from typing import Dict, List, Optional, Any
from dataclasses import dataclass
from abc import ABC, abstractmethod
import asyncio

@dataclass
class ConversationContext:
    """Maintains conversation state and context"""
    user_id: str
    session_id: str
    messages: List[Dict[str, Any]]
    user_profile: Dict[str, Any]
    conversation_state: Dict[str, Any]
    metadata: Dict[str, Any]

class AIAssistantArchitecture:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.components = self._initialize_components()
        
    def design_architecture(self):
        """Design comprehensive AI assistant architecture"""
        return {
            'core_components': {
                'nlu': self._design_nlu_component(),
                'dialog_manager': self._design_dialog_manager(),
                'response_generator': self._design_response_generator(),
                'context_manager': self._design_context_manager(),
                'integration_layer': self._design_integration_layer()
            },
            'data_flow': self._design_data_flow(),
            'deployment': self._design_deployment_architecture(),
            'scalability': self._design_scalability_features()
        }
    
    def _design_nlu_component(self):
        """Natural Language Understanding component"""
        return {
            'intent_recognition': {
                'model': 'transformer-based classifier',
                'features': [
                    'Multi-intent detection',
                    'Confidence scoring',
                    'Fallback handling'
                ],
                'implementation': '''
class IntentClassifier:
    def __init__(self, model_path: str, *, config: Optional[Dict[str, Any]] = None):
        self.model = self.load_model(model_path)
        self.intents = self.load_intent_schema()
        default_config = {"threshold": 0.65}
        self.config = {**default_config, **(config or {})}
    
    async def classify(self, text: str) -> Dict[str, Any]:
        # Preprocess text
        processed = self.preprocess(text)
        
        # Get model predictions
        predictions = await self.model.predict(processed)
        
        # Extract intents with confidence
        intents = []
        for intent, confidence in predictions:
            if confidence > self.config['threshold']:
                intents.append({
                    'name': intent,
                    'confidence': confidence,
                    'parameters': self.extract_parameters(text, intent)
                })
        
        return {
            'intents': intents,
            'primary_intent': intents[0] if intents else None,
            'requires_clarification': len(intents) > 1
        }
'''
            },
            'entity_extraction': {
                'model': 'NER with custom entities',
                'features': [
                    'Domain-specific entities',
                    'Contextual extraction',
                    'Entity resolution'
                ]
            },
            'sentiment_analysis': {
                'model': 'Fine-tuned sentiment classifier',
                'features': [
                    'Emotion detection',
                    'Urgency classification',
                    'User satisfaction tracking'
                ]
            }
        }
    
    def _design_dialog_manager(self):
        """Dialog management system"""
        return '''
class DialogManager:
    """Manages conversation flow and state"""
    
    def __init__(self):
        self.state_machine = ConversationStateMachine()
        self.policy_network = DialogPolicy()
        
    async def process_turn(self, 
                          context: ConversationContext, 
                          nlu_result: Dict[str, Any]) -> Dict[str, Any]:
        # Determine current state
        current_state = self.state_machine.get_state(context)
        
        # Apply dialog policy
        action = await self.policy_network.select_action(
            current_state, 
            nlu_result, 
            context
        )
        
        # Execute action
        result = await self.execute_action(action, context)
        
        # Update state