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import gradio as gr
from fastai.vision.all import *
import skimage
learn = load_learner('export.pkl')
categories = ('Lego Ninjago','Lego (non Ninjago)')
def predict(img):
img = PILImage.create(img)
pred, pred_idx, probs = learn.predict(img)
return dict(zip(categories, map(float,probs)))
title = "Lego Classifier"
description = "Classifies Lego into 'Ninjago' and 'Non Ninjago' with fastai. Created from the fastai 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 = ['ninjago.jpg', 'lego_nn.jpg']
interpretation = 'default'
enable_queue = True
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=2), title=title,
description=description, examples=examples, interpretation=interpretation, enable_queue=enable_queue).launch()
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