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import gradio as gr |
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import torch |
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model = torch.load('v4-epoch=19-val_loss=0.6964-val_accuracy=0.8964.ckpt') |
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import requests |
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from PIL import Image |
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from torchvision import transforms |
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labels = ['good', 'ill'] |
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def predict(inp): |
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img = transforms.ToTensor()(inp) |
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img = torchvision.transforms.Resize((800, 800))(img) |
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img = torchvision.transforms.CenterCrop(CROP)(img) |
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img = img.unsqueeze(0) |
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with torch.no_grad(): |
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prediction = torch.nn.functional.softmax(model(img)[0], dim=0) |
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confidences = {labels[i]: float(prediction[i]) for i in range(2)} |
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return confidences |
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import gradio as gr |
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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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).launch() |
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