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import gradio as gr |
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from fastai.vision.all import * |
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import skimage |
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learn = load_learner('export.pkl') |
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labels = learn.dls.vocab |
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def predict(img): |
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img = PILImage.create(img) |
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pred,pred_idx,probs = learn.predict(img) |
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prediction = str(pred) |
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return prediction |
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title = "Breast cancer detection with Deep Transfer Learning(ResNet18)." |
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description = "<p style='text-align: center'><b>As a radiologist or oncologist, it is crucial to know what is wrong with a breast x-ray image.<b><br><b>Upload the breast X-ray image to know what is wrong with a patients breast with or without inplant. This product is from the findings of my (Team) published research paper:<a href='https://iopscience.iop.org/article/10.1088/2057-1976/ad3cdf' target='_blank' style='color: blue;'>read paper</a>. Learn more about me: <a href='https://www.linkedin.com/in/fosberg-addai-53a6991a7/' target='_blank' style='color: blue;'>Fosberg Addai</a><b><p>" |
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article="<p style='text-align: center'>Web app is built and managed by Addai Fosberg<b></p>" |
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examples = ['img1.jpeg', 'img2.jpeg'] |
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enable_queue=True |
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gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,enable_queue=enable_queue).launch() |
