from fastai.vision.all import *

def is_cat(x): return x[0].isupper() 

path = untar_data(URLs.PETS)/'images'

learn=load_learner('model.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))}

import gradio as gr
iface = gr.Interface(fn=predict, inputs=gr.Image(), outputs=gr.Label(num_top_classes=3)).launch(share=True)
iface.launch()