Upload folder using huggingface_hub

README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 5.49.1
+sdk_version: 6.0.0
 app_file: run.py
 pinned: false
 hf_oauth: true

requirements.txt

@@ -1,5 +1,5 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@e05eb8df38a4ca20993e94ca4e209cf8110bb677#subdirectory=client/python
-https://gradio-pypi-previews.s3.amazonaws.com/e05eb8df38a4ca20993e94ca4e209cf8110bb677/gradio-5.49.1-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@d007e6cf617baba5c62e49ec2b7ce278aa863a79#subdirectory=client/python
+https://gradio-pypi-previews.s3.amazonaws.com/d007e6cf617baba5c62e49ec2b7ce278aa863a79/gradio-6.0.0-py3-none-any.whl
 torch
 torchvision
 requests

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classification\n", "### Simple image classification in Pytorch with Gradio's Image input and Label output.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch torchvision requests "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "import requests\n", "from torchvision import transforms  # type: ignore\n", "\n", "model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "def predict(inp):\n", "  inp = transforms.ToTensor()(inp).unsqueeze(0)\n", "  with torch.no_grad():\n", "    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)\n", "    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}\n", "  return confidences\n", "\n", "demo = gr.Interface(fn=predict,\n", "             inputs=gr.Image(type=\"pil\"),\n", "             outputs=gr.Label(num_top_classes=3),\n", "             examples=[[\"cheetah.jpg\"]],\n", "             )\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classification\n", "### Simple image classification in Pytorch with Gradio's Image input and Label output.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch torchvision requests "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "import requests\n", "from torchvision import transforms  # type: ignore\n", "\n", "model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "def predict(inp):\n", "  inp = transforms.ToTensor()(inp).unsqueeze(0)\n", "  with torch.no_grad():\n", "    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)\n", "    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}\n", "  return confidences\n", "\n", "demo = gr.Interface(fn=predict,\n", "             inputs=gr.Image(type=\"pil\"),\n", "             outputs=gr.Label(num_top_classes=3),\n", "             examples=[[\"cheetah.jpg\"]],\n", "             api_name=\"predict\"\n", "             )\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -18,6 +18,7 @@ demo = gr.Interface(fn=predict,
              inputs=gr.Image(type="pil"),
              outputs=gr.Label(num_top_classes=3),
              examples=[["cheetah.jpg"]],
+             api_name="predict"
              )
 
 demo.launch()