# diagnosis/ai_engine/model_loader.py
"""Singleton pattern for model loading

This loader provides a clean interface for getting detector instances.
Uses singleton pattern to ensure models are loaded only once.

Supports both:
- Legacy AdvancedStutterDetector (Whisper-based)
- New SpeechPathologyClassifier (Wav2Vec2-XLSR-53 based)
- New InferencePipeline (for real-time and batch processing)
"""
import logging
import sys
from pathlib import Path

logger = logging.getLogger(__name__)

# Add project root to path for imports
_project_root = Path(__file__).parent.parent.parent
if str(_project_root) not in sys.path:
    sys.path.insert(0, str(_project_root))

# Singleton instances
_detector_instance = None
_speech_pathology_model_instance = None
_inference_pipeline_instance = None


def get_stutter_detector():
    """
    Get or create singleton AdvancedStutterDetector instance (legacy).
    
    This ensures models are loaded only once and reused across requests.
    
    Returns:
        AdvancedStutterDetector: The singleton detector instance
        
    Raises:
        ImportError: If the detector class cannot be imported
    """
    global _detector_instance
    
    if _detector_instance is None:
        try:
            from .detect_stuttering import AdvancedStutterDetector
            logger.info("🔄 Initializing detector instance (first call)...")
            _detector_instance = AdvancedStutterDetector()
            logger.info("✅ Detector instance created successfully")
        except ImportError as e:
            logger.error(f"❌ Failed to import AdvancedStutterDetector: {e}")
            raise ImportError("No StutterDetector implementation available in detect_stuttering.py") from e
        except Exception as e:
            logger.error(f"❌ Failed to create detector instance: {e}")
            raise
    
    return _detector_instance


def get_speech_pathology_model(model_path: str = None):
    """
    Get or create singleton SpeechPathologyClassifier instance.
    
    Uses Wav2Vec2-XLSR-53 with custom multi-task head for fluency and articulation.
    
    Args:
        model_path: Optional path to saved model checkpoint
    
    Returns:
        SpeechPathologyClassifier: The singleton model instance
        
    Raises:
        ImportError: If the model class cannot be imported
        RuntimeError: If model cannot be loaded
    """
    global _speech_pathology_model_instance
    
    if _speech_pathology_model_instance is None:
        try:
            from models.speech_pathology_model import load_speech_pathology_model
            from config import default_model_config
            
            logger.info("🔄 Initializing SpeechPathologyClassifier (first call)...")
            _speech_pathology_model_instance = load_speech_pathology_model(
                model_name=default_model_config.model_name,
                classifier_hidden_dims=default_model_config.classifier_hidden_dims,
                dropout=default_model_config.dropout,
                device=default_model_config.device,
                use_fp16=default_model_config.use_fp16,
                model_path=model_path
            )
            logger.info("✅ SpeechPathologyClassifier instance created successfully")
        except ImportError as e:
            logger.error(f"❌ Failed to import SpeechPathologyClassifier: {e}")
            raise ImportError("Failed to import SpeechPathologyClassifier. Check models package.") from e
        except Exception as e:
            logger.error(f"❌ Failed to create SpeechPathologyClassifier instance: {e}", exc_info=True)
            raise RuntimeError(f"Failed to load SpeechPathologyClassifier: {e}") from e
    
    return _speech_pathology_model_instance


def get_inference_pipeline(model_path: str = None):
    """
    Get or create singleton InferencePipeline instance.
    
    Provides both batch and streaming inference capabilities with phone-level analysis.
    
    Args:
        model_path: Optional path to saved model checkpoint
    
    Returns:
        InferencePipeline: The singleton pipeline instance
        
    Raises:
        ImportError: If the pipeline class cannot be imported
        RuntimeError: If pipeline cannot be initialized
    """
    global _inference_pipeline_instance
    
    if _inference_pipeline_instance is None:
        try:
            from inference.inference_pipeline import InferencePipeline
            from config import default_audio_config, default_model_config, default_inference_config
            
            logger.info("🔄 Initializing InferencePipeline (first call)...")
            
            # Load model if path provided, otherwise let pipeline create it
            model = None
            if model_path:
                model = get_speech_pathology_model(model_path=model_path)
            
            _inference_pipeline_instance = InferencePipeline(
                model=model,
                audio_config=default_audio_config,
                model_config=default_model_config,
                inference_config=default_inference_config
            )
            logger.info("✅ InferencePipeline instance created successfully")
        except ImportError as e:
            logger.error(f"❌ Failed to import InferencePipeline: {e}")
            raise ImportError("Failed to import InferencePipeline. Check inference package.") from e
        except Exception as e:
            logger.error(f"❌ Failed to create InferencePipeline instance: {e}", exc_info=True)
            raise RuntimeError(f"Failed to initialize InferencePipeline: {e}") from e
    
    return _inference_pipeline_instance


def reset_detector():
    """
    Reset the singleton detector instance (useful for testing or reloading models).
    
    Note: This will force reloading of models on next get_stutter_detector() call.
    """
    global _detector_instance
    _detector_instance = None
    logger.info("🔄 Detector instance reset")


def reset_speech_pathology_model():
    """
    Reset the singleton SpeechPathologyClassifier instance.
    
    Note: This will force reloading of models on next get_speech_pathology_model() call.
    """
    global _speech_pathology_model_instance
    _speech_pathology_model_instance = None
    logger.info("🔄 SpeechPathologyClassifier instance reset")


def reset_inference_pipeline():
    """
    Reset the singleton InferencePipeline instance.
    
    Note: This will force reloading on next get_inference_pipeline() call.
    """
    global _inference_pipeline_instance
    _inference_pipeline_instance = None
    logger.info("🔄 InferencePipeline instance reset")


def reset_all():
    """
    Reset all singleton instances (useful for testing or complete reload).
    """
    reset_detector()
    reset_speech_pathology_model()
    reset_inference_pipeline()
    logger.info("🔄 All model instances reset")