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import gradio as gr | |
from fastai.vision.all import load_learner, PILImage | |
# Load the trained model | |
learn = load_learner('model.pkl') | |
# Define the prediction function | |
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))} | |
# Create Gradio interface | |
interface = gr.Interface( | |
fn=predict, | |
inputs=gr.Image(type="pil"), | |
outputs=gr.Label(num_top_classes=3), | |
title="Pet Breed Classifier", | |
description="A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces.", | |
examples=["example1.jpg", "example2.jpg", "example3.jpg"] # Replace with actual filenames | |
) | |
# Launch the interface | |
interface.launch() | |