from fastai.vision.all import *
import gradio as gr
# any external function used for labeling needs to be included in here
def is_cat(x): return x[0].isupper()
# this learner 'pkl' file is exactly the same as what you get when you trained it
# example : learn = vision_learner(dls,resnet18,metrics=error_rate)
# example : learn.fine_tune(3)
learn=load_learner('catsdogsmodel.pkl')
# Preping data for gradio, We are creating a dictionary for gradio. 
# One of the annoying things about 'gradio' is that it does not recognize tensor number and probabilities. 
# In fact, numpy either
categories = ('Dog', 'Cat')

# prediction, index & probabilities, gradio expects a dictinoary
def classify_image(img):
  pred,idx,probs = learn.predict(img)
  return dict(zip(categories,map(float, probs)))

image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label()
examples = ['dog.jpg','dog2.jpg','cat.jpg']

intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
# to create a public link, set 'share=True' in 'launch()'
intf.launch(inline=False)