Upload folder using huggingface_hub

requirements.txt

@@ -1,5 +1,5 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@327015b7dca90f17f174baee9f3c966a48fe4775#subdirectory=client/python
-https://gradio-pypi-previews.s3.amazonaws.com/327015b7dca90f17f174baee9f3c966a48fe4775/gradio-5.47.2-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@070463cf85f75f1a327a2c0daa6eea81467ad749#subdirectory=client/python
+https://gradio-pypi-previews.s3.amazonaws.com/070463cf85f75f1a327a2c0daa6eea81467ad749/gradio-5.47.2-py3-none-any.whl
 numpy
 tensorflow
 requests

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classifier"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy tensorflow requests "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/imagenet_labels.json https://github.com/gradio-app/gradio/raw/main/demo/image_classifier/files/imagenet_labels.json\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_classifier/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_classifier/images/lion.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import os\n", "import requests\n", "import tensorflow as tf  # type: ignore\n", "\n", "import gradio as gr\n", "\n", "inception_net = tf.keras.applications.MobileNetV2()  # load the model\n", "\n", "# Download human-readable labels for ImageNet.\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "def classify_image(inp):\n", "    inp = inp.reshape((-1, 224, 224, 3))\n", "    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)\n", "    prediction = inception_net.predict(inp).flatten()\n", "    return {labels[i]: float(prediction[i]) for i in range(1000)}\n", "\n", "image = gr.Image()\n", "label = gr.Label(num_top_classes=3)\n", "\n", "demo = gr.Interface(\n", "    fn=classify_image,\n", "    inputs=image,\n", "    outputs=label,\n", "    examples=[\n", "        os.path.join(os.path.abspath(''), \"images/cheetah1.jpg\"),\n", "        os.path.join(os.path.abspath(''), \"images/lion.jpg\")\n", "        ]\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n", "\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classifier"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy tensorflow requests "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/imagenet_labels.json https://github.com/gradio-app/gradio/raw/main/demo/image_classifier/files/imagenet_labels.json"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import requests\n", "import tensorflow as tf  # type: ignore\n", "\n", "import gradio as gr\n", "# get_image() returns the file path to sample images included with Gradio\n", "from gradio.media import get_image\n", "\n", "inception_net = tf.keras.applications.MobileNetV2()  # load the model\n", "\n", "# Download human-readable labels for ImageNet.\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "def classify_image(inp):\n", "    inp = inp.reshape((-1, 224, 224, 3))\n", "    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)\n", "    prediction = inception_net.predict(inp).flatten()\n", "    return {labels[i]: float(prediction[i]) for i in range(1000)}\n", "\n", "image = gr.Image()\n", "label = gr.Label(num_top_classes=3)\n", "\n", "demo = gr.Interface(\n", "    fn=classify_image,\n", "    inputs=image,\n", "    outputs=label,\n", "    examples=[\n", "        get_image(\"cheetah1.jpg\"),\n", "        get_image(\"lion.jpg\")\n", "        ]\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n", "\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -1,8 +1,9 @@
-import os
 import requests
 import tensorflow as tf  # type: ignore
 
 import gradio as gr
+# get_image() returns the file path to sample images included with Gradio
+from gradio.media import get_image
 
 inception_net = tf.keras.applications.MobileNetV2()  # load the model
 
@@ -24,8 +25,8 @@ demo = gr.Interface(
     inputs=image,
     outputs=label,
     examples=[
-        os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"),
-        os.path.join(os.path.dirname(__file__), "images/lion.jpg")
+        get_image("cheetah1.jpg"),
+        get_image("lion.jpg")
         ]
     )