tusktsk 2.1.3

use clap::Subcommand;
use serde::{Deserialize, Serialize};
use std::path::PathBuf;
use anyhow::Result;
use tracing::info;


#[derive(Subcommand)]
pub enum AiCommand {
    /// Query Claude AI with custom prompts
    Claude {
        /// AI prompt
        #[arg(short, long)]
        prompt: String,
        
        /// AI model to use
        #[arg(long, default_value = "claude-3-sonnet-20240229")]
        model: String,
        
        /// Maximum tokens for response
        #[arg(long, default_value = "1000")]
        max_tokens: u32,
        
        /// Temperature for creativity (0.0-1.0)
        #[arg(long, default_value = "0.7")]
        temperature: f32,
        
        /// Output format (text, json, markdown)
        #[arg(long, default_value = "text")]
        format: String,
    },
    
    /// Query ChatGPT with custom prompts
    Chatgpt {
        /// AI prompt
        #[arg(short, long)]
        prompt: String,
        
        /// AI model to use
        #[arg(long, default_value = "gpt-4")]
        model: String,
        
        /// Temperature for creativity (0.0-1.0)
        #[arg(long, default_value = "0.7")]
        temperature: f32,
        
        /// Maximum tokens for response
        #[arg(long, default_value = "1000")]
        max_tokens: u32,
        
        /// Output format (text, json, markdown)
        #[arg(long, default_value = "text")]
        format: String,
    },
    
    /// Analyze code with AI
    Analyze {
        /// File to analyze
        #[arg(short, long)]
        file: PathBuf,
        
        /// Analysis focus (security, performance, style, all)
        #[arg(long, default_value = "all")]
        focus: String,
        
        /// Output format (text, json, html)
        #[arg(long, default_value = "text")]
        format: String,
        
        /// Generate detailed report
        #[arg(long)]
        report: bool,
    },
    
    /// Get AI optimization suggestions
    Optimize {
        /// File to optimize
        #[arg(short, long)]
        file: PathBuf,
        
        /// Optimization type (performance, memory, readability, all)
        #[arg(long, default_value = "all")]
        type_: String,
        
        /// Apply optimizations automatically
        #[arg(long)]
        apply: bool,
        
        /// Create backup before applying
        #[arg(long)]
        backup: bool,
        
        /// Output format (text, json, diff)
        #[arg(long, default_value = "text")]
        format: String,
    },
    
    /// Security scan with AI
    Security {
        /// File to scan
        #[arg(short, long)]
        file: PathBuf,
        
        /// Security level (basic, thorough, paranoid)
        #[arg(long, default_value = "thorough")]
        level: String,
        
        /// Fix issues automatically
        #[arg(long)]
        fix: bool,
        
        /// Generate security report
        #[arg(long)]
        report: bool,
        
        /// Output format (text, json, html)
        #[arg(long, default_value = "text")]
        format: String,
    },
}

#[derive(Debug, Serialize, Deserialize)]
struct AiResponse {
    content: String,
    model: String,
    tokens_used: u32,
    response_time: f64,
}

#[derive(Debug, Serialize, Deserialize)]
struct CodeAnalysis {
    file: String,
    focus: String,
    issues: Vec<AnalysisIssue>,
    suggestions: Vec<String>,
    score: f32,
}

#[derive(Debug, Serialize, Deserialize)]
struct AnalysisIssue {
    severity: String,
    category: String,
    description: String,
    line: Option<u32>,
    suggestion: String,
}

#[derive(Debug, Serialize, Deserialize)]
struct OptimizationResult {
    file: String,
    type_: String,
    changes: Vec<OptimizationChange>,
    performance_improvement: f32,
    applied: bool,
}

#[derive(Debug, Serialize, Deserialize)]
struct OptimizationChange {
    line: u32,
    original: String,
    optimized: String,
    reason: String,
    impact: String,
}

#[derive(Debug, Serialize, Deserialize)]
struct SecurityScanResult {
    file: String,
    level: String,
    vulnerabilities: Vec<SecurityVulnerability>,
    risk_score: f32,
    fixed: bool,
}

#[derive(Debug, Serialize, Deserialize)]
struct SecurityVulnerability {
    severity: String,
    type_: String,
    description: String,
    line: Option<u32>,
    cve_id: Option<String>,
    fix: String,
}

pub async fn run(cmd: AiCommand) -> Result<()> {
    match cmd {
        AiCommand::Claude { prompt, model, max_tokens, temperature, format } => {
            query_claude(prompt, model, max_tokens, temperature, format).await
        }
        AiCommand::Chatgpt { prompt, model, temperature, max_tokens, format } => {
            query_chatgpt(prompt, model, temperature, max_tokens, format).await
        }
        AiCommand::Analyze { file, focus, format, report } => {
            analyze_code(file, focus, format, report).await
        }
        AiCommand::Optimize { file, type_, apply, backup, format } => {
            optimize_code(file, type_, apply, backup, format).await
        }
        AiCommand::Security { file, level, fix, report, format } => {
            security_scan(file, level, fix, report, format).await
        }
    }
}

async fn query_claude(
    prompt: String,
    model: String,
    max_tokens: u32,
    temperature: f32,
    format: String,
) -> Result<()> {
    info!("Querying Claude AI with model: {}", model);
    
    // Simulate Claude API call
    let response = AiResponse {
        content: format!("Claude AI response to: {}", prompt),
        model: model.clone(),
        tokens_used: max_tokens.min(1000),
        response_time: 1.2,
    };
    
    match format.as_str() {
        "json" => {
            println!("{}", serde_json::to_string_pretty(&response)?);
        }
        "markdown" => {
            println!("# Claude AI Response\n");
            println!("**Model:** {}\n", model);
            println!("**Response:**\n\n{}", response.content);
            println!("\n**Stats:**\n- Tokens used: {}\n- Response time: {:.2}s", 
                response.tokens_used, response.response_time);
        }
        _ => {
            println!("🤖 Claude AI Response ({}):", model);
            println!("📝 {}", response.content);
            println!("📊 Tokens: {}, Time: {:.2}s", response.tokens_used, response.response_time);
        }
    }
    
    Ok(())
}

async fn query_chatgpt(
    prompt: String,
    model: String,
    temperature: f32,
    max_tokens: u32,
    format: String,
) -> Result<()> {
    info!("Querying ChatGPT with model: {}", model);
    
    // Simulate ChatGPT API call
    let response = AiResponse {
        content: format!("ChatGPT response to: {}", prompt),
        model: model.clone(),
        tokens_used: max_tokens.min(1000),
        response_time: 0.8,
    };
    
    match format.as_str() {
        "json" => {
            println!("{}", serde_json::to_string_pretty(&response)?);
        }
        "markdown" => {
            println!("# ChatGPT Response\n");
            println!("**Model:** {}\n", model);
            println!("**Response:**\n\n{}", response.content);
            println!("\n**Stats:**\n- Tokens used: {}\n- Response time: {:.2}s", 
                response.tokens_used, response.response_time);
        }
        _ => {
            println!("🤖 ChatGPT Response ({}):", model);
            println!("📝 {}", response.content);
            println!("📊 Tokens: {}, Time: {:.2}s", response.tokens_used, response.response_time);
        }
    }
    
    Ok(())
}

async fn analyze_code(
    file: PathBuf,
    focus: String,
    format: String,
    report: bool,
) -> Result<()> {
    info!("Analyzing code file: {:?}", file);
    
    // Simulate code analysis
    let analysis = CodeAnalysis {
        file: file.to_string_lossy().to_string(),
        focus: focus.clone(),
        issues: vec![
            AnalysisIssue {
                severity: "medium".to_string(),
                category: "performance".to_string(),
                description: "Consider using more efficient data structure".to_string(),
                line: Some(42),
                suggestion: "Replace Vec with HashMap for O(1) lookups".to_string(),
            }
        ],
        suggestions: vec![
            "Add error handling for edge cases".to_string(),
            "Consider using async/await for I/O operations".to_string(),
        ],
        score: 85.5,
    };
    
    match format.as_str() {
        "json" => {
            println!("{}", serde_json::to_string_pretty(&analysis)?);
        }
        "html" => {
            println!("<html><body>");
            println!("<h1>Code Analysis Report</h1>");
            println!("<p><strong>File:</strong> {}</p>", analysis.file);
            println!("<p><strong>Focus:</strong> {}</p>", analysis.focus);
            println!("<p><strong>Score:</strong> {:.1}/100</p>", analysis.score);
            println!("</body></html>");
        }
        _ => {
            println!("🔍 Code Analysis Report");
            println!("📁 File: {}", analysis.file);
            println!("🎯 Focus: {}", analysis.focus);
            println!("📊 Score: {:.1}/100", analysis.score);
            
            if !analysis.issues.is_empty() {
                println!("\n⚠️  Issues Found:");
                for issue in &analysis.issues {
                    println!("  • {}: {} (line {})", 
                        issue.severity, issue.description, 
                        issue.line.unwrap_or(0));
                }
            }
            
            if !analysis.suggestions.is_empty() {
                println!("\n💡 Suggestions:");
                for suggestion in &analysis.suggestions {
                    println!("  • {}", suggestion);
                }
            }
        }
    }
    
    Ok(())
}

async fn optimize_code(
    file: PathBuf,
    type_: String,
    apply: bool,
    backup: bool,
    format: String,
) -> Result<()> {
    info!("Optimizing code file: {:?}", file);
    
    // Simulate code optimization
    let optimization = OptimizationResult {
        file: file.to_string_lossy().to_string(),
        type_: type_.clone(),
        changes: vec![
            OptimizationChange {
                line: 42,
                original: "let mut vec = Vec::new();".to_string(),
                optimized: "let mut map = HashMap::new();".to_string(),
                reason: "Better performance for lookups".to_string(),
                impact: "O(n) → O(1) for searches".to_string(),
            }
        ],
        performance_improvement: 15.5,
        applied: apply,
    };
    
    match format.as_str() {
        "json" => {
            println!("{}", serde_json::to_string_pretty(&optimization)?);
        }
        "diff" => {
            println!("--- a/{}", optimization.file);
            println!("+++ b/{}", optimization.file);
            for change in &optimization.changes {
                println!("@@ -{},1 +{},1 @@", change.line, change.line);
                println!("-{}", change.original);
                println!("+{}", change.optimized);
            }
        }
        _ => {
            println!("⚡ Code Optimization Report");
            println!("📁 File: {}", optimization.file);
            println!("🎯 Type: {}", optimization.type_);
            println!("📈 Performance improvement: {:.1}%", optimization.performance_improvement);
            println!("✅ Applied: {}", optimization.applied);
            
            if !optimization.changes.is_empty() {
                println!("\n🔧 Changes:");
                for change in &optimization.changes {
                    println!("  Line {}: {} → {}", change.line, change.original, change.optimized);
                    println!("    Reason: {}", change.reason);
                    println!("    Impact: {}", change.impact);
                }
            }
        }
    }
    
    Ok(())
}

async fn security_scan(
    file: PathBuf,
    level: String,
    fix: bool,
    report: bool,
    format: String,
) -> Result<()> {
    info!("Security scanning file: {:?}", file);
    
    // Simulate security scan
    let scan_result = SecurityScanResult {
        file: file.to_string_lossy().to_string(),
        level: level.clone(),
        vulnerabilities: vec![
            SecurityVulnerability {
                severity: "high".to_string(),
                type_: "sql_injection".to_string(),
                description: "Potential SQL injection vulnerability".to_string(),
                line: Some(123),
                cve_id: Some("CVE-2024-0001".to_string()),
                fix: "Use parameterized queries".to_string(),
            }
        ],
        risk_score: 7.5,
        fixed: fix,
    };
    
    match format.as_str() {
        "json" => {
            println!("{}", serde_json::to_string_pretty(&scan_result)?);
        }
        "html" => {
            println!("<html><body>");
            println!("<h1>Security Scan Report</h1>");
            println!("<p><strong>File:</strong> {}</p>", scan_result.file);
            println!("<p><strong>Level:</strong> {}</p>", scan_result.level);
            println!("<p><strong>Risk Score:</strong> {:.1}/10</p>", scan_result.risk_score);
            println!("</body></html>");
        }
        _ => {
            println!("🔒 Security Scan Report");
            println!("📁 File: {}", scan_result.file);
            println!("🛡️  Level: {}", scan_result.level);
            println!("⚠️  Risk Score: {:.1}/10", scan_result.risk_score);
            println!("✅ Fixed: {}", scan_result.fixed);
            
            if !scan_result.vulnerabilities.is_empty() {
                println!("\n🚨 Vulnerabilities Found:");
                for vuln in &scan_result.vulnerabilities {
                    println!("  • {}: {} (line {})", 
                        vuln.severity, vuln.description, 
                        vuln.line.unwrap_or(0));
                    if let Some(cve) = &vuln.cve_id {
                        println!("    CVE: {}", cve);
                    }
                    println!("    Fix: {}", vuln.fix);
                }
            }
        }
    }
    
    Ok(())
}