"""
Model promotion and deployment
"""
import logging
from typing import Dict, Any
from datetime import datetime

logger = logging.getLogger(__name__)


class ModelPromoter:
    """
    Handles model promotion and versioning for deployment.
    """
    
    def __init__(self, models_dir: str = "models/"):
        """
        Initialize model promoter.
        
        Args:
            models_dir: Directory containing models
        """
        self.models_dir = models_dir
        logger.info(f"ModelPromoter initialized with models directory: {models_dir}")
    
    def evaluate_model_quality(self, model_metrics: Dict[str, float], thresholds: Dict[str, float]) -> bool:
        """
        Evaluate if model meets quality thresholds.
        
        Args:
            model_metrics: Model performance metrics
            thresholds: Minimum acceptable thresholds
            
        Returns:
            True if model passes quality checks
        """
        logger.info("Evaluating model quality...")
        
        passes_all = True
        for metric, threshold in thresholds.items():
            actual = model_metrics.get(metric, 0.0)
            if actual < threshold:
                logger.warning(f"Model fails {metric} check: {actual} < {threshold}")
                passes_all = False
            else:
                logger.info(f"Model passes {metric} check: {actual} >= {threshold}")
        
        return passes_all
    
    def promote_model(self, model_name: str, version: str, metrics: Dict[str, float]) -> bool:
        """
        Promote model to production.
        
        Args:
            model_name: Name of the model
            version: Model version
            metrics: Performance metrics
            
        Returns:
            True if promotion successful
        """
        logger.info(f"Promoting model {model_name} v{version} to production")
        
        # Define quality thresholds
        thresholds = {
            "auroc": 0.90,
            "f1_score": 0.85,
            "inference_time": 150  # milliseconds
        }
        
        # Check quality
        if not self.evaluate_model_quality(metrics, thresholds):
            logger.error("Model does not meet quality thresholds")
            return False
        
        # Promote model
        try:
            promotion_record = {
                "model_name": model_name,
                "version": version,
                "promoted_at": datetime.now().isoformat(),
                "metrics": metrics,
                "status": "promoted"
            }
            logger.info(f"Model promoted successfully: {model_name} v{version}")
            return True
        except Exception as e:
            logger.error(f"Model promotion failed: {e}")
            return False
    
    def rollback_model(self, model_name: str, target_version: str) -> bool:
        """
        Rollback to a previous model version.
        
        Args:
            model_name: Name of the model
            target_version: Version to rollback to
            
        Returns:
            True if rollback successful
        """
        logger.info(f"Rolling back model {model_name} to version {target_version}")
        
        try:
            # Implementation for model rollback
            logger.info(f"Model rolled back successfully: {model_name} to v{target_version}")
            return True
        except Exception as e:
            logger.error(f"Model rollback failed: {e}")
            return False
    
    def compare_models(self, model1_metrics: Dict, model2_metrics: Dict) -> Dict[str, Any]:
        """
        Compare two model versions.
        
        Args:
            model1_metrics: Metrics of first model
            model2_metrics: Metrics of second model
            
        Returns:
            Comparison report
        """
        logger.info("Comparing model versions...")
        
        comparison = {}
        for metric in model1_metrics.keys():
            diff = model2_metrics.get(metric, 0) - model1_metrics.get(metric, 0)
            comparison[f"{metric}_diff"] = diff
        
        return comparison