import gradio as gr
from fastai.vision.all import *
import skimage as sk

#testing

learn = load_learner('export.pkl')
labels = learn.dls.vocab
EXAMPLES_PATH = Path('./')
examples = [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()]

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))}

gr.Interface(
    fn=predict, 
    examples=['train_0.jpg','train_147.jpg','train_251.jpg','train_255.jpg','train_256.jpg','train_258.jpg','train_272.jpg'], 
    inputs=gr.inputs.Image(shape=(512, 512)), 
    outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)