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import gradio as gr
from fastai.vision.all import *
import skimage
learn = load_learner('export.pkl')
labels = learn.dls.vocab
def predict(img):
img = PILImage.create(img)
pred, pred_idx, probs = learn.predict(img)
return {labels[i]: float(probs[i]) for i in range(len(labels))}
title = "Breast cancer detection with Deep Transfer Learning(ResNet18)."
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 patient's breast with or without implant. 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>"
article = "<p style='text-align: center'><b>Web app is built and managed by Addai Fosberg</b></p>"
examples = ['img1.jpeg', 'img2.jpeg']
iface = gr.Interface(
fn=predict,
inputs=gr.Image(shape=(512, 512)),
outputs=gr.Label(num_top_classes=3),
title=title,
description=description,
article=article,
examples=examples,
enable_queue=True
)
iface.launch() |