import gradio as gr
from fastai.vision.core import PILImage
from fastai.learner import load_learner

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 = "Bear Classifier"
description = ("A bear classifier trained on black/grizzly/teddy bear images downloaded from internet with fastai. "
               "Created as a demo for Gradio and HuggingFace Spaces.")
examples = ['black.jpg', 'grizzly.jpg', 'teddy.jpg']
grif = 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)
grif.launch(share=True)