import gradio as gr
from fastbook import *
from fastai.vision.widgets import *
from IPython.display import display


# learn_inf = load_learner('export.pkl')
# lbl_pred = widgets.Label()
# # lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
# out_pl = widgets.Output()
# btn_upload = widgets.FileUpload()

# # VBox([widgets.Label('Select your bear!'),
# #       btn_upload, btn_run, out_pl, lbl_pred])
# # def greet(name):
# #     return "Hello " + name + "!!"

# def on_click_classify(picture):
#     out_pl = widgets.Output()
#     img = PILImage.create(picture)
#     out_pl.clear_output()
#     with out_pl: display(img.to_thumb(128,128))
#     pred,pred_idx,probs = learn_inf.predict(img)
#     print('pred,pred_idx,probs', pred,pred_idx,probs)
#     lbl_pred.value = f'Prediction: {pred};\n Probability: {probs[pred_idx]:.04f}'
#     return lbl_pred.value
learn = load_learner('export.pkl')
categories = ('Hot Dog','No Hot Dog')

def on_click_classify(img):
    pred,pred_idx,probs = learn.predict(img)
    return dict(zip(categories, map(float,probs)))

image = gr.Image()
label = gr.Label()
examples = ['hot_dog.jpg', 'no_hot_dog.jpg', 'jian_yang.jpg']

iface = gr.Interface(fn=on_click_classify, inputs=image, outputs=label, examples=examples)
iface.launch()