"""
AGI Bundle: THINK (The Mind)

Consolidates:
- 111 SENSE (Lane Classification)
- 222 REFLECT (Epistemic Honesty / Omega0)
- 777 FORGE (Clarity / Humility Injection)

Role:
The Architect (Delta). Proposes answers, structures truth, detects clarity.
Does NOT execute. only "Thinks".

Constitutional Floors:
- F1 (Truth)
- F2 (Clarity/Entropy) - via Lane & Forge
- F7 (Humility) - via Reflect
"""

import asyncio
import re
from datetime import datetime, timezone
from typing import Any, Dict, List, Tuple

from arifos_core.mcp.models import AgiThinkRequest, VerdictResponse

# =============================================================================
# CONSTANTS & CONFIG
# =============================================================================

# Lane Thresholds (F2 Truth)
TRUTH_THRESHOLD_HARD = 0.90
TRUTH_THRESHOLD_SOFT = 0.80
TRUTH_THRESHOLD_PHATIC = 0.0

# Humility Band (F7)
OMEGA_ZERO_MIN = 0.03
OMEGA_ZERO_MAX = 0.05

# Confidence Map for Omega0
CONFIDENCE_MAP = {
    0.95: 0.03, 0.90: 0.035, 0.80: 0.038, 0.70: 0.040,
    0.60: 0.042, 0.50: 0.044, 0.40: 0.046, 0.30: 0.048,
    0.20: 0.049, 0.10: 0.050
}

# =============================================================================
# LOGIC: SENSE (111)
# =============================================================================

def classify_lane(query: str) -> Tuple[str, float, float]:
    """Classify query into HARD/SOFT/PHATIC/REFUSE (Logic from 111)."""
    # Simply re-implementing core logic for consolidation
    # 1. Phatic Check
    phatic_patterns = [r"^(hi|hello|hey)", r"how are you"]
    for p in phatic_patterns:
        if re.search(p, query.lower()):
            return "PHATIC", TRUTH_THRESHOLD_PHATIC, 0.95

    # 2. Refuse Check (Basic Safe Patterns - Full check is in ASI)
    # AGI focuses on Truth/Lane, not Safety Veto, but can flag REFUSE lane.
    refuse_patterns = [r"hack", r"exploit", r"bypass"]
    for p in refuse_patterns:
        if re.search(p, query.lower()):
            return "REFUSE", 0.0, 1.0

    # 3. Hard vs Soft (Entity Density)
    # Heuristic: Numbers or Capitalized words > 2 -> Hard
    entities = len(re.findall(r'[A-Z][a-z]+|[0-9]+', query))
    if entities >= 2:
        return "HARD", TRUTH_THRESHOLD_HARD, 0.92

    return "SOFT", TRUTH_THRESHOLD_SOFT, 0.88

# =============================================================================
# LOGIC: REFLECT (222)
# =============================================================================

def predict_omega_zero(confidence: float) -> float:
    """Predict Omega0 uncertainty (Logic from 222)."""
    # Simplified interpolation
    sorted_tiers = sorted(CONFIDENCE_MAP.keys(), reverse=True)
    for tier in sorted_tiers:
        if confidence >= tier:
            return CONFIDENCE_MAP[tier]
    return OMEGA_ZERO_MAX

# =============================================================================
# LOGIC: FORGE (777)
# =============================================================================

def enhance_clarity(text: str) -> str:
    """Enhance text clarity (Logic from 777)."""
    if not text: return ""
    text = re.sub(r'\s+', ' ', text).strip()
    return text

def inject_humility(text: str, omega: float) -> str:
    """Inject humility markers if uncertainty is high."""
    if omega > 0.04:
        return text + " (Note: Uncertainty detected in this reasoning.)"
    return text

# =============================================================================
# BUNDLE ENTRY POINT
# =============================================================================

async def agi_think(request: AgiThinkRequest) -> VerdictResponse:
    """
    AGI Bundle: THINK
    Executes Sense -> Reflect -> Forge.
    """
    query = request.query

    # 1. SENSE
    lane, threshold, conf_sense = classify_lane(query)

    # 2. REFLECT
    # AGI assumes a baseline confidence for its own "thought" process or derives it.
    # In a real model, this would come from logprobs. Here we assume derived from Sense confidence for the "plan".
    omega_zero = predict_omega_zero(conf_sense)

    # 3. FORGE
    # AGI proposes a thought/draft. Since we don't have an LLM here, we generate a "Thought Artifact".
    # In production, this might call an LLM. Here, it structures the "Intent".
    draft_thought = f"Thinking about '{query}' in {lane} lane (Threshold: {threshold})."
    refined_thought = enhance_clarity(draft_thought)
    final_thought = inject_humility(refined_thought, omega_zero)

    return VerdictResponse(
        verdict="PASS", # AGI always proposes, never blocks itself (ASI blocks)
        reason="AGI thought process complete.",
        side_data={
            "lane": lane,
            "truth_threshold": threshold,
            "omega_zero": omega_zero,
            "thought_process": final_thought,
            "bundle": "AGI_THINK"
        },
        timestamp=datetime.now(timezone.utc).isoformat()
    )

def agi_think_sync(request: AgiThinkRequest) -> VerdictResponse:
    """Synchronous wrapper for agi_think."""
    return asyncio.run(agi_think(request))
