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)