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

learn = load_learner(fname='pet_breeds.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 = "Pet Breed Classifier"
description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
examples = ["chihuahua.jpg", "siamese.jpg"]

gr.Interface(fn=predict,
             inputs=gr.Image(height=512, width=512),
             outputs=gr.Label(num_top_classes=3),
             title=title,
             description=description,
             article=article,
             examples=examples).launch()