anya-core 1.2.0

use std::error::Error;
use std::collections::{HashMap, HashSet};
use std::sync::Arc;
use tokio::sync::{RwLock, Mutex};
use serde::{Serialize, Deserialize};

/// Comprehensive ML Model Management System
#[derive(Debug)]
pub struct MLModelManager {
    /// Active model registry
    model_registry: Arc<RwLock<HashMap<String, MLModelMetadata>>>,
    
    /// Model performance tracking
    performance_tracker: Arc<RwLock<ModelPerformanceTracker>>,
    
    /// Governance integration
    governance_interface: Arc<Mutex<MLGovernanceInterface>>,
    
    /// Ethical AI compliance manager
    ethics_manager: Arc<RwLock<EthicalAIComplianceManager>>,
}

/// Detailed ML Model Metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MLModelMetadata {
    /// Unique model identifier
    pub id: String,
    
    /// Model type (classification, regression, etc.)
    pub model_type: ModelType,
    
    /// Specific governance use case
    pub governance_use_case: GovernanceUseCase,
    
    /// Model version
    pub version: String,
    
    /// Training data metadata
    pub training_metadata: TrainingMetadata,
    
    /// Current model status
    pub status: ModelStatus,
    
    /// Performance metrics
    pub performance_metrics: PerformanceMetrics,
    
    /// Ethical compliance score
    pub ethics_score: f64,
}

/// Types of ML Models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ModelType {
    Classification,
    Regression,
    Clustering,
    ReinforcementLearning,
    NaturalLanguageProcessing,
}

/// Governance-Specific Use Cases
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum GovernanceUseCase {
    ProposalScoring,
    RiskAssessment,
    SentimentAnalysis,
    ResourceAllocation,
    DecisionPrediction,
    ComplianceMonitoring,
}

/// Model Training Metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingMetadata {
    /// Training dataset size
    pub dataset_size: usize,
    
    /// Training timestamp
    pub trained_at: std::time::SystemTime,
    
    /// Data sources used
    pub data_sources: Vec<String>,
    
    /// Training environment details
    pub training_environment: TrainingEnvironment,
}

/// Training Environment Specifications
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingEnvironment {
    /// Computational resources used
    pub compute_resources: ComputeResources,
    
    /// Hardware specifications
    pub hardware: HardwareSpecs,
}

/// Computational Resources
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputeResources {
    /// GPU resources
    pub gpu_count: usize,
    
    /// CPU cores
    pub cpu_cores: usize,
    
    /// Memory used (GB)
    pub memory_gb: f64,
}

/// Hardware Specifications
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareSpecs {
    /// Hardware vendor
    pub vendor: String,
    
    /// Hardware model
    pub model: String,
}

/// Current Model Status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ModelStatus {
    Active,
    Training,
    Deprecated,
    Experimental,
    Suspended,
}

/// Performance Metrics for ML Models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    /// Accuracy score
    pub accuracy: f64,
    
    /// Precision score
    pub precision: f64,
    
    /// Recall score
    pub recall: f64,
    
    /// F1 score
    pub f1_score: f64,
    
    /// Model inference time (ms)
    pub inference_time_ms: f64,
}

/// Model Performance Tracking
#[derive(Debug)]
pub struct ModelPerformanceTracker {
    /// Historical performance records
    performance_history: HashMap<String, Vec<PerformanceMetrics>>,
    
    /// Comparative performance analysis
    comparative_analysis: HashMap<String, CompetitivePerformance>,
}

/// Competitive Performance Metrics
#[derive(Debug, Clone)]
pub struct CompetitivePerformance {
    /// Relative performance compared to baseline
    pub relative_performance: f64,
    
    /// Performance trend
    pub performance_trend: PerformanceTrend,
}

/// Performance Trend Indicators
#[derive(Debug, Clone)]
pub enum PerformanceTrend {
    Improving,
    Stable,
    Declining,
}

/// ML Governance Interface
#[derive(Debug)]
pub struct MLGovernanceInterface {
    /// Approved model use cases
    approved_use_cases: HashSet<GovernanceUseCase>,
    
    /// Governance parameters
    governance_parameters: HashMap<String, serde_json::Value>,
}

/// Ethical AI Compliance Manager
#[derive(Debug)]
pub struct EthicalAIComplianceManager {
    /// Ethical guidelines
    ethical_guidelines: Vec<EthicalGuideline>,
    
    /// Compliance scoring mechanism
    compliance_scoring: ComplianceScoring,
}

/// Ethical Guidelines for AI
#[derive(Debug, Clone)]
pub enum EthicalGuideline {
    Transparency,
    Fairness,
    Accountability,
    PrivacyPreservation,
    BiasMinimization,
}

/// Compliance Scoring Mechanism
#[derive(Debug, Clone)]
pub struct ComplianceScoring {
    /// Overall compliance score
    pub total_score: f64,
    
    /// Individual guideline scores
    pub guideline_scores: HashMap<EthicalGuideline, f64>,
}

impl MLModelManager {
    /// Create a new ML Model Management System
    pub fn new() -> Self {
        Self {
            model_registry: Arc::new(RwLock::new(HashMap::new())),
            performance_tracker: Arc::new(RwLock::new(ModelPerformanceTracker {
                performance_history: HashMap::new(),
                comparative_analysis: HashMap::new(),
            })),
            governance_interface: Arc::new(Mutex::new(MLGovernanceInterface {
                approved_use_cases: HashSet::from([
                    GovernanceUseCase::ProposalScoring,
                    GovernanceUseCase::RiskAssessment,
                    GovernanceUseCase::SentimentAnalysis,
                ]),
                governance_parameters: HashMap::new(),
            })),
            ethics_manager: Arc::new(RwLock::new(EthicalAIComplianceManager {
                ethical_guidelines: vec![
                    EthicalGuideline::Transparency,
                    EthicalGuideline::Fairness,
                    EthicalGuideline::Accountability,
                ],
                compliance_scoring: ComplianceScoring {
                    total_score: 0.85, // Initial high compliance score
                    guideline_scores: HashMap::new(),
                },
            })),
        }
    }
    
    /// Register a new ML model
    pub async fn register_model(&self, model: MLModelMetadata) -> Result<(), String> {
        let mut registry = self.model_registry.write().await;
        
        // Validate model before registration
        self.validate_model(&model)?;
        
        registry.insert(model.id.clone(), model);
        Ok(())
    }
    
    /// Validate ML model against governance and ethical standards
    fn validate_model(&self, model: &MLModelMetadata) -> Result<(), String> {
        // Check governance use case approval
        let governance_interface = self.governance_interface.lock().await;
        if !governance_interface.approved_use_cases.contains(&model.governance_use_case) {
            return Err(format!("Unapproved governance use case: {:?}", model.governance_use_case));
        }
        
        // Check ethical compliance
        let ethics_manager = self.ethics_manager.read().await;
        if model.ethics_score < 0.7 {
            return Err(format!("Insufficient ethical compliance score: {}", model.ethics_score));
        }
        
        Ok(())
    }
    
    /// Update model performance
    pub async fn update_performance(&self, model_id: &str, new_metrics: PerformanceMetrics) -> Result<(), String> {
        let mut performance_tracker = self.performance_tracker.write().await;
        
        // Update performance history
        performance_tracker.performance_history
            .entry(model_id.to_string())
            .or_insert_with(Vec::new)
            .push(new_metrics.clone());
        
        // Update comparative analysis
        performance_tracker.comparative_analysis
            .entry(model_id.to_string())
            .or_insert(CompetitivePerformance {
                relative_performance: new_metrics.accuracy,
                performance_trend: PerformanceTrend::Stable,
            });
        
        Ok(())
    }
    
    /// Get recommended models for a specific governance use case
    pub async fn get_recommended_models(&self, use_case: GovernanceUseCase) -> Vec<MLModelMetadata> {
        let registry = self.model_registry.read().await;
        
        registry.values()
            .filter(|model| model.governance_use_case == use_case && model.status == ModelStatus::Active)
            .cloned()
            .collect()
    }
}

/// Default configuration for ML governance in Anya DAO
impl MLModelManager {
    /// Create a default ML management configuration for DAO governance
    pub fn default_dao_configuration() -> Self {
        let mut manager = Self::new();
        
        // Pre-configure governance models
        let proposal_scoring_model = MLModelMetadata {
            id: "anya_proposal_scorer_v1".to_string(),
            model_type: ModelType::Classification,
            governance_use_case: GovernanceUseCase::ProposalScoring,
            version: "1.0.0".to_string(),
            training_metadata: TrainingMetadata {
                dataset_size: 10000,
                trained_at: std::time::SystemTime::now(),
                data_sources: vec![
                    "historical_proposals".to_string(),
                    "community_feedback".to_string(),
                ],
                training_environment: TrainingEnvironment {
                    compute_resources: ComputeResources {
                        gpu_count: 4,
                        cpu_cores: 32,
                        memory_gb: 128.0,
                    },
                    hardware: HardwareSpecs {
                        vendor: "NVIDIA".to_string(),
                        model: "DGX A100".to_string(),
                    },
                },
            },
            status: ModelStatus::Active,
            performance_metrics: PerformanceMetrics {
                accuracy: 0.85,
                precision: 0.82,
                recall: 0.88,
                f1_score: 0.85,
                inference_time_ms: 12.5,
            },
            ethics_score: 0.92,
        };
        
        // Async block to register model (simplified for example)
        tokio::spawn(async move {
            let _ = manager.register_model(proposal_scoring_model).await;
        });
        
        manager
    }
}