import gradio as gr
from fastai.vision.all import *
import skimage
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 = "Pet Classifier"
description = "A test with happy and sad 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>"

gr.Interface(
    fn=predict,
    inputs=gr.components.Image(),
    outputs=gr.components.Label(num_top_classes=2),
    title=title,
    description=description,
    article=article,
).launch()