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import gradio as gr
from transformers import ViTHybridImageProcessor, ViTHybridForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "google/vit-hybrid-base-bit-384"
feature_extractor = ViTHybridImageProcessor.from_pretrained(model_name)
model = ViTHybridForImageClassification.from_pretrained(model_name)
# Function for prediction
def classify_image(image):
inputs = feature_extractor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
return model.config.id2label[predicted_class_idx]
# Gradio UI
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil"),
outputs="text",
title="ViT-Hybrid Image Classifier",
description="Upload an image to classify it using the ViT-Hybrid model.",
)
if __name__ == "__main__":
iface.launch()
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