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


def is_cat(x): return x[0].isupper()


learn = load_learner('model.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 = "Cat or Dog 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.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3),
             title=title,
             # description=description, article=article, examples=examples,
             interpretation=interpretation,
             enable_queue=enable_queue).launch()