app.py

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/code/josemauriciodelgado/breast-cancer-detection/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()