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

learn = load_learner('ripeorrotten_apple.pkl')

labels = ('ripe', 'rotten')
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 = "Apple Ripeness Classifier"
description = "Is your apple ripe or rotten? Use this AQCC (apple quality control classifier) trained on web images with fastai."
#article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
examples = ['apple-fruit-ripe.jpg','rotten.jpg','rotting.jpg']

gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=3),
    title=title,
    description=description,
#    article=article,
    examples=examples
).launch()