#!/usr/bin/env python3
"""MLflow Tagging Script for Clinical Baseline"""

import mlflow
import argparse
from datetime import datetime

def tag_clinical_baseline(run_id: str, gpu_memory: int):
    """Tag MLflow run with clinical baseline information"""
    
    # Set MLflow tracking URI
    mlflow.set_tracking_uri("http://localhost:5001")
    
    # Clinical baseline tags
    tags = {
        "clinical.baseline": "true",
        "clinical.version": "1.0",
        "clinical.sign_off": "pending",
        "clinical.reviewer": "TBD",
        "clinical.date": datetime.now().strftime('%Y-%m-%d'),
        
        # Technical tags
        "model.architecture": "UNETR",
        "model.modality": "multimodal",
        "dataset.source": "MSD",
        "dataset.task": "Task01_BrainTumour",
        
        # Hardware tags
        "hardware.gpu_memory": f"{gpu_memory}GB",
        "hardware.amp": "true",
        
        # Git information (would be populated in real deployment)
        "git.branch": "main",
        "git.commit": "TBD",
        
        # Clinical workflow
        "workflow.type": "clinical_training",
        "workflow.stage": "baseline",
        "workflow.status": "completed"
    }
    
    # Apply tags to run
    with mlflow.start_run(run_id=run_id):
        for key, value in tags.items():
            mlflow.set_tag(key, value)
    
    print(f"✅ Tagged run {run_id} with clinical baseline information")

def main():
    parser = argparse.ArgumentParser(description='Tag MLflow run for clinical baseline')
    parser.add_argument('--run-id', required=True, help='MLflow run ID')
    parser.add_argument('--gpu-memory', type=int, default=0, help='GPU memory in GB')
    
    args = parser.parse_args()
    tag_clinical_baseline(args.run_id, args.gpu_memory)

if __name__ == "__main__":
    main()
