "from PIL import Image\n", "import numpy as np\n", "import urllib.request\n", "\n", "\n", "source_dir = pathlib.Path(__file__).parent\n", "\n", "urllib.request.urlretrieve(\n", "  'https://gradio-builds.s3.amazonaws.com/demo-files/base.png',\n", "   str(source_dir / \"base.png\")\n", ")\n", "urllib.request.urlretrieve(\n", "    \"https://gradio-builds.s3.amazonaws.com/demo-files/buildings.png\",\n", "    str(source_dir / \"buildings.png\")\n", ")\n", "\n", "base_image = Image.open(str(source_dir / \"base.png\"))\n", "building_image = Image.open(str(source_dir / \"buildings.png\"))\n", "\n", "# Create segmentation mask\n", "building_image = np.asarray(building_image)[:, :, -1] > 0\n", "\n", "css = \"footer {display: none !important;} .gradio-container {min-height: 0px !important;}\"\n", "\n", "with gr.Blocks(css=css) as demo:\n", "    gr.AnnotatedImage(\n", "        value=(base_image, [(building_image, \"buildings\")]),\n", "        height=500,\n", "    )\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: annotatedimage_component"]}, {"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 pathlib\n", "from PIL import Image\n", "import numpy as np\n", "import urllib.request\n", "\n", "\n", "source_dir = pathlib.Path(__file__).parent\n", "\n", "urllib.request.urlretrieve(\n", "  'https://gradio-builds.s3.amazonaws.com/demo-files/base.png',\n", "   str(source_dir / \"base.png\")\n", ")\n", "urllib.request.urlretrieve(\n", "    \"https://gradio-builds.s3.amazonaws.com/demo-files/buildings.png\",\n", "    str(source_dir / \"buildings.png\")\n", ")\n", "\n", "base_image = Image.open(str(source_dir / \"base.png\"))\n", "building_image = Image.open(str(source_dir / \"buildings.png\"))\n", "\n", "# Create segmentation mask\n", "building_image = np.asarray(building_image)[:, :, -1] > 0\n", "\n", "with gr.Blocks() as demo:\n", "    gr.AnnotatedImage(\n", "        value=(base_image, [(building_image, \"buildings\")]),\n", "        height=500,\n", "    )\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -22,9 +22,7 @@ building_image = Image.open(str(source_dir / "buildings.png"))
 # Create segmentation mask
 building_image = np.asarray(building_image)[:, :, -1] > 0
 
-css = "footer {display: none !important;} .gradio-container {min-height: 0px !important;}"
-
-with gr.Blocks(css=css) as demo:
+with gr.Blocks() as demo:
     gr.AnnotatedImage(
         value=(base_image, [(building_image, "buildings")]),
         height=500,