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import gradio as gr | |
from fastai.vision.all import * | |
import skimage | |
learn = load_learner('export.pkl') | |
examples = [['Images/PKG - Breast-Metastases-MSKCC/Breast-Metastases-MSKCC/HobI16-053768896760.jpeg'], ['Images/PKG - CPTAC-BRCA/BRCA/01BR001-4ffefc66-d0ba-4a36-b4fa-35bd91.jpeg']] | |
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 classification" | |
description = "Demo for breast cancer classification using histopathology images." | |
article="<p style='text-align: center'><a href='https://www.kaggle.com/josemauriciodelgado/breast-cancer-classifier/edit' target='_blank'>Notebook</a></p>" | |
interpretation='default' | |
enable_queue=True | |
gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=6),title=title,description=description,article=article,examples=examples , interpretation=interpretation,enable_queue=enable_queue).launch() |