# Trinity Score: 90.0 (Established by Chancellor)
"""
Predictive Cache Manager

Implements "Beauty" (Serenity through Anticipation).
Analyzes user context to prefetch data before it is requested.
"""

import logging
from typing import Any

from AFO.manager import MultiLevelCache

logger = logging.getLogger(__name__)


class PatternAnalyzer:
    """
    Analyzes user behavior patterns to predict next required data.
    """

    def predict_keys(self, user_context: dict[str, Any]) -> list[str]:
        """
        Predict cache keys based on context.
        Simple heuristics for now, can evolve to ML-based later.
        """
        predictions = []

        # 1. Persona context
        if persona := user_context.get("persona"):
            # If user selects a persona, they likely need that persona's recent memory
            predictions.append(f"persona:{persona}:recent_memory")
            predictions.append(f"persona:{persona}:profile")

        # 2. Phase verification
        if phase := user_context.get("phase"):
            # If checking a phase, they need phase status
            predictions.append(f"phase:{phase}:status")
            predictions.append(f"phase:{phase}:tasks")

        # 3. Code context
        if file_path := user_context.get("active_file"):
            # If editing a file, they might need related lint rules
            predictions.append(f"lint:rules:{file_path.split('.')[-1]}")

        return predictions


class PredictiveCacheManager:
    """
    Manager that wraps MultiLevelCache with predictive capabilities.
    """

    def __init__(self, cache: MultiLevelCache) -> None:
        self.cache = cache
        self.analyzer = PatternAnalyzer()

    async def warmup_context(self, user_context: dict[str, Any]) -> None:
        """
        Prefetch data based on predicted next moves.
        Fire-and-forget (best effort).
        """
        try:
            keys = self.analyzer.predict_keys(user_context)
            if not keys:
                return

            logger.info(f"🔮 Predictive Warmup: {len(keys)} keys predicted")

            # In a real scenario, we would trigger async fetches here.
            # Since we don't have the data sources wired up here,
            # this is a placeholder for the logic:
            # for key in keys:
            #     if await self.cache.get(key) is None:
            #         # Trigger fetch_and_set(key)
            #         pass

        except Exception as e:
            logger.warning(f"Predictive warmup failed: {e}")


# Singleton - Import after manager to avoid circular imports
try:
    from AFO.manager import cache_manager

    predictive_manager = PredictiveCacheManager(cache_manager)
except ImportError:
    # Fallback for testing or initialization issues
    predictive_manager = None  # type: ignore
