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Parent(s):
92b7f6a
Update app.py
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app.py
CHANGED
@@ -5,11 +5,11 @@ from torchvision.models import resnet18, ResNet18_Weights
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from torch import nn
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from PIL import Image # pip install pillow
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labels = ['
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# Same data transformation that was used for inputs (except data augmentation)
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data_transform = transforms.Compose([
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transforms.Resize(size=(
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225])
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@@ -30,14 +30,14 @@ def predict(img):
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confidences = {labels[i]: float(predictions[i]) for i in range(len(labels))}
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return confidences
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title = "
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description = "
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demo=gr.Interface(fn=predict,
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inputs=gr.Image(type="pil"),
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outputs=gr.Label(num_top_classes=len(labels)),
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title=title,
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description=description,
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examples=["2.jpg", "
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demo.launch('share=True')
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from torch import nn
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from PIL import Image # pip install pillow
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labels = ['fractured','not fractured']
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# Same data transformation that was used for inputs (except data augmentation)
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data_transform = transforms.Compose([
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transforms.Resize(size=(128, 128)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225])
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confidences = {labels[i]: float(predictions[i]) for i in range(len(labels))}
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return confidences
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title = "Bone Fractures"
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description = "Bone fractures classifier trained on the Kaggle dataset using Resnet18"
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demo=gr.Interface(fn=predict,
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inputs=gr.Image(type="pil"),
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outputs=gr.Label(num_top_classes=len(labels)),
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title=title,
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description=description,
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examples=["2.jpg", "6.jpg"])
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demo.launch('share=True')
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