turash/bugulma/backend/internal/repository/trust_metrics_repository.go

package repository

import (
	"bugulma/backend/internal/domain"
	"context"
	"math"
	"time"

	"gorm.io/gorm"
)

// TrustMetricsRepository implements domain.TrustMetricsRepository with GORM
type TrustMetricsRepository struct {
	*BaseRepository[domain.TrustMetrics]
}

// NewTrustMetricsRepository creates a new GORM-based trust metrics repository
func NewTrustMetricsRepository(db *gorm.DB) domain.TrustMetricsRepository {
	return &TrustMetricsRepository{
		BaseRepository: NewBaseRepository[domain.TrustMetrics](db),
	}
}

// GetByOrganization retrieves all trust metrics for an organization
func (r *TrustMetricsRepository) GetByOrganization(ctx context.Context, orgID string) ([]*domain.TrustMetrics, error) {
	return r.FindWhereWithContext(ctx, "organization_id = ?", orgID)
}

// GetByType retrieves trust metrics of a specific type for an organization
func (r *TrustMetricsRepository) GetByType(ctx context.Context, orgID, metricType string) ([]*domain.TrustMetrics, error) {
	return r.FindWhereWithContext(ctx, "organization_id = ? AND metric_type = ?", orgID, metricType)
}

// GetLatestProfile retrieves the latest comprehensive quality profile for an organization
func (r *TrustMetricsRepository) GetLatestProfile(ctx context.Context, orgID string) (*domain.DataQualityProfile, error) {
	// Get all metrics for the organization
	metrics, err := r.GetByOrganization(ctx, orgID)
	if err != nil {
		return nil, err
	}

	// Convert []*domain.TrustMetrics to []domain.TrustMetrics
	metricsSlice := make([]domain.TrustMetrics, len(metrics))
	for i, metric := range metrics {
		metricsSlice[i] = *metric
	}

	// Calculate profile scores
	profile := &domain.DataQualityProfile{
		OrganizationID:   orgID,
		LastAssessed:     time.Now(),
		AssessmentPeriod: 30 * 24 * time.Hour, // 30 days
		Metrics:          metricsSlice,
	}

	// Calculate completeness score
	completeness := r.calculateCompletenessScore(metrics)
	profile.Completeness = completeness

	// Calculate accuracy score
	accuracy := r.calculateAccuracyScore(metrics)
	profile.Accuracy = accuracy

	// Calculate overall score (weighted average)
	profile.OverallScore = (completeness*0.4 + accuracy*0.4 + profile.Timeliness*0.1 + profile.Consistency*0.1)

	// Generate recommendations
	profile.Recommendations = r.generateRecommendations(profile)

	return profile, nil
}

// calculateCompletenessScore calculates how complete the organization's data is
func (r *TrustMetricsRepository) calculateCompletenessScore(metrics []*domain.TrustMetrics) float64 {
	if len(metrics) == 0 {
		return 0.0
	}

	// Look for completeness metrics
	for _, metric := range metrics {
		if metric.MetricName == "data_completeness" && metric.MetricType == "organization" {
			return metric.Value
		}
	}

	// Default calculation based on number of metrics
	score := float64(len(metrics)) / 10.0 // Assume 10 key metrics
	if score > 1.0 {
		score = 1.0
	}
	return score
}

// calculateAccuracyScore calculates data accuracy based on metrics
func (r *TrustMetricsRepository) calculateAccuracyScore(metrics []*domain.TrustMetrics) float64 {
	accuracyMetrics := 0
	totalScore := 0.0

	for _, metric := range metrics {
		if metric.MetricName == "data_accuracy" || metric.MetricName == "verification_score" {
			accuracyMetrics++
			totalScore += metric.Value
		}
	}

	if accuracyMetrics == 0 {
		return 0.5 // Default neutral score
	}

	return totalScore / float64(accuracyMetrics)
}

// generateRecommendations generates improvement recommendations
func (r *TrustMetricsRepository) generateRecommendations(profile *domain.DataQualityProfile) []string {
	var recommendations []string

	if profile.Completeness < 0.7 {
		recommendations = append(recommendations, "Complete missing profile information to improve trust scores")
	}

	if profile.Accuracy < 0.8 {
		recommendations = append(recommendations, "Verify and update outdated information")
	}

	if profile.OverallScore < 0.6 {
		recommendations = append(recommendations, "Consider third-party verification to build credibility")
	}

	if len(recommendations) == 0 {
		recommendations = append(recommendations, "Profile quality is good - continue maintaining data accuracy")
	}

	return recommendations
}

// VerifiedDataRepository implements operations for verified data
type VerifiedDataRepository struct {
	*BaseRepository[domain.VerifiedData]
}

// NewVerifiedDataRepository creates a new verified data repository
func NewVerifiedDataRepository(db *gorm.DB) *VerifiedDataRepository {
	return &VerifiedDataRepository{
		BaseRepository: NewBaseRepository[domain.VerifiedData](db),
	}
}

// GetByOrganization retrieves verified data for an organization
func (r *VerifiedDataRepository) GetByOrganization(ctx context.Context, orgID string) ([]*domain.VerifiedData, error) {
	return r.FindWhereWithContext(ctx, "organization_id = ?", orgID)
}

// GetByStatus retrieves verified data by status
func (r *VerifiedDataRepository) GetByStatus(ctx context.Context, status domain.VerificationStatus) ([]*domain.VerifiedData, error) {
	return r.FindWhereWithContext(ctx, "status = ?", status)
}

// HistoricalSuccessRepository implements operations for historical success metrics
type HistoricalSuccessRepository struct {
	*BaseRepository[domain.HistoricalSuccess]
}

// NewHistoricalSuccessRepository creates a new historical success repository
func NewHistoricalSuccessRepository(db *gorm.DB) *HistoricalSuccessRepository {
	return &HistoricalSuccessRepository{
		BaseRepository: NewBaseRepository[domain.HistoricalSuccess](db),
	}
}

// GetByOrganization retrieves historical success metrics for an organization
func (r *HistoricalSuccessRepository) GetByOrganization(ctx context.Context, orgID string) ([]*domain.HistoricalSuccess, error) {
	return r.FindWhereWithContext(ctx, "organization_id = ?", orgID)
}

// GetByMetricType retrieves metrics of a specific type
func (r *HistoricalSuccessRepository) GetByMetricType(ctx context.Context, orgID, metricType string) ([]*domain.HistoricalSuccess, error) {
	return r.FindWhereWithContext(ctx, "organization_id = ? AND metric_type = ?", orgID, metricType)
}

// CalculateTrustScore calculates overall trust score for an organization
func (r *HistoricalSuccessRepository) CalculateTrustScore(ctx context.Context, orgID string) (*domain.TrustScore, error) {
	// Get trust metrics
	trustRepo := NewTrustMetricsRepository(r.DB())
	metrics, err := trustRepo.GetByOrganization(ctx, orgID)
	if err != nil {
		return nil, err
	}

	// Get historical success metrics
	historical, err := r.GetByOrganization(ctx, orgID)
	if err != nil {
		return nil, err
	}

	// Calculate component scores
	trustScore := &domain.TrustScore{
		OrganizationID: orgID,
		LastCalculated: time.Now(),
		ScoreBreakdown: make(map[string]float64),
	}

	// Data quality score
	dataQualityScore := calculateDataQualityScore(metrics)
	trustScore.DataQualityScore = dataQualityScore
	trustScore.ScoreBreakdown["data_quality"] = dataQualityScore

	// Verification score
	verificationScore := calculateVerificationScore(metrics)
	trustScore.VerificationScore = verificationScore
	trustScore.ScoreBreakdown["verification"] = verificationScore

	// Historical score
	historicalScore := calculateHistoricalScore(historical)
	trustScore.HistoricalScore = historicalScore
	trustScore.ScoreBreakdown["historical"] = historicalScore

	// Peer review score - calculated from historical success metrics
	// Organizations with successful matches and high completion rates have better peer review scores
	peerReviewScore := calculatePeerReviewScore(historical, metrics)
	trustScore.PeerReviewScore = peerReviewScore
	trustScore.ScoreBreakdown["peer_review"] = peerReviewScore

	// Overall score (weighted average)
	trustScore.OverallScore = (dataQualityScore*0.3 + verificationScore*0.3 + historicalScore*0.3 + trustScore.PeerReviewScore*0.1)

	// Confidence level based on data availability
	trustScore.ConfidenceLevel = calculateConfidenceLevel(metrics, historical)

	// Risk factors and recommendations
	trustScore.RiskFactors = identifyRiskFactors(trustScore)
	trustScore.Recommendations = generateTrustRecommendations(trustScore)

	return trustScore, nil
}

// Helper functions for trust score calculation
func calculateDataQualityScore(metrics []*domain.TrustMetrics) float64 {
	if len(metrics) == 0 {
		return 0.3 // Low default
	}

	totalScore := 0.0
	count := 0

	for _, metric := range metrics {
		if metric.MetricType == "organization" || metric.MetricType == "resource_flow" {
			totalScore += metric.Value
			count++
		}
	}

	if count == 0 {
		return 0.3
	}

	return totalScore / float64(count)
}

func calculateVerificationScore(metrics []*domain.TrustMetrics) float64 {
	for _, metric := range metrics {
		if metric.MetricName == "verification_score" {
			return metric.Value
		}
	}
	return 0.0 // No verification data
}

func calculateHistoricalScore(historical []*domain.HistoricalSuccess) float64 {
	if len(historical) == 0 {
		return 0.4 // Neutral default for new organizations
	}

	totalScore := 0.0
	count := 0

	for _, h := range historical {
		if h.MetricType == "completion_rate" {
			totalScore += h.Value
			count++
		}
	}

	if count == 0 {
		return 0.4
	}

	return totalScore / float64(count)
}

func calculateConfidenceLevel(metrics []*domain.TrustMetrics, historical []*domain.HistoricalSuccess) float64 {
	dataPoints := len(metrics) + len(historical)

	// Confidence increases with more data points
	if dataPoints < 5 {
		return 0.3
	} else if dataPoints < 15 {
		return 0.6
	} else {
		return 0.9
	}
}

func identifyRiskFactors(trustScore *domain.TrustScore) []domain.RiskFactor {
	var risks []domain.RiskFactor

	if trustScore.DataQualityScore < 0.5 {
		risks = append(risks, domain.RiskFactor{
			Type:        "data_quality",
			Description: "Low data quality score indicates incomplete or inaccurate information",
			Severity:    0.7,
			Impact:      "high",
			Mitigation:  "Complete profile information and verify data accuracy",
		})
	}

	if trustScore.VerificationScore < 0.3 {
		risks = append(risks, domain.RiskFactor{
			Type:        "verification",
			Description: "Limited verification reduces trustworthiness",
			Severity:    0.6,
			Impact:      "medium",
			Mitigation:  "Pursue third-party verification or certification",
		})
	}

	if trustScore.HistoricalScore < 0.4 {
		risks = append(risks, domain.RiskFactor{
			Type:        "historical",
			Description: "Limited historical performance data",
			Severity:    0.4,
			Impact:      "low",
			Mitigation:  "Build track record through successful partnerships",
		})
	}

	return risks
}

func generateTrustRecommendations(trustScore *domain.TrustScore) []string {
	var recommendations []string

	if trustScore.OverallScore < 0.5 {
		recommendations = append(recommendations, "Focus on improving data quality and verification to build trust")
		recommendations = append(recommendations, "Consider obtaining industry certifications or third-party verification")
	}

	if trustScore.DataQualityScore < 0.7 {
		recommendations = append(recommendations, "Complete all profile information and keep data current")
	}

	if trustScore.VerificationScore < 0.5 {
		recommendations = append(recommendations, "Pursue verification for key data elements")
	}

	if trustScore.ConfidenceLevel < 0.6 {
		recommendations = append(recommendations, "Build more comprehensive data profile to increase confidence")
	}

	if len(recommendations) == 0 {
		recommendations = append(recommendations, "Trust profile is strong - maintain current standards")
	}

	return recommendations
}

// calculatePeerReviewScore calculates peer review score based on historical performance
// This uses historical success metrics to infer peer satisfaction
func calculatePeerReviewScore(historical []*domain.HistoricalSuccess, metrics []*domain.TrustMetrics) float64 {
	if len(historical) == 0 {
		return 0.4 // Neutral default for new organizations
	}

	// Calculate average completion rate (indicates successful partnerships)
	totalCompletion := 0.0
	completionCount := 0

	// Calculate average satisfaction if available
	totalSatisfaction := 0.0
	satisfactionCount := 0

	for _, h := range historical {
		if h.MetricType == "completion_rate" {
			totalCompletion += h.Value
			completionCount++
		} else if h.MetricType == "satisfaction" {
			totalSatisfaction += h.Value
			satisfactionCount++
		}
	}

	// Base score from completion rate (0.6 weight)
	completionScore := 0.4
	if completionCount > 0 {
		avgCompletion := totalCompletion / float64(completionCount)
		completionScore = avgCompletion * 0.6
	}

	// Satisfaction score (0.4 weight)
	satisfactionScore := 0.3
	if satisfactionCount > 0 {
		avgSatisfaction := totalSatisfaction / float64(satisfactionCount)
		satisfactionScore = avgSatisfaction * 0.4
	}

	// Combine scores
	peerScore := completionScore + satisfactionScore

	// Boost score if organization has many successful partnerships
	if len(historical) > 10 {
		peerScore = math.Min(1.0, peerScore*1.1)
	}

	return math.Max(0.0, math.Min(1.0, peerScore))
}