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

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 = ('Pet breed classifier trained on the Oxford Pets dataset' +
               'with the fastai library and the ResNet50 neural network architecture. ' +
               'Based on the tutorial by Dr Tanishq Abraham.')
article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"

subfolder = Path('pets')
search_pattern = str(subfolder/'*.jpg')
jpg_files = glob.glob(search_pattern)

gr.Interface(fn=predict,
             inputs=gr.Image(),
             outputs=gr.Label(num_top_classes=3),
             title=title,
             description=description,
             article=article,
             examples=jpg_files,
             examples_per_page=37
            ).launch(share=True)