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requirements.txt

@@ -1,2 +1,2 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@de997e67c9a7feb9e2eccebf92969366dbd67eba#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/de997e67c9a7feb9e2eccebf92969366dbd67eba/gradio-4.39.0-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@9b42ba8f1006c05d60a62450d3036ce0d6784f86#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/9b42ba8f1006c05d60a62450d3036ce0d6784f86/gradio-4.39.0-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", "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}
+{"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", "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()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -4,7 +4,6 @@ from PIL import Image
 import numpy as np
 import urllib.request
 
-
 source_dir = pathlib.Path(__file__).parent
 
 urllib.request.urlretrieve(
@@ -28,4 +27,4 @@ with gr.Blocks() as demo:
         height=500,
     )
 
-demo.launch()
+demo.launch()