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| # app.py | |
| import gradio as gr | |
| import torch | |
| import requests | |
| from PIL import Image | |
| from torchvision import transforms | |
| model = torch.hub.load('pytorch/vision', '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 = 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(999)} | |
| return confidences | |
| # create gradio interface, with text input and dict output | |
| gr.Interface(title="Image Classification in PyTorch", | |
| fn=predict, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Label(num_top_classes=3), | |
| examples=["lion.jpg", "cheetah.jpg"]).launch() | |
| # run the app | |
| gr.launch(server_port=7680, enable_queue=False, share=True) | |