app.py

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

learn = load_learner('export.pkl')

labels = ('Lego (non Ninjago)', 'Lego Ninjago')


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 = "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.jpeg', 'lego.jpeg']
interpretation = 'default'
enable_queue = True

gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(192, 192)), outputs=gr.outputs.Label(), title=title,
             description=description, examples=examples, interpretation=interpretation, enable_queue=enable_queue).launch()