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# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb. | |
# %% auto 0 | |
__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'interface', 'classify_image'] | |
# %% app.ipynb 2 | |
from fastai.vision.all import * | |
import gradio as gr | |
# %% app.ipynb 3 | |
learn = load_learner('model.pkl') | |
# %% app.ipynb 7 | |
categories = ('ash', 'chestnut', 'ginkgo biloba', 'silver maple', 'willow oak') | |
def classify_image(img): | |
pred, idx, probs = learn.predict(img) | |
# Change each probability to a float, since Gradio doesn't support Tensors or NumPy | |
return dict(zip(categories, map(float, probs))) | |
# %% app.ipynb 10 | |
image = gr.inputs.Image(shape=(192, 192)) | |
label = gr.outputs.Label() | |
examples = ['images/ash.jpg', 'images/chestnut.jpg', 'images/ginkgo_biloba.jpg', | |
'images/silver_maple.jpg', 'images/willow_oak.jpg'] | |
interface = gr.Interface(fn=classify_image, inputs=image, | |
outputs=label, examples=examples) | |
interface.launch(inline=False) | |