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import gradio as gr |
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from fastai.vision.all import * |
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from tools import * |
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import skimage |
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learn = load_learner('panda-model-1.pkl') |
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labels = learn.dls.vocab |
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def predict(img): |
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print(type(img)) |
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print(img.shape) |
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img = get_crops(PILImage.create(img)) |
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pred,pred_idx,probs = learn.predict(img) |
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return {labels[i]: float(probs[i]) for i in range(len(labels))} |
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title = "Prostate cANcer graDe Assessment model" |
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description = "A model to predict the ISUP grade for prostate cancer based on whole-slide images of digitized H&E-stained biopsies." |
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examples = ['example.jpg', 'example2.jpg', 'example3.jpg'] |
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gr.Interface(fn=predict,inputs=gr.inputs.Image(),outputs=gr.outputs.Label(num_top_classes=5),title=title,description=description,examples=examples).launch() |
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