# AUTOGENERATED! DO NOT EDIT! File to edit: ../dog-breeds.ipynb.

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

# %% ../dog-breeds.ipynb 2
from fastai import *
from fastai.vision.all import *
import gradio as gr

# %% ../dog-breeds.ipynb 13
# Load model wherever you plan to use it for inference
learn_inf = load_learner('export2.pkl')

# %% ../dog-breeds.ipynb 15
categories = ['Abyssinian', 'Bengal', 'Birman', 'Bombay', 'British_Shorthair', 
              'Egyptian_Mau', 'Maine_Coon', 'Persian', 'Ragdoll', 'Russian_Blue', 
              'Siamese', 'Sphynx', 'american_bulldog', 'american_pit_bull_terrier', 
              'basset_hound', 'beagle', 'boxer', 'chihuahua', 'english_cocker_spaniel', 
              'english_setter', 'german_shorthaired', 'great_pyrenees', 'havanese', 
              'japanese_chin', 'keeshond', 'leonberger', 'miniature_pinscher', 
              'newfoundland', 'pomeranian', 'pug', 'saint_bernard', 'samoyed', 
              'scottish_terrier', 'shiba_inu', 'staffordshire_bull_terrier', 
              'wheaten_terrier', 'yorkshire_terrier']


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

# %% ../dog-breeds.ipynb 16
image = gr.Image(shape=(192,192))
label = gr.Label()
examples =  ['dog.jpg', 'cat.jpg']

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