# import gradio as gr
# def greet (name): return f"Hello {name}!" 
# gr.Interface(fn=greet, inputs="text", outputs="text"). launch()

_all_ = ['is_cat', 'learn', 'classify_image'
, 'categories',
'image',
'label',  'examples', 'intf']
# Cell
from fastai.vision.al1 import * 
import gradio as gr
def is_cat(x): return x[0].isupper()
# Cell
learn = load_learner('model.pkl')
# Cell
categories = ('Dog', 'Cat')
format_float = lambda x: "{:.10f}".format(float(x))

def classify_image(img):
    pred,idx,probs = learn.predict(img)
    return dict (zip(categories, map (format_float,probs)))

image = gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
examples = ['dog.jpg'
, 'cat.jpg', 'cos.png']
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch (inline=False)