__all__ = ['learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']

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

learn = load_learner("Lecture2_Big_Cat_Model.pkl")
#categories = ("Leopard", "Cougar", "Tiger", "Lion", "Cheetah", "SnowLeopard")
labels = learn.dls.vocab

def classify_images(img):
  pred,idx,probs = learn.predict(img)
  return {labels[i]: float(probs[i]) for i in range(len(labels))}

# image = gr.inputs.Image(shape=(192, 192))
# label = gr.outputs.Label()
# examples = ["Lion.jpg","Cheetah.jpg","Tiger.jpeg"]

#gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch(share=True)

#intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf = gr.Interface(
  fn=classify_images,
  inputs=gr.Image(),
  outputs=gr.Label(),
  examples = ["Lion.jpeg","Cheetah.jpeg","Tiger.jpeg"])
  
intf.launch(share=True, inline=False)

#<<<<<<< HEAD
#intf.launch(inline=False, share=True)
#=======
#intf.launch(inline=False)
#>>>>>>> bb7397bf5239a4735ba43cd8a14438b3e82638ff


# def greet(name):
#     return "Hello " + name + "!!"
# 
# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()