gradcam
Browse files- app.py +2 -2
- requirements.txt +1 -0
app.py
CHANGED
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@@ -53,13 +53,13 @@ def predict(inp):
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with torch.no_grad():
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prediction = model(img).softmax(1).numpy()
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confidences = {labels[i]: float(prediction[0][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(),
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-
outputs=gr.Label(num_top_classes=1),
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).launch()
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with torch.no_grad():
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prediction = model(img).softmax(1).numpy()
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confidences = {labels[i]: float(prediction[0][i]) for i in range(2)}
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+
return confidences, img
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import gradio as gr
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gr.Interface(fn=predict,
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inputs=gr.Image(),
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+
outputs=[gr.Label(num_top_classes=1), "image"],
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).launch()
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requirements.txt
CHANGED
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@@ -3,3 +3,4 @@ torchvision
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timm==0.8.15dev0
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albumentations
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opencv-python-headless
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timm==0.8.15dev0
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albumentations
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opencv-python-headless
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+
grad-cam
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