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 = ['/kaggle/input/butterfly-images40-species/test/ADONIS/1.jpg',
           '/kaggle/input/butterfly-images40-species/test/AFRICAN GIANT SWALLOWTAIL/2.jpg',
           '/kaggle/input/butterfly-images40-species/test/AMERICAN SNOOT/3.jpg']
title = "ButterFly Image Classifier 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 = ).launch(share=True)