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import argparse
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import torch
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from model import BiomedClipClassifier, predict_from_paths
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--weights", type=str, default=".")
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parser.add_argument("--mri", type=str, required=True, help="Path to NIfTI MRI file")
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parser.add_argument("--text", type=str, required=True, help="Clinical text")
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args = parser.parse_args()
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = BiomedClipClassifier.from_pretrained(args.weights, device=device)
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pred, probs = predict_from_paths(model, args.mri, args.text, device=device)
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print("Prediction:", pred)
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print("Probabilities [CN, MCI, Dementia]:", [round(p, 4) for p in probs])
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if __name__ == "__main__":
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main()
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