#!/usr/bin/env python3
"""
TMLPD Improvement Council - Multi-LLM Decision Making

Uses multiple AI providers to analyze, debate, and vote on
the best improvement path for TMLPD v2.0.

Council Members:
1. Anthropic Claude (Architectural perspective)
2. OpenAI GPT-4 (Practical implementation)
3. Google Gemini (Research alignment)
4. Cerebras Llama (Cost/benefit analysis)
"""

import asyncio
import json
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Any

# Mock implementations (in real system, use actual API calls)
class CouncilMember:
    """A council member with specific perspective"""

    def __init__(self, name: str, provider: str, perspective: str):
        self.name = name
        self.provider = provider
        self.perspective = perspective

    async def deliberate(self, proposals: List[Dict]) -> Dict[str, Any]:
        """
        Analyze proposals and provide recommendations

        Returns: Ranking with reasoning
        """
        # In production, call actual LLM API
        # For now, return perspective-based analysis

        analysis = {
            "member": self.name,
            "provider": self.provider,
            "perspective": self.perspective,
            "rankings": [],
            "reasoning": ""
        }

        # Different perspectives weight factors differently
        if self.perspective == "architectural":
            # Prioritize: System design, scalability, maintainability
            priority_factors = ["scalability", "maintainability", "abstraction"]
            analysis["reasoning"] = (
                "Focus on solid architectural foundations that enable "
                "future growth. Multi-provider system and difficulty-aware "
                "routing provide the best extensibility."
            )

        elif self.perspective == "practical":
            # Prioritize: Implementation speed, user value, quick wins
            priority_factors = ["implementation_speed", "user_value", "quick_wins"]
            analysis["reasoning"] = (
                "Prioritize features that provide immediate user value. "
                "CLI interface and error handling give instant usability "
                "improvements."
            )

        elif self.perspective == "research":
            # Prioritize: Research backing, innovation, alignment with trends
            priority_factors = ["research_support", "innovation", "trend_alignment"]
            analysis["reasoning"] = (
                "Emphasize improvements with strong research backing. "
                "Advanced memory systems and difficulty-aware routing have "
                "solid arXiv validation."
            )

        elif self.perspective == "cost_benefit":
            # Prioritize: Cost reduction, ROI, efficiency
            priority_factors = ["cost_savings", "roi", "efficiency"]
            analysis["reasoning"] = (
                "Focus on improvements that reduce operational costs and "
                "increase efficiency. Multi-provider routing enables 40-60% "
                "cost reduction (MONK benchmarks)."
            )

        # Score each proposal
        for proposal in proposals:
            score = self._score_proposal(proposal, priority_factors)
            analysis["rankings"].append({
                "proposal": proposal["name"],
                "score": score,
                "justification": self._justify_score(proposal, score)
            })

        # Sort by score
        analysis["rankings"].sort(key=lambda x: x["score"], reverse=True)

        return analysis

    def _score_proposal(self, proposal: Dict, factors: List[str]) -> float:
        """Score a proposal based on perspective factors"""
        score = 0.0

        # Base score from value rating
        score += proposal.get("value", 0) * 20  # 0-100 points

        # Adjust for effort (lower effort = better)
        effort_days = proposal.get("effort_days", 5)
        score += (10 - effort_days) * 5  # Prefer quicker implementations

        # Research backing boost
        if proposal.get("research_backed"):
            if "research" in factors:
                score += 15

        # Impact boost
        impact = proposal.get("impact", 0)
        if impact >= 4 and "user_value" in factors:
            score += 10

        return min(score, 100)

    def _justify_score(self, proposal: Dict, score: float) -> str:
        """Generate justification for the score"""
        justifications = []

        if score >= 80:
            justifications.append("Critical priority")
        elif score >= 60:
            justifications.append("High priority")
        elif score >= 40:
            justifications.append("Medium priority")
        else:
            justifications.append("Lower priority")

        if proposal.get("research_backed"):
            justifications.append("Strong research support")

        if proposal.get("effort_days", 5) <= 2:
            justifications.append("Quick to implement")

        return ", ".join(justifications)


class ImprovementCouncil:
    """Council of AI experts for decision making"""

    def __init__(self):
        self.members = [
            CouncilMember(
                name="Claude (Architect)",
                provider="anthropic",
                perspective="architectural"
            ),
            CouncilMember(
                name="GPT-4 (Pragmatist)",
                provider="openai",
                perspective="practical"
            ),
            CouncilMember(
                name="Gemini (Researcher)",
                provider="google",
                perspective="research"
            ),
            CouncilMember(
                name="Llama (Cost Analyst)",
                provider="cerebras",
                perspective="cost_benefit"
            )
        ]

        self.proposals = [
            {
                "name": "Multi-Provider System with Health Monitoring",
                "effort_days": 3,
                "value": 5,
                "impact": 5,
                "research_backed": True,
                "citations": [
                    "https://arxiv.org/html/2506.12508v1",
                    "https://arxiv.org/abs/2511.15755"
                ],
                "description": "Enable switching between Anthropic, OpenAI, Cerebras, etc. with health monitoring and automatic failover. MONK benchmarks show 40-60% cost reduction."
            },
            {
                "name": "Difficulty-Aware Routing",
                "effort_days": 2,
                "value": 5,
                "impact": 5,
                "research_backed": True,
                "citations": [
                    "https://arxiv.org/html/2509.11079v2"
                ],
                "description": "Classify tasks by difficulty (trivial/simple/medium/complex/expert) and route to optimal providers. Research shows 35% decision quality improvement."
            },
            {
                "name": "Advanced Memory System (Memoria-inspired)",
                "effort_days": 4,
                "value": 5,
                "impact": 5,
                "research_backed": True,
                "citations": [
                    "https://www.arxiv.org/abs/2512.12686",
                    "https://arxiv.org/abs/2502.12110"
                ],
                "description": "Multi-tier memory: episodic (JSON), semantic (vector DB), working (in-memory). Research shows 50% improvement in long-term coherence."
            },
            {
                "name": "Workflow Executors (Chaining & Parallelization)",
                "effort_days": 3,
                "value": 5,
                "impact": 4,
                "research_backed": True,
                "citations": [
                    "https://arxiv.org/abs/2511.15755"
                ],
                "description": "Implement chaining (sequential) and parallelization (concurrent) executors. Unlocks the 15% workflow use case."
            },
            {
                "name": "CLI Interface with Rich Output",
                "effort_days": 3,
                "value": 4,
                "impact": 4,
                "research_backed": False,
                "description": "Command-line tool with tmlpd execute, route, memory commands. Makes TMLPD practical for daily use."
            },
            {
                "name": "Function Calling / Tool Use Enhancement",
                "effort_days": 2,
                "value": 4,
                "impact": 4,
                "research_backed": True,
                "citations": [
                    "https://arxiv.org/html/2409.00920v2"
                ],
                "description": "Skills as callable functions with structured parameters. Research shows 40% reliability improvement."
            },
            {
                "name": "Git-Versioned Context Management",
                "effort_days": 2,
                "value": 3,
                "impact": 3,
                "research_backed": True,
                "citations": [
                    "https://arxiv.org/abs/2508.00031"
                ],
                "description": "Git-like branching and versioning for checkpoints. Enables experiment tracking and reproducibility."
            },
            {
                "name": "Better Error Messages & Logging",
                "effort_days": 1,
                "value": 3,
                "impact": 3,
                "research_backed": False,
                "description": "Actionable error messages with suggestions. Comprehensive logging for debugging."
            }
        ]

    async def deliberate(self) -> Dict[str, Any]:
        """
        Run council deliberation process

        Steps:
        1. Each member analyzes proposals independently
        2. Aggregate rankings
        3. Identify consensus and disagreements
        4. Generate final recommendation
        """
        print("\n" + "=" * 70)
        print("  TMLPD IMPROVEMENT COUNCIL - MULTI-LLM DECISION MAKING")
        print("=" * 70 + "\n")

        # Step 1: Individual deliberations
        print("Step 1: Gathering individual council member opinions...\n")

        analyses = []
        for member in self.members:
            print(f"  🤔 {member.name} ({member.provider}) deliberating...")
            analysis = await member.deliberate(self.proposals)
            analyses.append(analysis)
            print(f"     ✓ Complete")

        # Step 2: Aggregate rankings
        print("\nStep 2: Aggregating rankings and identifying consensus...\n")

        aggregated = self._aggregate_rankings(analyses)

        # Step 3: Display results
        self._display_council_results(analyses, aggregated)

        # Step 4: Generate final recommendation
        recommendation = self._generate_recommendation(aggregated)

        # Save results
        self._save_council_decision(analyses, aggregated, recommendation)

        return {
            "individual_analyses": analyses,
            "aggregated_rankings": aggregated,
            "recommendation": recommendation
        }

    def _aggregate_rankings(self, analyses: List[Dict]) -> Dict[str, Any]:
        """Aggregate rankings from all council members"""

        # Calculate average scores for each proposal
        proposal_scores = {p["name"]: {"total": 0, "count": 0, "scores": []} for p in self.proposals}

        for analysis in analyses:
            for ranking in analysis["rankings"]:
                proposal_name = ranking["proposal"]
                score = ranking["score"]

                proposal_scores[proposal_name]["total"] += score
                proposal_scores[proposal_name]["count"] += 1
                proposal_scores[proposal_name]["scores"].append(score)

        # Calculate averages
        aggregated = []
        for proposal_name, scores in proposal_scores.items():
            avg_score = scores["total"] / scores["count"]
            std_dev = self._calculate_std_dev(scores["scores"])

            # Find consensus level (low std_dev = high consensus)
            if std_dev < 10:
                consensus = "strong"
            elif std_dev < 20:
                consensus = "moderate"
            else:
                consensus = "weak"

            aggregated.append({
                "proposal": proposal_name,
                "average_score": avg_score,
                "std_deviation": std_dev,
                "consensus": consensus
            })

        # Sort by average score
        aggregated.sort(key=lambda x: x["average_score"], reverse=True)

        return aggregated

    def _calculate_std_dev(self, scores: List[float]) -> float:
        """Calculate standard deviation"""
        if len(scores) < 2:
            return 0.0

        mean = sum(scores) / len(scores)
        variance = sum((s - mean) ** 2 for s in scores) / len(scores)
        return variance ** 0.5

    def _display_council_results(self, analyses: List[Dict], aggregated: List[Dict]):
        """Display council deliberation results"""

        print("\n" + "=" * 70)
        print("  COUNCIL MEMBER OPINIONS")
        print("=" * 70 + "\n")

        for analysis in analyses:
            print(f"🤖 {analysis['member']} ({analysis['provider']})")
            print(f"   Perspective: {analysis['perspective']}")
            print(f"   Reasoning: {analysis['reasoning']}")
            print(f"\n   Top Rankings:")

            for i, ranking in enumerate(analysis["rankings"][:3], 1):
                print(f"   {i}. {ranking['proposal']}: {ranking['score']:.1f}/100")
                print(f"      {ranking['justification']}")

            print()

        print("\n" + "=" * 70)
        print("  AGGREGATED COUNCIL RANKINGS")
        print("=" * 70 + "\n")

        for i, item in enumerate(aggregated, 1):
            consensus_indicator = {
                "strong": "🤝 Strong consensus",
                "moderate": "🤷 Moderate agreement",
                "weak": "⚠️  Disagreement"
            }

            print(f"{i}. **{item['proposal']}**")
            print(f"   Score: {item['average_score']:.1f}/100")
            print(f"   Consensus: {consensus_indicator[item['consensus']]}")
            print()

    def _generate_recommendation(self, aggregated: List[Dict]) -> Dict[str, Any]:
        """Generate final council recommendation"""

        # Top 3 proposals
        top_3 = aggregated[:3]

        # Check if there's strong consensus on #1
        if top_3[0]["consensus"] == "strong":
            implementation_strategy = "sequential"
            reasoning = (
                "Strong council consensus on top priority. "
                "Implement sequentially for best results."
            )
        else:
            implementation_strategy = "parallel"
            reasoning = (
                "Moderate disagreement on priorities. "
                "Consider parallel implementation of top 2-3 items."
            )

        recommendation = {
            "primary_recommendation": top_3[0]["proposal"],
            "implementation_strategy": implementation_strategy,
            "reasoning": reasoning,
            "proposed_roadmap": []
        }

        # Generate roadmap based on strategy
        if implementation_strategy == "sequential":
            # Sequential roadmap
            total_weeks = 0
            for item in aggregated:
                proposal = next(p for p in self.proposals if p["name"] == item["proposal"])
                effort_days = proposal.get("effort_days", 5)
                weeks = effort_days / 5

                total_weeks += weeks

                recommendation["proposed_roadmap"].append({
                    "phase": len(recommendation["proposed_roadmap"]) + 1,
                    "proposal": item["proposal"],
                    "duration_weeks": weeks,
                    "cumulative_weeks": total_weeks
                })
        else:
            # Parallel roadmap (top items in parallel)
            recommendation["proposed_roadmap"] = [
                {
                    "phase": 1,
                    "proposal": "Parallel implementation of top 3",
                    "duration_weeks": 3,
                    "items": [item["proposal"] for item in top_3]
                }
            ]

        return recommendation

    def _save_council_decision(
        self,
        analyses: List[Dict],
        aggregated: List[Dict],
        recommendation: Dict
    ):
        """Save council decision to file"""

        decision_doc = {
            "timestamp": datetime.now().isoformat(),
            "council_members": [m["member"] for m in analyses],
            "individual_analyses": analyses,
            "aggregated_rankings": aggregated,
            "recommendation": recommendation
        }

        output_path = Path("/Users/Subho/tmlpd-skill/docs/COUNCIL_DECISION.json")

        with open(output_path, 'w') as f:
            json.dump(decision_doc, f, indent=2)

        print(f"\n✅ Council decision saved to: {output_path}")


async def main():
    """Run improvement council"""

    council = ImprovementCouncil()
    decision = await council.deliberate()

    print("\n" + "=" * 70)
    print("  FINAL COUNCIL RECOMMENDATION")
    print("=" * 70 + "\n")

    rec = decision["recommendation"]

    print(f"🎯 Primary Recommendation:")
    print(f"   {rec['primary_recommendation']}")
    print()
    print(f"📋 Implementation Strategy:")
    print(f"   {rec['implementation_strategy']}")
    print()
    print(f"💡 Reasoning:")
    print(f"   {rec['reasoning']}")
    print()
    print(f"🗓️  Proposed Roadmap:")
    print()

    for phase in rec["proposed_roadmap"]:
        print(f"   Phase {phase['phase']}:")
        if "proposal" in phase:
            print(f"   - {phase['proposal']}")
            print(f"   - Duration: {phase['duration_weeks']:.1f} weeks")
        else:
            print(f"   - {phase['proposal']}")
            print(f"   - Duration: {phase['duration_weeks']:.1f} weeks")
            print(f"   - Items: {', '.join(phase['items'])}")
        print()

    print("=" * 70)
    print("\n✨ Council deliberation complete!")
    print("   See docs/COUNCIL_DECISION.json for full details.\n")


if __name__ == "__main__":
    asyncio.run(main())
