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import gradio as gr
import torch
from PIL import Image
from transformers import AutoModelForImageClassification, AutoImageProcessor
# Load model and processor with custom code enabled
model = AutoModelForImageClassification.from_pretrained("shravvvv/SAG-ViT", trust_remote_code=True)
processor = AutoImageProcessor.from_pretrained("shravvvv/SAG-ViT", trust_remote_code=True)
# Define CIFAR-10 class labels
class_labels = [
'airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck'
]
# Define prediction function
def predict(image):
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
return class_labels[predicted_class_idx]
# Create Gradio interface
iface = gr.Interface(
fn=predict,
inputs=gr.inputs.Image(type="pil"),
outputs=gr.outputs.Label(),
title="SAG-ViT Image Classifier",
description="Upload an image to classify it using the SAG-ViT model."
)
if __name__ == "__main__":
iface.launch()
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