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


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 Breed Classifier"
description = "A pet breed classifier trained on the Oxford Pets dataset"
article="<p style='text-align: center'><h6>Dont have any links to share yet</h6></p>"
examples = ['siamese.jpg']
interpretation='default'
enable_queue=True





gr.Interface(
    fn=predict,
    inputs=gr.Image(),
    outputs=gr.Label(num_top_classes=3),
    title=title, 
    description=description,
    article=article, 
    examples=examples, 
    #interpretation=interpretation, 
    #enable_queue=enable_queue
).launch(share=True, debug=True)