app.py

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

learn = load_learner('resnet_emoji50.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 = "Perfect Window/Damaged_Window Classifier"
description = "A Window/Damaged_Window classifier trained on the google random images. Please use the examples to try it out. Created as a demo."
examples = ['w2.jpeg','w3.jpg','w4.jpg','w5.jpg']
interpretation='default'
enable_queue=True

gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()