app.py

import gradio as gr
import torch
import requests
from torchvision import transforms

# model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")

def predict(inp):
  # inp = transforms.ToTensor()(inp).unsqueeze(0)
  # with torch.no_grad():
  #   prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
  #   confidences = {labels[i]: float(prediction[i]) for i in range(1000)}    

  confidences = {labels[i]: i for i in range(1000)}    
  return confidences

demo = gr.Interface(fn=predict, 
             inputs=gr.inputs.Image(type="pil"),
             outputs=gr.outputs.Label(num_top_classes=3),
             examples=[["chair.jpg"]],
             share=True,
             )
             
demo.launch()