import gradio as gr
from fastai.learner import load_learner
from fastai.vision.all import PILImage

def label_func(f): return f[0].isupper()
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 = "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 = ['siamese.jpg']
interpretation = 'default'
enable_queue = True

gr.Interface(
    fn=predict,
    inputs=gr.Image(type="filepath"),
    outputs=gr.Label(num_top_classes=3)
).launch(share=True)