import gradio as gr
from fastai.vision.all import *
import pathlib
plt = platform.system()
if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
learn = load_learner('model.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 = "Shirt Fit Classifier"
description = "A Loose vs. Fitted shirt classifier"
article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
examples = ['demo1.jpg', 'demo2.jpg', 'demo3.jpg', 'demo4.jpg']
interpretation='default'
enable_queue=True

gr.Interface(fn=predict,
             inputs=gr.inputs.Image(shape=(300, 300)),
             outputs=gr.outputs.Label(num_top_classes=2),
             title=title,
             description=description,
             article=article,
             examples=examples,
             interpretation=interpretation,
             enable_queue=enable_queue).launch()