Upload folder using huggingface_hub

README.md

@@ -1,12 +1,12 @@
+
 ---
-title: Image Classifier Interface Load 3-x
-emoji: 🌖
-colorFrom: red
-colorTo: yellow
+title: image_classifier_interface_load_3-x 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
-sdk_version: 4.3.0
-app_file: app.py
+sdk_version: 3.50.1
+app_file: run.py
 pinned: false
+hf_oauth: true
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

run.ipynb

@@ -0,0 +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}

run.py

@@ -0,0 +1,32 @@
+import gradio as gr
+import pathlib
+
+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"],
+    ["label", "text"],
+    examples=[
+        [str(current_dir / "cheetah1.jpeg"), None],
+        [str(current_dir / "cheetah1.jpg"), "cheetah"],
+        [str(current_dir / "lion.jpg"), "lion"],
+    ],
+    cache_examples=False,
+)
+demo = gr.TabbedInterface([using_img_classifier_as_function, img_classifier])
+
+if __name__ == "__main__":
+    demo.launch()