app.py

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

learn = load_learner('export.pkl')

label = learn.dls.vocab
def predict(img):
  img = PILImage.create(img)
  pred,pred_idx,probs = learn.predicts(img)
  return {labels[i]: float(probs[i]) for i in range(len(labels))}

title = "Dog Breed Classifier"
description = "A dog breed classifier trained on 10 most popular breed."
examples = []
interpretation='default'
enable_queue=True

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