"""
Core MedicalNLP class for processing medical text.
"""

import logging
from typing import List, Dict, Any, Optional

logger = logging.getLogger(__name__)


class MedicalNLP:
    """
    Main class for medical natural language processing.
    
    This class provides a unified interface for various medical NLP tasks
    including entity extraction, text preprocessing, and classification.
    """
    
    def __init__(self, model_name: Optional[str] = None):
        """
        Initialize the MedicalNLP processor.
        
        Args:
            model_name: Optional name of the pre-trained model to use
        """
        self.model_name = model_name or "default"
        self._initialized = False
        logger.info(f"Initialized MedicalNLP with model: {self.model_name}")
    
    def extract_entities(self, text: str) -> List[Dict[str, Any]]:
        """
        Extract medical entities from text.
        
        Args:
            text: Input medical text
            
        Returns:
            List of extracted entities with their properties
        """
        # Placeholder implementation
        entities = []
        logger.info(f"Extracting entities from text: {text[:50]}...")
        
        # TODO: Implement actual entity extraction logic
        # This would typically use models like BioBERT, ClinicalBERT, etc.
        
        return entities
    
    def preprocess_text(self, text: str) -> str:
        """
        Preprocess medical text for analysis.
        
        Args:
            text: Raw medical text
            
        Returns:
            Preprocessed text
        """
        # Placeholder implementation
        logger.info(f"Preprocessing text: {text[:50]}...")
        
        # TODO: Implement text preprocessing
        # - Remove special characters
        # - Normalize medical abbreviations
        # - Handle medical terminology
        
        return text
    
    def classify_text(self, text: str, categories: List[str]) -> Dict[str, float]:
        """
        Classify medical text into predefined categories.
        
        Args:
            text: Medical text to classify
            categories: List of possible categories
            
        Returns:
            Dictionary mapping categories to confidence scores
        """
        # Placeholder implementation
        logger.info(f"Classifying text into categories: {categories}")
        
        # TODO: Implement text classification
        # This would typically use a trained classifier
        
        return {category: 0.0 for category in categories}
    
    def summarize_text(self, text: str, max_length: int = 100) -> str:
        """
        Generate a summary of medical text.
        
        Args:
            text: Medical text to summarize
            max_length: Maximum length of summary
            
        Returns:
            Generated summary
        """
        # Placeholder implementation
        logger.info(f"Generating summary for text: {text[:50]}...")
        
        # TODO: Implement text summarization
        # This could use extractive or abstractive methods
        
        return text[:max_length] if len(text) > max_length else text
