soma_core/cli/

meta_reflective.rs

use crate::memory::SymbolicContext;
use crate::ops::{default_operator_registry, SomaOperator};
use anyhow::Result;
use crossterm::{
    event::{self, Event, KeyCode, KeyEvent},
    execute,
    style::{Color, Print, ResetColor, SetForegroundColor},
    terminal::{disable_raw_mode, enable_raw_mode, Clear, ClearType},
};

use std::collections::HashMap;
use std::io;
use std::time::{SystemTime, UNIX_EPOCH};

/// Meta-reflective analysis system for system introspection and optimization
pub struct MetaReflectiveSystem {
    pub registry: HashMap<String, Box<dyn SomaOperator>>,
    pub analysis_sessions: Vec<MetaAnalysisSession>,
    pub system_metrics: SystemMetrics,
    pub optimization_history: Vec<OptimizationRecommendation>,
}

/// Session tracking for meta-reflective analysis
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct MetaAnalysisSession {
    pub session_id: String,
    pub timestamp: u64,
    pub system_state: SymbolicContext,
    pub analysis_results: SymbolicContext,
    pub performance_score: f64,
    pub optimization_suggestions: Vec<String>,
    pub cognitive_state: String,
}

/// System performance metrics for meta-analysis
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct SystemMetrics {
    pub operator_count: usize,
    pub active_sessions: usize,
    pub total_operations: usize,
    pub error_count: usize,
    pub average_performance: f64,
    pub cognitive_efficiency: f64,
    pub system_uptime: u64,
}

/// Optimization recommendations with priorities
#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub struct OptimizationRecommendation {
    pub id: String,
    pub category: OptimizationCategory,
    pub priority: Priority,
    pub description: String,
    pub suggested_action: String,
    pub estimated_impact: f64,
    pub implementation_complexity: f64,
}

#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub enum OptimizationCategory {
    Cognitive,
    Performance,
    Memory,
    Reasoning,
    Architecture,
    Efficiency,
}

#[derive(Debug, Clone, serde::Serialize, serde::Deserialize)]
pub enum Priority {
    Critical,
    High,
    Medium,
    Low,
}

/// Interactive CLI mode for meta-reflective analysis
#[derive(Debug, Clone)]
pub enum MetaReflectiveMode {
    SystemIntrospection,
    PerformanceAnalysis,
    CognitiveAssessment,
    OptimizationRecommendations,
    SessionHistory,
    RealtimeMonitoring,
    ExportAnalysis,
}

impl MetaReflectiveSystem {
    /// Create new meta-reflective analysis system
    pub fn new() -> Self {
        Self {
            registry: default_operator_registry(),
            analysis_sessions: Vec::new(),
            system_metrics: SystemMetrics::default(),
            optimization_history: Vec::new(),
        }
    }

    /// Start interactive meta-reflective analysis CLI
    pub fn start_interactive_analysis(&mut self) -> Result<()> {
        enable_raw_mode()?;
        let mut stdout = io::stdout();

        execute!(stdout, Clear(ClearType::All))?;
        self.display_header(&mut stdout)?;

        let mut current_mode = MetaReflectiveMode::SystemIntrospection;
        let mut session_context = SymbolicContext::new();
        self.setup_initial_context(&mut session_context);

        loop {
            self.display_mode_interface(&mut stdout, &current_mode)?;
            self.display_current_analysis(&mut stdout, &session_context, &current_mode)?;

            match event::read()? {
                Event::Key(KeyEvent { code, .. }) => {
                    match code {
                        KeyCode::Char('q') => break,
                        KeyCode::Char('1') => current_mode = MetaReflectiveMode::SystemIntrospection,
                        KeyCode::Char('2') => current_mode = MetaReflectiveMode::PerformanceAnalysis,
                        KeyCode::Char('3') => current_mode = MetaReflectiveMode::CognitiveAssessment,
                        KeyCode::Char('4') => current_mode = MetaReflectiveMode::OptimizationRecommendations,
                        KeyCode::Char('5') => current_mode = MetaReflectiveMode::SessionHistory,
                        KeyCode::Char('6') => current_mode = MetaReflectiveMode::RealtimeMonitoring,
                        KeyCode::Char('7') => current_mode = MetaReflectiveMode::ExportAnalysis,
                        KeyCode::Char('r') => {
                            self.run_meta_analysis(&mut session_context)?;
                        }
                        KeyCode::Char('s') => {
                            self.save_analysis_session(&session_context)?;
                        }
                        KeyCode::Char('o') => {
                            self.generate_optimization_recommendations(&session_context)?;
                        }
                        KeyCode::Enter => {
                            self.execute_mode_action(&mut session_context, &current_mode)?;
                        }
                        _ => {}
                    }
                }
                _ => {}
            }

            execute!(stdout, Clear(ClearType::All))?;
            self.display_header(&mut stdout)?;
        }

        disable_raw_mode()?;
        execute!(stdout, Clear(ClearType::All))?;
        Ok(())
    }

    /// Display header with system status
    fn display_header(&self, stdout: &mut io::Stdout) -> Result<()> {
        execute!(
            stdout,
            SetForegroundColor(Color::Cyan),
            Print("🧠 SOMA Meta-Reflective Analysis System\n"),
            SetForegroundColor(Color::White),
            Print("==========================================\n\n"),
            ResetColor
        )?;

        // Display current system metrics
        execute!(
            stdout,
            SetForegroundColor(Color::Green),
            Print(format!("📊 System Status: {} operators | {} sessions | Uptime: {}s\n",
                self.system_metrics.operator_count,
                self.system_metrics.active_sessions,
                self.system_metrics.system_uptime
            )),
            SetForegroundColor(Color::Yellow),
            Print(format!("⚡ Performance: {:.3} | Efficiency: {:.3} | Errors: {}\n\n",
                self.system_metrics.average_performance,
                self.system_metrics.cognitive_efficiency,
                self.system_metrics.error_count
            )),
            ResetColor
        )?;

        Ok(())
    }

    /// Display mode interface options
    fn display_mode_interface(&self, stdout: &mut io::Stdout, current_mode: &MetaReflectiveMode) -> Result<()> {
        let mode_name = match current_mode {
            MetaReflectiveMode::SystemIntrospection => "🔍 System Introspection",
            MetaReflectiveMode::PerformanceAnalysis => "📈 Performance Analysis", 
            MetaReflectiveMode::CognitiveAssessment => "🧠 Cognitive Assessment",
            MetaReflectiveMode::OptimizationRecommendations => "🎯 Optimization Recommendations",
            MetaReflectiveMode::SessionHistory => "📋 Session History",
            MetaReflectiveMode::RealtimeMonitoring => "⚡ Real-time Monitoring",
            MetaReflectiveMode::ExportAnalysis => "💾 Export Analysis",
        };

        execute!(
            stdout,
            SetForegroundColor(Color::Magenta),
            Print(format!("Current Mode: {}\n", mode_name)),
            ResetColor,
            Print("─────────────────────────────────────────────────────────────\n"),
            Print("🔧 Controls:\n"),
            Print("  [1] System Introspection  [2] Performance Analysis  [3] Cognitive Assessment\n"),
            Print("  [4] Optimizations        [5] Session History      [6] Real-time Monitor\n"),
            Print("  [7] Export Analysis      [r] Run Analysis         [s] Save Session\n"),
            Print("  [o] Generate Optimizations [Enter] Execute Action  [q] Quit\n\n"),
        )?;

        Ok(())
    }

    /// Display current analysis based on mode
    fn display_current_analysis(&self, stdout: &mut io::Stdout, context: &SymbolicContext, mode: &MetaReflectiveMode) -> Result<()> {
        match mode {
            MetaReflectiveMode::SystemIntrospection => {
                self.display_system_introspection(stdout, context)?;
            }
            MetaReflectiveMode::PerformanceAnalysis => {
                self.display_performance_analysis(stdout, context)?;
            }
            MetaReflectiveMode::CognitiveAssessment => {
                self.display_cognitive_assessment(stdout, context)?;
            }
            MetaReflectiveMode::OptimizationRecommendations => {
                self.display_optimization_recommendations(stdout)?;
            }
            MetaReflectiveMode::SessionHistory => {
                self.display_session_history(stdout)?;
            }
            MetaReflectiveMode::RealtimeMonitoring => {
                self.display_realtime_monitoring(stdout, context)?;
            }
            MetaReflectiveMode::ExportAnalysis => {
                self.display_export_options(stdout)?;
            }
        }
        Ok(())
    }

    /// Display system introspection analysis
    fn display_system_introspection(&self, stdout: &mut io::Stdout, context: &SymbolicContext) -> Result<()> {
        execute!(
            stdout,
            SetForegroundColor(Color::Blue),
            Print("🔍 System Introspection Analysis\n"),
            Print("─────────────────────────────\n"),
            ResetColor
        )?;

        // Run introspection if we have analysis data
        if let Some(introspect_op) = self.registry.get("introspect") {
            if let Ok(analysis) = introspect_op.execute(context) {
                execute!(
                    stdout,
                    Print(format!("🧮 Context Size: {}\n", analysis.get("context_size").unwrap_or(&"0".to_string()))),
                    Print(format!("🌊 Reasoning Depth: {}\n", analysis.get("reasoning_depth").unwrap_or(&"0".to_string()))),
                    Print(format!("📊 Complexity Score: {}\n", analysis.get("complexity_score").unwrap_or(&"0".to_string()))),
                    Print(format!("⚠️  Bottleneck: {}\n", analysis.get("bottleneck_detected").unwrap_or(&"none".to_string()))),
                )?;
            }
        }

        // Display context composition
        let context_map = context.flatten();
        execute!(
            stdout,
            Print(format!("\n📋 Context Composition ({} keys):\n", context_map.len())),
        )?;

        for (i, (key, value)) in context_map.iter().take(5).enumerate() {
            let truncated_value = if value.len() > 40 {
                format!("{}...", &value[..37])
            } else {
                value.clone()
            };
            execute!(
                stdout,
                Print(format!("  {}. {}: {}\n", i + 1, key, truncated_value)),
            )?;
        }

        if context_map.len() > 5 {
            execute!(
                stdout,
                Print(format!("  ... and {} more keys\n", context_map.len() - 5)),
            )?;
        }

        Ok(())
    }

    /// Display performance analysis
    fn display_performance_analysis(&self, stdout: &mut io::Stdout, _context: &SymbolicContext) -> Result<()> {
        execute!(
            stdout,
            SetForegroundColor(Color::Green),
            Print("📈 Performance Analysis\n"),
            Print("─────────────────────\n"),
            ResetColor
        )?;

        // Calculate performance metrics
        let total_operations = self.system_metrics.total_operations;
        let error_rate = if total_operations > 0 {
            (self.system_metrics.error_count as f64 / total_operations as f64) * 100.0
        } else {
            0.0
        };

        execute!(
            stdout,
            Print(format!("🎯 Average Performance: {:.3}\n", self.system_metrics.average_performance)),
            Print(format!("⚡ Cognitive Efficiency: {:.3}\n", self.system_metrics.cognitive_efficiency)),
            Print(format!("📊 Total Operations: {}\n", total_operations)),
            Print(format!("❌ Error Rate: {:.2}%\n", error_rate)),
            Print(format!("⏱️  System Uptime: {}s\n", self.system_metrics.system_uptime)),
        )?;

        // Performance trend analysis
        execute!(
            stdout,
            Print("\n📊 Performance Metrics:\n"),
        )?;

        if self.analysis_sessions.len() > 1 {
            let recent_sessions = &self.analysis_sessions[self.analysis_sessions.len().saturating_sub(5)..];
            let avg_recent_performance: f64 = recent_sessions.iter()
                .map(|s| s.performance_score)
                .sum::<f64>() / recent_sessions.len() as f64;

            execute!(
                stdout,
                Print(format!("  📈 Recent 5 sessions avg: {:.3}\n", avg_recent_performance)),
            )?;

            let trend = if avg_recent_performance > self.system_metrics.average_performance {
                "📈 Improving"
            } else if avg_recent_performance < self.system_metrics.average_performance {
                "📉 Declining"  
            } else {
                "➡️  Stable"
            };

            execute!(
                stdout,
                Print(format!("  🎯 Performance trend: {}\n", trend)),
            )?;
        }

        Ok(())
    }

    /// Display cognitive assessment
    fn display_cognitive_assessment(&self, stdout: &mut io::Stdout, context: &SymbolicContext) -> Result<()> {
        execute!(
            stdout,
            SetForegroundColor(Color::Magenta),
            Print("🧠 Cognitive Assessment\n"),
            Print("─────────────────────\n"),
            ResetColor
        )?;

        // Run meta-reflective analysis
        if let Some(meta_op) = self.registry.get("meta_reflective") {
            if let Ok(analysis) = meta_op.execute(context) {
                execute!(
                    stdout,
                    Print(format!("🎯 Performance Score: {}\n", 
                        analysis.get("system_performance_score").unwrap_or(&"0".to_string()))),
                    Print(format!("🧩 Reasoning Patterns: {}\n", 
                        analysis.get("reasoning_patterns_detected").unwrap_or(&"0".to_string()))),
                    Print(format!("🌊 Symbolic Depth: {}\n", 
                        analysis.get("symbolic_depth").unwrap_or(&"1".to_string()))),
                    Print(format!("⚡ Cognitive Efficiency: {}\n", 
                        analysis.get("cognitive_efficiency").unwrap_or(&"1.0".to_string()))),
                    Print(format!("🌟 Emergence Level: {}\n", 
                        analysis.get("emergence_level").unwrap_or(&"moderate".to_string()))),
                    Print(format!("🔮 Meta-Cognitive State: {}\n", 
                        analysis.get("meta_cognitive_state").unwrap_or(&"optimal".to_string()))),
                )?;

                // Display optimization suggestions
                execute!(
                    stdout,
                    Print("\n🎯 System Optimizations (Θ-suggestions):\n"),
                )?;

                for i in 0..3 {
                    if let Some(optimization) = analysis.get(&format!("optimization_Θ_{}", i)) {
                        execute!(
                            stdout,
                            Print(format!("  {}. {}\n", i + 1, optimization)),
                        )?;
                    }
                }
            }
        }

        // Run cognitive load analysis
        if let Some(load_op) = self.registry.get("cognitive_load") {
            if let Ok(load_analysis) = load_op.execute(context) {
                execute!(
                    stdout,
                    Print(format!("\n🏋️ Cognitive Load Analysis:\n")),
                    Print(format!("  📊 Load Score: {}\n", 
                        load_analysis.get("load_score").unwrap_or(&"0".to_string()))),
                    Print(format!("  📈 Load Level: {}\n", 
                        load_analysis.get("load_level").unwrap_or(&"low".to_string()))),
                    Print(format!("  🔧 Optimization Needed: {}\n", 
                        load_analysis.get("optimization_needed").unwrap_or(&"false".to_string()))),
                )?;
            }
        }

        Ok(())
    }

    /// Display optimization recommendations
    fn display_optimization_recommendations(&self, stdout: &mut io::Stdout) -> Result<()> {
        execute!(
            stdout,
            SetForegroundColor(Color::Yellow),
            Print("🎯 Optimization Recommendations\n"),
            Print("──────────────────────────────\n"),
            ResetColor
        )?;

        if self.optimization_history.is_empty() {
            execute!(
                stdout,
                Print("📝 No optimization recommendations yet.\n"),
                Print("   Press [o] to generate recommendations based on current analysis.\n"),
            )?;
        } else {
            // Display recent recommendations
            let recent_optimizations = &self.optimization_history[self.optimization_history.len().saturating_sub(5)..];
            
            for (i, opt) in recent_optimizations.iter().enumerate() {
                let priority_icon = match opt.priority {
                    Priority::Critical => "🚨",
                    Priority::High => "⚠️ ",
                    Priority::Medium => "🔶",
                    Priority::Low => "🔵",
                };

                let category_icon = match opt.category {
                    OptimizationCategory::Cognitive => "🧠",
                    OptimizationCategory::Performance => "⚡",
                    OptimizationCategory::Memory => "💾",
                    OptimizationCategory::Reasoning => "🤔",
                    OptimizationCategory::Architecture => "🏗️ ",
                    OptimizationCategory::Efficiency => "🎯",
                };

                execute!(
                    stdout,
                    Print(format!("{}. {} {} {} Priority\n", 
                        i + 1, priority_icon, category_icon, 
                        format!("{:?}", opt.priority).to_uppercase())),
                    Print(format!("   📋 {}\n", opt.description)),
                    Print(format!("   🎯 Action: {}\n", opt.suggested_action)),
                    Print(format!("   📊 Impact: {:.2} | Complexity: {:.2}\n\n", 
                        opt.estimated_impact, opt.implementation_complexity)),
                )?;
            }
        }

        Ok(())
    }

    /// Display session history
    fn display_session_history(&self, stdout: &mut io::Stdout) -> Result<()> {
        execute!(
            stdout,
            SetForegroundColor(Color::Cyan),
            Print("📋 Session History\n"),
            Print("────────────────\n"),
            ResetColor
        )?;

        if self.analysis_sessions.is_empty() {
            execute!(
                stdout,
                Print("📝 No analysis sessions recorded yet.\n"),
                Print("   Press [r] to run a meta-analysis and create a session.\n"),
            )?;
        } else {
            let recent_sessions = &self.analysis_sessions[self.analysis_sessions.len().saturating_sub(10)..];
            
            for (i, session) in recent_sessions.iter().enumerate() {
                execute!(
                    stdout,
                    Print(format!("{}. Session {} (timestamp: {})\n", 
                        i + 1, &session.session_id[..8], session.timestamp)),
                    Print(format!("   🎯 Performance: {:.3} | State: {}\n", 
                        session.performance_score, session.cognitive_state)),
                    Print(format!("   💡 Optimizations: {} suggestions\n", 
                        session.optimization_suggestions.len())),
                )?;

                if !session.optimization_suggestions.is_empty() {
                    execute!(
                        stdout,
                        Print(format!("   📋 Top suggestion: {}\n", 
                            session.optimization_suggestions[0])),
                    )?;
                }

                execute!(stdout, Print("\n"))?;
            }
        }

        Ok(())
    }

    /// Display real-time monitoring
    fn display_realtime_monitoring(&self, stdout: &mut io::Stdout, context: &SymbolicContext) -> Result<()> {
        execute!(
            stdout,
            SetForegroundColor(Color::Red),
            Print("⚡ Real-time System Monitoring\n"),
            Print("────────────────────────────\n"),
            ResetColor
        )?;

        let current_time = SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_secs();
        
        execute!(
            stdout,
            Print(format!("🕐 Current Time: {}\n", current_time)),
            Print(format!("🔧 Active Context Keys: {}\n", context.flatten().len())),
            Print(format!("📊 Session Count: {}\n", self.analysis_sessions.len())),
            Print(format!("🎯 Last Performance: {:.3}\n", 
                self.analysis_sessions.last().map(|s| s.performance_score).unwrap_or(0.0))),
        )?;

        // Live system metrics
        execute!(
            stdout,
            Print("\n📊 Live Metrics:\n"),
            Print(format!("  🚀 Operators Available: {}\n", self.registry.len())),
            Print(format!("  💾 Memory Usage: {} context keys\n", context.flatten().len())),
            Print(format!("  ⚡ Processing Efficiency: {:.3}\n", self.system_metrics.cognitive_efficiency)),
        )?;

        // System health indicators
        let health_status = if self.system_metrics.error_count == 0 {
            "🟢 Excellent"
        } else if self.system_metrics.error_count < 3 {
            "🟡 Good"
        } else {
            "🔴 Needs Attention"
        };

        execute!(
            stdout,
            Print(format!("\n🏥 System Health: {}\n", health_status)),
        )?;

        Ok(())
    }

    /// Display export options
    fn display_export_options(&self, stdout: &mut io::Stdout) -> Result<()> {
        execute!(
            stdout,
            SetForegroundColor(Color::Green),
            Print("💾 Export Analysis Data\n"),
            Print("──────────────────────\n"),
            ResetColor,
            Print("Available export formats:\n"),
            Print("  📄 JSON - Complete analysis data\n"),
            Print("  📊 CSV - Performance metrics\n"),
            Print("  📋 TXT - Human-readable report\n"),
            Print("  🧠 XML - Structured cognitive data\n\n"),
            Print("Export includes:\n"),
            Print("  • All analysis sessions\n"),
            Print("  • Optimization recommendations\n"),
            Print("  • System performance metrics\n"),
            Print("  • Meta-cognitive assessments\n\n"),
            Print("Press [Enter] to export all data to JSON\n"),
        )?;

        Ok(())
    }

    /// Execute mode-specific actions
    fn execute_mode_action(&mut self, context: &mut SymbolicContext, mode: &MetaReflectiveMode) -> Result<()> {
        match mode {
            MetaReflectiveMode::SystemIntrospection => {
                self.run_introspection_analysis(context)?;
            }
            MetaReflectiveMode::PerformanceAnalysis => {
                self.update_performance_metrics();
            }
            MetaReflectiveMode::CognitiveAssessment => {
                self.run_cognitive_assessment(context)?;
            }
            MetaReflectiveMode::OptimizationRecommendations => {
                self.generate_optimization_recommendations(context)?;
            }
            MetaReflectiveMode::SessionHistory => {
                // Display detailed session view
            }
            MetaReflectiveMode::RealtimeMonitoring => {
                self.update_realtime_metrics(context);
            }
            MetaReflectiveMode::ExportAnalysis => {
                self.export_analysis_data()?;
            }
        }
        Ok(())
    }

    /// Run comprehensive meta-analysis
    pub fn run_meta_analysis(&mut self, context: &mut SymbolicContext) -> Result<()> {
        // Update system context with current metrics
        context.set("operator_count", &self.registry.len().to_string());
        context.set("system_uptime", &self.system_metrics.system_uptime.to_string());
        context.set("errors_count", &self.system_metrics.error_count.to_string());
        context.set("total_operations", &self.system_metrics.total_operations.to_string());

        // Add reasoning patterns to context
        context.set("reasoning_pattern_meta", "meta_reflective_analysis");
        context.set("reasoning_pattern_optimization", "system_optimization");
        context.set("strategy_introspection", "active");

        // Run meta-reflective operator
        if let Some(meta_op) = self.registry.get("meta_reflective") {
            let analysis_result = meta_op.execute(context)?;
            
            // Extract results
            let performance_score = analysis_result
                .get("system_performance_score")
                .unwrap_or(&"0.8".to_string())
                .parse::<f64>()
                .unwrap_or(0.8);

            let cognitive_state = analysis_result
                .get("meta_cognitive_state")
                .unwrap_or(&"optimal".to_string())
                .clone();

            // Collect optimization suggestions
            let mut optimizations = Vec::new();
            for i in 0..5 {
                if let Some(opt) = analysis_result.get(&format!("optimization_Θ_{}", i)) {
                    optimizations.push(opt.clone());
                }
            }

            // Create session record
            let session = MetaAnalysisSession {
                session_id: format!("meta_{}", SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_millis()),
                timestamp: SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_secs(),
                system_state: context.clone(),
                analysis_results: analysis_result,
                performance_score,
                optimization_suggestions: optimizations,
                cognitive_state,
            };

            self.analysis_sessions.push(session);
            
            // Update system metrics
            self.update_performance_metrics();
        }

        Ok(())
    }

    /// Save analysis session to file
    fn save_analysis_session(&self, _context: &SymbolicContext) -> Result<()> {
        if let Some(latest_session) = self.analysis_sessions.last() {
            let filename = format!("meta_analysis_{}.json", latest_session.session_id);
            let session_json = serde_json::to_string_pretty(&latest_session)?;
            std::fs::write(&filename, session_json)?;
            // Note: In a real CLI, we'd show a success message
        }
        Ok(())
    }

    /// Generate optimization recommendations
    fn generate_optimization_recommendations(&mut self, context: &SymbolicContext) -> Result<()> {
        // Run analysis to generate recommendations
        if let Some(meta_op) = self.registry.get("meta_reflective") {
            let analysis = meta_op.execute(context)?;
            
            // Generate specific recommendations based on analysis
            let performance_score = analysis
                .get("system_performance_score")
                .unwrap_or(&"0.8".to_string())
                .parse::<f64>()
                .unwrap_or(0.8);

            if performance_score < 0.7 {
                self.optimization_history.push(OptimizationRecommendation {
                    id: format!("opt_{}", SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_millis()),
                    category: OptimizationCategory::Performance,
                    priority: Priority::High,
                    description: "System performance below optimal threshold".to_string(),
                    suggested_action: "Review cognitive load distribution and optimize bottlenecks".to_string(),
                    estimated_impact: 0.25,
                    implementation_complexity: 0.4,
                });
            }

            // Check cognitive efficiency
            if self.system_metrics.cognitive_efficiency < 0.8 {
                self.optimization_history.push(OptimizationRecommendation {
                    id: format!("opt_{}", SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_millis()),
                    category: OptimizationCategory::Cognitive,
                    priority: Priority::Medium,
                    description: "Cognitive efficiency could be improved".to_string(),
                    suggested_action: "Implement attention focusing and reduce context complexity".to_string(),
                    estimated_impact: 0.15,
                    implementation_complexity: 0.3,
                });
            }

            // Check for high error rates
            if self.system_metrics.error_count > 5 {
                self.optimization_history.push(OptimizationRecommendation {
                    id: format!("opt_{}", SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_millis()),
                    category: OptimizationCategory::Efficiency,
                    priority: Priority::Critical,
                    description: "High error count detected in system operations".to_string(),
                    suggested_action: "Investigate error sources and implement robust error handling".to_string(),
                    estimated_impact: 0.4,
                    implementation_complexity: 0.6,
                });
            }
        }

        Ok(())
    }

    /// Run introspection analysis
    fn run_introspection_analysis(&self, context: &mut SymbolicContext) -> Result<()> {
        if let Some(introspect_op) = self.registry.get("introspect") {
            let _analysis = introspect_op.execute(context)?;
            // Results are displayed in the UI
        }
        Ok(())
    }

    /// Run cognitive assessment
    fn run_cognitive_assessment(&self, context: &mut SymbolicContext) -> Result<()> {
        // Run multiple cognitive operators
        if let Some(load_op) = self.registry.get("cognitive_load") {
            let _load_analysis = load_op.execute(context)?;
        }

        if let Some(attention_op) = self.registry.get("attention_focus") {
            let _attention_analysis = attention_op.execute(context)?;
        }

        Ok(())
    }

    /// Update performance metrics
    fn update_performance_metrics(&mut self) {
        self.system_metrics.total_operations += 1;
        
        if !self.analysis_sessions.is_empty() {
            let total_score: f64 = self.analysis_sessions.iter()
                .map(|s| s.performance_score)
                .sum();
            self.system_metrics.average_performance = total_score / self.analysis_sessions.len() as f64;
        }

        // Update cognitive efficiency based on error rate
        if self.system_metrics.total_operations > 0 {
            self.system_metrics.cognitive_efficiency = 
                1.0 - (self.system_metrics.error_count as f64 / self.system_metrics.total_operations as f64);
        }

        self.system_metrics.active_sessions = self.analysis_sessions.len();
        self.system_metrics.system_uptime += 1; // Simplified increment
    }

    /// Update real-time metrics
    fn update_realtime_metrics(&mut self, context: &SymbolicContext) {
        let context_size = context.flatten().len();
        // Update metrics based on current context
        if context_size > 50 {
            self.system_metrics.error_count += 1; // Simulated complexity error
        }
    }

    /// Export analysis data
    fn export_analysis_data(&self) -> Result<()> {
        let export_data = serde_json::json!({
            "system_metrics": self.system_metrics,
            "analysis_sessions": self.analysis_sessions,
            "optimization_history": self.optimization_history,
            "export_timestamp": SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_secs()
        });

        let filename = format!("soma_meta_analysis_export_{}.json", 
            SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_secs());
        
        std::fs::write(&filename, serde_json::to_string_pretty(&export_data)?)?;
        Ok(())
    }

    /// Setup initial context for analysis
    pub fn setup_initial_context(&mut self, context: &mut SymbolicContext) {
        context.set("session_start", &SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_secs().to_string());
        context.set("operator_registry_size", &self.registry.len().to_string());
        context.set("analysis_mode", "meta_reflective");
        context.set("cognitive_focus", "system_optimization");

        // Initialize system metrics
        self.system_metrics.operator_count = self.registry.len();
        self.system_metrics.system_uptime = SystemTime::now().duration_since(UNIX_EPOCH).unwrap().as_secs();
    }
}

impl Default for SystemMetrics {
    fn default() -> Self {
        Self {
            operator_count: 0,
            active_sessions: 0,
            total_operations: 0,
            error_count: 0,
            average_performance: 0.85,
            cognitive_efficiency: 0.9,
            system_uptime: 0,
        }
    }
}

// Make structs serializable for export
use serde::{Deserialize, Serialize};

#[derive(Serialize, Deserialize)]
struct SerializableSystemMetrics {
    operator_count: usize,
    active_sessions: usize,
    total_operations: usize,
    error_count: usize,
    average_performance: f64,
    cognitive_efficiency: f64,
    system_uptime: u64,
}

impl From<&SystemMetrics> for SerializableSystemMetrics {
    fn from(metrics: &SystemMetrics) -> Self {
        Self {
            operator_count: metrics.operator_count,
            active_sessions: metrics.active_sessions,
            total_operations: metrics.total_operations,
            error_count: metrics.error_count,
            average_performance: metrics.average_performance,
            cognitive_efficiency: metrics.cognitive_efficiency,
            system_uptime: metrics.system_uptime,
        }
    }
}

/// Create a new meta-reflective analysis session
pub fn create_meta_reflective_session() -> Result<MetaReflectiveSystem> {
    Ok(MetaReflectiveSystem::new())
}

/// Run meta-reflective analysis on the provided context
pub fn analyze_meta_reflective(context: &SymbolicContext) -> Result<SymbolicContext> {
    let registry = default_operator_registry();
    
    if let Some(meta_op) = registry.get("meta_reflective") {
        meta_op.execute(context)
    } else {
        Err(anyhow::anyhow!("MetaReflectiveOperator not found in registry"))
    }
}

/// Process meta-reflective workflow for CLI integration
pub fn process_meta_reflective_workflow(context: &SymbolicContext) -> Result<SymbolicContext> {
    let mut system = MetaReflectiveSystem::new();
    let mut enhanced_context = context.clone();
    
    // Setup context for meta-analysis
    system.setup_initial_context(&mut enhanced_context);
    
    // Run comprehensive analysis
    system.run_meta_analysis(&mut enhanced_context)?;
    
    Ok(enhanced_context)
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_meta_reflective_system_creation() {
        let system = MetaReflectiveSystem::new();
        assert!(system.registry.len() > 0);
        assert_eq!(system.analysis_sessions.len(), 0);
        assert_eq!(system.optimization_history.len(), 0);
    }

    #[test]
    fn test_analyze_meta_reflective() {
        let mut context = SymbolicContext::new();
        context.set("operator_count", "15");
        context.set("errors_count", "0");
        context.set("reasoning_pattern", "optimization");

        let result = analyze_meta_reflective(&context).unwrap();
        
        assert!(result.get("system_performance_score").is_some());
        assert!(result.get("meta_cognitive_state").is_some());
        assert!(result.get("reasoning_patterns_detected").is_some());
    }

    #[test]
    fn test_meta_reflective_session_tracking() {
        let mut system = MetaReflectiveSystem::new();
        let mut context = SymbolicContext::new();
        
        system.setup_initial_context(&mut context);
        system.run_meta_analysis(&mut context).unwrap();
        
        assert_eq!(system.analysis_sessions.len(), 1);
        assert!(system.analysis_sessions[0].performance_score > 0.0);
    }

    #[test]
    fn test_optimization_recommendations() {
        let mut system = MetaReflectiveSystem::new();
        let context = SymbolicContext::new();
        
        // Set low performance scenario
        system.system_metrics.cognitive_efficiency = 0.5;
        system.system_metrics.error_count = 10;
        
        system.generate_optimization_recommendations(&context).unwrap();
        
        assert!(system.optimization_history.len() > 0);
        assert!(system.optimization_history.iter()
            .any(|opt| matches!(opt.priority, Priority::Critical)));
    }

    #[test]
    fn test_performance_metrics_update() {
        let mut system = MetaReflectiveSystem::new();
        let initial_operations = system.system_metrics.total_operations;
        
        system.update_performance_metrics();
        
        assert_eq!(system.system_metrics.total_operations, initial_operations + 1);
    }

    #[test]
    fn test_context_setup() {
        let mut system = MetaReflectiveSystem::new();
        let mut context = SymbolicContext::new();
        
        system.setup_initial_context(&mut context);
        
        assert!(context.get("session_start").is_some());
        assert!(context.get("operator_registry_size").is_some());
        assert_eq!(context.get("analysis_mode").unwrap(), "meta_reflective");
    }
}