# orchestrator_engine.py
import uuid
import logging
from datetime import datetime

logger = logging.getLogger(__name__)

class MVPOrchestrator:
    def __init__(self, llm_router, context_manager, agents):
        self.llm_router = llm_router
        self.context_manager = context_manager
        self.agents = agents
        self.execution_trace = []
        logger.info("MVPOrchestrator initialized")
        
    async def process_request(self, session_id: str, user_input: str) -> dict:
        """
        Main orchestration flow with academic differentiation
        """
        logger.info(f"Processing request for session {session_id}")
        logger.info(f"User input: {user_input[:100]}")
        
        try:
            # Step 1: Generate unique interaction ID
            interaction_id = self._generate_interaction_id(session_id)
            logger.info(f"Generated interaction ID: {interaction_id}")
            
            # Step 2: Context management
            logger.info("Step 2: Managing context...")
            context = await self.context_manager.manage_context(session_id, user_input)
            logger.info(f"Context retrieved: {len(context.get('interactions', []))} interactions")
            
            # Step 3: Intent recognition with CoT
            logger.info("Step 3: Recognizing intent...")
            intent_result = await self.agents['intent_recognition'].execute(
                user_input=user_input,
                context=context
            )
            logger.info(f"Intent detected: {intent_result.get('primary_intent', 'unknown')}")
            
            # Step 4: Agent execution planning
            logger.info("Step 4: Creating execution plan...")
            execution_plan = await self._create_execution_plan(intent_result, context)
            
            # Step 5: Parallel agent execution
            logger.info("Step 5: Executing agents...")
            agent_results = await self._execute_agents(execution_plan, user_input, context)
            logger.info(f"Agent execution complete: {len(agent_results)} results")
            
            # Step 6: Response synthesis
            logger.info("Step 6: Synthesizing response...")
            final_response = await self.agents['response_synthesis'].execute(
                agent_outputs=agent_results,
                user_input=user_input,
                context=context
            )
            
            # Step 7: Safety and bias check
            logger.info("Step 7: Safety check...")
            safety_checked = await self.agents['safety_check'].execute(
                response=final_response,
                context=context
            )
            
            result = self._format_final_output(safety_checked, interaction_id)
            logger.info(f"Request processing complete. Response length: {len(str(result.get('response', '')))}")
            return result
            
        except Exception as e:
            logger.error(f"Error in process_request: {e}", exc_info=True)
            return {
                "response": f"Error processing request: {str(e)}",
                "error": str(e),
                "interaction_id": str(uuid.uuid4())[:8]
            }
    
    def _generate_interaction_id(self, session_id: str) -> str:
        """
        Generate unique interaction identifier
        """
        timestamp = datetime.now().isoformat()
        unique_id = str(uuid.uuid4())[:8]
        return f"{session_id}_{unique_id}_{int(datetime.now().timestamp())}"
    
    async def _create_execution_plan(self, intent_result: dict, context: dict) -> dict:
        """
        Create execution plan based on intent recognition
        """
        # TODO: Implement agent selection and sequencing logic
        return {
            "agents_to_execute": [],
            "execution_order": "parallel",
            "priority": "normal"
        }
    
    async def _execute_agents(self, execution_plan: dict, user_input: str, context: dict) -> dict:
        """
        Execute agents in parallel or sequential order based on plan
        """
        # TODO: Implement parallel/sequential agent execution
        return {}
    
    def _format_final_output(self, response: dict, interaction_id: str) -> dict:
        """
        Format final output with tracing and metadata
        """
        return {
            "interaction_id": interaction_id,
            "response": response.get("final_response", ""),
            "confidence_score": response.get("confidence_score", 0.0),
            "agent_trace": self.execution_trace,
            "timestamp": datetime.now().isoformat(),
            "metadata": {
                "agents_used": response.get("agents_used", []),
                "processing_time": response.get("processing_time", 0),
                "token_count": response.get("token_count", 0)
            }
        }
    
    def get_execution_trace(self) -> list:
        """
        Return execution trace for debugging and analysis
        """
        return self.execution_trace
    
    def clear_execution_trace(self):
        """
        Clear the execution trace
        """
        self.execution_trace = []