#!/usr/bin/env nu
# AI-Enhanced CLI Commands for AuroraFrame
#
# Integrates with the Nushell MCP server to provide AI-powered development commands:
# - Content generation and enhancement
# - Schema design and validation
# - Error resolution and debugging
# - Asset optimization and generation

# Configuration for MCP server
let MCP_SERVER_PATH = "./auroraframe-mcp-server/mcp-server.nu"
let MCP_CONFIG = {
    timeout: 30000  # 30 seconds
    max_retries: 3
}

# Main AI command dispatcher
export def "ai" [
    command: string  # AI command to execute
    ...args: string  # Command arguments
    --debug(-d)      # Enable debug output
] {
    match $command {
        "generate" => (ai_generate ...$args --debug=$debug)
        "enhance" => (ai_enhance ...$args --debug=$debug)
        "schema" => (ai_schema ...$args --debug=$debug)
        "fix" => (ai_fix ...$args --debug=$debug)
        "analyze" => (ai_analyze ...$args --debug=$debug)
        "help" => (ai_help)
        _ => {
            print $"❌ Unknown AI command: ($command)"
            ai_help
        }
    }
}

# AI Content Generation
export def "ai generate" [
    type: string = "content"  # Type: content, page, schema, docs
    ...args: string          # Additional arguments
    --prompt(-p): string     # Generation prompt
    --schema(-s): string     # Schema file to use
    --format(-f): string = "markdown"  # Output format
    --output(-o): string     # Output file
    --debug(-d)             # Debug mode
] {
    if $debug { print "🧠 AI Generate: Starting content generation..." }

    match $type {
        "content" => (generate_content $prompt $schema $format $output $debug)
        "page" => (generate_page $args $prompt $debug)
        "schema" => (generate_schema_from_prompt $prompt $debug)
        "docs" => (generate_documentation $args $debug)
        _ => {
            print $"❌ Unknown generation type: ($type)"
            print "Available types: content, page, schema, docs"
        }
    }
}

# AI Content Enhancement
export def "ai enhance" [
    target: string           # File or content to enhance
    --type(-t): string = "seo"  # Enhancement type: seo, readability, structure, metadata
    --output(-o): string     # Output file
    --debug(-d)             # Debug mode
] {
    if $debug { print $"🚀 AI Enhance: Enhancing ($target) for ($type)" }

    if not ($target | path exists) {
        print $"❌ File not found: ($target)"
        return
    }

    let content = (open $target)
    let enhancement_types = [$type]

    let mcp_request = {
        method: "tools/call"
        params: {
            name: "enhance_content"
            arguments: {
                content: $content
                enhancements: $enhancement_types
            }
        }
    }

    let result = (call_mcp_server $mcp_request $debug)

    if "error" in $result {
        print $"❌ Enhancement failed: ($result.error)"
        return
    }

    let enhanced_content = $result.content.0.text

    if ($output | is-not-empty) {
        $enhanced_content | save $output
        print $"✅ Enhanced content saved to: ($output)"
    } else {
        print $enhanced_content
    }
}

# AI Schema Operations
export def "ai schema" [
    operation: string        # Operation: generate, validate, migrate
    ...args: string         # Schema files or descriptions
    --prompt(-p): string    # Schema description
    --examples(-e): string  # Example data file
    --debug(-d)            # Debug mode
] {
    if $debug { print $"⚙️ AI Schema: ($operation) operation" }

    match $operation {
        "generate" => (generate_schema_ai $prompt $examples $debug)
        "validate" => (validate_schema_ai $args $debug)
        "migrate" => (migrate_schema_ai $args $debug)
        "analyze" => (analyze_schema_ai $args $debug)
        _ => {
            print $"❌ Unknown schema operation: ($operation)"
            print "Available operations: generate, validate, migrate, analyze"
        }
    }
}

# AI Error Resolution
export def "ai fix" [
    --auto(-a)              # Automatically apply fixes
    --error(-e): string     # Specific error to fix
    --file(-f): string      # File with errors
    --debug(-d)            # Debug mode
] {
    if $debug { print "🔧 AI Fix: Analyzing and resolving errors..." }

    # Check for recent build errors
    let build_log = try { open "build.log" } catch { "" }
    let errors = if ($error | is-not-empty) {
        [$error]
    } else if ($file | is-not-empty) {
        (extract_errors_from_file $file)
    } else if ($build_log | is-not-empty) {
        (extract_errors_from_log $build_log)
    } else {
        print "❌ No errors found to fix. Specify --error, --file, or ensure build.log exists."
        return
    }

    for error_info in $errors {
        if $debug { print $"🔍 Analyzing error: ($error_info.message)" }

        let mcp_request = {
            method: "tools/call"
            params: {
                name: "resolve_error"
                arguments: {
                    error: $error_info
                    project_context: (get_project_context)
                }
            }
        }

        let result = (call_mcp_server $mcp_request $debug)

        if "error" in $result {
            print $"❌ Error analysis failed: ($result.error)"
            continue
        }

        print $result.content.0.text

        if $auto {
            print "🤖 Auto-fix mode enabled - would apply suggested fixes here"
            # TODO: Implement automatic fix application
        }
    }
}

# AI Analysis and Optimization
export def "ai analyze" [
    target: string           # What to analyze: build, performance, content, schema
    --file(-f): string      # Specific file to analyze
    --debug(-d)            # Debug mode
] {
    if $debug { print $"📊 AI Analyze: Analyzing ($target)" }

    match $target {
        "build" => (analyze_build_performance $debug)
        "performance" => (analyze_site_performance $file $debug)
        "content" => (analyze_content_quality $file $debug)
        "schema" => (analyze_schema_usage $file $debug)
        _ => {
            print $"❌ Unknown analysis target: ($target)"
            print "Available targets: build, performance, content, schema"
        }
    }
}

# Generate content using AI
def generate_content [prompt: string, schema_file: string, format: string, output: string, debug: bool] {
    if ($prompt | is-empty) {
        print "❌ Content prompt is required. Use --prompt or -p"
        return
    }

    let schema_data = if ($schema_file | is-not-empty) and ($schema_file | path exists) {
        open $schema_file
    } else {
        {}
    }

    let mcp_request = {
        method: "tools/call"
        params: {
            name: "generate_content"
            arguments: {
                schema: $schema_data
                prompt: $prompt
                format: $format
            }
        }
    }

    let result = (call_mcp_server $mcp_request $debug)

    if "error" in $result {
        print $"❌ Content generation failed: ($result.error)"
        return
    }

    let generated_content = $result.content.0.text

    if ($output | is-not-empty) {
        $generated_content | save $output
        print $"✅ Content generated and saved to: ($output)"
    } else {
        print $generated_content
    }
}

# Generate page with AI assistance
def generate_page [args: list, prompt: string, debug: bool] {
    if ($args | length) == 0 {
        print "❌ Page name is required: ai generate page <name>"
        return
    }

    let page_name = ($args | first)
    let page_type = ($args | get 1? | default "blog")

    if $debug { print $"📄 Generating page: ($page_name) of type ($page_type)" }

    # Create page directory
    let page_dir = $"pages/($page_name)"
    mkdir $page_dir
    mkdir $"($page_dir)/src"
    mkdir $"($page_dir)/src/assets"
    mkdir $"($page_dir)/src/assets/css"

    # Generate page configuration with AI
    let config_prompt = $"Generate a page configuration for a ($page_type) page named '($page_name)'. ($prompt)"

    # TODO: Call MCP server to generate config.toml
    # For now, create a basic template
    let config_content = $"# AI-Generated Configuration for ($page_name)
[page]
name = \"($page_name)\"
title = \"($page_name | str title-case)\"
type = \"($page_type)\"
description = \"AI-generated ($page_type) page\"

[markdown]
enabled = true
processor = \"pulldown-cmark\"

[highlight]
enabled = \"auto\"
theme = \"github-dark\"
"

    $config_content | save $"($page_dir)/config.toml"

    # Generate initial content
    let content_prompt = if ($prompt | is-not-empty) { $prompt } else { $"Create initial content for a ($page_type) page about ($page_name)" }

    generate_content $content_prompt "" "markdown" $"($page_dir)/src/content.md" $debug

    print $"✅ AI-generated page created: ($page_dir)"
}

# Generate schema from natural language
def generate_schema_from_prompt [prompt: string, debug: bool] {
    if ($prompt | is-empty) {
        print "❌ Schema description is required. Use --prompt or -p"
        return
    }

    let mcp_request = {
        method: "tools/call"
        params: {
            name: "generate_schema"
            arguments: {
                description: $prompt
                examples: []
            }
        }
    }

    let result = (call_mcp_server $mcp_request $debug)

    if "error" in $result {
        print $"❌ Schema generation failed: ($result.error)"
        return
    }

    print $result.content.0.text
}

# Call MCP server with request
def call_mcp_server [request: record, debug: bool] {
    if $debug { print $"📡 Calling MCP server with: ($request.params.name)" }

    # For now, simulate MCP server response
    # In production, this would call the actual Nushell MCP server
    let mock_responses = {
        generate_content: {
            content: [{
                type: "text"
                text: "# AI-Generated Content\n\nThis is AI-generated content based on your prompt. In a real implementation, this would come from the OpenAI API via our MCP server.\n\n## Features\n- Type-safe configuration\n- AI-powered generation\n- Lightning-fast builds"
            }]
        }
        enhance_content: {
            content: [{
                type: "text"
                text: "Enhanced content with improved SEO, readability, and structure..."
            }]
        }
        generate_schema: {
            content: [{
                type: "text"
                text: "Generated KCL Schema:\n\n```kcl\nschema GeneratedSchema:\n    title: str\n    description: str\n    tags: [str]\n```"
            }]
        }
    }

    let tool_name = $request.params.name
    if $tool_name in ($mock_responses | columns) {
        $mock_responses | get $tool_name
    } else {
        { error: $"Mock MCP server: tool ($tool_name) not implemented" }
    }
}

# Extract errors from build log
def extract_errors_from_log [build_log: string] {
    # Simple error extraction - in reality, this would be more sophisticated
    let error_lines = ($build_log | lines | where { |line| ($line | str contains "error") or ($line | str contains "Error") })

    $error_lines | each { |line|
        {
            message: $line
            code: "build_error"
            context: $line
        }
    }
}

# Get project context for error resolution
def get_project_context [] {
    {
        framework: "AuroraFrame"
        version: "1.0.0"
        features: ["KCL", "Rust", "Nushell", "MCP", "AI"]
        pages: (try { ls pages/ | get name } catch { [] })
    }
}

# AI Help
def ai_help [] {
    print "🧠 AuroraFrame AI Commands

USAGE:
    ai <command> [options]

AI COMMANDS:
    generate    Generate content, pages, schemas, or documentation
    enhance     Improve existing content with AI
    schema      Work with KCL schemas (generate, validate, migrate)
    fix         Automatically resolve errors and issues
    analyze     Analyze build performance, content quality, etc.

EXAMPLES:
    # Generate a blog post
    ai generate content --prompt \"Write about Rust performance\" --format markdown

    # Create an entire page with AI
    ai generate page tech-blog --prompt \"Focus on web development\"

    # Enhance existing content for SEO
    ai enhance content.md --type seo

    # Generate a schema from description
    ai schema generate --prompt \"Schema for a blog post with author, tags, and SEO\"

    # Fix build errors automatically
    ai fix --auto

    # Analyze build performance
    ai analyze build

For more information, visit: https://docs.auroraframe.dev/ai-commands"
}

# Quick AI shortcuts
export def "create --ai" [
    type: string            # Page type
    name: string           # Page name
    --prompt(-p): string   # AI prompt for generation
] {
    ai generate page $name --prompt $prompt
}

export def "enhance" [
    file: string           # File to enhance
    --ai                   # Use AI enhancement
    --type(-t): string = "seo"  # Enhancement type
] {
    if $ai {
        ai enhance $file --type $type
    } else {
        print "❌ Non-AI enhancement not implemented. Use --ai flag."
    }
}

export def "fix --ai" [
    --auto(-a)             # Auto-apply fixes
] {
    ai fix --auto=$auto
}