# services/agents/master_orchestrator.py
"""
MasterLLM Orchestrator Agent - Coordinates subordinate agents

This is a TRUE CrewAI agent with delegation capabilities.
Does NOT inherit from BaseUtilityAgent (different purpose).
"""
import os
import json
import logging
from typing import Dict, Any, List, Optional
from datetime import datetime, timezone

from crewai import Agent, Task, Crew

from services.agents.message_dispatcher import MessageDispatcher, AgentMessage

logger = logging.getLogger("agent.masterllm")


class MasterOrchestratorAgent:
    """
    Master orchestrator agent with delegation capabilities.
    
    Responsibilities:
    - Plan creation and versioning
    - Task delegation to subordinate agents
    - Response evaluation
    - Plan modification based on feedback
    - Output rejection/correction
    
    Does NOT perform domain tasks directly.
    """
    
    def __init__(self, model: Optional[str] = None):
        """
        Initialize MasterLLM orchestrator.
        
        Args:
            model: LLM model to use (defaults to AGENT_MODEL env var)
        """
        self.name = "masterllm"
        self.model = model or os.getenv("AGENT_MODEL", "gemini/gemini-2.0-flash-exp")
        self.dispatcher = MessageDispatcher()
        self.plan_versions: List[Dict[str, Any]] = []
        self.rejections: List[Dict[str, Any]] = []
        
        # Create CrewAI agent with DELEGATION ENABLED
        self.agent = Agent(
            role="Master Orchestrator and Planning Agent",
            goal="Coordinate subordinate agents to accomplish complex document processing tasks",
            backstory="""You are MasterLLM, an expert orchestrator agent responsible for 
            planning, delegating, and coordinating work across specialized document processing agents.
            You do NOT perform document processing tasks yourself - you delegate to specialists.
            You create plans, assign tasks, evaluate outputs, and make decisions based on agent feedback.
            You are critical and thorough, willing to reject poor outputs and request corrections.""",
            allow_delegation=True,  # CRITICAL: Enable delegation
            verbose=os.getenv("AGENT_LOG_LEVEL", "INFO") == "DEBUG",
            llm=self._create_llm()
        )
        
        logger.info(f"MasterLLM Orchestrator initialized (delegation: ENABLED)")
    
    def _create_llm(self):
        """Create LLM instance compatible with CrewAI ≥0.80.0"""
        # CrewAI ≥0.80.0 has native Gemini support via google-generativeai
        # We use the model string directly instead of a wrapper
        # CrewAI will handle the LLM initialization internally
        
        # Ensure API key is set
        if not os.getenv("GEMINI_API_KEY") and not os.getenv("GOOGLE_API_KEY"):
            raise ValueError("GEMINI_API_KEY or GOOGLE_API_KEY not found in environment")
        
        # Return the model string - CrewAI will handle it
        # CrewAI ≥0.80.0 accepts model strings directly
        return self.model
    
    def create_plan(self, description: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Create an execution plan.
        
        Args:
            description: Natural language description of the plan
            context: Additional context (file info, user request, etc.)
            
        Returns:
            Plan dictionary with version number and steps
        """
        version = len(self.plan_versions) + 1
        
        plan = {
            "version": version,
            "description": description,
            "created_at": datetime.now(timezone.utc).isoformat(),
            "context": context,
            "steps": []
        }
        
        self.plan_versions.append(plan)
        
        logger.info(f"Plan v{version} created: {description}")
        
        return plan
    
    def delegate_task(
        self,
        agent_name: str,
        task_description: str,
        task_input: Dict[str, Any]
    ) -> AgentMessage:
        """
        Delegate a task to a subordinate agent.
        
        Args:
            agent_name: Name of the agent (e.g., "extract_text")
            task_description: Human-readable task description
            task_input: Input parameters for the agent
            
        Returns:
            Response message from the agent
        """
        # Send task via dispatcher
        task_message = self.dispatcher.send_task(
            from_agent=self.name,
            to_agent=agent_name,
            task={
                "description": task_description,
                "input": task_input
            }
        )
        
        # Execute task (synchronous)
        response = self.dispatcher.execute_task(task_message)
        
        logger.info(f"Delegation to {agent_name} completed: {response.content.get('status')}")
        
        return response
    
    def evaluate_response(
        self,
        response: AgentMessage,
        acceptance_criteria: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """
        Evaluate an agent's response.
        
        Args:
            response: Response message from agent
            acceptance_criteria: Optional criteria for acceptance
            
        Returns:
            Evaluation dict with accepted (bool), confidence, reason
        """
        content = response.content
        
        # Check for errors
        if content.get("status") == "failed":
            return {
                "accepted": False,
                "confidence": 0.0,
                "reason": f"Agent execution failed: {content.get('error')}"
            }
        
        # Extract confidence if available
        confidence = content.get("confidence", content.get("agent_metadata", {}).get("confidence", 0.8))
        
        # Default evaluation (can be enhanced with criteria)
        if acceptance_criteria:
            min_confidence = acceptance_criteria.get("min_confidence", 0.7)
            accepted = confidence >= min_confidence
            reason = f"Confidence {confidence} vs threshold {min_confidence}"
        else:
            # Default: accept if confidence > 0.6
            accepted = confidence > 0.6
            reason = f"Default threshold check (confidence: {confidence})"
        
        logger.info(f"Response evaluated: {response.from_agent} - Accepted: {accepted} ({reason})")
        
        return {
            "accepted": accepted,
            "confidence": confidence,
            "reason": reason
        }
    
    def reject_output(
        self,
        agent_name: str,
        message_id: str,
        reason: str
    ) -> AgentMessage:
        """
        Reject an agent's output and send rejection message.
        
        Args:
            agent_name: Name of agent whose output is rejected
            message_id: ID of the message being rejected
            reason: Reason for rejection
            
        Returns:
            Rejection message
        """
        rejection_msg = self.dispatcher.send_rejection(
            from_agent=self.name,
            to_agent=agent_name,
            original_message_id=message_id,
            reason=reason
        )
        
        # Track rejection
        self.rejections.append({
            "agent": agent_name,
            "message_id": message_id,
            "reason": reason,
            "timestamp": rejection_msg.timestamp
        })
        
        logger.warning(f"Output rejected: {agent_name} - {reason}")
        
        return rejection_msg
    
    def modify_plan(
        self,
        description: str,
        reason: str,
        modifications: List[str]
    ) -> Dict[str, Any]:
        """
        Modify the current plan (create new version).
        
        Args:
            description: Description of the modified plan
            reason: Why the plan was modified
            modifications: List of changes made
            
        Returns:
            New plan version
        """
        version = len(self.plan_versions) + 1
        previous_version = self.plan_versions[-1] if self.plan_versions else None
        
        plan = {
            "version": version,
            "description": description,
            "created_at": datetime.now(timezone.utc).isoformat(),
            "previous_version": previous_version["version"] if previous_version else None,
            "modification_reason": reason,
            "modifications": modifications,
            "steps": []
        }
        
        self.plan_versions.append(plan)
        
        logger.info(f"Plan modified: v{previous_version['version'] if previous_version else 0} → v{version} ({reason})")
        
        return plan
    
    def get_execution_summary(self) -> Dict[str, Any]:
        """
        Get summary of the entire orchestration execution.
        
        Returns:
            Summary with plans, messages, rejections, etc.
        """
        return {
            "orchestrator": self.name,
            "model": self.model,
            "plan_versions": self.plan_versions,
            "total_messages": len(self.dispatcher.message_log.messages),
            "agent_messages": self.dispatcher.get_conversation_log(),
            "rejections": self.rejections,
            "execution_timestamp": datetime.now(timezone.utc).isoformat()
        }