# This script is used to create a Gradio interface in which we have a 
# dog vs cat classifier using the fastai library. For more explanation,
# visit the Google Colab notebook associated. 

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

# Define label function
def is_cat(x): return x[0].isupper()

# Load our model
learner = load_learner('model.pkl')


# Transform our model to obtain results that Gradio can handle with
categories = ('Dog', 'Cat')

def classify_image(img):
  # We are saying that this predictions returns: the prediction, its index and the prediction probability
  pred,idx,probs = learn.predict(img)

  # Here we return a dictionary with categories as keys and its probabilities as values
  return dict(zip(categories, map(float, probs)))


# Create the Gradio interface
image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label()
examples =  ['dogg.jpg', 'cat.jpg', 'dunno.jpg']

intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False, share=True)