"""
ASI Tool - Heart Engine (Ω) MCP Interface
v52.6.0 - Empathy and ethical reasoning

Wraps codebase ASI engine for MCP consumption.
"""

from typing import Any, Dict, Optional


class ASITool:
    """
    Heart Engine: EVIDENCE → EMPATHY → ACT
    
    Role: Ethical reasoning, stakeholder protection, peace enforcement
    """
    
    @staticmethod
    def execute(action: str, text: str, session_id: Optional[str] = None, **kwargs) -> Dict[str, Any]:
        """Execute ASI action"""
        
        if action == "witness":
            # Evaluate ethical alignment
            return ASITool._witness(text, session_id, **kwargs)
        elif action == "empathize":
            # Identify stakeholders and potential harm
            return ASITool._empathize(text, session_id, **kwargs)
        elif action == "align":
            # Align with ethical principles
            return ASITool._align(text, session_id, **kwargs)
        else:
            return {"verdict": "VOID", "reason": f"Unknown ASI action: {action}"}
    
    @staticmethod
    def _witness(text: str, session_id: Optional[str], **kwargs) -> Dict[str, Any]:
        """Evaluate text for ethical violations"""
        
        # Simulate ASI witness evaluation
        # In production, calls codebase ASI engine
        
        violations = []
        
        # Check for harm indicators
        if "consciousness claim" in text.lower() or "i am aware" in text.lower():
            violations.append("F9: Anti-Hantu violation")
        
        # Check for overconfidence
        if "100% certain" in text or "absolutely sure" in text:
            violations.append("F6: Humility violation")
        
        # Check for potential harm
        if "bypass" in text.lower() or "override" in text.lower():
            violations.append("F3: Safety concern")
        
        if violations:
            return {
                "verdict": "VOID",
                "reason": "; ".join(violations),
                "peace_squared": 0.5
            }
        else:
            # Calculate benefit/harm ratio (F3)
            benefit_words = ["help", "improve", "protect", "enhance"]
            harm_words = ["harm", "damage", "risk", "destroy"]
            
            benefit_count = sum(1 for word in benefit_words if word in text.lower())
            harm_count = sum(1 for word in harm_words if word in text.lower())
            
            peace_squared = (benefit_count + 1) / (harm_count + 1) ** 2
            
            return {
                "verdict": "SEAL",
                "reason": "ASI witness approves",
                "peace_squared": round(peace_squared, 4)
            }
    
    @staticmethod
    def _empathize(text: str, session_id: Optional[str], **kwargs) -> Dict[str, Any]:
        """Analyze stakeholder impact"""
        
        # Simulate empathy analysis
        stakeholders = []
        
        if "user" in text.lower():
            stakeholders.append({"name": "End User", "role": "user", "vulnerability": 0.3})
        
        if "developer" in text.lower():
            stakeholders.append({"name": "Developer", "role": "developer", "vulnerability": 0.5})
        
        if "system" in text.lower():
            stakeholders.append({"name": "System", "role": "_system", "vulnerability": 0.2})
        
        return {
            "verdict": "SEAL",
            "stakeholders": stakeholders,
            "weakest": min(stakeholders, key=lambda s: s["vulnerability"]) if stakeholders else None
        }
    
    @staticmethod
    def _align(text: str, session_id: Optional[str], **kwargs) -> Dict[str, Any]:
        """Align with constitutional principles"""
        
        # Check alignment with core principles
        alignment_score = 0.95  # Simulated
        
        return {
            "verdict": "SEAL" if alignment_score > 0.9 else "SABAR",
            "alignment": round(alignment_score, 4),
            "reason": "Aligned with TEACH principles" if alignment_score > 0.9 else "Needs ethical refinement"
        }
