app.py

#!/usr/bin/env python3
import gradio as gr
from fastai.vision.all import *
import skimage

import pathlib
plat = platform.system()
if plat == 'Linux': pathlib.WindowsPath = pathlib.PosixPath

learn = load_learner('export.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 = "Dog/Cat Breed Classifier"
description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces. Thank you to Tanishq Abraham for making this demo."
article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
examples = ['scratch.jpg', 'doge.jpg', 'doge_weird.jpg']
# examples = None
interpretation='default'
enable_queue=True
gr.Interface(fn=predict,inputs=gr.components.Image(shape=(512, 512)),outputs=gr.components.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation).launch(enable_queue=enable_queue)