# orchestrator_engine.py
import uuid
from datetime import datetime

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 = []
        
    async def process_request(self, session_id: str, user_input: str) -> dict:
        """
        Main orchestration flow with academic differentiation
        """
        # Step 1: Generate unique interaction ID
        interaction_id = self._generate_interaction_id(session_id)
        
        # Step 2: Context management
        context = await self.context_manager.manage_context(session_id, user_input)
        
        # Step 3: Intent recognition with CoT
        intent_result = await self.agents['intent_recognition'].execute(
            user_input=user_input,
            context=context
        )
        
        # Step 4: Agent execution planning
        execution_plan = await self._create_execution_plan(intent_result, context)
        
        # Step 5: Parallel agent execution
        agent_results = await self._execute_agents(execution_plan, user_input, context)
        
        # Step 6: Response synthesis
        final_response = await self.agents['response_synthesis'].execute(
            agent_outputs=agent_results,
            user_input=user_input,
            context=context
        )
        
        # Step 7: Safety and bias check
        safety_checked = await self.agents['safety_check'].execute(
            response=final_response,
            context=context
        )
        
        return self._format_final_output(safety_checked, interaction_id)
    
    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 = []