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__all__ = ['learn', 'categories', 'classify_image', 'image', 'label', 'examples', 'intf']
# Cell
from fastai.vision.all import *
import gradio as gr
# Cell
learn = load_learner('bear_model.pkl')
# Cell
categories = ('black', 'grizzly', 'teddy')
def classify_image(img):
pred, idx, probs = learn.predict(img)
if max(probs).item() < 0.6:
return "Less than 60% confidence it's one of these bears."
else:
return dict(zip(categories, map(float, probs)))
# Cell
# Description texts
gColab = """
My model training code is in [Google Colab Notebook](https://colab.research.google.com/drive/1N592yRBIituoNB8kIh8TekGbkzufuP-P?usp=sharing)
"""
heading = """
## What bear is it--grizzly, black, or teddy?
"""
# Example images
examples = ['images/grizzly_bear.jpg', 'images/sloth_stuffed_animal.jpg', 'images/teddy_bear.jpeg', 'images/sailboat.jpg']
with gr.Blocks() as app:
gr.Markdown(heading)
# Define inputs and outputs
with gr.Row():
with gr.Column():
image = gr.Image(height=224, width=224, label="Input Image")
with gr.Column():
label = gr.Label(label="Classification Result")
# Add image examples
gr.Examples(examples=examples, inputs=image, outputs=label, fn=classify_image)
# Define what happens when submitted
image.change(fn=classify_image, inputs=image, outputs=label)
# Place description sub-block here
gr.Markdown(gColab)
app.launch(inline=False) |