app.py

import gradio as gr
from fastai.vision.all import *
import skimage

learn = load_learner('export 1.pkl')

labels = learn.dls.vocab
def predict(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    prediction = str(pred)
    
    return prediction


title = "Lung cancer detection with Deep Transfer Learning(ResNet152 model)"
description = "<p style='text-align: center'><b>As a radiologist or oncologist, it is crucial to know what is wrong with a lung CT image.<b><br><b>Upload the lung CT-image to know what is wrong with a patient's lung CT image<b><p>"
article="<p style='text-align: center'>Web app is built and managed by Mr.<b></p>"
examples = ['img 1.png', 'img 2.png']
enable_queue=True
#interpretation='default'

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()