"""
bbb_consensus_miner.py

Part of Pillar 3: Metabolic Loop Architecture
Purpose: Mines the BBB Cooling Ledger for consensus patterns to inform active perception.

DITEMPA BUKAN DIBERI - Forged v50.4
"""

import json
from dataclasses import dataclass
from datetime import datetime, timedelta, timezone
from typing import Any, Dict, List, Optional


@dataclass
class ConsensusPattern:
    """Pattern extracted from consensus history."""
    floor: str                # e.g., "F6"
    drift: float              # Observed drift from threshold (e.g., -0.05)
    severity: float           # 0.0 - 1.0
    frequency: int            # Occurrences in window
    recommendation: str       # "TIGHTEN", "LOOSEN", "MAINTAIN"

class ConsensusMiner:
    """
    Mines cooling ledger for retroactive patterns.
    Functions as the hippocampus (Pattern Memory) for the Metabolic Loop.
    """

    def __init__(self, ledger_path: str = "audit_trail/"):
        self.ledger_path = ledger_path

    def mine_patterns(self, window_days: int = 7) -> List[ConsensusPattern]:
        """
        Scan ledger for patterns over the lookback window.

        Note: In Phase 1, this simulates mining by analyzing simulated logs.
        Real implementation will walk the filesystem or query a DB.
        """
        # TODO: Implement real filesystem walk of self.ledger_path
        # For now, return a placeholder pattern to verify wiring

        simulated_drift = 0.04 # Simulating F6 drift

        return [
            ConsensusPattern(
                floor="F6",
                drift=simulated_drift,
                severity=0.4,
                frequency=3,
                recommendation="TIGHTEN"
            )
        ]

    def get_active_feedback(self) -> Dict[str, Any]:
        """
        Get actionable feedback for current cycle (Input to 111_sense).
        """
        patterns = self.mine_patterns()

        feedback = {
            "metrics": {
                "drift_f6": 0.0,
                "volatility": 0.0
            },
            "alerts": []
        }

        for p in patterns:
            if p.floor == "F6" and p.recommendation == "TIGHTEN":
                feedback["metrics"]["drift_f6"] = p.drift
                feedback["alerts"].append("F6_DRIFT_DETECTED")

        return feedback

# Global Miner Instance
MINER = ConsensusMiner()
