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# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
# %% auto 0
__all__ = ['single_classifier', 'multi_class_classifier', 'multi_label_classifier', 'binary_labels', 'multi_class_labels',
'multi_label_labels', 'label_func', 'single_classification', 'multi_class_classification',
'multi_label_classification']
# %% app.ipynb 1
import gradio as gr
import nbdev
from fastai.vision.all import *
import os
# %% app.ipynb 2
def label_func(f): return 'Cat' if f[0].isupper() else 'Dog'
# %% app.ipynb 3
single_classifier = load_learner('models/dog-cat-classifier.pkl')
multi_class_classifier = load_learner('models/breeds-classifier.pkl')
multi_label_classifier = load_learner('models/multi-label-classification.pkl')
# %% app.ipynb 4
binary_labels = single_classifier.dls.vocab
def single_classification(img):
img = PILImage.create(img)
pred, pred_idx, probs = single_classifier.predict(img)
return dict(zip(binary_labels, map(float, probs)))
# %% app.ipynb 5
multi_class_labels = multi_class_classifier.dls.vocab
def multi_class_classification(img):
img = PILImage.create(img)
pred, pred_idx, probs = multi_class_classifier.predict(img)
return dict(zip(multi_class_labels, map(float, probs)))
# %% app.ipynb 6
multi_label_labels = multi_label_classifier.dls.vocab
def multi_label_classification(img):
img = PILImage.create(img)
pred, pred_idx, probs = multi_label_classifier.predict(img)
return dict(zip(multi_label_labels, map(float, probs)))
# %% app.ipynb 7
with gr.Blocks() as demo:
gr.Markdown("This demo allowing you to try different vision classification models - \
From binary classification through multi-class and multi-label classification and finally segmentation.")
with gr.Tab("Binary"):
with gr.Row():
with gr.Column():
b_image_input = gr.inputs.Image(shape = (460,460))
with gr.Row():
b_button = gr.Button("Run")
b_examples = 'models/Examples/Pets'
examples = gr.Examples(examples=[b_examples + '/shiba_inu_44.jpg', b_examples + '/Bengal_132.jpg',], inputs = b_image_input)
binary_out = gr.Label(num_top_classes=len(binary_labels))
with gr.Tab("MultiClass"):
with gr.Row():
with gr.Column():
m_image_input = gr.inputs.Image(shape = (460,460))
with gr.Row():
m_button = gr.Button("Run")
m_examples = 'models/Examples/Pets'
examples = gr.Examples(examples=[os.path.join(m_examples, s) for s in os.listdir(m_examples) if s.endswith('jpg')], inputs = m_image_input)
multi_out = gr.Label(num_top_classes=len(multi_class_labels))
with gr.Tab("MultiLabel"):
with gr.Row():
with gr.Column():
ml_image_input = gr.inputs.Image(shape = (460,460))
with gr.Row():
ml_button = gr.Button("Run")
ml_examples = 'models/Examples/Pascal'
examples = gr.Examples(examples=[os.path.join(ml_examples, s) for s in os.listdir(ml_examples) if s.endswith('jpg')], inputs = ml_image_input)
multil_out = gr.Label(num_top_classes=len(multi_label_labels))
b_button.click(single_classification, inputs=b_image_input, outputs=binary_out)
m_button.click(multi_class_classification, inputs=m_image_input, outputs=multi_out)
ml_button.click(multi_label_classification, inputs=ml_image_input, outputs=multil_out)
demo.launch()
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