import gradio as gr


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 = "Gradio test"
description = "Quick Fastai classifier for bird/forest."
examples = ['examples/bird.jpg', 'examples/tree.jpg', 'examples/rainforest.jpg']
interpretation='default'
enable_queue=True

gr.Interface(fn=predict,inputs=gr.Image(),outputs=gr.Label(num_top_classes=2),title=title,description=description,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
iface.launch()