# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.

# %% auto 0
__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']

# %% ../app.ipynb 0
from fastai.vision.all import *
import PIL
import gradio as gr

def is_cat(x):
    return x[0].isupper()
# %% ../app.ipynb 3
learn = load_learner('model.pkl')

# %% ../app.ipynb 5
categories = ('Abyssian', 'American Bulldog', 'American Pit Bull Terrier', 'Basset Hound', 'Beagle','Bengal',
                'Birman', 'Bombay', 'Boxer','British Shorthair', 'Chihuahua',
            'Egyptian Mau', 'English Cocker Spaniel', 'English Setter', 'German Shorthaired', 'Great Pyrenees', 'Havanese', 'Japanese Chin', 
            'Keeshond', 'Leonberger', 'Main Coon', 'Miniature Pinscher', 'Newfoundland', 'Persian', 'Pomeranian', 'Pug', 'Ragdoll', 'Russian Blue', 
            'St.Bernard', 'Samyoed', 'Scottish Terrier', 'Shiba Inu', 'Siamese', 'Sphynx',  
             'Staffordshire Bull Terrier', 'Wheaten Terrier', 'Yorkshire Terrier')

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

# %% ../app.ipynb 
image = gr.Image(shape=(192,192))
label = gr.Label()
examples = ['americanBulldog.jpg', 'bernard.jpg', 'ragdoll.jpg', 'ragdoll1.jpg']
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False)