torsh_hub/

visualization.rs

//! Visualization tools for model analytics and profiling
//!
//! This module provides comprehensive visualization capabilities for:
//! - Model performance metrics and benchmarks
//! - Usage analytics and trends
//! - Training progress and fine-tuning metrics
//! - System resource utilization
//! - Model architecture diagrams

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::analytics::{ModelPerformanceData, ModelUsageStats, RealTimeMetrics};
use crate::fine_tuning::TrainingHistory;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::PathBuf;
use std::time::{Duration, SystemTime};
use torsh_core::error::{Result, TorshError};

/// Visualization engine for generating charts and graphs
pub struct VisualizationEngine {
    config: VisualizationConfig,
    chart_renderer: ChartRenderer,
    dashboard_generator: DashboardGenerator,
    export_manager: ExportManager,
}

impl VisualizationEngine {
    /// Get chart renderer
    pub fn chart_renderer(&self) -> &ChartRenderer {
        &self.chart_renderer
    }

    /// Get dashboard generator
    pub fn dashboard_generator(&self) -> &DashboardGenerator {
        &self.dashboard_generator
    }
}

/// Configuration for visualization settings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VisualizationConfig {
    pub theme: VisualizationTheme,
    pub default_chart_size: ChartSize,
    pub color_palette: ColorPalette,
    pub animation_enabled: bool,
    pub high_dpi_enabled: bool,
    pub export_formats: Vec<ExportFormat>,
}

/// Visual themes for charts and dashboards
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum VisualizationTheme {
    Light,
    Dark,
    HighContrast,
    Custom(CustomTheme),
}

/// Custom theme configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CustomTheme {
    pub background_color: String,
    pub text_color: String,
    pub primary_color: String,
    pub secondary_color: String,
    pub accent_color: String,
    pub grid_color: String,
}

/// Chart size configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChartSize {
    pub width: u32,
    pub height: u32,
}

/// Color palette for charts
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ColorPalette {
    pub primary_colors: Vec<String>,
    pub gradient_colors: Vec<String>,
    pub status_colors: StatusColors,
}

/// Status-specific colors
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatusColors {
    pub success: String,
    pub warning: String,
    pub error: String,
    pub info: String,
}

/// Export format options
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ExportFormat {
    PNG,
    SVG,
    PDF,
    HTML,
    JSON,
}

/// Chart renderer for creating various chart types
pub struct ChartRenderer {
    config: VisualizationConfig,
}

impl ChartRenderer {
    /// Get visualization configuration
    pub fn config(&self) -> &VisualizationConfig {
        &self.config
    }
}

/// Dashboard generator for creating comprehensive dashboards
pub struct DashboardGenerator {
    templates: HashMap<String, DashboardTemplate>,
}

impl DashboardGenerator {
    /// Get dashboard templates
    pub fn templates(&self) -> &HashMap<String, DashboardTemplate> {
        &self.templates
    }
}

/// Export manager for saving visualizations
pub struct ExportManager {
    output_directory: PathBuf,
}

/// Different types of charts available
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ChartType {
    Line,
    Bar,
    Pie,
    Scatter,
    Heatmap,
    Histogram,
    BoxPlot,
    Radar,
    Treemap,
    Sankey,
}

/// Chart data structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChartData {
    pub title: String,
    pub chart_type: ChartType,
    pub datasets: Vec<Dataset>,
    pub x_axis: Axis,
    pub y_axis: Axis,
    pub legend: Option<Legend>,
    pub annotations: Vec<Annotation>,
}

/// Dataset for chart data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Dataset {
    pub label: String,
    pub data: Vec<DataPoint>,
    pub color: Option<String>,
    pub style: Option<LineStyle>,
    pub fill: bool,
}

/// Individual data point
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataPoint {
    pub x: f64,
    pub y: f64,
    pub label: Option<String>,
    pub metadata: HashMap<String, String>,
}

/// Axis configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Axis {
    pub title: String,
    pub min: Option<f64>,
    pub max: Option<f64>,
    pub scale: AxisScale,
    pub format: Option<String>,
}

/// Axis scaling options
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AxisScale {
    Linear,
    Logarithmic,
    Time,
    Category,
}

/// Line style options
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LineStyle {
    Solid,
    Dashed,
    Dotted,
    DashDot,
}

/// Legend configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Legend {
    pub position: LegendPosition,
    pub columns: u32,
    pub font_size: u32,
}

/// Legend position options
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LegendPosition {
    Top,
    Bottom,
    Left,
    Right,
    TopLeft,
    TopRight,
    BottomLeft,
    BottomRight,
}

/// Chart annotations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Annotation {
    pub annotation_type: AnnotationType,
    pub x: f64,
    pub y: f64,
    pub text: String,
    pub color: Option<String>,
}

/// Annotation types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AnnotationType {
    Point,
    Line,
    Rectangle,
    Circle,
    Arrow,
    Text,
}

/// Dashboard template structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DashboardTemplate {
    pub name: String,
    pub description: String,
    pub layout: DashboardLayout,
    pub widgets: Vec<DashboardWidget>,
    pub refresh_interval: Option<Duration>,
}

/// Dashboard layout configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DashboardLayout {
    pub columns: u32,
    pub rows: u32,
    pub padding: u32,
    pub margin: u32,
}

/// Dashboard widget
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DashboardWidget {
    pub widget_type: WidgetType,
    pub title: String,
    pub position: WidgetPosition,
    pub size: WidgetSize,
    pub data_source: DataSource,
    pub update_interval: Option<Duration>,
}

/// Widget types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum WidgetType {
    Chart(ChartType),
    Metric,
    Table,
    Text,
    Image,
    Gauge,
    Progress,
    StatusIndicator,
}

/// Widget position
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WidgetPosition {
    pub x: u32,
    pub y: u32,
}

/// Widget size
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WidgetSize {
    pub width: u32,
    pub height: u32,
}

/// Data source for widgets
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DataSource {
    Analytics(String),
    Metrics(String),
    Performance(String),
    Usage(String),
    Custom(String),
}

/// Performance visualization data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceVisualization {
    pub model_id: String,
    pub inference_time_chart: ChartData,
    pub throughput_chart: ChartData,
    pub resource_utilization_chart: ChartData,
    pub bottleneck_analysis: Vec<BottleneckVisualization>,
}

/// Bottleneck visualization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BottleneckVisualization {
    pub bottleneck_type: String,
    pub severity: String,
    pub impact_chart: ChartData,
    pub timeline: Vec<TimelineEvent>,
}

/// Timeline event for bottleneck analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimelineEvent {
    pub timestamp: SystemTime,
    pub event_type: String,
    pub description: String,
    pub severity: String,
}

/// Usage analytics visualization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UsageVisualization {
    pub usage_trends: ChartData,
    pub popular_models: ChartData,
    pub user_patterns: ChartData,
    pub geographic_distribution: Option<ChartData>,
    pub time_series_usage: ChartData,
}

/// Training progress visualization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingVisualization {
    pub loss_curve: ChartData,
    pub accuracy_curve: ChartData,
    pub learning_rate_schedule: ChartData,
    pub gradient_norms: ChartData,
    pub validation_metrics: ChartData,
}

impl Default for VisualizationConfig {
    fn default() -> Self {
        Self {
            theme: VisualizationTheme::Light,
            default_chart_size: ChartSize {
                width: 800,
                height: 600,
            },
            color_palette: ColorPalette::default(),
            animation_enabled: true,
            high_dpi_enabled: true,
            export_formats: vec![ExportFormat::PNG, ExportFormat::SVG, ExportFormat::HTML],
        }
    }
}

impl VisualizationConfig {
    /// Create a dark theme configuration preset optimized for dark backgrounds
    /// with carefully selected colors for good contrast and readability
    pub fn dark_theme() -> Self {
        Self {
            theme: VisualizationTheme::Dark,
            color_palette: ColorPalette {
                primary_colors: vec![
                    "#FF7979".to_string(),
                    "#74B9FF".to_string(),
                    "#00B894".to_string(),
                    "#FDCB6E".to_string(),
                    "#E17055".to_string(),
                    "#A29BFE".to_string(),
                    "#FD79A8".to_string(),
                    "#81ECEC".to_string(),
                ],
                gradient_colors: vec![
                    "#2C3E50".to_string(),
                    "#34495E".to_string(),
                    "#7F8C8D".to_string(),
                    "#95A5A6".to_string(),
                ],
                status_colors: StatusColors {
                    success: "#00B894".to_string(),
                    warning: "#FDCB6E".to_string(),
                    error: "#E74C3C".to_string(),
                    info: "#74B9FF".to_string(),
                },
            },
            ..Default::default()
        }
    }

    /// Create a high contrast configuration preset for accessibility compliance
    /// using maximum contrast colors suitable for users with visual impairments
    pub fn high_contrast() -> Self {
        Self {
            theme: VisualizationTheme::HighContrast,
            color_palette: ColorPalette {
                primary_colors: vec![
                    "#000000".to_string(),
                    "#FFFFFF".to_string(),
                    "#FF0000".to_string(),
                    "#00FF00".to_string(),
                    "#0000FF".to_string(),
                    "#FFFF00".to_string(),
                    "#FF00FF".to_string(),
                    "#00FFFF".to_string(),
                ],
                gradient_colors: vec!["#000000".to_string(), "#FFFFFF".to_string()],
                status_colors: StatusColors {
                    success: "#00FF00".to_string(),
                    warning: "#FFFF00".to_string(),
                    error: "#FF0000".to_string(),
                    info: "#0000FF".to_string(),
                },
            },
            ..Default::default()
        }
    }

    /// Create a configuration optimized for print/publication output
    /// with high DPI settings, static output, and print-friendly colors
    pub fn print_optimized() -> Self {
        Self {
            animation_enabled: false,
            high_dpi_enabled: true,
            export_formats: vec![ExportFormat::PDF, ExportFormat::SVG, ExportFormat::PNG],
            color_palette: ColorPalette {
                primary_colors: vec![
                    "#2C3E50".to_string(),
                    "#E74C3C".to_string(),
                    "#3498DB".to_string(),
                    "#2ECC71".to_string(),
                    "#F39C12".to_string(),
                    "#9B59B6".to_string(),
                    "#1ABC9C".to_string(),
                    "#E67E22".to_string(),
                ],
                gradient_colors: vec!["#BDC3C7".to_string(), "#95A5A6".to_string()],
                status_colors: StatusColors {
                    success: "#27AE60".to_string(),
                    warning: "#F39C12".to_string(),
                    error: "#E74C3C".to_string(),
                    info: "#3498DB".to_string(),
                },
            },
            ..Default::default()
        }
    }
}

impl Default for ColorPalette {
    fn default() -> Self {
        Self {
            primary_colors: vec![
                "#FF6B6B".to_string(),
                "#4ECDC4".to_string(),
                "#45B7D1".to_string(),
                "#96CEB4".to_string(),
                "#FECA57".to_string(),
                "#FF9FF3".to_string(),
                "#54A0FF".to_string(),
                "#5F27CD".to_string(),
            ],
            gradient_colors: vec![
                "#667eea".to_string(),
                "#764ba2".to_string(),
                "#f093fb".to_string(),
                "#f5576c".to_string(),
            ],
            status_colors: StatusColors {
                success: "#28a745".to_string(),
                warning: "#ffc107".to_string(),
                error: "#dc3545".to_string(),
                info: "#17a2b8".to_string(),
            },
        }
    }
}

impl VisualizationEngine {
    /// Create a new visualization engine
    pub fn new(config: VisualizationConfig, output_dir: PathBuf) -> Result<Self> {
        std::fs::create_dir_all(&output_dir).map_err(|e| TorshError::IoError(e.to_string()))?;

        Ok(Self {
            chart_renderer: ChartRenderer::new(config.clone()),
            dashboard_generator: DashboardGenerator::new(),
            export_manager: ExportManager::new(output_dir),
            config,
        })
    }

    /// Create performance visualization from analytics data
    pub fn create_performance_visualization(
        &self,
        data: &ModelPerformanceData,
    ) -> Result<PerformanceVisualization> {
        let inference_time_chart = self.create_inference_time_chart(data)?;
        let throughput_chart = self.create_throughput_chart(data)?;
        let resource_utilization_chart = self.create_resource_utilization_chart(data)?;
        let bottleneck_analysis = self.create_bottleneck_analysis(data)?;

        Ok(PerformanceVisualization {
            model_id: data.model_id.clone(),
            inference_time_chart,
            throughput_chart,
            resource_utilization_chart,
            bottleneck_analysis,
        })
    }

    /// Create usage analytics visualization
    pub fn create_usage_visualization(
        &self,
        stats: &[ModelUsageStats],
    ) -> Result<UsageVisualization> {
        let usage_trends = self.create_usage_trends_chart(stats)?;
        let popular_models = self.create_popular_models_chart(stats)?;
        let user_patterns = self.create_user_patterns_chart(stats)?;
        let time_series_usage = self.create_time_series_usage_chart(stats)?;

        Ok(UsageVisualization {
            usage_trends,
            popular_models,
            user_patterns,
            geographic_distribution: None, // Would require geographic data
            time_series_usage,
        })
    }

    /// Create training progress visualization
    pub fn create_training_visualization(
        &self,
        history: &TrainingHistory,
    ) -> Result<TrainingVisualization> {
        let loss_curve = self.create_loss_curve_chart(history)?;
        let accuracy_curve = self.create_accuracy_curve_chart(history)?;
        let learning_rate_schedule = self.create_lr_schedule_chart(history)?;
        let gradient_norms = self.create_gradient_norms_chart(history)?;
        let validation_metrics = self.create_validation_metrics_chart(history)?;

        Ok(TrainingVisualization {
            loss_curve,
            accuracy_curve,
            learning_rate_schedule,
            gradient_norms,
            validation_metrics,
        })
    }

    /// Create real-time dashboard
    pub fn create_realtime_dashboard(
        &self,
        _metrics: &RealTimeMetrics,
    ) -> Result<DashboardTemplate> {
        let dashboard = DashboardTemplate {
            name: "Real-time Model Hub Dashboard".to_string(),
            description: "Live monitoring of model hub metrics".to_string(),
            layout: DashboardLayout {
                columns: 3,
                rows: 2,
                padding: 10,
                margin: 20,
            },
            widgets: vec![
                DashboardWidget {
                    widget_type: WidgetType::Metric,
                    title: "Active Models".to_string(),
                    position: WidgetPosition { x: 0, y: 0 },
                    size: WidgetSize {
                        width: 1,
                        height: 1,
                    },
                    data_source: DataSource::Metrics("active_models".to_string()),
                    update_interval: Some(Duration::from_secs(5)),
                },
                DashboardWidget {
                    widget_type: WidgetType::Chart(ChartType::Line),
                    title: "Requests per Second".to_string(),
                    position: WidgetPosition { x: 1, y: 0 },
                    size: WidgetSize {
                        width: 2,
                        height: 1,
                    },
                    data_source: DataSource::Metrics("rps".to_string()),
                    update_interval: Some(Duration::from_secs(1)),
                },
                DashboardWidget {
                    widget_type: WidgetType::Gauge,
                    title: "CPU Usage".to_string(),
                    position: WidgetPosition { x: 0, y: 1 },
                    size: WidgetSize {
                        width: 1,
                        height: 1,
                    },
                    data_source: DataSource::Metrics("cpu_usage".to_string()),
                    update_interval: Some(Duration::from_secs(2)),
                },
                DashboardWidget {
                    widget_type: WidgetType::Gauge,
                    title: "Memory Usage".to_string(),
                    position: WidgetPosition { x: 1, y: 1 },
                    size: WidgetSize {
                        width: 1,
                        height: 1,
                    },
                    data_source: DataSource::Metrics("memory_usage".to_string()),
                    update_interval: Some(Duration::from_secs(2)),
                },
                DashboardWidget {
                    widget_type: WidgetType::Chart(ChartType::Bar),
                    title: "Error Rate".to_string(),
                    position: WidgetPosition { x: 2, y: 1 },
                    size: WidgetSize {
                        width: 1,
                        height: 1,
                    },
                    data_source: DataSource::Metrics("error_rate".to_string()),
                    update_interval: Some(Duration::from_secs(10)),
                },
            ],
            refresh_interval: Some(Duration::from_secs(5)),
        };

        Ok(dashboard)
    }

    /// Export visualization to file
    pub fn export_visualization<T: Serialize>(
        &self,
        visualization: &T,
        filename: &str,
        format: ExportFormat,
    ) -> Result<PathBuf> {
        self.export_manager.export(visualization, filename, format)
    }

    /// Create inference time chart
    fn create_inference_time_chart(&self, data: &ModelPerformanceData) -> Result<ChartData> {
        let dataset = Dataset {
            label: "Inference Time".to_string(),
            data: data
                .inference_times
                .iter()
                .enumerate()
                .map(|(i, duration)| DataPoint {
                    x: i as f64,
                    y: duration.as_millis() as f64,
                    label: None,
                    metadata: HashMap::new(),
                })
                .collect(),
            color: Some(self.config.color_palette.primary_colors[0].clone()),
            style: Some(LineStyle::Solid),
            fill: false,
        };

        Ok(ChartData {
            title: "Model Inference Time".to_string(),
            chart_type: ChartType::Line,
            datasets: vec![dataset],
            x_axis: Axis {
                title: "Request Number".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Linear,
                format: None,
            },
            y_axis: Axis {
                title: "Time (ms)".to_string(),
                min: Some(0.0),
                max: None,
                scale: AxisScale::Linear,
                format: Some("%.1f ms".to_string()),
            },
            legend: Some(Legend {
                position: LegendPosition::TopRight,
                columns: 1,
                font_size: 12,
            }),
            annotations: vec![],
        })
    }

    /// Create throughput chart
    fn create_throughput_chart(&self, data: &ModelPerformanceData) -> Result<ChartData> {
        let dataset = Dataset {
            label: "Requests per Second".to_string(),
            data: data
                .throughput_data
                .iter()
                .enumerate()
                .map(|(i, measurement)| DataPoint {
                    x: i as f64,
                    y: measurement.requests_per_second as f64,
                    label: None,
                    metadata: HashMap::new(),
                })
                .collect(),
            color: Some(self.config.color_palette.primary_colors[1].clone()),
            style: Some(LineStyle::Solid),
            fill: true,
        };

        Ok(ChartData {
            title: "Model Throughput".to_string(),
            chart_type: ChartType::Line,
            datasets: vec![dataset],
            x_axis: Axis {
                title: "Time".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Time,
                format: None,
            },
            y_axis: Axis {
                title: "Requests/Second".to_string(),
                min: Some(0.0),
                max: None,
                scale: AxisScale::Linear,
                format: Some("%.1f RPS".to_string()),
            },
            legend: Some(Legend {
                position: LegendPosition::TopLeft,
                columns: 1,
                font_size: 12,
            }),
            annotations: vec![],
        })
    }

    /// Create resource utilization chart
    fn create_resource_utilization_chart(&self, data: &ModelPerformanceData) -> Result<ChartData> {
        let cpu_dataset = Dataset {
            label: "CPU Usage".to_string(),
            data: data
                .resource_utilization
                .cpu_usage
                .iter()
                .enumerate()
                .map(|(i, usage)| DataPoint {
                    x: i as f64,
                    y: *usage as f64,
                    label: None,
                    metadata: HashMap::new(),
                })
                .collect(),
            color: Some(self.config.color_palette.primary_colors[2].clone()),
            style: Some(LineStyle::Solid),
            fill: false,
        };

        let memory_dataset = Dataset {
            label: "Memory Usage".to_string(),
            data: data
                .resource_utilization
                .memory_usage
                .iter()
                .enumerate()
                .map(|(i, usage)| DataPoint {
                    x: i as f64,
                    y: (*usage as f64) / (1024.0 * 1024.0), // Convert to MB
                    label: None,
                    metadata: HashMap::new(),
                })
                .collect(),
            color: Some(self.config.color_palette.primary_colors[3].clone()),
            style: Some(LineStyle::Dashed),
            fill: false,
        };

        Ok(ChartData {
            title: "Resource Utilization".to_string(),
            chart_type: ChartType::Line,
            datasets: vec![cpu_dataset, memory_dataset],
            x_axis: Axis {
                title: "Time".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Linear,
                format: None,
            },
            y_axis: Axis {
                title: "Usage".to_string(),
                min: Some(0.0),
                max: None,
                scale: AxisScale::Linear,
                format: None,
            },
            legend: Some(Legend {
                position: LegendPosition::TopRight,
                columns: 1,
                font_size: 12,
            }),
            annotations: vec![],
        })
    }

    /// Create bottleneck analysis visualization
    fn create_bottleneck_analysis(
        &self,
        data: &ModelPerformanceData,
    ) -> Result<Vec<BottleneckVisualization>> {
        let mut visualizations = Vec::new();

        for bottleneck in &data.bottlenecks {
            let impact_chart = ChartData {
                title: format!("{:?} Bottleneck Impact", bottleneck.bottleneck_type),
                chart_type: ChartType::Bar,
                datasets: vec![Dataset {
                    label: "Impact".to_string(),
                    data: vec![DataPoint {
                        x: 0.0,
                        y: bottleneck.impact_percentage as f64,
                        label: Some(bottleneck.description.clone()),
                        metadata: HashMap::new(),
                    }],
                    color: Some(match bottleneck.severity {
                        crate::analytics::BottleneckSeverity::Low => {
                            self.config.color_palette.status_colors.info.clone()
                        }
                        crate::analytics::BottleneckSeverity::Medium => {
                            self.config.color_palette.status_colors.warning.clone()
                        }
                        crate::analytics::BottleneckSeverity::High => {
                            self.config.color_palette.status_colors.error.clone()
                        }
                        crate::analytics::BottleneckSeverity::Critical => "#8B0000".to_string(),
                    }),
                    style: None,
                    fill: true,
                }],
                x_axis: Axis {
                    title: "Bottleneck".to_string(),
                    min: None,
                    max: None,
                    scale: AxisScale::Category,
                    format: None,
                },
                y_axis: Axis {
                    title: "Impact (%)".to_string(),
                    min: Some(0.0),
                    max: Some(100.0),
                    scale: AxisScale::Linear,
                    format: Some("%.1f%%".to_string()),
                },
                legend: None,
                annotations: vec![],
            };

            visualizations.push(BottleneckVisualization {
                bottleneck_type: format!("{:?}", bottleneck.bottleneck_type),
                severity: format!("{:?}", bottleneck.severity),
                impact_chart,
                timeline: vec![], // Would be populated with actual timeline data
            });
        }

        Ok(visualizations)
    }

    // Additional chart creation methods would be implemented here...
    fn create_usage_trends_chart(&self, stats: &[ModelUsageStats]) -> Result<ChartData> {
        // Aggregate daily usage across all models
        let mut daily_totals: HashMap<String, u64> = HashMap::new();

        for stat in stats {
            for (date, count) in &stat.daily_usage {
                *daily_totals.entry(date.clone()).or_insert(0) += count;
            }
        }

        // Sort dates chronologically
        let mut sorted_dates: Vec<_> = daily_totals.keys().cloned().collect();
        sorted_dates.sort();

        // Create dataset
        let dataset = Dataset {
            label: "Daily Usage".to_string(),
            data: sorted_dates
                .iter()
                .enumerate()
                .map(|(i, date)| {
                    let count = daily_totals.get(date).copied().unwrap_or(0);
                    DataPoint {
                        x: i as f64,
                        y: count as f64,
                        label: Some(date.clone()),
                        metadata: HashMap::new(),
                    }
                })
                .collect(),
            color: Some("#3b82f6".to_string()), // Blue
            style: Some(LineStyle::Solid),
            fill: true,
        };

        Ok(ChartData {
            title: "Usage Trends Over Time".to_string(),
            chart_type: ChartType::Line,
            datasets: vec![dataset],
            x_axis: Axis {
                title: "Date".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Category,
                format: None,
            },
            y_axis: Axis {
                title: "Usage Count".to_string(),
                min: Some(0.0),
                max: None,
                scale: AxisScale::Linear,
                format: Some("d".to_string()),
            },
            legend: None,
            annotations: vec![],
        })
    }

    fn create_popular_models_chart(&self, stats: &[ModelUsageStats]) -> Result<ChartData> {
        // Sort models by popularity score (descending) and take top 10
        let mut sorted_stats: Vec<_> = stats.iter().collect();
        sorted_stats.sort_by(|a, b| {
            b.popularity_score
                .partial_cmp(&a.popularity_score)
                .expect("popularity_score should be comparable")
        });
        let top_models = sorted_stats.iter().take(10);

        let dataset = Dataset {
            label: "Popularity Score".to_string(),
            data: top_models
                .enumerate()
                .map(|(i, stat)| DataPoint {
                    x: i as f64,
                    y: stat.popularity_score as f64,
                    label: Some(stat.model_id.clone()),
                    metadata: HashMap::new(),
                })
                .collect(),
            color: Some("#8b5cf6".to_string()), // Purple
            style: Some(LineStyle::Solid),
            fill: false,
        };

        Ok(ChartData {
            title: "Popular Models".to_string(),
            chart_type: ChartType::Bar,
            datasets: vec![dataset],
            x_axis: Axis {
                title: "Model".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Linear,
                format: None,
            },
            y_axis: Axis {
                title: "Popularity Score".to_string(),
                min: Some(0.0),
                max: None,
                scale: AxisScale::Linear,
                format: Some(".2f".to_string()),
            },
            legend: None,
            annotations: vec![],
        })
    }

    fn create_user_patterns_chart(&self, stats: &[ModelUsageStats]) -> Result<ChartData> {
        // Aggregate usage patterns across all models by hour
        let mut hourly_totals = [0u64; 24];
        for stat in stats {
            for (hour, &count) in stat.hourly_patterns.iter().enumerate() {
                hourly_totals[hour] += count;
            }
        }

        let dataset = Dataset {
            label: "Usage Count".to_string(),
            data: hourly_totals
                .iter()
                .enumerate()
                .map(|(hour, &count)| DataPoint {
                    x: hour as f64,
                    y: count as f64,
                    label: Some(format!("{:02}:00", hour)),
                    metadata: HashMap::new(),
                })
                .collect(),
            color: Some("#f59e0b".to_string()), // Orange
            style: Some(LineStyle::Solid),
            fill: false,
        };

        Ok(ChartData {
            title: "User Patterns (Hourly Usage)".to_string(),
            chart_type: ChartType::Bar,
            datasets: vec![dataset],
            x_axis: Axis {
                title: "Hour of Day".to_string(),
                min: Some(0.0),
                max: Some(23.0),
                scale: AxisScale::Linear,
                format: Some("d".to_string()),
            },
            y_axis: Axis {
                title: "Usage Count".to_string(),
                min: Some(0.0),
                max: None,
                scale: AxisScale::Linear,
                format: Some("d".to_string()),
            },
            legend: None,
            annotations: vec![],
        })
    }

    fn create_time_series_usage_chart(&self, stats: &[ModelUsageStats]) -> Result<ChartData> {
        // Create a multi-line chart showing usage trends for top 5 models
        let mut sorted_stats: Vec<_> = stats.iter().collect();
        sorted_stats.sort_by(|a, b| b.total_inferences.cmp(&a.total_inferences));
        let top_models = sorted_stats.iter().take(5);

        let colors = [
            "#3b82f6", // Blue
            "#ef4444", // Red
            "#10b981", // Green
            "#f59e0b", // Orange
            "#8b5cf6", // Purple
        ];

        let mut datasets = vec![];

        for (idx, stat) in top_models.enumerate() {
            // Sort dates for this model
            let mut dates: Vec<_> = stat.daily_usage.keys().cloned().collect();
            dates.sort();

            let data: Vec<DataPoint> = dates
                .iter()
                .enumerate()
                .map(|(i, date)| {
                    let count = stat.daily_usage.get(date).copied().unwrap_or(0);
                    DataPoint {
                        x: i as f64,
                        y: count as f64,
                        label: Some(date.clone()),
                        metadata: HashMap::new(),
                    }
                })
                .collect();

            if !data.is_empty() {
                datasets.push(Dataset {
                    label: stat.model_id.clone(),
                    data,
                    color: Some(colors[idx % colors.len()].to_string()),
                    style: Some(LineStyle::Solid),
                    fill: false,
                });
            }
        }

        Ok(ChartData {
            title: "Time Series Usage by Model".to_string(),
            chart_type: ChartType::Line,
            datasets,
            x_axis: Axis {
                title: "Date".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Category,
                format: None,
            },
            y_axis: Axis {
                title: "Usage Count".to_string(),
                min: Some(0.0),
                max: None,
                scale: AxisScale::Linear,
                format: Some("d".to_string()),
            },
            legend: Some(Legend {
                position: LegendPosition::TopRight,
                font_size: 12,
                columns: 1,
            }),
            annotations: vec![],
        })
    }

    fn create_loss_curve_chart(&self, history: &TrainingHistory) -> Result<ChartData> {
        let epochs: Vec<f64> = (1..=history.loss.len()).map(|e| e as f64).collect();

        let mut datasets = vec![Dataset {
            label: "Training Loss".to_string(),
            data: history
                .loss
                .iter()
                .enumerate()
                .map(|(i, &loss)| DataPoint {
                    x: epochs[i],
                    y: loss,
                    label: None,
                    metadata: HashMap::new(),
                })
                .collect(),
            color: Some("#3b82f6".to_string()), // Blue
            style: Some(LineStyle::Solid),
            fill: false,
        }];

        // Add validation loss if available
        if !history.val_loss.is_empty() {
            datasets.push(Dataset {
                label: "Validation Loss".to_string(),
                data: history
                    .val_loss
                    .iter()
                    .enumerate()
                    .map(|(i, &loss)| DataPoint {
                        x: (i + 1) as f64,
                        y: loss,
                        label: None,
                        metadata: HashMap::new(),
                    })
                    .collect(),
                color: Some("#ef4444".to_string()), // Red
                style: Some(LineStyle::Dashed),
                fill: false,
            });
        }

        Ok(ChartData {
            title: "Training Loss".to_string(),
            chart_type: ChartType::Line,
            datasets,
            x_axis: Axis {
                title: "Epoch".to_string(),
                min: Some(1.0),
                max: Some(history.loss.len() as f64),
                scale: AxisScale::Linear,
                format: Some("d".to_string()),
            },
            y_axis: Axis {
                title: "Loss".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Linear,
                format: Some(".4f".to_string()),
            },
            legend: Some(Legend {
                position: LegendPosition::TopRight,
                font_size: 12,
                columns: 1,
            }),
            annotations: vec![],
        })
    }

    fn create_accuracy_curve_chart(&self, history: &TrainingHistory) -> Result<ChartData> {
        // Look for accuracy metrics in the history
        let mut datasets = vec![];

        if let Some(train_accuracy) = history.metrics.get("accuracy") {
            let epochs: Vec<f64> = (1..=train_accuracy.len()).map(|e| e as f64).collect();
            datasets.push(Dataset {
                label: "Training Accuracy".to_string(),
                data: train_accuracy
                    .iter()
                    .enumerate()
                    .map(|(i, &acc)| DataPoint {
                        x: epochs[i],
                        y: acc,
                        label: None,
                        metadata: HashMap::new(),
                    })
                    .collect(),
                color: Some("#3b82f6".to_string()), // Blue
                style: Some(LineStyle::Solid),
                fill: false,
            });
        }

        if let Some(val_accuracy) = history.metrics.get("val_accuracy") {
            let epochs: Vec<f64> = (1..=val_accuracy.len()).map(|e| e as f64).collect();
            datasets.push(Dataset {
                label: "Validation Accuracy".to_string(),
                data: val_accuracy
                    .iter()
                    .enumerate()
                    .map(|(i, &acc)| DataPoint {
                        x: epochs[i],
                        y: acc,
                        label: None,
                        metadata: HashMap::new(),
                    })
                    .collect(),
                color: Some("#10b981".to_string()), // Green
                style: Some(LineStyle::Dashed),
                fill: false,
            });
        }

        let num_epochs = datasets.first().map(|d| d.data.len()).unwrap_or(0);

        Ok(ChartData {
            title: "Training Accuracy".to_string(),
            chart_type: ChartType::Line,
            datasets,
            x_axis: Axis {
                title: "Epoch".to_string(),
                min: Some(1.0),
                max: Some(num_epochs as f64),
                scale: AxisScale::Linear,
                format: Some("d".to_string()),
            },
            y_axis: Axis {
                title: "Accuracy".to_string(),
                min: Some(0.0),
                max: Some(1.0),
                scale: AxisScale::Linear,
                format: Some(".2%".to_string()),
            },
            legend: Some(Legend {
                position: LegendPosition::BottomRight,
                font_size: 12,
                columns: 1,
            }),
            annotations: vec![],
        })
    }

    fn create_lr_schedule_chart(&self, history: &TrainingHistory) -> Result<ChartData> {
        let epochs: Vec<f64> = (1..=history.learning_rates.len())
            .map(|e| e as f64)
            .collect();

        let dataset = Dataset {
            label: "Learning Rate".to_string(),
            data: history
                .learning_rates
                .iter()
                .enumerate()
                .map(|(i, &lr)| DataPoint {
                    x: epochs[i],
                    y: lr,
                    label: None,
                    metadata: HashMap::new(),
                })
                .collect(),
            color: Some("#10b981".to_string()), // Green
            style: Some(LineStyle::Solid),
            fill: false,
        };

        Ok(ChartData {
            title: "Learning Rate Schedule".to_string(),
            chart_type: ChartType::Line,
            datasets: vec![dataset],
            x_axis: Axis {
                title: "Epoch".to_string(),
                min: Some(1.0),
                max: Some(history.learning_rates.len() as f64),
                scale: AxisScale::Linear,
                format: Some("d".to_string()),
            },
            y_axis: Axis {
                title: "Learning Rate".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Logarithmic,
                format: Some(".2e".to_string()),
            },
            legend: Some(Legend {
                position: LegendPosition::TopRight,
                font_size: 12,
                columns: 1,
            }),
            annotations: vec![],
        })
    }

    fn create_gradient_norms_chart(&self, history: &TrainingHistory) -> Result<ChartData> {
        let mut datasets = vec![];

        // Look for gradient norm metrics (grad_norm, gradient_norm, etc.)
        for (metric_name, values) in &history.metrics {
            if metric_name.to_lowercase().contains("grad")
                && (metric_name.to_lowercase().contains("norm")
                    || metric_name.to_lowercase().contains("magnitude"))
            {
                let epochs: Vec<f64> = (1..=values.len()).map(|e| e as f64).collect();
                datasets.push(Dataset {
                    label: metric_name.clone(),
                    data: values
                        .iter()
                        .enumerate()
                        .map(|(i, &val)| DataPoint {
                            x: epochs[i],
                            y: val,
                            label: None,
                            metadata: HashMap::new(),
                        })
                        .collect(),
                    color: None, // Auto-assign colors
                    style: Some(LineStyle::Solid),
                    fill: false,
                });
            }
        }

        // If no gradient norms found, create empty chart with message
        let num_epochs = datasets.first().map(|d| d.data.len()).unwrap_or(1);

        Ok(ChartData {
            title: "Gradient Norms".to_string(),
            chart_type: ChartType::Line,
            datasets,
            x_axis: Axis {
                title: "Epoch".to_string(),
                min: Some(1.0),
                max: Some(num_epochs as f64),
                scale: AxisScale::Linear,
                format: Some("d".to_string()),
            },
            y_axis: Axis {
                title: "Gradient Norm".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Logarithmic,
                format: Some(".2e".to_string()),
            },
            legend: Some(Legend {
                position: LegendPosition::TopRight,
                font_size: 12,
                columns: 1,
            }),
            annotations: vec![],
        })
    }

    fn create_validation_metrics_chart(&self, history: &TrainingHistory) -> Result<ChartData> {
        let mut datasets = vec![];

        // Collect all validation metrics (those starting with "val_")
        for (metric_name, values) in &history.metrics {
            if metric_name.starts_with("val_") {
                let epochs: Vec<f64> = (1..=values.len()).map(|e| e as f64).collect();
                datasets.push(Dataset {
                    label: metric_name.clone(),
                    data: values
                        .iter()
                        .enumerate()
                        .map(|(i, &val)| DataPoint {
                            x: epochs[i],
                            y: val,
                            label: None,
                            metadata: HashMap::new(),
                        })
                        .collect(),
                    color: None, // Auto-assign colors
                    style: Some(LineStyle::Solid),
                    fill: false,
                });
            }
        }

        let num_epochs = datasets.first().map(|d| d.data.len()).unwrap_or(0);

        Ok(ChartData {
            title: "Validation Metrics".to_string(),
            chart_type: ChartType::Line,
            datasets,
            x_axis: Axis {
                title: "Epoch".to_string(),
                min: Some(1.0),
                max: Some(num_epochs as f64),
                scale: AxisScale::Linear,
                format: Some("d".to_string()),
            },
            y_axis: Axis {
                title: "Metric Value".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Linear,
                format: Some(".4f".to_string()),
            },
            legend: Some(Legend {
                position: LegendPosition::TopRight,
                font_size: 12,
                columns: 1,
            }),
            annotations: vec![],
        })
    }

    fn create_placeholder_chart(&self, title: &str) -> ChartData {
        ChartData {
            title: title.to_string(),
            chart_type: ChartType::Line,
            datasets: vec![],
            x_axis: Axis {
                title: "X Axis".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Linear,
                format: None,
            },
            y_axis: Axis {
                title: "Y Axis".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Linear,
                format: None,
            },
            legend: None,
            annotations: vec![],
        }
    }
}

impl ChartRenderer {
    fn new(config: VisualizationConfig) -> Self {
        Self { config }
    }

    /// Render chart to HTML
    pub fn render_to_html(&self, chart: &ChartData) -> Result<String> {
        // Implementation would generate HTML with embedded JavaScript for interactive charts
        let html = format!(
            r#"
            <!DOCTYPE html>
            <html>
            <head>
                <title>{}</title>
                <script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
            </head>
            <body>
                <div id="chart" style="width:100%;height:600px;"></div>
                <script>
                    // Chart implementation would go here
                    Plotly.newPlot('chart', [], {{}});
                </script>
            </body>
            </html>
            "#,
            chart.title
        );
        Ok(html)
    }

    /// Render chart to SVG
    pub fn render_to_svg(&self, chart: &ChartData) -> Result<String> {
        // Implementation would generate SVG markup
        Ok(format!("<svg><!-- {} chart --></svg>", chart.title))
    }
}

impl DashboardGenerator {
    fn new() -> Self {
        Self {
            templates: HashMap::new(),
        }
    }

    /// Generate dashboard HTML
    pub fn generate_dashboard_html(&self, template: &DashboardTemplate) -> Result<String> {
        // Implementation would generate a complete dashboard HTML page
        let html = format!(
            r#"
            <!DOCTYPE html>
            <html>
            <head>
                <title>{}</title>
                <link rel="stylesheet" href="dashboard.css">
                <script src="dashboard.js"></script>
            </head>
            <body>
                <div class="dashboard">
                    <h1>{}</h1>
                    <div class="dashboard-grid">
                        <!-- Widgets would be generated here -->
                    </div>
                </div>
            </body>
            </html>
            "#,
            template.name, template.name
        );
        Ok(html)
    }
}

impl ExportManager {
    fn new(output_directory: PathBuf) -> Self {
        Self { output_directory }
    }

    /// Export data to specified format
    pub fn export<T: Serialize>(
        &self,
        data: &T,
        filename: &str,
        format: ExportFormat,
    ) -> Result<PathBuf> {
        let output_path = match format {
            ExportFormat::JSON => {
                let path = self.output_directory.join(format!("{}.json", filename));
                let json = serde_json::to_string_pretty(data)
                    .map_err(|e| TorshError::SerializationError(e.to_string()))?;
                std::fs::write(&path, json).map_err(|e| TorshError::IoError(e.to_string()))?;
                path
            }
            ExportFormat::HTML => {
                let path = self.output_directory.join(format!("{}.html", filename));
                // Implementation would convert data to HTML
                std::fs::write(&path, "<!-- HTML export -->")?;
                path
            }
            ExportFormat::PNG | ExportFormat::SVG | ExportFormat::PDF => {
                let extension = match format {
                    ExportFormat::PNG => "png",
                    ExportFormat::SVG => "svg",
                    ExportFormat::PDF => "pdf",
                    _ => {
                        return Err(TorshError::InvalidArgument(format!(
                            "Unsupported binary export format: {:?}",
                            format
                        )));
                    }
                };
                let path = self
                    .output_directory
                    .join(format!("{}.{}", filename, extension));
                // Implementation would render to binary format
                std::fs::write(&path, b"binary data placeholder")?;
                path
            }
        };

        Ok(output_path)
    }
}

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

    #[test]
    fn test_visualization_config_default() {
        let config = VisualizationConfig::default();
        assert_eq!(config.default_chart_size.width, 800);
        assert_eq!(config.default_chart_size.height, 600);
        assert!(config.animation_enabled);
        assert!(config.high_dpi_enabled);
    }

    #[test]
    fn test_color_palette_default() {
        let palette = ColorPalette::default();
        assert!(!palette.primary_colors.is_empty());
        assert_eq!(palette.status_colors.success, "#28a745");
        assert_eq!(palette.status_colors.error, "#dc3545");
    }

    #[test]
    fn test_chart_data_creation() {
        let chart = ChartData {
            title: "Test Chart".to_string(),
            chart_type: ChartType::Line,
            datasets: vec![],
            x_axis: Axis {
                title: "X".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Linear,
                format: None,
            },
            y_axis: Axis {
                title: "Y".to_string(),
                min: None,
                max: None,
                scale: AxisScale::Linear,
                format: None,
            },
            legend: None,
            annotations: vec![],
        };

        assert_eq!(chart.title, "Test Chart");
        assert!(matches!(chart.chart_type, ChartType::Line));
    }
}