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

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

learn = load_learner('model.pkl')

categories = ('Dog','Cat')

labels = learn.dls.vocab
def predict(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    return dict(zip(categories, map(float,probs)))

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 = ['dog.jpeg', 'cat.jpeg', 'dogcat.jpeg']
interpretation='default'
enable_queue=True

image = gr.Image(height=192, width=192)
label = gr.Label(num_top_classes=3)

intf = gr.Interface(
    fn=predict,
    inputs=image,
    outputs=label,
    examples=examples,
    title=title,
    description=description
)
intf.launch()

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