inferno-ai 0.10.3

use crate::InfernoError;
use crate::backends::InferenceParams;
use crate::multimodal::{
    AudioFeatures, ModelCapabilities, MultiModalConfig, MultiModalProcessor, ProcessingStatus,
    VisionFeatures,
};
use clap::{Args, Subcommand};
use serde_json;
use std::collections::HashMap;
use std::path::PathBuf;
use tokio::time::{Duration, sleep};

#[derive(Args)]
pub struct MultiModalArgs {
    #[command(subcommand)]
    pub command: MultiModalCommands,
}

#[derive(Subcommand)]
pub enum MultiModalCommands {
    /// Process single media file with optional text prompt
    Process {
        /// Model to use for processing
        #[arg(short, long)]
        model: String,

        /// Input file path
        #[arg(short, long)]
        input: PathBuf,

        /// Optional text prompt to accompany media
        #[arg(short, long)]
        prompt: Option<String>,

        /// Maximum tokens to generate
        #[arg(long, default_value = "500")]
        max_tokens: u32,

        /// Temperature for generation
        #[arg(long, default_value = "0.7")]
        temperature: f32,

        /// Output format (json, text)
        #[arg(short, long, default_value = "text")]
        output_format: String,

        /// Save result to file
        #[arg(short = 'o', long)]
        output_file: Option<PathBuf>,

        /// Show processing progress
        #[arg(long)]
        _show_progress: bool,
    },

    /// Process base64 encoded media
    ProcessBase64 {
        /// Model to use for processing
        #[arg(short, long)]
        model: String,

        /// Base64 encoded media data
        #[arg(short, long)]
        data: String,

        /// Media type (jpg, png, mp3, wav, mp4, etc.)
        #[arg(short = 't', long)]
        media_type: String,

        /// Optional text prompt
        #[arg(short, long)]
        prompt: Option<String>,

        /// Maximum tokens to generate
        #[arg(long, default_value = "500")]
        max_tokens: u32,

        /// Temperature for generation
        #[arg(long, default_value = "0.7")]
        temperature: f32,

        /// Output format (json, text)
        #[arg(short, long, default_value = "text")]
        output_format: String,
    },

    /// Batch process multiple media files
    Batch {
        /// Model to use for processing
        #[arg(short, long)]
        model: String,

        /// Directory containing media files
        #[arg(short, long)]
        input_dir: PathBuf,

        /// File pattern to match (e.g., "*.jpg", "*.mp3")
        #[arg(short, long, default_value = "*")]
        pattern: String,

        /// Common text prompt for all files
        #[arg(short, long)]
        prompt: Option<String>,

        /// Output directory for results
        #[arg(short, long)]
        output_dir: PathBuf,

        /// Maximum concurrent processing jobs
        #[arg(long, default_value = "3")]
        max_concurrent: u32,

        /// Continue processing on errors
        #[arg(long)]
        continue_on_error: bool,
    },

    /// Show active processing sessions
    Sessions {
        /// Show detailed session information
        #[arg(short, long)]
        detailed: bool,

        /// Refresh interval in seconds for live monitoring
        #[arg(short, long)]
        refresh: Option<u32>,
    },

    /// Get session status
    Status {
        /// Session ID to check
        session_id: String,

        /// Follow session progress
        #[arg(short, long)]
        follow: bool,
    },

    /// Cancel processing session
    Cancel {
        /// Session ID to cancel
        session_id: String,
    },

    /// Show supported media formats
    Formats {
        /// Filter by media type (image, audio, video)
        #[arg(short, long)]
        media_type: Option<String>,

        /// Show example usage
        #[arg(short, long)]
        examples: bool,
    },

    /// Show model capabilities
    Capabilities {
        /// Model name to check
        #[arg(short, long)]
        model: Option<String>,

        /// Show only models supporting specific media type
        #[arg(short = 't', long)]
        media_type: Option<String>,

        /// Output format (table, json)
        #[arg(short, long, default_value = "table")]
        format: String,
    },

    /// Register model capabilities
    RegisterModel {
        /// Model name
        #[arg(short, long)]
        model: String,

        /// Capabilities configuration file (JSON)
        #[arg(short, long)]
        config_file: PathBuf,
    },

    /// Analyze media file without inference
    Analyze {
        /// Input file path
        #[arg(short, long)]
        input: PathBuf,

        /// Show detailed metadata
        #[arg(short, long)]
        detailed: bool,

        /// Output format (json, yaml, table)
        #[arg(short, long, default_value = "table")]
        format: String,
    },

    /// Convert media between formats
    Convert {
        /// Input file path
        #[arg(short, long)]
        input: PathBuf,

        /// Output file path
        #[arg(short, long)]
        output: PathBuf,

        /// Target format
        #[arg(short, long)]
        format: String,

        /// Quality settings (0-100 for images, bitrate for audio)
        #[arg(short, long)]
        quality: Option<u32>,

        /// Additional conversion parameters
        #[arg(short, long)]
        params: Vec<String>,
    },
}

/// Configuration for processing single media files
/// Reduces function signature from 9 parameters to 2
pub struct ProcessFileConfig {
    pub model: String,
    pub input: PathBuf,
    pub prompt: Option<String>,
    pub max_tokens: u32,
    pub temperature: f32,
    pub output_format: String,
    pub output_file: Option<PathBuf>,
    pub show_progress: bool,
}

/// Configuration for processing base64-encoded media
/// Reduces function signature from 8 parameters to 2
pub struct ProcessBase64Config {
    pub model: String,
    pub data: String,
    pub media_type: String,
    pub prompt: Option<String>,
    pub max_tokens: u32,
    pub temperature: f32,
    pub output_format: String,
}

/// Configuration for batch processing media files
/// Reduces function signature from 8 parameters to 2
pub struct BatchProcessConfig {
    pub model: String,
    pub input_dir: PathBuf,
    pub pattern: String,
    pub prompt: Option<String>,
    pub output_dir: PathBuf,
    pub max_concurrent: u32,
    pub continue_on_error: bool,
}

pub async fn handle_multimodal_command(args: MultiModalArgs) -> Result<(), InfernoError> {
    let config = MultiModalConfig::default();
    let processor = MultiModalProcessor::new(config);
    processor.initialize().await.map_err(|e| {
        InfernoError::InvalidArgument(format!("Failed to initialize processor: {}", e))
    })?;

    match args.command {
        MultiModalCommands::Process {
            model,
            input,
            prompt,
            max_tokens,
            temperature,
            output_format,
            output_file,
            _show_progress,
        } => {
            let config = ProcessFileConfig {
                model,
                input,
                prompt,
                max_tokens,
                temperature,
                output_format,
                output_file,
                show_progress: _show_progress,
            };
            handle_process_command(&processor, config).await
        }

        MultiModalCommands::ProcessBase64 {
            model,
            data,
            media_type,
            prompt,
            max_tokens,
            temperature,
            output_format,
        } => {
            let config = ProcessBase64Config {
                model,
                data,
                media_type,
                prompt,
                max_tokens,
                temperature,
                output_format,
            };
            handle_process_base64_command(&processor, config).await
        }

        MultiModalCommands::Batch {
            model,
            input_dir,
            pattern,
            prompt,
            output_dir,
            max_concurrent,
            continue_on_error,
        } => {
            let config = BatchProcessConfig {
                model,
                input_dir,
                pattern,
                prompt,
                output_dir,
                max_concurrent,
                continue_on_error,
            };
            handle_batch_command(&processor, config).await
        }

        MultiModalCommands::Sessions { detailed, refresh } => {
            handle_sessions_command(&processor, detailed, refresh).await
        }

        MultiModalCommands::Status { session_id, follow } => {
            handle_status_command(&processor, session_id, follow).await
        }

        MultiModalCommands::Cancel { session_id } => {
            handle_cancel_command(&processor, session_id).await
        }

        MultiModalCommands::Formats {
            media_type,
            examples,
        } => handle_formats_command(&processor, media_type, examples).await,

        MultiModalCommands::Capabilities {
            model,
            media_type,
            format,
        } => handle_capabilities_command(&processor, model, media_type, format).await,

        MultiModalCommands::RegisterModel { model, config_file } => {
            handle_register_model_command(&processor, model, config_file).await
        }

        MultiModalCommands::Analyze {
            input,
            detailed,
            format,
        } => handle_analyze_command(&processor, input, detailed, format).await,

        MultiModalCommands::Convert {
            input,
            output,
            format,
            quality,
            params,
        } => handle_convert_command(input, output, format, quality, params).await,
    }
}

async fn handle_process_command(
    processor: &MultiModalProcessor,
    config: ProcessFileConfig,
) -> Result<(), InfernoError> {
    // Validate inputs
    if config.model.is_empty() {
        return Err(InfernoError::InvalidArgument(
            "Model name cannot be empty".to_string(),
        ));
    }
    if !config.input.exists() {
        return Err(InfernoError::InvalidArgument(format!(
            "Input file does not exist: {:?}",
            config.input
        )));
    }

    println!("Processing file: {:?}", config.input);

    let params = InferenceParams {
        max_tokens: config.max_tokens,
        temperature: config.temperature,
        top_k: 40,
        top_p: 0.9,
        stream: false,
        stop_sequences: vec![],
        seed: None,
    };

    let result = processor
        .process_file(&config.model, &config.input, config.prompt, params)
        .await
        .map_err(|e| InfernoError::InvalidArgument(format!("Processing failed: {}", e)))?;

    let output_content = match config.output_format.as_str() {
        "json" => serde_json::to_string_pretty(&result).map_err(|e| {
            InfernoError::InvalidArgument(format!("JSON serialization failed: {}", e))
        })?,
        "text" => format_text_output(&result),
        _ => {
            return Err(InfernoError::InvalidArgument(format!(
                "Unsupported output format: {}",
                config.output_format
            )));
        }
    };

    if let Some(output_path) = config.output_file {
        tokio::fs::write(&output_path, &output_content).await?;
        println!("Results saved to: {:?}", output_path);
    } else {
        println!("{}", output_content);
    }

    Ok(())
}

async fn handle_process_base64_command(
    processor: &MultiModalProcessor,
    config: ProcessBase64Config,
) -> Result<(), InfernoError> {
    // Validate inputs
    if config.model.is_empty() {
        return Err(InfernoError::InvalidArgument(
            "Model name cannot be empty".to_string(),
        ));
    }
    if config.data.is_empty() {
        return Err(InfernoError::InvalidArgument(
            "Base64 data cannot be empty".to_string(),
        ));
    }
    if config.media_type.is_empty() {
        return Err(InfernoError::InvalidArgument(
            "Media type cannot be empty".to_string(),
        ));
    }

    // Validate supported media types
    let supported_types = [
        "jpg", "jpeg", "png", "gif", "webp", "bmp", "tiff", // images
        "mp3", "wav", "flac", "ogg", "m4a", "aac", // audio
        "mp4", "avi", "mov", "mkv", "webm", // video
    ];
    if !supported_types.contains(&config.media_type.to_lowercase().as_str()) {
        return Err(InfernoError::InvalidArgument(format!(
            "Unsupported media type '{}'. Supported types: {}",
            config.media_type,
            supported_types.join(", ")
        )));
    }

    println!("Processing base64 data ({} type)", config.media_type);

    let params = InferenceParams {
        max_tokens: config.max_tokens,
        temperature: config.temperature,
        top_k: 40,
        top_p: 0.9,
        stream: false,
        stop_sequences: vec![],
        seed: None,
    };

    let result = processor
        .process_base64(
            &config.model,
            &config.data,
            &config.media_type,
            config.prompt,
            params,
        )
        .await
        .map_err(|e| InfernoError::InvalidArgument(format!("Processing failed: {}", e)))?;

    let output_content = match config.output_format.as_str() {
        "json" => serde_json::to_string_pretty(&result).map_err(|e| {
            InfernoError::InvalidArgument(format!("JSON serialization failed: {}", e))
        })?,
        "text" => format_text_output(&result),
        _ => {
            return Err(InfernoError::InvalidArgument(format!(
                "Unsupported output format: {}",
                config.output_format
            )));
        }
    };

    println!("{}", output_content);
    Ok(())
}

async fn handle_batch_command(
    processor: &MultiModalProcessor,
    config: BatchProcessConfig,
) -> Result<(), InfernoError> {
    // Validate inputs
    if config.model.is_empty() {
        return Err(InfernoError::InvalidArgument(
            "Model name cannot be empty".to_string(),
        ));
    }
    if !config.input_dir.exists() {
        return Err(InfernoError::InvalidArgument(format!(
            "Input directory does not exist: {:?}",
            config.input_dir
        )));
    }
    if !config.input_dir.is_dir() {
        return Err(InfernoError::InvalidArgument(format!(
            "Input path is not a directory: {:?}",
            config.input_dir
        )));
    }
    if config.max_concurrent == 0 {
        return Err(InfernoError::InvalidArgument(
            "Max concurrent jobs must be at least 1".to_string(),
        ));
    }

    println!("Starting batch processing...");
    println!("Input directory: {:?}", config.input_dir);
    println!("Pattern: {}", config.pattern);
    println!("Output directory: {:?}", config.output_dir);
    println!("Max concurrent jobs: {}", config.max_concurrent);

    // Create output directory
    tokio::fs::create_dir_all(&config.output_dir).await?;

    // Find matching files
    let files = find_matching_files(&config.input_dir, &config.pattern).await?;
    println!("Found {} files to process", files.len());

    if files.is_empty() {
        println!("No files found matching pattern");
        return Ok(());
    }

    let params = InferenceParams {
        max_tokens: 500,
        temperature: 0.7,
        top_k: 40,
        top_p: 0.9,
        stream: false,
        stop_sequences: vec![],
        seed: None,
    };

    let mut processed = 0;
    let mut failed = 0;
    let continue_on_error = config.continue_on_error;

    // Process files in batches
    for chunk in files.chunks(config.max_concurrent as usize) {
        let mut tasks = Vec::new();

        for file_path in chunk {
            let processor_ref = processor;
            let model_ref = &config.model;
            let prompt_ref = &config.prompt;
            let params_ref = params.clone();
            let output_dir_ref = &config.output_dir;

            let task = async move {
                let result = processor_ref
                    .process_file(model_ref, file_path, prompt_ref.clone(), params_ref)
                    .await;

                match result {
                    Ok(res) => {
                        // Save result to output directory
                        let file_stem = file_path
                            .file_stem()
                            .and_then(|s| s.to_str())
                            .unwrap_or("unknown");
                        let output_file = output_dir_ref.join(format!("{}_result.json", file_stem));

                        if let Err(e) = tokio::fs::write(
                            &output_file,
                            serde_json::to_string_pretty(&res).unwrap_or_default(),
                        )
                        .await
                        {
                            eprintln!("Failed to save result for {:?}: {}", file_path, e);
                            return false;
                        }

                        println!("✅ Processed: {:?} -> {:?}", file_path, output_file);
                        true
                    }
                    Err(e) => {
                        eprintln!("❌ Failed to process {:?}: {}", file_path, e);
                        !continue_on_error
                    }
                }
            };

            tasks.push(task);
        }

        // Wait for all tasks in this chunk to complete
        for task in tasks {
            if task.await {
                processed += 1;
            } else {
                failed += 1;
                if !continue_on_error {
                    break;
                }
            }
        }

        if failed > 0 && !continue_on_error {
            break;
        }
    }

    println!("\nBatch processing completed:");
    println!("✅ Processed: {}", processed);
    println!("❌ Failed: {}", failed);

    Ok(())
}

async fn handle_sessions_command(
    processor: &MultiModalProcessor,
    detailed: bool,
    refresh: Option<u32>,
) -> Result<(), InfernoError> {
    if let Some(interval) = refresh {
        // Live monitoring mode
        loop {
            print!("\x1B[2J\x1B[1;1H"); // Clear screen
            println!(
                "🔄 Active Processing Sessions (refreshing every {}s)",
                interval
            );
            println!("{}", "=".repeat(60));

            display_sessions(processor, detailed).await?;

            sleep(Duration::from_secs(interval as u64)).await;
        }
    } else {
        // Single snapshot
        display_sessions(processor, detailed).await?;
    }

    Ok(())
}

async fn display_sessions(
    processor: &MultiModalProcessor,
    detailed: bool,
) -> Result<(), InfernoError> {
    let sessions = processor
        .list_active_sessions()
        .await
        .map_err(|e| InfernoError::InvalidArgument(format!("Failed to list sessions: {}", e)))?;

    if sessions.is_empty() {
        println!("No active processing sessions");
        return Ok(());
    }

    if detailed {
        for session in sessions {
            println!("\n📋 Session Details:");
            println!("ID: {}", session.id);
            println!("Model: {}", session.model_id);
            println!("Status: {:?}", session.status);
            println!("Progress: {:.1}%", session.progress);
            println!(
                "Created: {}",
                session.created_at.format("%Y-%m-%d %H:%M:%S")
            );
            println!(
                "Updated: {}",
                session.updated_at.format("%Y-%m-%d %H:%M:%S")
            );
        }
    } else {
        println!(
            "{:<15} {:<20} {:<12} {:<8} {:<20}",
            "Session ID", "Model", "Status", "Progress", "Created"
        );
        println!("{}", "-".repeat(80));

        for session in sessions {
            let short_id = if session.id.len() > 12 {
                format!("{}...", &session.id[..9])
            } else {
                session.id
            };

            println!(
                "{:<15} {:<20} {:<12} {:<8} {:<20}",
                short_id,
                session.model_id,
                format!("{:?}", session.status),
                format!("{:.1}%", session.progress),
                session.created_at.format("%H:%M:%S")
            );
        }
    }

    Ok(())
}

async fn handle_status_command(
    processor: &MultiModalProcessor,
    session_id: String,
    follow: bool,
) -> Result<(), InfernoError> {
    if follow {
        loop {
            match processor.get_session_status(&session_id).await {
                Ok(Some(session)) => {
                    print!(
                        "\r🔄 Session {} - Status: {:?} - Progress: {:.1}%",
                        &session_id[..8.min(session_id.len())],
                        session.status,
                        session.progress
                    );
                    std::io::Write::flush(&mut std::io::stdout()).unwrap();

                    if matches!(
                        session.status,
                        ProcessingStatus::Completed
                            | ProcessingStatus::Failed
                            | ProcessingStatus::Cancelled
                    ) {
                        println!();
                        match session.status {
                            ProcessingStatus::Completed => {
                                println!("✅ Processing completed successfully!")
                            }
                            ProcessingStatus::Failed => println!("❌ Processing failed!"),
                            ProcessingStatus::Cancelled => println!("⚠️ Processing was cancelled"),
                            _ => {}
                        }
                        break;
                    }
                }
                Ok(None) => {
                    println!("Session not found: {}", session_id);
                    break;
                }
                Err(e) => {
                    println!("Error getting session status: {}", e);
                    break;
                }
            }

            sleep(Duration::from_secs(2)).await;
        }
    } else {
        match processor.get_session_status(&session_id).await {
            Ok(Some(session)) => {
                println!("Session ID: {}", session.id);
                println!("Model: {}", session.model_id);
                println!("Status: {:?}", session.status);
                println!("Progress: {:.1}%", session.progress);
                println!(
                    "Created: {}",
                    session.created_at.format("%Y-%m-%d %H:%M:%S")
                );
                println!(
                    "Updated: {}",
                    session.updated_at.format("%Y-%m-%d %H:%M:%S")
                );
            }
            Ok(None) => {
                println!("Session not found: {}", session_id);
            }
            Err(e) => {
                return Err(InfernoError::InvalidArgument(format!(
                    "Failed to get session status: {}",
                    e
                )));
            }
        }
    }

    Ok(())
}

async fn handle_cancel_command(
    processor: &MultiModalProcessor,
    session_id: String,
) -> Result<(), InfernoError> {
    // Validate inputs
    if session_id.is_empty() {
        return Err(InfernoError::InvalidArgument(
            "Session ID cannot be empty".to_string(),
        ));
    }

    processor
        .cancel_session(&session_id)
        .await
        .map_err(|e| InfernoError::InvalidArgument(format!("Failed to cancel session: {}", e)))?;

    println!("Session {} has been cancelled", session_id);
    Ok(())
}

async fn handle_formats_command(
    processor: &MultiModalProcessor,
    media_type: Option<String>,
    examples: bool,
) -> Result<(), InfernoError> {
    let formats = processor.get_supported_formats();

    match media_type {
        Some(mt) => {
            if let Some(format_list) = formats.get(&mt) {
                println!("Supported {} formats:", mt);
                for format in format_list {
                    println!("  • {}", format);
                }
            } else {
                println!("Unknown media type: {}", mt);
                println!(
                    "Available types: {}",
                    formats.keys().cloned().collect::<Vec<_>>().join(", ")
                );
            }
        }
        None => {
            println!("📁 Supported Media Formats:");
            println!("{}", "=".repeat(40));

            for (media_type, format_list) in formats {
                println!("\n🎯 {}:", media_type.to_uppercase());
                let chunks: Vec<_> = format_list.chunks(6).collect();
                for chunk in chunks {
                    println!("   {}", chunk.join(", "));
                }
            }
        }
    }

    if examples {
        println!("\n💡 Usage Examples:");
        println!("{}", "=".repeat(20));
        println!("# Process an image with text prompt");
        println!(
            "inferno multimodal process -m gpt-4-vision -i image.jpg -p \"What's in this image?\""
        );
        println!("\n# Batch process audio files");
        println!(
            "inferno multimodal batch -m whisper-large -i ./audio/ -p transcribe -o ./results/"
        );
        println!("\n# Analyze video metadata");
        println!("inferno multimodal analyze -i video.mp4 --detailed");
    }

    Ok(())
}

async fn handle_capabilities_command(
    _processor: &MultiModalProcessor,
    model: Option<String>,
    media_type: Option<String>,
    format: String,
) -> Result<(), InfernoError> {
    // Mock capabilities data since we don't have a real model registry
    let mut capabilities = HashMap::new();

    capabilities.insert(
        "gpt-4-vision".to_string(),
        ModelCapabilities {
            supports_text: true,
            supports_images: true,
            supports_audio: false,
            supports_video: false,
            max_context_length: Some(8000),
            supported_languages: vec!["en".to_string(), "es".to_string(), "fr".to_string()],
            vision_features: Some(VisionFeatures {
                object_detection: true,
                ocr: true,
                scene_understanding: true,
                face_recognition: false,
                image_generation: false,
                max_image_size: (2048, 2048),
            }),
            audio_features: None,
        },
    );

    capabilities.insert(
        "whisper-large".to_string(),
        ModelCapabilities {
            supports_text: true,
            supports_images: false,
            supports_audio: true,
            supports_video: false,
            max_context_length: None,
            supported_languages: vec![
                "en".to_string(),
                "es".to_string(),
                "fr".to_string(),
                "de".to_string(),
            ],
            vision_features: None,
            audio_features: Some(AudioFeatures {
                speech_to_text: true,
                audio_classification: false,
                music_analysis: false,
                voice_synthesis: false,
                noise_reduction: true,
                max_audio_length_seconds: 3600,
            }),
        },
    );

    let filtered_capabilities: HashMap<String, ModelCapabilities> = match (&model, &media_type) {
        (Some(m), _) => capabilities
            .into_iter()
            .filter(|(name, _)| name == m)
            .collect(),
        (None, Some(mt)) => capabilities
            .into_iter()
            .filter(|(_, caps)| match mt.as_str() {
                "text" => caps.supports_text,
                "image" => caps.supports_images,
                "audio" => caps.supports_audio,
                "video" => caps.supports_video,
                _ => false,
            })
            .collect(),
        (None, None) => capabilities,
    };

    if filtered_capabilities.is_empty() {
        println!("No models found matching criteria");
        return Ok(());
    }

    match format.as_str() {
        "json" => {
            println!(
                "{}",
                serde_json::to_string_pretty(&filtered_capabilities).map_err(|e| {
                    InfernoError::InvalidArgument(format!("JSON serialization failed: {}", e))
                })?
            );
        }
        "table" => {
            println!("🤖 Model Capabilities:");
            println!("{}", "=".repeat(80));

            for (model_name, caps) in filtered_capabilities {
                println!("\n📋 Model: {}", model_name);
                println!("   Text: {}", if caps.supports_text { "✅" } else { "❌" });
                println!(
                    "   Images: {}",
                    if caps.supports_images { "✅" } else { "❌" }
                );
                println!(
                    "   Audio: {}",
                    if caps.supports_audio { "✅" } else { "❌" }
                );
                println!(
                    "   Video: {}",
                    if caps.supports_video { "✅" } else { "❌" }
                );

                if let Some(max_ctx) = caps.max_context_length {
                    println!("   Max Context: {} tokens", max_ctx);
                }

                if !caps.supported_languages.is_empty() {
                    println!("   Languages: {}", caps.supported_languages.join(", "));
                }

                if let Some(vision) = caps.vision_features {
                    println!("   Vision Features:");
                    println!(
                        "     • Object Detection: {}",
                        if vision.object_detection {
                            "✅"
                        } else {
                            "❌"
                        }
                    );
                    println!("     • OCR: {}", if vision.ocr { "✅" } else { "❌" });
                    println!(
                        "     • Scene Understanding: {}",
                        if vision.scene_understanding {
                            "✅"
                        } else {
                            "❌"
                        }
                    );
                    println!(
                        "     • Max Image Size: {}x{}",
                        vision.max_image_size.0, vision.max_image_size.1
                    );
                }

                if let Some(audio) = caps.audio_features {
                    println!("   Audio Features:");
                    println!(
                        "     • Speech to Text: {}",
                        if audio.speech_to_text { "✅" } else { "❌" }
                    );
                    println!(
                        "     • Audio Classification: {}",
                        if audio.audio_classification {
                            "✅"
                        } else {
                            "❌"
                        }
                    );
                    println!(
                        "     • Music Analysis: {}",
                        if audio.music_analysis { "✅" } else { "❌" }
                    );
                    println!(
                        "     • Max Audio Length: {}s",
                        audio.max_audio_length_seconds
                    );
                }
            }
        }
        _ => {
            return Err(InfernoError::InvalidArgument(format!(
                "Unsupported format: {}",
                format
            )));
        }
    }

    Ok(())
}

async fn handle_register_model_command(
    processor: &MultiModalProcessor,
    model: String,
    config_file: PathBuf,
) -> Result<(), InfernoError> {
    // Validate inputs
    if model.is_empty() {
        return Err(InfernoError::InvalidArgument(
            "Model name cannot be empty".to_string(),
        ));
    }
    if !config_file.exists() {
        return Err(InfernoError::InvalidArgument(format!(
            "Config file does not exist: {:?}",
            config_file
        )));
    }

    let config_content = tokio::fs::read_to_string(&config_file).await?;

    let capabilities: ModelCapabilities = serde_json::from_str(&config_content)
        .map_err(|e| InfernoError::InvalidArgument(format!("Invalid JSON config: {}", e)))?;

    processor
        .register_model_capabilities(model.clone(), capabilities)
        .await
        .map_err(|e| InfernoError::InvalidArgument(format!("Failed to register model: {}", e)))?;

    println!("✅ Model '{}' capabilities registered successfully", model);
    Ok(())
}

async fn handle_analyze_command(
    _processor: &MultiModalProcessor,
    input: PathBuf,
    detailed: bool,
    format: String,
) -> Result<(), InfernoError> {
    // Validate inputs
    if !input.exists() {
        return Err(InfernoError::InvalidArgument(format!(
            "Input file does not exist: {:?}",
            input
        )));
    }

    println!("Analyzing file: {:?}", input);

    // Mock analysis - in real implementation would extract actual metadata
    let file_metadata = tokio::fs::metadata(&input).await?;

    let file_extension = input
        .extension()
        .and_then(|ext| ext.to_str())
        .unwrap_or("unknown");

    let analysis = create_mock_analysis(&input, &file_metadata, file_extension, detailed);

    match format.as_str() {
        "json" => {
            println!(
                "{}",
                serde_json::to_string_pretty(&analysis).map_err(|e| {
                    InfernoError::InvalidArgument(format!("JSON serialization failed: {}", e))
                })?
            );
        }
        "table" => {
            print_analysis_table(&analysis);
        }
        _ => {
            return Err(InfernoError::InvalidArgument(format!(
                "Unsupported format: {}",
                format
            )));
        }
    }

    Ok(())
}

async fn handle_convert_command(
    input: PathBuf,
    output: PathBuf,
    format: String,
    quality: Option<u32>,
    params: Vec<String>,
) -> Result<(), InfernoError> {
    // Validate inputs
    if !input.exists() {
        return Err(InfernoError::InvalidArgument(format!(
            "Input file does not exist: {:?}",
            input
        )));
    }
    if format.is_empty() {
        return Err(InfernoError::InvalidArgument(
            "Target format cannot be empty".to_string(),
        ));
    }
    if let Some(q) = quality {
        if q > 100 {
            return Err(InfernoError::InvalidArgument(format!(
                "Quality must be between 0 and 100, got: {}",
                q
            )));
        }
    }

    println!(
        "Converting {:?} to {:?} (format: {})",
        input, output, format
    );

    if let Some(q) = quality {
        println!("Quality setting: {}", q);
    }

    if !params.is_empty() {
        println!("Additional parameters: {:?}", params);
    }

    // Mock conversion - in real implementation would use media processing libraries
    let input_data = tokio::fs::read(&input).await?;

    // Simulate conversion process
    println!("🔄 Converting...");
    tokio::time::sleep(Duration::from_millis(500)).await;

    // Write mock converted data
    tokio::fs::write(&output, &input_data).await?;

    println!("✅ Conversion completed: {:?}", output);
    Ok(())
}

// Helper functions

async fn find_matching_files(dir: &PathBuf, pattern: &str) -> Result<Vec<PathBuf>, InfernoError> {
    let mut files = Vec::new();
    let mut entries = tokio::fs::read_dir(dir).await?;

    while let Some(entry) = entries.next_entry().await? {
        let path = entry.path();
        if path.is_file() && (pattern == "*" || path.to_string_lossy().contains(pattern)) {
            files.push(path);
        }
    }

    Ok(files)
}

fn format_text_output(result: &crate::multimodal::MultiModalResult) -> String {
    let mut output = String::new();

    output.push_str("🔥 Multi-Modal Processing Result\n");
    output.push_str(&format!("{}\n", "=".repeat(40)));
    output.push_str(&format!("Session ID: {}\n", result.id));
    output.push_str(&format!("Model: {}\n", result.model_used));
    output.push_str(&format!("Input: {}\n", result.input_summary));
    output.push_str(&format!(
        "Processing Time: {}ms\n",
        result.processing_time_ms
    ));
    output.push_str(&format!(
        "Created: {}\n\n",
        result.created_at.format("%Y-%m-%d %H:%M:%S")
    ));

    output.push_str("📋 Processed Components:\n");
    for (i, component) in result.processed_components.iter().enumerate() {
        output.push_str(&format!(
            "  {}. {} - {}\n",
            i + 1,
            component.component_type,
            component.description
        ));
        if component.processing_time_ms > 0 {
            output.push_str(&format!(
                "     Processing time: {}ms\n",
                component.processing_time_ms
            ));
        }
    }

    output.push_str(&format!("\n🎯 Result:\n{}\n", result.inference_result));

    if let Some(scores) = &result.confidence_scores {
        output.push_str("\n📊 Confidence Scores:\n");
        for (key, score) in scores {
            output.push_str(&format!("  • {}: {:.2}\n", key, score));
        }
    }

    output
}

fn create_mock_analysis(
    path: &PathBuf,
    metadata: &std::fs::Metadata,
    extension: &str,
    detailed: bool,
) -> serde_json::Value {
    let mut analysis = serde_json::json!({
        "file_path": path,
        "file_size_bytes": metadata.len(),
        "file_extension": extension,
        "file_type": determine_file_type(extension),
        "last_modified": metadata.modified().ok().map(|t| {
            chrono::DateTime::<chrono::Utc>::from(t).format("%Y-%m-%d %H:%M:%S").to_string()
        })
    });

    if detailed {
        match extension {
            "jpg" | "jpeg" | "png" | "bmp" | "gif" | "webp" | "tiff" => {
                analysis["image_analysis"] = serde_json::json!({
                    "format": extension.to_uppercase(),
                    "estimated_dimensions": "1920x1080",
                    "estimated_channels": 3,
                    "color_space": "RGB",
                    "has_exif": true
                });
            }
            "mp3" | "wav" | "flac" | "ogg" | "m4a" | "aac" => {
                analysis["audio_analysis"] = serde_json::json!({
                    "format": extension.to_uppercase(),
                    "estimated_duration": "120.5 seconds",
                    "estimated_sample_rate": "44100 Hz",
                    "estimated_channels": 2,
                    "estimated_bitrate": "128 kbps"
                });
            }
            "mp4" | "avi" | "mov" | "mkv" | "webm" => {
                analysis["video_analysis"] = serde_json::json!({
                    "format": extension.to_uppercase(),
                    "estimated_duration": "300.0 seconds",
                    "estimated_resolution": "1920x1080",
                    "estimated_frame_rate": "30 fps",
                    "has_audio": true,
                    "estimated_video_codec": "H.264",
                    "estimated_audio_codec": "AAC"
                });
            }
            _ => {
                analysis["file_analysis"] = serde_json::json!({
                    "type": "unknown",
                    "mime_type": "application/octet-stream"
                });
            }
        }
    }

    analysis
}

fn determine_file_type(extension: &str) -> &'static str {
    match extension.to_lowercase().as_str() {
        "jpg" | "jpeg" | "png" | "bmp" | "gif" | "webp" | "tiff" => "image",
        "mp3" | "wav" | "flac" | "ogg" | "m4a" | "aac" => "audio",
        "mp4" | "avi" | "mov" | "mkv" | "webm" => "video",
        "txt" | "md" | "json" | "xml" | "csv" => "text",
        _ => "unknown",
    }
}

fn print_analysis_table(analysis: &serde_json::Value) {
    println!("📊 File Analysis Results:");
    println!("{}", "=".repeat(50));

    if let Some(path) = analysis.get("file_path") {
        println!("File: {}", path.as_str().unwrap_or("unknown"));
    }

    if let Some(size) = analysis.get("file_size_bytes") {
        let size_mb = size.as_u64().unwrap_or(0) as f64 / 1024.0 / 1024.0;
        println!(
            "Size: {:.2} MB ({} bytes)",
            size_mb,
            size.as_u64().unwrap_or(0)
        );
    }

    if let Some(file_type) = analysis.get("file_type") {
        println!("Type: {}", file_type.as_str().unwrap_or("unknown"));
    }

    if let Some(ext) = analysis.get("file_extension") {
        println!("Extension: {}", ext.as_str().unwrap_or("unknown"));
    }

    if let Some(modified) = analysis.get("last_modified") {
        println!("Modified: {}", modified.as_str().unwrap_or("unknown"));
    }

    // Print specific analysis based on media type
    if let Some(img_analysis) = analysis.get("image_analysis") {
        println!("\n🖼️  Image Analysis:");
        if let Some(dims) = img_analysis.get("estimated_dimensions") {
            println!("  Dimensions: {}", dims.as_str().unwrap_or("unknown"));
        }
        if let Some(channels) = img_analysis.get("estimated_channels") {
            println!("  Channels: {}", channels.as_u64().unwrap_or(0));
        }
        if let Some(color_space) = img_analysis.get("color_space") {
            println!(
                "  Color Space: {}",
                color_space.as_str().unwrap_or("unknown")
            );
        }
    }

    if let Some(audio_analysis) = analysis.get("audio_analysis") {
        println!("\n🎵 Audio Analysis:");
        if let Some(duration) = audio_analysis.get("estimated_duration") {
            println!("  Duration: {}", duration.as_str().unwrap_or("unknown"));
        }
        if let Some(sample_rate) = audio_analysis.get("estimated_sample_rate") {
            println!(
                "  Sample Rate: {}",
                sample_rate.as_str().unwrap_or("unknown")
            );
        }
        if let Some(channels) = audio_analysis.get("estimated_channels") {
            println!("  Channels: {}", channels.as_u64().unwrap_or(0));
        }
        if let Some(bitrate) = audio_analysis.get("estimated_bitrate") {
            println!("  Bitrate: {}", bitrate.as_str().unwrap_or("unknown"));
        }
    }

    if let Some(video_analysis) = analysis.get("video_analysis") {
        println!("\n🎬 Video Analysis:");
        if let Some(duration) = video_analysis.get("estimated_duration") {
            println!("  Duration: {}", duration.as_str().unwrap_or("unknown"));
        }
        if let Some(resolution) = video_analysis.get("estimated_resolution") {
            println!("  Resolution: {}", resolution.as_str().unwrap_or("unknown"));
        }
        if let Some(frame_rate) = video_analysis.get("estimated_frame_rate") {
            println!("  Frame Rate: {}", frame_rate.as_str().unwrap_or("unknown"));
        }
        if let Some(video_codec) = video_analysis.get("estimated_video_codec") {
            println!(
                "  Video Codec: {}",
                video_codec.as_str().unwrap_or("unknown")
            );
        }
        if let Some(audio_codec) = video_analysis.get("estimated_audio_codec") {
            println!(
                "  Audio Codec: {}",
                audio_codec.as_str().unwrap_or("unknown")
            );
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use std::io::Write;
    use tempfile::NamedTempFile;

    #[test]
    fn test_process_file_config_empty_model_validation() {
        let config = ProcessFileConfig {
            model: "".to_string(),
            input: PathBuf::from("/tmp/test.jpg"),
            prompt: None,
            max_tokens: 500,
            temperature: 0.7,
            output_format: "text".to_string(),
            output_file: None,
            show_progress: false,
        };
        assert!(config.model.is_empty());
    }

    #[test]
    fn test_process_base64_config_empty_data_validation() {
        let config = ProcessBase64Config {
            model: "test-model".to_string(),
            data: "".to_string(),
            media_type: "jpg".to_string(),
            prompt: None,
            max_tokens: 500,
            temperature: 0.7,
            output_format: "text".to_string(),
        };
        assert!(config.data.is_empty());
    }

    #[test]
    fn test_batch_process_config_zero_concurrent_validation() {
        let config = BatchProcessConfig {
            model: "test-model".to_string(),
            input_dir: PathBuf::from("/tmp"),
            pattern: "*".to_string(),
            prompt: None,
            output_dir: PathBuf::from("/tmp/output"),
            max_concurrent: 0,
            continue_on_error: false,
        };
        assert_eq!(config.max_concurrent, 0);
    }

    #[test]
    fn test_determine_file_type_image() {
        assert_eq!(determine_file_type("jpg"), "image");
        assert_eq!(determine_file_type("jpeg"), "image");
        assert_eq!(determine_file_type("png"), "image");
        assert_eq!(determine_file_type("gif"), "image");
        assert_eq!(determine_file_type("webp"), "image");
    }

    #[test]
    fn test_determine_file_type_audio() {
        assert_eq!(determine_file_type("mp3"), "audio");
        assert_eq!(determine_file_type("wav"), "audio");
        assert_eq!(determine_file_type("flac"), "audio");
        assert_eq!(determine_file_type("ogg"), "audio");
    }

    #[test]
    fn test_determine_file_type_video() {
        assert_eq!(determine_file_type("mp4"), "video");
        assert_eq!(determine_file_type("avi"), "video");
        assert_eq!(determine_file_type("mov"), "video");
        assert_eq!(determine_file_type("mkv"), "video");
    }

    #[test]
    fn test_determine_file_type_text() {
        assert_eq!(determine_file_type("txt"), "text");
        assert_eq!(determine_file_type("md"), "text");
        assert_eq!(determine_file_type("json"), "text");
    }

    #[test]
    fn test_determine_file_type_unknown() {
        assert_eq!(determine_file_type("xyz"), "unknown");
        assert_eq!(determine_file_type(""), "unknown");
    }

    #[test]
    fn test_create_mock_analysis_basic() {
        let temp_file = NamedTempFile::new().unwrap();
        let path = temp_file.path().to_path_buf();
        let metadata = std::fs::metadata(&path).unwrap();

        let analysis = create_mock_analysis(&path, &metadata, "jpg", false);

        assert!(analysis.get("file_path").is_some());
        assert!(analysis.get("file_size_bytes").is_some());
        assert!(analysis.get("file_extension").is_some());
        assert!(analysis.get("file_type").is_some());
    }

    #[test]
    fn test_create_mock_analysis_detailed_image() {
        let temp_file = NamedTempFile::new().unwrap();
        let path = temp_file.path().to_path_buf();
        let metadata = std::fs::metadata(&path).unwrap();

        let analysis = create_mock_analysis(&path, &metadata, "png", true);

        assert!(analysis.get("image_analysis").is_some());
        let img_analysis = analysis.get("image_analysis").unwrap();
        assert!(img_analysis.get("format").is_some());
        assert!(img_analysis.get("estimated_dimensions").is_some());
    }

    #[test]
    fn test_create_mock_analysis_detailed_audio() {
        let temp_file = NamedTempFile::new().unwrap();
        let path = temp_file.path().to_path_buf();
        let metadata = std::fs::metadata(&path).unwrap();

        let analysis = create_mock_analysis(&path, &metadata, "mp3", true);

        assert!(analysis.get("audio_analysis").is_some());
        let audio_analysis = analysis.get("audio_analysis").unwrap();
        assert!(audio_analysis.get("format").is_some());
        assert!(audio_analysis.get("estimated_duration").is_some());
    }

    #[test]
    fn test_create_mock_analysis_detailed_video() {
        let temp_file = NamedTempFile::new().unwrap();
        let path = temp_file.path().to_path_buf();
        let metadata = std::fs::metadata(&path).unwrap();

        let analysis = create_mock_analysis(&path, &metadata, "mp4", true);

        assert!(analysis.get("video_analysis").is_some());
        let video_analysis = analysis.get("video_analysis").unwrap();
        assert!(video_analysis.get("format").is_some());
        assert!(video_analysis.get("estimated_resolution").is_some());
    }

    #[test]
    fn test_supported_media_types() {
        let supported_types = [
            "jpg", "jpeg", "png", "gif", "webp", "bmp", "tiff", // images
            "mp3", "wav", "flac", "ogg", "m4a", "aac", // audio
            "mp4", "avi", "mov", "mkv", "webm", // video
        ];

        // Test that all supported types are recognized
        for media_type in &supported_types {
            assert!(
                supported_types.contains(media_type),
                "Type {} should be supported",
                media_type
            );
        }

        // Test unsupported type
        assert!(!supported_types.contains(&"invalid"));
    }
}