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)} return confidences def run(): demo = gr.Interface( fn=predict, inputs=gr.inputs.Image(type="pil"), outputs=gr.outputs.Label(num_top_classes=3), ) demo.launch(server_name="0.0.0.0", server_port=7860) if __name__ == "__main__": run()