bear-classifier / app.py
Zak Croft
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import gradio as gr
from fastai.vision.all import *
import skimage
learn = load_learner('model.pkl')
labels = learn.dls.vocab
def predict(img):
img = PILImage.create(img)
pred,pred_idx,probs = learn.predict(img)
return {labels[i]: float(probs[i]) for i in range(len(labels))}
article="<p style='text-align: center'><a href='https://www.linkedin.com/in/zakcroft' target='_blank'>Me</a></p>"
demo = gr.Interface(fn=predict,
inputs=gr.Image(width=512, height=512),
outputs=gr.Label(num_top_classes=3),
title="Bear Classifier",
description="Grizzly, black or Teddy?",
article=article,
examples=['black.jpeg', 'grizzly.jpeg', 'teddy.jpeg',
'black-2.jpeg', 'grizzly-2.webp', 'teddy-2.webp'],
# interpretation='default',
# enable_queue=True
flagging_options=['should be Black', 'should be Grizzly', 'should be Teddy', 'should be None']
).launch(share=True)
# demo = gr.Interface(
# fn=predict,
# inputs=gr.inputs.Image(shape=(512, 512)),
# outputs=gr.outputs.Label(num_top_classes=3),
# title=title,
# description=description
# ,article=article,
# examples=examples,
# interpretation=interpretation,
# enable_queue=enable_queue).launch()
if __name__ == "__main__":
demo.launch(show_api=False)