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from fastai.vision.all import *
import gradio as gr
def get_x(r): return path/r['fname']
def get_y(r): return r['car_body_style']
learn = load_learner('export.pkl')
labels = learn.dls.vocab
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 = 'Car Body Style Classifier'
description = 'A car body style classifier trained on the [Cars Dataset](http://ai.stanford.edu/~jkrause/cars/car_dataset.html).'
examples = ['convertible.jpg', 'coupe.jpg', 'van.jpg']
gr.Interface(fn=predict,
inputs=gr.inputs.Image(shape=(512, 512)),
outputs=gr.outputs.Label(num_top_classes=3),
title=title,
description=description,
examples=examples,
enable_queue=True).launch(share=True) |