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# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
# %% auto 0
__all__ = ['learner', 'cats', 'img', 'lbl', 'examples', 'title', 'description', 'article', 'interface', 'classify_img']
# %% app.ipynb 3
from fastai.vision.all import *
import gradio as gr
# %% app.ipynb 6
learner = load_learner('model/export.pkl')
# %% app.ipynb 9
cats = ('Black Bear', 'Grizzly Bear', 'Teddy Bear',)
def classify_img(img):
preds, idx, probs = learner.predict(img)
return dict(zip(cats, map(float, probs)))
# %% app.ipynb 12
img = gr.Image()
lbl = gr.Label()
examples = [str(img_path) for img_path in Path('example_images/').rglob('*.jpg')]
title = 'Bear Classifier'
description = 'My first AI model that can tell you whether an image contains a grizzly bear, a black bear, or a teddy bear. This model was trained on the ' \
'ResNet18 architecture and used the fastai library.'
article = "<p style='text-align: center; font-size: 36px'><a href='https://forbo7.github.io/forblog/posts/2_bear_classifier_model.html' " \
"targets='_blank'><Blog Post</a></p>"
# %% app.ipynb 15
interface = gr.Interface(
fn=classify_img,
inputs='image',
outputs='label',
examples=examples,
title=title,
description=description,
article=article
)
interface.launch(inline=False, enable_queue=True)
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