app.py

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

learn = load_learner('example.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 = "Bear Classifier"
description = "A bear classifier trained on random pulled internet images with fastai. Created as a demo for Gradio and HuggingFace Spaces."
article="<p style='text-align: center'><a href='https://www.lauraku.com' target='_blank'>My website</a></p>"
examples = ['grizzly.jpeg', 'black.jpeg', 'teddy.jpeg']
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,article=article,examples=examples,interpretation=interpretation).launch(enable_queue=enable_queue)