torsh_hub/

cli.rs

//! Command-line interface for torsh-hub
//!
//! This module provides a comprehensive CLI for interacting with the torsh-hub
//! model repository and management system.

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::{
    analytics::{AnalyticsManager, ExportFormat},
    fine_tuning::{FineTuner, FineTuningConfig},
    model_info::{ModelInfo, Version},
    registry::{ModelRegistry, RegistryEntry, SearchQuery},
};
use clap::{Args, Parser, Subcommand, ValueEnum};
use serde::{Deserialize, Serialize};
use std::path::PathBuf;
use std::time::Duration;
use torsh_core::error::{Result, TorshError};

/// ToRSh Hub CLI - Model repository and management system
#[derive(Parser)]
#[command(name = "torsh-hub")]
#[command(about = "A CLI tool for managing ToRSh machine learning models")]
#[command(version = "0.1.0-alpha.2")]
#[command(long_about = None)]
pub struct Cli {
    #[command(subcommand)]
    pub command: Commands,

    /// Configuration file path
    #[arg(short, long, global = true)]
    pub config: Option<PathBuf>,

    /// Verbose output
    #[arg(short, long, global = true)]
    pub verbose: bool,

    /// Output format
    #[arg(short, long, global = true, value_enum, default_value_t = OutputFormat::Table)]
    pub output: OutputFormat,

    /// Hub URL (default: official ToRSh Hub)
    #[arg(long, global = true)]
    pub hub_url: Option<String>,

    /// Authentication token
    #[arg(long, global = true)]
    pub token: Option<String>,
}

#[derive(Subcommand)]
pub enum Commands {
    /// Model management commands
    #[command(subcommand)]
    Model(ModelCommands),

    /// Search for models
    Search(SearchArgs),

    /// Download a model
    Download(DownloadArgs),

    /// Upload a model
    Upload(UploadArgs),

    /// Fine-tune a model
    #[command(subcommand)]
    FineTune(FineTuneCommands),

    /// Analytics and monitoring
    #[command(subcommand)]
    Analytics(AnalyticsCommands),

    /// Configuration management
    #[command(subcommand)]
    Config(ConfigCommands),

    /// Cache management
    #[command(subcommand)]
    Cache(CacheCommands),

    /// Authentication
    #[command(subcommand)]
    Auth(AuthCommands),

    /// Registry management
    #[command(subcommand)]
    Registry(RegistryCommands),
}

#[derive(Subcommand)]
pub enum ModelCommands {
    /// List models
    List(ListArgs),
    /// Show model information
    Info(InfoArgs),
    /// Load a model for inference
    Load(LoadArgs),
    /// Validate a model
    Validate(ValidateArgs),
    /// Convert model format
    Convert(ConvertArgs),
    /// Compare models
    Compare(CompareArgs),
}

#[derive(Subcommand)]
pub enum FineTuneCommands {
    /// Start fine-tuning
    Start(FineTuneStartArgs),
    /// Resume fine-tuning
    Resume(FineTuneResumeArgs),
    /// Stop fine-tuning
    Stop(FineTuneStopArgs),
    /// List fine-tuning jobs
    List(FineTuneListArgs),
    /// Show fine-tuning status
    Status(FineTuneStatusArgs),
}

#[derive(Subcommand)]
pub enum AnalyticsCommands {
    /// Show usage statistics
    Usage(UsageArgs),
    /// Show performance metrics
    Performance(PerformanceArgs),
    /// Export analytics data
    Export(ExportArgs),
    /// Show real-time dashboard
    Dashboard,
    /// Generate analytics report
    Report(ReportArgs),
}

#[derive(Subcommand)]
pub enum ConfigCommands {
    /// Show current configuration
    Show,
    /// Set configuration value
    Set(ConfigSetArgs),
    /// Reset configuration to defaults
    Reset,
    /// Validate configuration
    Validate,
}

#[derive(Subcommand)]
pub enum CacheCommands {
    /// List cached models
    List,
    /// Clear cache
    Clear(CacheClearArgs),
    /// Show cache statistics
    Stats,
    /// Optimize cache
    Optimize,
}

#[derive(Subcommand)]
pub enum AuthCommands {
    /// Login to hub
    Login(LoginArgs),
    /// Logout from hub
    Logout,
    /// Show current user
    Whoami,
    /// Refresh authentication token
    Refresh,
}

#[derive(Subcommand)]
pub enum RegistryCommands {
    /// Add a model to registry
    Add(RegistryAddArgs),
    /// Remove a model from registry
    Remove(RegistryRemoveArgs),
    /// Update model metadata
    Update(RegistryUpdateArgs),
    /// Sync with remote registry
    Sync,
}

#[derive(Args)]
pub struct SearchArgs {
    /// Search query
    pub query: String,

    /// Model category filter
    #[arg(short, long)]
    pub category: Option<String>,

    /// Model type filter (e.g., vision, nlp, audio)
    #[arg(short = 't', long)]
    pub model_type: Option<String>,

    /// Minimum accuracy threshold
    #[arg(long)]
    pub min_accuracy: Option<f32>,

    /// Maximum model size (in MB)
    #[arg(long)]
    pub max_size: Option<u64>,

    /// Sort by field
    #[arg(short, long, value_enum, default_value_t = SortBy::Relevance)]
    pub sort: SortBy,

    /// Number of results to show
    #[arg(short, long, default_value_t = 10)]
    pub limit: usize,
}

#[derive(Args)]
pub struct DownloadArgs {
    /// Model identifier (name or URL)
    pub model: String,

    /// Output directory
    #[arg(short, long)]
    pub output: Option<PathBuf>,

    /// Force download even if cached
    #[arg(short, long)]
    pub force: bool,

    /// Download specific revision/version
    #[arg(short, long)]
    pub revision: Option<String>,

    /// Resume incomplete download
    #[arg(long)]
    pub resume: bool,

    /// Verify download integrity
    #[arg(long, default_value_t = true)]
    pub verify: bool,
}

#[derive(Args)]
pub struct UploadArgs {
    /// Path to model directory or file
    pub path: PathBuf,

    /// Model name
    #[arg(short, long)]
    pub name: String,

    /// Model description
    #[arg(short, long)]
    pub description: Option<String>,

    /// Model tags
    #[arg(short, long)]
    pub tags: Vec<String>,

    /// Model category
    #[arg(short, long)]
    pub category: Option<String>,

    /// Model license
    #[arg(short, long)]
    pub license: Option<String>,

    /// Make model private
    #[arg(long)]
    pub private: bool,

    /// Skip validation
    #[arg(long)]
    pub skip_validation: bool,
}

#[derive(Args)]
pub struct ListArgs {
    /// Filter by category
    #[arg(short, long)]
    pub category: Option<String>,

    /// Show only owned models
    #[arg(long)]
    pub owned: bool,

    /// Show only public models
    #[arg(long)]
    pub public: bool,

    /// Show detailed information
    #[arg(short, long)]
    pub detailed: bool,
}

#[derive(Args)]
pub struct InfoArgs {
    /// Model identifier
    pub model: String,

    /// Show detailed technical information
    #[arg(long)]
    pub technical: bool,

    /// Show usage statistics
    #[arg(long)]
    pub stats: bool,

    /// Show download links
    #[arg(long)]
    pub downloads: bool,
}

#[derive(Args)]
pub struct LoadArgs {
    /// Model identifier
    pub model: String,

    /// Device to load model on
    #[arg(short, long, value_enum, default_value_t = Device::Auto)]
    pub device: Device,

    /// Precision to use
    #[arg(short, long, value_enum)]
    pub precision: Option<Precision>,

    /// Optimization level
    #[arg(short, long, value_enum, default_value_t = OptimizationLevel::Standard)]
    pub optimization: OptimizationLevel,

    /// Enable profiling
    #[arg(long)]
    pub profile: bool,
}

#[derive(Args)]
pub struct ValidateArgs {
    /// Model path or identifier
    pub model: String,

    /// Validation strictness level
    #[arg(short, long, value_enum, default_value_t = ValidationLevel::Standard)]
    pub level: ValidationLevel,

    /// Fix validation issues automatically
    #[arg(long)]
    pub fix: bool,
}

#[derive(Args)]
pub struct ConvertArgs {
    /// Input model path
    pub input: PathBuf,

    /// Output model path
    pub output: PathBuf,

    /// Target format
    #[arg(short, long, value_enum)]
    pub format: ModelFormat,

    /// Optimization options
    #[arg(short, long)]
    pub optimize: Vec<String>,
}

#[derive(Args)]
pub struct CompareArgs {
    /// First model identifier
    pub model1: String,

    /// Second model identifier
    pub model2: String,

    /// Comparison metrics
    #[arg(short, long)]
    pub metrics: Vec<String>,

    /// Test dataset path
    #[arg(short, long)]
    pub dataset: Option<PathBuf>,
}

#[derive(Args)]
pub struct FineTuneStartArgs {
    /// Base model identifier
    pub model: String,

    /// Training dataset path
    #[arg(short, long)]
    pub dataset: PathBuf,

    /// Validation dataset path
    #[arg(short, long)]
    pub validation: Option<PathBuf>,

    /// Learning rate
    #[arg(short, long, default_value_t = 1e-4)]
    pub learning_rate: f32,

    /// Number of epochs
    #[arg(short, long, default_value_t = 10)]
    pub epochs: usize,

    /// Batch size
    #[arg(short, long, default_value_t = 32)]
    pub batch_size: usize,

    /// Fine-tuning strategy
    #[arg(short, long, value_enum, default_value_t = Strategy::Full)]
    pub strategy: Strategy,

    /// Output directory for fine-tuned model
    #[arg(short, long)]
    pub output: PathBuf,

    /// Configuration file
    #[arg(short, long)]
    pub config_file: Option<PathBuf>,
}

#[derive(Args)]
pub struct FineTuneResumeArgs {
    /// Job ID or checkpoint path
    pub job_or_checkpoint: String,
}

#[derive(Args)]
pub struct FineTuneStopArgs {
    /// Job ID
    pub job_id: String,

    /// Save checkpoint before stopping
    #[arg(long, default_value_t = true)]
    pub save_checkpoint: bool,
}

#[derive(Args)]
pub struct FineTuneListArgs {
    /// Show only active jobs
    #[arg(short, long)]
    pub active: bool,

    /// Show detailed information
    #[arg(short, long)]
    pub detailed: bool,
}

#[derive(Args)]
pub struct FineTuneStatusArgs {
    /// Job ID
    pub job_id: String,

    /// Follow status updates
    #[arg(short, long)]
    pub follow: bool,
}

#[derive(Args)]
pub struct UsageArgs {
    /// Model identifier (optional)
    pub model: Option<String>,

    /// Time period
    #[arg(short, long, value_enum, default_value_t = TimePeriod::LastWeek)]
    pub period: TimePeriod,

    /// Show breakdown by category
    #[arg(long)]
    pub by_category: bool,
}

#[derive(Args)]
pub struct PerformanceArgs {
    /// Model identifier (optional)
    pub model: Option<String>,

    /// Show detailed profiling data
    #[arg(short, long)]
    pub detailed: bool,

    /// Compare with baseline
    #[arg(long)]
    pub baseline: Option<String>,
}

#[derive(Args)]
pub struct ExportArgs {
    /// Output file path
    pub output: PathBuf,

    /// Export format
    #[arg(short, long, value_enum, default_value_t = ExportFormat::JSON)]
    pub format: ExportFormat,

    /// Start date (YYYY-MM-DD)
    #[arg(long)]
    pub start_date: Option<String>,

    /// End date (YYYY-MM-DD)
    #[arg(long)]
    pub end_date: Option<String>,

    /// Include user data
    #[arg(long)]
    pub include_users: bool,
}

#[derive(Args)]
pub struct ReportArgs {
    /// Model identifier (optional)
    pub model: Option<String>,

    /// Report type
    #[arg(short, long, value_enum, default_value_t = ReportType::Summary)]
    pub report_type: ReportType,

    /// Output file path
    #[arg(short, long)]
    pub output: Option<PathBuf>,
}

#[derive(Args)]
pub struct ConfigSetArgs {
    /// Configuration key
    pub key: String,

    /// Configuration value
    pub value: String,
}

#[derive(Args)]
pub struct CacheClearArgs {
    /// Clear specific model
    pub model: Option<String>,

    /// Clear models older than duration
    #[arg(long)]
    pub older_than: Option<String>,

    /// Clear all cached models
    #[arg(long)]
    pub all: bool,
}

#[derive(Args)]
pub struct LoginArgs {
    /// Username or email
    pub username: Option<String>,

    /// API token (if provided, skips interactive login)
    #[arg(long)]
    pub token: Option<String>,

    /// Hub URL
    #[arg(long)]
    pub hub_url: Option<String>,
}

#[derive(Args)]
pub struct RegistryAddArgs {
    /// Model path
    pub path: PathBuf,

    /// Model name
    #[arg(short, long)]
    pub name: String,

    /// Version
    #[arg(short, long)]
    pub version: Option<String>,
}

#[derive(Args)]
pub struct RegistryRemoveArgs {
    /// Model name
    pub name: String,

    /// Version (remove specific version)
    #[arg(short, long)]
    pub version: Option<String>,
}

#[derive(Args)]
pub struct RegistryUpdateArgs {
    /// Model name
    pub name: String,

    /// New description
    #[arg(long)]
    pub description: Option<String>,

    /// New tags
    #[arg(long)]
    pub tags: Option<Vec<String>>,
}

#[derive(ValueEnum, Clone, Debug)]
pub enum OutputFormat {
    Table,
    JSON,
    YAML,
    CSV,
}

#[derive(ValueEnum, Clone, Debug)]
pub enum SortBy {
    Relevance,
    Name,
    Date,
    Downloads,
    Size,
    Accuracy,
}

#[derive(ValueEnum, Clone, Debug)]
pub enum Device {
    Auto,
    CPU,
    CUDA,
    Metal,
    WebGPU,
}

#[derive(ValueEnum, Clone, Debug)]
pub enum Precision {
    FP32,
    FP16,
    BF16,
    INT8,
    INT4,
}

#[derive(ValueEnum, Clone, Debug)]
pub enum OptimizationLevel {
    None,
    Basic,
    Standard,
    Aggressive,
}

#[derive(ValueEnum, Clone, Debug)]
pub enum ValidationLevel {
    Basic,
    Standard,
    Strict,
}

#[derive(ValueEnum, Clone, Debug)]
pub enum ModelFormat {
    Torsh,
    ONNX,
    PyTorch,
    TensorFlow,
    TensorRT,
}

#[derive(ValueEnum, Clone, Debug)]
pub enum Strategy {
    Full,
    Partial,
    HeadOnly,
    LoRA,
    Adapter,
}

#[derive(ValueEnum, Clone, Debug)]
pub enum TimePeriod {
    LastHour,
    LastDay,
    LastWeek,
    LastMonth,
    LastYear,
    All,
}

#[derive(ValueEnum, Clone, Debug)]
pub enum ReportType {
    Summary,
    Detailed,
    Performance,
    Usage,
    Trends,
}

/// CLI application state
pub struct CliApp {
    pub config: CliConfig,
    pub registry: ModelRegistry,
    pub analytics: Option<AnalyticsManager>,
}

/// CLI configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CliConfig {
    pub hub_url: String,
    pub cache_dir: PathBuf,
    pub download_timeout: Duration,
    pub max_concurrent_downloads: usize,
    pub default_device: String,
    pub auth_token: Option<String>,
    pub analytics_enabled: bool,
}

impl Default for CliConfig {
    fn default() -> Self {
        Self {
            hub_url: "https://hub.torsh.dev".to_string(),
            cache_dir: dirs::cache_dir().unwrap_or_default().join("torsh-hub"),
            download_timeout: Duration::from_secs(300),
            max_concurrent_downloads: 4,
            default_device: "auto".to_string(),
            auth_token: None,
            analytics_enabled: true,
        }
    }
}

impl CliApp {
    /// Create new CLI application
    pub fn new(config_path: Option<PathBuf>) -> Result<Self> {
        let config = if let Some(path) = config_path {
            Self::load_config(&path)?
        } else {
            CliConfig::default()
        };

        let registry = ModelRegistry::new(config.cache_dir.clone())?;

        let analytics = if config.analytics_enabled {
            Some(AnalyticsManager::new(config.cache_dir.join("analytics"))?)
        } else {
            None
        };

        Ok(Self {
            config,
            registry,
            analytics,
        })
    }

    /// Load configuration from file
    fn load_config(path: &PathBuf) -> Result<CliConfig> {
        let content = std::fs::read_to_string(path)
            .map_err(|e| torsh_core::error::TorshError::IoError(e.to_string()))?;

        let config: CliConfig = toml::from_str(&content)
            .map_err(|e| torsh_core::error::TorshError::SerializationError(e.to_string()))?;

        Ok(config)
    }

    /// Save configuration to file
    pub fn save_config(&self, path: &PathBuf) -> Result<()> {
        let content = toml::to_string_pretty(&self.config)
            .map_err(|e| torsh_core::error::TorshError::SerializationError(e.to_string()))?;

        std::fs::write(path, content)
            .map_err(|e| torsh_core::error::TorshError::IoError(e.to_string()))?;

        Ok(())
    }

    /// Execute search command
    pub async fn execute_search(&mut self, args: &SearchArgs) -> Result<()> {
        let category = if let Some(cat) = &args.category {
            Some(cat.parse::<crate::registry::ModelCategory>()?)
        } else {
            None
        };

        let query = SearchQuery {
            text: Some(args.query.clone()),
            category,
            architecture: args.model_type.clone(),
            min_accuracy: args.min_accuracy,
            max_model_size_mb: args.max_size.map(|size| size as f32),
            limit: args.limit,
            ..Default::default()
        };

        let results = self.registry.search(&query);

        self.display_search_results(&results, &args.sort)?;
        Ok(())
    }

    /// Execute download command
    pub async fn execute_download(&mut self, args: &DownloadArgs) -> Result<()> {
        println!("Downloading model: {}", args.model);

        if let Some(analytics) = &mut self.analytics {
            let action = crate::analytics::UserAction {
                action_type: crate::analytics::ActionType::ModelDownload,
                timestamp: std::time::SystemTime::now(),
                model_id: Some(args.model.clone()),
                parameters: std::collections::HashMap::new(),
                success: true,
                error_message: None,
            };
            analytics.record_user_action("cli_user", action)?;
        }

        // Implementation would handle actual download
        println!("Download completed successfully");
        Ok(())
    }

    /// Execute fine-tuning start command
    pub async fn execute_fine_tune_start(&mut self, args: &FineTuneStartArgs) -> Result<()> {
        let config = if let Some(config_path) = &args.config_file {
            self.load_fine_tune_config(config_path)?
        } else {
            match args.strategy {
                Strategy::Full => {
                    let num_classes = self
                        .detect_num_classes(
                            &args.model,
                            &Some(args.dataset.to_string_lossy().to_string()),
                        )
                        .await
                        .unwrap_or(1000);
                    crate::fine_tuning::utils::image_classification_config(num_classes)
                }
                Strategy::HeadOnly => {
                    let num_classes = self
                        .detect_num_classes(
                            &args.model,
                            &Some(args.dataset.to_string_lossy().to_string()),
                        )
                        .await
                        .unwrap_or(1000);
                    crate::fine_tuning::utils::image_classification_config(num_classes)
                }
                Strategy::LoRA => {
                    let rank = self
                        .determine_lora_rank(&args.model, args.batch_size)
                        .await
                        .unwrap_or(64);
                    crate::fine_tuning::utils::lora_config(rank)
                }
                Strategy::Adapter => {
                    let num_classes = self
                        .detect_num_classes(
                            &args.model,
                            &Some(args.dataset.to_string_lossy().to_string()),
                        )
                        .await
                        .unwrap_or(1000);
                    let mut config =
                        crate::fine_tuning::utils::image_classification_config(num_classes);
                    self.configure_adapter_strategy(&mut config, &args.model)
                        .await
                        .unwrap_or(());
                    config
                }
                _ => FineTuningConfig::default(),
            }
        };

        // Load model info (placeholder)
        let model_info = ModelInfo::new(
            args.model.clone(),
            "user".to_string(),
            Version::new(1, 0, 0),
        );
        let _trainer = FineTuner::new(config, model_info)?;

        println!("Starting fine-tuning for model: {}", args.model);
        println!("Fine-tuning configuration:");
        println!("  Learning rate: {}", args.learning_rate);
        println!("  Epochs: {}", args.epochs);
        println!("  Batch size: {}", args.batch_size);
        println!("  Strategy: {:?}", args.strategy);

        // Implementation would start actual fine-tuning
        Ok(())
    }

    /// Execute analytics usage command
    pub async fn execute_analytics_usage(&self, args: &UsageArgs) -> Result<()> {
        if let Some(analytics) = &self.analytics {
            let report = analytics.generate_report(args.model.as_deref())?;
            self.display_analytics_report(&report)?;
        } else {
            println!("Analytics not enabled");
        }
        Ok(())
    }

    /// Display search results
    fn display_search_results(&self, results: &[&RegistryEntry], _sort_by: &SortBy) -> Result<()> {
        if results.is_empty() {
            println!("No models found");
            return Ok(());
        }

        println!("Found {} models:", results.len());
        println!(
            "{:<30} {:<15} {:<20} {:<10}",
            "Name", "Category", "Author", "Downloads"
        );
        println!("{}", "-".repeat(80));

        for model in results {
            println!(
                "{:<30} {:<15} {:<20} {:<10}",
                model.name,
                format!("{:?}", model.category),
                model.author,
                model.downloads
            );
        }

        Ok(())
    }

    /// Display analytics report
    fn display_analytics_report(&self, report: &crate::analytics::AnalyticsReport) -> Result<()> {
        println!(
            "Analytics Report - {}",
            chrono::DateTime::<chrono::Utc>::from(report.timestamp)
        );

        if let Some(model_id) = &report.model_id {
            println!("Model: {}", model_id);
        }

        if let Some(usage_stats) = &report.usage_stats {
            println!("\nUsage Statistics:");
            println!("  Total loads: {}", usage_stats.total_loads);
            println!("  Total inferences: {}", usage_stats.total_inferences);
            println!(
                "  Average inference time: {:?}",
                usage_stats.average_inference_time
            );
            println!("  Error rate: {:.2}%", usage_stats.error_rate * 100.0);
        }

        println!("\nReal-time Metrics:");
        println!(
            "  Active models: {}",
            report.real_time_metrics.active_models
        );
        println!(
            "  Active sessions: {}",
            report.real_time_metrics.total_active_sessions
        );
        println!(
            "  Current memory usage: {} MB",
            report.real_time_metrics.current_memory_usage / 1024 / 1024
        );
        println!(
            "  Requests per second: {:.2}",
            report.real_time_metrics.requests_per_second
        );

        if !report.trending_models.is_empty() {
            println!("\nTrending Models:");
            for model in &report.trending_models {
                println!(
                    "  {}: {:.2} (growth: {:.1}%)",
                    model.model_id,
                    model.trend_score,
                    model.growth_rate * 100.0
                );
            }
        }

        Ok(())
    }

    /// Load fine-tuning configuration from file
    fn load_fine_tune_config(&self, path: &PathBuf) -> Result<FineTuningConfig> {
        let content = std::fs::read_to_string(path)
            .map_err(|e| torsh_core::error::TorshError::IoError(e.to_string()))?;

        let config: FineTuningConfig = toml::from_str(&content)
            .map_err(|e| torsh_core::error::TorshError::SerializationError(e.to_string()))?;

        Ok(config)
    }

    /// Automatically detect the number of classes from dataset structure or model metadata
    async fn detect_num_classes(&self, model: &str, data_path: &Option<String>) -> Result<usize> {
        // Try to detect from data path structure first
        if let Some(ref path) = data_path {
            if let Ok(count) = self.count_class_directories(path) {
                return Ok(count);
            }
        }

        // Fallback: Try to infer from model metadata
        if let Ok(metadata) = self.get_model_metadata(model).await {
            if let Some(classes) = metadata.get("num_classes") {
                if let Some(count) = classes.as_u64() {
                    return Ok(count as usize);
                }
            }
        }

        // Default fallback based on common datasets
        Ok(match model {
            m if m.contains("imagenet") => 1000,
            m if m.contains("cifar10") => 10,
            m if m.contains("cifar100") => 100,
            m if m.contains("mnist") => 10,
            _ => 1000, // Generic default
        })
    }

    /// Determine optimal LoRA rank based on model size and batch size
    async fn determine_lora_rank(&self, model: &str, batch_size: usize) -> Result<usize> {
        // Try to get model parameter count
        let param_count = self
            .estimate_model_parameters(model)
            .await
            .unwrap_or(25_000_000); // 25M default

        // Calculate rank based on model size and memory constraints
        let base_rank = match param_count {
            n if n < 10_000_000 => 16,     // Small models: rank 16
            n if n < 100_000_000 => 64,    // Medium models: rank 64
            n if n < 1_000_000_000 => 128, // Large models: rank 128
            _ => 256,                      // Very large models: rank 256
        };

        // Adjust based on batch size (smaller batch = can afford larger rank)
        let adjusted_rank = match batch_size {
            n if n <= 8 => (base_rank as f32 * 1.5) as usize,
            n if n <= 16 => base_rank,
            n if n <= 32 => (base_rank as f32 * 0.8) as usize,
            _ => (base_rank as f32 * 0.6) as usize,
        };

        // Ensure rank is a power of 2 and within reasonable bounds
        let final_rank = adjusted_rank.next_power_of_two().min(512).max(8);

        Ok(final_rank)
    }

    /// Configure adapter strategy based on model architecture
    async fn configure_adapter_strategy(
        &self,
        _config: &mut FineTuningConfig,
        model: &str,
    ) -> Result<()> {
        // Determine adapter placement based on model type
        let adapter_config = if model.contains("bert") || model.contains("transformer") {
            // For transformer models, place adapters after attention and FFN layers
            serde_json::json!({
                "adapter_layers": ["attention", "ffn"],
                "adapter_dim": 64,
                "adapter_alpha": 16,
                "adapter_dropout": 0.1
            })
        } else if model.contains("resnet") || model.contains("efficientnet") {
            // For CNN models, place adapters after major blocks
            serde_json::json!({
                "adapter_layers": ["conv_blocks"],
                "adapter_dim": 32,
                "adapter_alpha": 8,
                "adapter_dropout": 0.05
            })
        } else {
            // Generic adapter configuration
            serde_json::json!({
                "adapter_layers": ["all"],
                "adapter_dim": 64,
                "adapter_alpha": 16,
                "adapter_dropout": 0.1
            })
        };

        // Store adapter configuration in the fine-tuning config
        // This would require extending FineTuningConfig to support adapter settings
        println!("Adapter configuration: {}", adapter_config);

        Ok(())
    }

    /// Count the number of class directories in a dataset path
    fn count_class_directories(&self, path: &str) -> Result<usize> {
        use std::fs;

        let path = std::path::Path::new(path);
        if !path.exists() {
            return Err(TorshError::IoError(format!(
                "Data path does not exist: {}",
                path.display()
            )));
        }

        // Look for typical dataset structures (train/, val/, test/)
        let train_path = path.join("train");
        let classes_path = if train_path.exists() {
            train_path
        } else {
            path.to_path_buf()
        };

        let mut class_count = 0;
        if let Ok(entries) = fs::read_dir(classes_path) {
            for entry in entries.flatten() {
                if entry.file_type().map(|ft| ft.is_dir()).unwrap_or(false) {
                    class_count += 1;
                }
            }
        }

        if class_count == 0 {
            return Err(TorshError::Other(
                "No class directories found in dataset path".to_string(),
            ));
        }

        Ok(class_count)
    }

    /// Get model metadata from hub or local cache
    async fn get_model_metadata(&self, model: &str) -> Result<serde_json::Value> {
        // Try to fetch metadata from the model registry
        let _registry = crate::registry::ModelRegistry::new("./torsh_registry");

        // This is a simplified implementation - in practice, you'd have a proper metadata API
        let metadata = serde_json::json!({
            "name": model,
            "architecture": self.infer_architecture(model),
            "num_classes": self.infer_default_classes(model)
        });

        Ok(metadata)
    }

    /// Estimate the number of parameters in a model
    async fn estimate_model_parameters(&self, model: &str) -> Result<usize> {
        // Rough parameter estimates for common models
        let param_count = match model {
            m if m.contains("resnet18") => 11_689_512,
            m if m.contains("resnet50") => 25_557_032,
            m if m.contains("resnet101") => 44_549_160,
            m if m.contains("efficientnet-b0") => 5_288_548,
            m if m.contains("efficientnet-b1") => 7_794_184,
            m if m.contains("efficientnet-b2") => 9_109_994,
            m if m.contains("bert-base") => 110_000_000,
            m if m.contains("bert-large") => 340_000_000,
            m if m.contains("gpt2") => 117_000_000,
            m if m.contains("t5-small") => 60_000_000,
            m if m.contains("t5-base") => 220_000_000,
            _ => 25_000_000, // Default estimate: 25M parameters
        };

        Ok(param_count)
    }

    /// Infer model architecture from name
    fn infer_architecture(&self, model: &str) -> &str {
        if model.contains("resnet") {
            "resnet"
        } else if model.contains("efficientnet") {
            "efficientnet"
        } else if model.contains("bert") {
            "bert"
        } else if model.contains("gpt") {
            "gpt"
        } else if model.contains("t5") {
            "t5"
        } else {
            "unknown"
        }
    }

    /// Infer default number of classes based on model name
    fn infer_default_classes(&self, model: &str) -> usize {
        if model.contains("imagenet") {
            1000
        } else if model.contains("cifar10") {
            10
        } else if model.contains("cifar100") {
            100
        } else if model.contains("mnist") {
            10
        } else {
            1000 // ImageNet default
        }
    }
}

/// Main CLI entry point
pub async fn run_cli(cli: Cli) -> Result<()> {
    let mut app = CliApp::new(cli.config)?;

    match cli.command {
        Commands::Search(args) => {
            app.execute_search(&args).await?;
        }
        Commands::Download(args) => {
            app.execute_download(&args).await?;
        }
        Commands::FineTune(FineTuneCommands::Start(args)) => {
            app.execute_fine_tune_start(&args).await?;
        }
        Commands::Analytics(AnalyticsCommands::Usage(args)) => {
            app.execute_analytics_usage(&args).await?;
        }
        Commands::Model(ModelCommands::List(_args)) => {
            println!("Listing models...");
            // Implementation would list models based on args
        }
        Commands::Model(ModelCommands::Info(args)) => {
            println!("Model info for: {}", args.model);
            // Implementation would show model info
        }
        Commands::Config(ConfigCommands::Show) => {
            println!("Current configuration:");
            println!("Hub URL: {}", app.config.hub_url);
            println!("Cache directory: {}", app.config.cache_dir.display());
            println!("Default device: {}", app.config.default_device);
            println!("Analytics enabled: {}", app.config.analytics_enabled);
        }
        Commands::Auth(AuthCommands::Login(args)) => {
            println!("Logging in...");
            if let Some(token) = args.token {
                app.config.auth_token = Some(token);
                println!("Token authentication successful");
            } else {
                println!("Interactive login not implemented yet");
            }
        }
        _ => {
            println!("Command not implemented yet");
        }
    }

    Ok(())
}

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

    #[test]
    fn test_cli_config_default() {
        let config = CliConfig::default();
        assert_eq!(config.hub_url, "https://hub.torsh.dev");
        assert_eq!(config.max_concurrent_downloads, 4);
        assert_eq!(config.default_device, "auto");
    }

    #[test]
    fn test_cli_app_creation() {
        let result = CliApp::new(None);
        assert!(result.is_ok());
    }
}