# import gradio as gr

# def greet(name):
#     return "Hello " + name + "!!"

# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()

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

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

model_path = Path('models')
image_path = Path('images')

cloud_categories = (
  'Cirrus', 
  'Cirrostratus',
  'Cirrocumulus',
  'Altostratus',
  'Altocumulus',
  'Stratus',
  'Stratocumulus',
  'Nimbostratus',
  'Cumulus',
  'Cumulonimbus',
  'Lenticular'
)

cloud_examples = [image_path / f"{c}.jpg" for c in cloud_categories]
cloud_examples


def is_cat(x): return x[0].isupper()

learn = load_learner('model.pkl')

categories = ('Dog', 'Cat')

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', 'cat.jpg', 'dunno.jpg']

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