gradio/demo/demo2.py

import gradio as gr
import torch
from torchvision import transforms
import requests
from PIL import Image
​
model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
​
# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
​
def predict(inp):
  inp = Image.fromarray(inp.astype('uint8'), 'RGB')
  inp = transforms.ToTensor()(inp).unsqueeze(0)
  with torch.no_grad():
    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
  return {labels[i]: float(prediction[i]) for i in range(1000)}
​
inputs = gr.inputs.Image()
outputs = gr.outputs.Label(num_top_classes=3)
gr.Interface(fn=predict, inputs=inputs, outputs=outputs).launch()