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from fastai.vision.all import PILImage, load_learner
from gradio import Interface
from gradio.components import Image, Label
TITLE = "Chicken Breed Classifier"
DESCRIPTION = """A chicken breed classifier trained using the dataset here: https://www.kaggle.com/datasets/edkenthazledine/chicken-breeds
There are many breeds of chicken, and getting lots of pictures of them is hard!
This can identify (to varying degrees of accuracy, the model is ~90% accurate): American Gamefowl, Australorp, Burford Brown, Crevecoeur, Derbyshire Redcap, Easter Egger, Light Sussex, Sapphire Gem, Speckled Sussex, Wyandotte
"""
EXAMPLES = ["wyandotte.jpg"]
learn = load_learner("export_10b_90p.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))}
iface = Interface(
fn=predict,
inputs=Image(shape=(512, 512)),
outputs=Label(num_top_classes=3),
title=TITLE,
description=DESCRIPTION,
examples=EXAMPLES,
)
iface.launch(enable_queue=True)
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