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import gradio as gr |
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import torch |
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from torchvision import transforms |
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from PIL import Image |
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import requests |
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model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval() |
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response = requests.get("https://git.io/JJkYN") |
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labels = response.text.split("\n") |
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def predict(inp): |
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inp = transforms.ToTensor()(inp).unsqueeze(0) |
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with torch.no_grad(): |
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prediction = torch.nn.functional.softmax(model(inp)[0], dim=0) |
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confidences = {labels[i]: float(prediction[i]) for i in range(1000)} |
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return confidences |
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gr.Interface(fn=predict, |
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inputs=gr.Image(type="pil"), |
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outputs=gr.Label(num_top_classes=3), |
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examples=["imgs/lion.jpg", |
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"imgs/car.jpg", |
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"imgs/cheetah.jpg", |
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"imgs/banana.jpg", |
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"imgs/bus.jpg", |
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"imgs/parfum.jpg", |
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"imgs/alligator.jpg", |
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"imgs/arc.jpg"]).launch() |
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