app.py

from fastai.vision.all import *
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))}

examples = ['adonis.jpg',
           'atlas moth.jpg',
           'american snoot.jpg']
title = "ButterFly Image Classifier🦋"
description = "A ResNet18 feature extractor computer vision model to classify images of butterfly in 100 classes!"
article = "[Chris Olande.](https://github.com/Chrisolande)"

image = gr.Image()
label = gr.Label(num_top_classes = 3)
gr.Interface(fn=predict, inputs=image, outputs=label, examples = examples, title = title, description = description, article = article).launch(share=True)