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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()
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