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


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


learn = load_learner("model.pkl")


labels = learn.dls.vocab

categories = ("Dog", "Cat")


def predict(img):
    img = PILImage.create(img)
    _, _, probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))
    # return {labels[i]: float(probs[i]) for i in range(len(labels))}


title = "Cat or Dog Classifier"
description = "Classifier to determine if a photo is a cat or not."
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.PNG", "poodle.jpg", "panda.jpg"]
interpretation = "default"
enable_queue = True

gr.Interface(
    fn=predict,
    inputs=gr.components.Image(shape=(512, 512)),
    outputs=gr.components.Label(num_top_classes=3),
    title=title,
    description=description,
    article=article,
    examples=examples,
    interpretation=interpretation,
).launch(enable_queue=enable_queue)