"""
AGI Neural Core (The Thinker)
Authority: F2 (Truth) + F6 (Context)
Metabolic Stages: 111, 222, 333
"""
import logging
import math
import random
import time
from typing import Any, Dict, List

logger = logging.getLogger("agi_kernel")

class AGINeuralCore:
    """
    The Orthogonal Thinking Kernel.
    Pure Logic. No Side Effects.
    """

    @staticmethod
    async def sense(query: str, context_meta: Dict[str, Any] = None) -> Dict[str, Any]:
        """Stage 111: Active Context Sensing (Energy/Entropy)."""
        timestamp = time.time()

        # Calculate Entropy
        prob = [float(query.count(c)) / len(query) for c in dict.fromkeys(list(query))]
        entropy = -sum([p * math.log(p) / math.log(2.0) for p in prob])

        # Energy State
        is_urgent = "!" in query or query.isupper()
        energy_state = "HIGH" if is_urgent else "BALANCED"

        origin = context_meta.get("origin", "Unknown") if context_meta else "User_Direct"
        lane = "HARD" if any(x in query.lower() for x in ["what", "when", "who", "fact", "truth"]) else "SOFT"

        return {
            "meta": {
                "timestamp": timestamp,
                "entropy_score": round(entropy, 3),
                "energy_state": energy_state,
                "origin_context": origin,
                "lane_classification": lane
            }
        }

    @staticmethod
    async def reflect(thought: str, thought_number: int, total_thoughts: int, next_needed: bool) -> Dict[str, Any]:
        """Stage 222: Sequential Reflection."""
        # In a real implementation, this would invoke the SequentialThinking model
        # For the kernel, we validate the step
        return {
            "status": "Reflected",
            "thought_index": f"{thought_number}/{total_thoughts}",
            "requires_more": next_needed,
            "integrity_hash": str(hash(thought))
        }

    @staticmethod
    async def atlas_tac_analysis(inputs: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Stage 333: TAC Engine (Theory of Anomalous Contrast)."""
        # Simulate Contrast Mining
        consensus_score = random.uniform(0.7, 1.0)
        contrast_heat = 1.0 - consensus_score

        insight = "High Divergence - Mining Truth" if contrast_heat > 0.5 else "Consensus Detected"

        return {
            "tac_metrics": {
                "contrast_heat": contrast_heat,
                "useful_heat": True,
                "anomaly_detected": contrast_heat > 0.8
            },
            "insight": insight
        }
