app.py

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

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

learn = load_learner('model.pkl')

categories = ('Dog', 'Cat')

def predict(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    return dict(zip(categories, map(float,probs)))

title = "Pet Breed Classifier"
description = "A pet breed classifier trained on the Oxford Pets dataset with fastai."
examples = [['cat.jpeg'],
			 ['dog.jpeg'],
			 ['cat2.jpeg'],
			 ['dog2.jpeg'],
			 ['cat3.jpeg'],
			 ['dog3.jpeg'],
			 ['cat4.jpeg'],
			 ['dog4.jpeg']]
interpretation='default'
enable_queue=True

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