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README.md

@@ -1,12 +1,11 @@
+
 ---
-title: Annotatedimage Component Main
-emoji: 📊
-colorFrom: green
-colorTo: blue
+title: annotatedimage_component_main 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
 sdk_version: 3.35.2
-app_file: app.py
+app_file: run.py
 pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

@@ -0,0 +1,2 @@
+
+https://gradio-main-build.s3.amazonaws.com/c7081e5747b941a3dc0018b3216d62ac41e17dd4/gradio-3.35.2-py3-none-any.whl

run.ipynb

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+{"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}

run.py

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+import gradio as gr
+import pathlib
+from PIL import Image
+import numpy as np
+import urllib.request
+
+
+source_dir = pathlib.Path(__file__).parent
+
+urllib.request.urlretrieve(
+  'https://gradio-builds.s3.amazonaws.com/demo-files/base.png',
+   str(source_dir / "base.png")
+)
+urllib.request.urlretrieve(
+    "https://gradio-builds.s3.amazonaws.com/demo-files/buildings.png",
+    str(source_dir / "buildings.png")
+)
+
+base_image = Image.open(str(source_dir / "base.png"))
+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:
+    gr.AnnotatedImage(
+        value=(base_image, [(building_image, "buildings")]),
+        height=500,
+    )
+
+demo.launch()