| import requests | |
| import torch | |
| from PIL import Image | |
| from torchvision import transforms | |
| import gradio as gr | |
| 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.Image() | |
| outputs = gr.Label(num_top_classes=3) | |
| demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs) | |
| if __name__ == "__main__": | |
| demo.launch() | |