import logging
import asyncio
from typing import List, Dict, Any

logger = logging.getLogger(__name__)

class WarRoom:
    """
    Orchestrates a "Multi-Agent Debate" for critical security decisions.
    Inspired by BettaFish's ForumEngine.
    """

    AGENT_PERSONAS = {
        "ExploitAnalyst": "You focus on proof-of-concept availability and technical severity.",
        "ComplianceGuardian": "You focus on data privacy, legal risks, and regulatory impact.",
        "StrategicScorer": "You evaluate the overall probability of exploitation in the current environment."
    }

    async def host_vulnerability_debate(self, threat_summary: str) -> Dict[str, Any]:
        """
        Spawns specialized agent personas to debate a threat and reach a consensus.
        """
        logger.info("Initializing OmniClaw War Room debate...")
        
        # In a real system, these would call LLM prompts for each persona.
        # For now, we simulate the 'Chain of Thought' consensus mechanism.
        
        debate_log = []
        
        # Round 1: Analysis
        debate_log.append({"agent": "ExploitAnalyst", "statement": f"Checking PoC status for: {threat_summary}. Risk: High if RCE is confirmed."})
        debate_log.append({"agent": "ComplianceGuardian", "statement": f"Evaluating GDPR impact. High risk to PII data."})
        
        # Round 2: Rebuttal/Refinement
        debate_log.append({"agent": "StrategicScorer", "statement": "Combined risk is 8.5/10. Recommending immediate isolation."})
        
        # Consensus
        consensus = {
            "severity": "CRITICAL",
            "score": 8.5,
            "rationale": "ExploitAnalyst confirms PoC; ComplianceGuardian notes high data risk.",
            "debate_log": debate_log
        }
        
        logger.info("Consensus reached in War Room.")
        return consensus

war_room = WarRoom()
