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# AUTOGENERATED! DO NOT EDIT! File to edit: Deployment.ipynb. | |
# %% auto 0 | |
__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'inf', 'get_y', 'classify_images'] | |
# %% Deployment.ipynb 2 | |
from fastai.vision.all import * | |
import gradio as gr | |
def get_y(path): | |
return parent_label(path).split(' and ') | |
# %% Deployment.ipynb 3 | |
learn = load_learner('export.pkl') | |
# %% Deployment.ipynb 8 | |
categories = learn.dls.vocab | |
def classify_images(img): | |
"""classifies images and returns the probabilities on each categories.""" | |
pred, pred_idx, probs = learn.predict(img) | |
return dict(zip(categories, map(float, probs))) | |
# %% Deployment.ipynb 10 | |
image = gr.inputs.Image(shape=(192, 192)) | |
label = gr.outputs.Label() | |
# If you have more or less examples, edit this list. | |
examples = ['apple.jpg', 'orange.jpg', 'pear.jpg', 'apple and orange.jpg', | |
'pear and orange.jpg', 'apple and pear.jpg', 'apple and pear and orange.jpg', | |
'random images.jpg'] | |
inf = gr.Interface(fn=classify_images, inputs=image, outputs=label, examples=examples) | |
inf.launch(inline=False) | |