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))}

gr.Interface(fn=predict, inputs=gr.Image(height = 512, width = 512), outputs=gr.Label(num_top_classes=3),
            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'],
            ).launch(share=True)