import torch model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval() import requests from torchvision import transforms # Download human-readable labels for ImageNet. 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)} return confidences import gradio as gr gr.Interface(fn=predict, inputs=gr.inputs.Image(type="pil"), outputs=gr.outputs.Label(num_top_classes=3), examples=["lion.jpg", "cheetah.jpg"], theme="default", css=".footer{display:none !important}").launch()