anya-core 1.2.0

// DAO Agent - Machine Learning Governance Agent for Anya Core
// Implements intelligent governance mechanisms for the DAO

use crate::AnyaError;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use tokio::sync::RwLock;

/// DAO Governance Agent for intelligent decision-making
#[derive(Debug)]
pub struct DaoAgent {
    /// Agent identifier
    id: String,
    /// Governance parameters
    config: DaoAgentConfig,
    /// Current governance state
    state: RwLock<DaoState>,
    /// ML model for governance decisions
    model: RwLock<GovernanceModel>,
}

impl DaoAgent {
    /// Get the agent ID
    pub fn agent_id(&self) -> &str {
        &self.id
    }

    /// Update agent ID if needed
    pub fn set_agent_id(&mut self, new_id: String) {
        self.id = new_id;
    }
}

/// Configuration for the DAO Agent
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DaoAgentConfig {
    /// Voting threshold for proposals
    pub voting_threshold: f64,
    /// Quorum requirement
    pub quorum_requirement: f64,
    /// ML model parameters
    pub model_params: ModelParameters,
    /// Governance rules
    pub governance_rules: GovernanceRules,
}

/// Current state of the DAO
#[derive(Debug, Default)]
pub struct DaoState {
    /// Active proposals
    pub active_proposals: HashMap<String, Proposal>,
    /// Member participation metrics
    pub participation_metrics: ParticipationMetrics,
    /// Governance history
    pub governance_history: Vec<GovernanceEvent>,
}

/// ML model for governance decisions
#[derive(Debug)]
pub struct GovernanceModel {
    /// Decision weights
    weights: HashMap<String, f64>,
    /// Training data
    training_data: Vec<TrainingSample>,
    /// Model accuracy metrics
    accuracy: f64,
}

impl GovernanceModel {
    /// Create a new governance model
    pub fn new() -> Self {
        Self {
            weights: HashMap::new(),
            training_data: Vec::new(),
            accuracy: 0.0,
        }
    }

    /// Get current accuracy
    pub fn get_accuracy(&self) -> f64 {
        self.accuracy
    }

    /// Update model weights based on new data
    pub fn update_weights(&mut self, new_weights: HashMap<String, f64>) {
        self.weights.extend(new_weights);
    }

    /// Add training sample
    pub fn add_training_sample(&mut self, sample: TrainingSample) {
        self.training_data.push(sample);
    }

    /// Train the model and update accuracy
    pub fn train(&mut self) -> Result<(), AnyaError> {
        if self.training_data.is_empty() {
            return Err(AnyaError::ML("No training data available".to_string()));
        }

        // Simple training simulation - in real implementation this would be more sophisticated
        let mut total_accuracy = 0.0;
        for sample in &self.training_data {
            // Simulate prediction accuracy based on sample complexity
            let sample_accuracy = 1.0 - (sample.complexity * 0.1);
            total_accuracy += sample_accuracy.clamp(0.0, 1.0);
        }

        self.accuracy = total_accuracy / self.training_data.len() as f64;
        Ok(())
    }

    /// Get decision weights for a specific proposal
    pub fn get_decision_weights(&self, proposal_type: &str) -> Option<f64> {
        self.weights.get(proposal_type).copied()
    }
}

/// Governance proposal
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Proposal {
    /// Proposal ID
    pub id: String,
    /// Proposal title
    pub title: String,
    /// Proposal description
    pub description: String,
    /// Proposer DID
    pub proposer: String,
    /// Voting deadline
    pub deadline: u64,
    /// Current votes
    pub votes: HashMap<String, Vote>,
    /// Proposal status
    pub status: ProposalStatus,
}

/// Vote on a proposal
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Vote {
    /// Voter DID
    pub voter: String,
    /// Vote choice (yes/no/abstain)
    pub choice: VoteChoice,
    /// Voting weight
    pub weight: f64,
    /// Timestamp
    pub timestamp: u64,
}

/// Vote choices
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum VoteChoice {
    Yes,
    No,
    Abstain,
}

/// Proposal status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ProposalStatus {
    Active,
    Passed,
    Rejected,
    Expired,
}

/// ML model parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelParameters {
    /// Learning rate
    pub learning_rate: f64,
    /// Training epochs
    pub epochs: u32,
    /// Regularization factor
    pub regularization: f64,
}

/// Governance rules
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GovernanceRules {
    /// Minimum proposal threshold
    pub min_proposal_threshold: f64,
    /// Maximum voting period
    pub max_voting_period: u64,
    /// Participation requirements
    pub participation_requirements: ParticipationRequirements,
}

/// Participation requirements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParticipationRequirements {
    /// Minimum stake required
    pub min_stake: u64,
    /// Minimum participation history
    pub min_participation_history: u32,
}

/// Participation metrics
#[derive(Debug, Default, Clone)]
pub struct ParticipationMetrics {
    /// Total members
    pub total_members: u64,
    /// Active members
    pub active_members: u64,
    /// Voting participation rate
    pub voting_participation_rate: f64,
    /// Average proposal quality score
    pub avg_proposal_quality: f64,
}

/// Governance event for history tracking
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GovernanceEvent {
    /// Event ID
    pub id: String,
    /// Event type
    pub event_type: GovernanceEventType,
    /// Timestamp
    pub timestamp: u64,
    /// Event data
    pub data: serde_json::Value,
}

/// Types of governance events
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum GovernanceEventType {
    ProposalCreated,
    VoteCast,
    ProposalFinalized,
    RuleUpdated,
    MemberAdded,
    MemberRemoved,
}

/// Training sample for ML governance model
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingSample {
    /// Proposal ID
    pub proposal_id: String,
    /// Features extracted from the proposal
    pub features: HashMap<String, f64>,
    /// Outcome (0.0 = rejected, 1.0 = accepted)
    pub outcome: f64,
    /// Complexity score for training difficulty
    pub complexity: f64,
    /// Timestamp when sample was created
    pub timestamp: u64,
}

impl TrainingSample {
    /// Create a new training sample
    pub fn new(proposal_id: String, features: HashMap<String, f64>, outcome: f64) -> Self {
        Self {
            proposal_id,
            features,
            outcome,
            complexity: 0.5, // Default complexity
            timestamp: chrono::Utc::now().timestamp() as u64,
        }
    }
}

impl DaoAgent {
    /// Create a new DAO Agent
    pub fn new(id: String, config: DaoAgentConfig) -> Self {
        Self {
            id,
            config,
            state: RwLock::new(DaoState::default()),
            model: RwLock::new(GovernanceModel::new()),
        }
    }

    /// Initialize the DAO Agent
    pub async fn initialize(&self) -> Result<(), AnyaError> {
        // Initialize ML model
        let mut model = self.model.write().await;
        model.initialize(&self.config.model_params)?;

        // Load historical data if available
        self.load_governance_history().await?;

        Ok(())
    }

    /// Create a new proposal
    pub async fn create_proposal(
        &self,
        title: String,
        description: String,
        proposer: String,
        deadline: u64,
    ) -> Result<String, AnyaError> {
        let proposal_id = format!("prop_{}", uuid::Uuid::new_v4());

        let proposal = Proposal {
            id: proposal_id.clone(),
            title,
            description,
            proposer,
            deadline,
            votes: HashMap::new(),
            status: ProposalStatus::Active,
        };

        let mut state = self.state.write().await;
        state.active_proposals.insert(proposal_id.clone(), proposal);

        // Record governance event
        let event = GovernanceEvent {
            id: format!("event_{}", uuid::Uuid::new_v4()),
            event_type: GovernanceEventType::ProposalCreated,
            timestamp: chrono::Utc::now().timestamp() as u64,
            data: serde_json::to_value(&proposal_id)?,
        };
        state.governance_history.push(event);

        Ok(proposal_id)
    }

    /// Cast a vote on a proposal
    pub async fn cast_vote(
        &self,
        proposal_id: String,
        voter: String,
        choice: VoteChoice,
        weight: f64,
    ) -> Result<(), AnyaError> {
        let mut state = self.state.write().await;

        if let Some(proposal) = state.active_proposals.get_mut(&proposal_id) {
            let vote = Vote {
                voter: voter.clone(),
                choice: choice.clone(),
                weight,
                timestamp: chrono::Utc::now().timestamp() as u64,
            };

            proposal.votes.insert(voter.clone(), vote);

            // Record governance event
            let event = GovernanceEvent {
                id: format!("event_{}", uuid::Uuid::new_v4()),
                event_type: GovernanceEventType::VoteCast,
                timestamp: chrono::Utc::now().timestamp() as u64,
                data: serde_json::json!({
                    "proposal_id": proposal_id,
                    "voter": voter,
                    "choice": choice
                }),
            };
            state.governance_history.push(event);

            Ok(())
        } else {
            Err(AnyaError::NotFound(format!(
                "Proposal {proposal_id} not found"
            )))
        }
    }

    /// Analyze proposal outcomes using ML
    pub async fn analyze_proposal(&self, proposal_id: &str) -> Result<ProposalAnalysis, AnyaError> {
        let state = self.state.read().await;
        let model = self.model.read().await;

        if let Some(proposal) = state.active_proposals.get(proposal_id) {
            let analysis = model.analyze_proposal(proposal)?;
            Ok(analysis)
        } else {
            Err(AnyaError::NotFound(format!(
                "Proposal {proposal_id} not found"
            )))
        }
    }

    /// Update governance rules based on ML insights
    pub async fn update_governance_rules(&self) -> Result<(), AnyaError> {
        let model = self.model.read().await;
        let _insights = model.generate_governance_insights()?;

        // Apply insights to update rules
        // This would involve updating the governance parameters
        // based on ML model recommendations

        Ok(())
    }

    /// Get participation metrics
    pub async fn get_participation_metrics(&self) -> Result<ParticipationMetrics, AnyaError> {
        let state = self.state.read().await;
        Ok(state.participation_metrics.clone())
    }

    /// Load governance history from storage
    async fn load_governance_history(&self) -> Result<(), AnyaError> {
        // Load historical governance data for ML training
        // This would connect to the storage layer
        Ok(())
    }
}

/// Analysis result for a proposal
#[derive(Debug)]
pub struct ProposalAnalysis {
    /// Predicted outcome
    pub predicted_outcome: ProposalStatus,
    /// Confidence score
    pub confidence: f64,
    /// Participation prediction
    pub expected_participation: f64,
    /// Quality score
    pub quality_score: f64,
}

impl Default for GovernanceModel {
    fn default() -> Self {
        Self::new()
    }
}

impl GovernanceModel {
    /// Initialize the model with parameters
    pub fn initialize(&mut self, _params: &ModelParameters) -> Result<(), AnyaError> {
        // Initialize ML model with given parameters
        // This would set up the actual ML algorithms
        Ok(())
    }

    /// Analyze a proposal using the ML model
    pub fn analyze_proposal(&self, proposal: &Proposal) -> Result<ProposalAnalysis, AnyaError> {
        // Extract features from the proposal
        let features = self.extract_features(proposal)?;

        // Use ML model to predict outcome
        let predicted_outcome = self.predict_outcome(&features)?;
        let confidence = self.calculate_confidence(&features)?;
        let expected_participation = self.predict_participation(&features)?;
        let quality_score = self.assess_quality(&features)?;

        Ok(ProposalAnalysis {
            predicted_outcome,
            confidence,
            expected_participation,
            quality_score,
        })
    }

    /// Generate insights for governance rule updates
    pub fn generate_governance_insights(&self) -> Result<GovernanceInsights, AnyaError> {
        // Analyze historical data to generate insights
        // This would use ML to suggest improvements
        Ok(GovernanceInsights::default())
    }

    /// Extract features from a proposal for ML analysis
    fn extract_features(&self, proposal: &Proposal) -> Result<Vec<f64>, AnyaError> {
        // Extract relevant features for ML model
        // Using vec![] macro as suggested by Clippy
        let features = vec![
            proposal.description.len() as f64, // Description length
            proposal.votes.len() as f64,       // Current vote count
        ];

        // Add more sophisticated feature extraction here

        Ok(features)
    }

    /// Predict the outcome of a proposal
    fn predict_outcome(&self, features: &[f64]) -> Result<ProposalStatus, AnyaError> {
        // Use ML model to predict outcome
        // This is a simplified implementation
        if features.iter().sum::<f64>() > 100.0 {
            Ok(ProposalStatus::Passed)
        } else {
            Ok(ProposalStatus::Rejected)
        }
    }

    /// Calculate confidence in prediction
    fn calculate_confidence(&self, _features: &[f64]) -> Result<f64, AnyaError> {
        // Calculate confidence score based on model certainty
        Ok(0.85) // Placeholder
    }

    /// Predict participation rate
    fn predict_participation(&self, _features: &[f64]) -> Result<f64, AnyaError> {
        // Predict expected participation
        Ok(0.65) // Placeholder
    }

    /// Assess proposal quality
    fn assess_quality(&self, _features: &[f64]) -> Result<f64, AnyaError> {
        // Assess the quality of the proposal
        Ok(0.75) // Placeholder
    }
}

/// Governance insights generated by ML
#[derive(Debug, Default)]
pub struct GovernanceInsights {
    /// Recommended rule changes
    pub recommended_changes: Vec<String>,
    /// Participation improvement suggestions
    pub participation_suggestions: Vec<String>,
    /// Quality improvement recommendations
    pub quality_recommendations: Vec<String>,
}

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

    #[tokio::test]
    async fn test_dao_agent_creation() {
        let config = DaoAgentConfig {
            voting_threshold: 0.6,
            quorum_requirement: 0.3,
            model_params: ModelParameters {
                learning_rate: 0.01,
                epochs: 100,
                regularization: 0.001,
            },
            governance_rules: GovernanceRules {
                min_proposal_threshold: 0.1,
                max_voting_period: 86400 * 7, // 7 days
                participation_requirements: ParticipationRequirements {
                    min_stake: 1000,
                    min_participation_history: 5,
                },
            },
        };

        let agent = DaoAgent::new("test_dao".to_string(), config);
        assert_eq!(agent.id, "test_dao");
    }

    #[tokio::test]
    async fn test_proposal_creation() {
        let config = DaoAgentConfig {
            voting_threshold: 0.6,
            quorum_requirement: 0.3,
            model_params: ModelParameters {
                learning_rate: 0.01,
                epochs: 100,
                regularization: 0.001,
            },
            governance_rules: GovernanceRules {
                min_proposal_threshold: 0.1,
                max_voting_period: 86400 * 7,
                participation_requirements: ParticipationRequirements {
                    min_stake: 1000,
                    min_participation_history: 5,
                },
            },
        };

        let agent = DaoAgent::new("test_dao".to_string(), config);

        let proposal_id = agent
            .create_proposal(
                "Test Proposal".to_string(),
                "A test proposal for unit testing".to_string(),
                "test_proposer".to_string(),
                chrono::Utc::now().timestamp() as u64 + 86400, // 1 day from now
            )
            .await
            .unwrap();

        assert!(!proposal_id.is_empty());
    }
}