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run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classifier_interface_load"]}, {"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_classifier_interface_load/cheetah1.jpeg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interface_load/cheetah1.jpg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interface_load/lion.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pathlib\n", "\n", "current_dir = pathlib.Path(__file__).parent\n", "\n", "images = [str(current_dir / \"cheetah1.jpeg\"), str(current_dir / \"cheetah1.jpg\"), str(current_dir / \"lion.jpg\")]\n", "\n", "\n", "img_classifier = gr.load(\n", "    \"models/google/vit-base-patch16-224\", examples=images, cache_examples=False\n", ")\n", "\n", "\n", "def func(img, text):\n", "    return img_classifier(img), text\n", "\n", "\n", "using_img_classifier_as_function = gr.Interface(\n", "    func,\n", "    [gr.Image(type=\"filepath\"), \"text\"],\n", "    [\"label\", \"text\"],\n", "    examples=[\n", "        [str(current_dir / \"cheetah1.jpeg\"), None],\n", "        [str(current_dir / \"cheetah1.jpg\"), \"cheetah\"],\n", "        [str(current_dir / \"lion.jpg\"), \"lion\"],\n", "    ],\n", "    cache_examples=False,\n", ")\n", "demo = gr.TabbedInterface([using_img_classifier_as_function, img_classifier])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classifier_interface_load"]}, {"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_classifier_interface_load/cheetah1.jpeg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interface_load/cheetah1.jpg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interface_load/lion.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pathlib\n", "\n", "current_dir = pathlib.Path(__file__).parent\n", "\n", "images = [str(current_dir / \"cheetah1.jpeg\"), str(current_dir / \"cheetah1.jpg\"), str(current_dir / \"lion.jpg\")]\n", "\n", "img_classifier = gr.load(\n", "    \"models/google/vit-base-patch16-224\", examples=images, cache_examples=False\n", ")\n", "\n", "def func(img, text):\n", "    return img_classifier(img), text\n", "\n", "using_img_classifier_as_function = gr.Interface(\n", "    func,\n", "    [gr.Image(type=\"filepath\"), \"text\"],\n", "    [\"label\", \"text\"],\n", "    examples=[\n", "        [str(current_dir / \"cheetah1.jpeg\"), None],\n", "        [str(current_dir / \"cheetah1.jpg\"), \"cheetah\"],\n", "        [str(current_dir / \"lion.jpg\"), \"lion\"],\n", "    ],\n", "    cache_examples=False,\n", ")\n", "demo = gr.TabbedInterface([using_img_classifier_as_function, img_classifier])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -5,16 +5,13 @@ current_dir = pathlib.Path(__file__).parent
 
 images = [str(current_dir / "cheetah1.jpeg"), str(current_dir / "cheetah1.jpg"), str(current_dir / "lion.jpg")]
 
-
 img_classifier = gr.load(
     "models/google/vit-base-patch16-224", examples=images, cache_examples=False
 )
 
-
 def func(img, text):
     return img_classifier(img), text
 
-
 using_img_classifier_as_function = gr.Interface(
     func,
     [gr.Image(type="filepath"), "text"],
@@ -29,4 +26,4 @@ using_img_classifier_as_function = gr.Interface(
 demo = gr.TabbedInterface([using_img_classifier_as_function, img_classifier])
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()