"""
ML integration plugin for the address formatter.

This plugin integrates machine learning features into address formatting.
"""
from typing import Dict, Any, List, Optional

from address_formatter.plugins.interface import FormatterPlugin, PluginMetadata


class MLIntegrationPlugin(FormatterPlugin):
    """
    Plugin that provides machine learning capabilities for address formatting.
    
    This plugin can:
    - Extract structured components from unstructured address text
    - Predict missing components in partial addresses
    - Classify address types
    """
    
    def __init__(self):
        """Initialize plugin attributes before calling initialize()."""
        # Set default values for attributes
        self.ml_available = False
        self.predictor = None
    
    @property
    def metadata(self) -> PluginMetadata:
        """Get plugin metadata."""
        return PluginMetadata(
            name="ml_integration",
            version="1.0.0",
            description="Provides machine learning capabilities for address processing",
            author="Address Formatter Team",
            priority=200,
            tags=["ml", "extraction", "prediction"]
        )
    
    def initialize(self) -> None:
        """Initialize the plugin by setting up ML components."""
        # Already set default values in __init__, now try to load ML components
        try:
            # Try to import ML libraries
            # These lines would be uncommented in a real implementation
            # import numpy as np
            # import spacy
            # from sentence_transformers import SentenceTransformer
            
            # For now, we'll just leave this as False to avoid errors
            # self.ml_available = True
            
            # Initialize predictor (would be a real ML model in production)
            # self.predictor = SomeMLModel()
            pass
            
        except ImportError:
            # ML libraries not available - already set to False in __init__
            pass
    
    def pre_format(self, components: Dict[str, Any], 
                  options: Dict[str, Any]) -> Dict[str, Any]:
        """
        Process address components before formatting.
        
        Uses ML capabilities to predict missing components or extract
        components from unstructured text.
        """
        if not self.ml_available:
            return components
        
        result = components.copy()
        
        # Check if we have unstructured text to process
        unstructured_text = result.get("unstructured_text", "")
        if unstructured_text and options.get("extract_from_text", False):
            # Extract structured components from unstructured text
            # In a real implementation, this would use the predictor
            extracted = {}  # self.predictor.extract_components(unstructured_text)
            
            # Only use extracted components for fields that aren't already present
            for key, value in extracted.items():
                if key not in result or not result[key]:
                    result[key] = value
        
        # Check if we should predict missing components
        if options.get("predict_missing", False):
            # Use ML to predict missing components
            # In a real implementation, this would use the predictor
            # result = self.predictor.predict_missing_components(result)
            pass
        
        return result
    
    def post_format(self, formatted_address: str, 
                   components: Dict[str, Any], 
                   options: Dict[str, Any]) -> str:
        """
        Process formatted address after formatting.
        
        No post-processing is performed in this plugin.
        """
        # This plugin doesn't modify the formatted address
        return formatted_address 