#!/usr/bin/env python
# coding: utf-8

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from fastai.vision.widgets import *
from fastai.vision.core import *
from fastai.learner import *


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learn_inf = load_learner('rackets.pkl')


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btn_upload = widgets.FileUpload()
btn_classify = widgets.Button(description='Classify')
out_image = widgets.Output()
lbl_preds = widgets.Label()


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def fn_classify(change):
    img = PILImage.create(btn_upload.data[-1])
    out_image.clear_output()
    with out_image: display(img.to_thumb(128,128))
    pred,pred_idx,probs = learn_inf.predict(img)
    lbl_preds.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'

btn_classify.on_click(fn_classify)


# ## Do you play tennis?
# 
# I play squash. Often when people see me with my squash racket, they ask 'Do you play tennis?' As my first FastAI project, I thought I'd find out how difficult it is to distinguish a squash and tennis racket, by seeing how fastai's transfer learning models would perform.
# 
# The result is the following web app, where you can upload an image and the fastai model will provides its prediction. Try it out! 

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VBox([widgets.Label(), 
      btn_upload, btn_classify, out_image, lbl_preds])


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