/**
 * @module cli/commands/neural
 * @description Neural CLI commands: semantic similarity, clustering,
 * hierarchy, neighbors, outlier detection, and visualization data export.
 * Registered in `src/cli/index.ts` as `similar` / `cluster` / `related` /
 * `hierarchy` / `outliers` / `visualize`. Each command is one-shot: it
 * initializes the shared Brainy instance, runs the neural operation, then
 * closes the store and exits explicitly.
 */

import inquirer from 'inquirer'
import chalk from 'chalk'
import ora from 'ora'
import fs from 'node:fs'
import path from 'node:path'
import { Brainy } from '../../brainy.js'

interface CommandArguments {
  action?: string;
  id?: string;
  query?: string;
  threshold?: number;
  format?: string;
  output?: string;
  limit?: number;
  algorithm?: string;
  dimensions?: number;
  explain?: boolean;
  _: string[];
}

let brainyInstance: Brainy | null = null

const getBrainy = (): Brainy => {
  if (!brainyInstance) {
    brainyInstance = new Brainy()
  }
  return brainyInstance
}

async function handleSimilarCommand(neural: any, argv: CommandArguments): Promise<void> {
  const spinner = ora('🧠 Calculating semantic similarity...').start()
  
  try {
    let itemA: string, itemB: string
    
    if (argv.id && argv.query) {
      itemA = argv.id
      itemB = argv.query
    } else if (argv._ && argv._.length >= 3) {
      itemA = argv._[1]
      itemB = argv._[2]
    } else {
      spinner.stop()
      const answers = await inquirer.prompt([
        {
          type: 'input',
          name: 'itemA',
          message: 'First item (ID or text):',
          validate: (input: string) => input.length > 0
        },
        {
          type: 'input',
          name: 'itemB',
          message: 'Second item (ID or text):',
          validate: (input: string) => input.length > 0
        }
      ])
      itemA = answers.itemA
      itemB = answers.itemB
      spinner.start()
    }
    
    const result = await neural.similar(itemA, itemB, {
      explain: argv.explain,
      includeBreakdown: argv.explain
    })
    
    spinner.succeed('✅ Similarity calculated')
    
    if (typeof result === 'number') {
      console.log(`\n🔗 Similarity: ${chalk.cyan((result * 100).toFixed(1))}%`)
    } else {
      console.log(`\n🔗 Similarity: ${chalk.cyan((result.score * 100).toFixed(1))}%`)
      if (result.explanation) {
        console.log(`💭 Explanation: ${result.explanation}`)
      }
      if (result.breakdown) {
        console.log('\n📊 Breakdown:')
        console.log(`  Semantic: ${chalk.yellow((result.breakdown.semantic! * 100).toFixed(1))}%`)
        if (result.breakdown.taxonomic !== undefined) {
          console.log(`  Taxonomic: ${chalk.yellow((result.breakdown.taxonomic * 100).toFixed(1))}%`)
        }
        if (result.breakdown.contextual !== undefined) {
          console.log(`  Contextual: ${chalk.yellow((result.breakdown.contextual * 100).toFixed(1))}%`)
        }
      }
      if (result.hierarchy) {
        console.log(`\n🌳 Hierarchy: ${result.hierarchy.sharedParent ? 
          `Shared parent at distance ${result.hierarchy.distance}` : 
          'No shared parent found'}`)
      }
    }
    
    if (argv.output) {
      await saveToFile(argv.output, result, argv.format!)
    }
    
  } catch (error) {
    spinner.fail('💥 Failed to calculate similarity')
    throw error
  }
}

async function handleClustersCommand(neural: any, argv: CommandArguments): Promise<void> {
  let spinner: any = null
  try {
    let options: any = {
      algorithm: argv.algorithm as any,
      threshold: argv.threshold,
      maxClusters: argv.limit
    }

    // Interactive mode if no algorithm specified or using defaults
    if (!argv.algorithm || argv.algorithm === 'hierarchical') {
      const answers = await inquirer.prompt([
        {
          type: 'list',
          name: 'algorithm',
          message: 'Choose clustering algorithm:',
          default: argv.algorithm || 'hierarchical',
          choices: [
            { name: '🌳 Hierarchical (Tree-based, best for natural grouping)', value: 'hierarchical' },
            { name: '📊 K-Means (Fixed number of clusters)', value: 'kmeans' },
            { name: '🎯 DBSCAN (Density-based, finds arbitrary shapes)', value: 'dbscan' }
          ]
        },
        {
          type: 'number',
          name: 'maxClusters',
          message: 'Maximum number of clusters:',
          default: argv.limit || 5,
          when: (answers: any) => answers.algorithm === 'kmeans'
        },
        {
          type: 'number',
          name: 'threshold',
          message: 'Similarity threshold (0-1):',
          default: argv.threshold || 0.7,
          validate: (input: number) => (input >= 0 && input <= 1) || 'Must be between 0 and 1'
        }
      ])

      options = {
        algorithm: answers.algorithm,
        threshold: answers.threshold,
        maxClusters: answers.maxClusters || options.maxClusters
      }
    }

    const spinner = ora('🎯 Finding semantic clusters...').start()
    const clusters = await neural.clusters(argv.query || options)
    
    spinner.succeed(`✅ Found ${clusters.length} clusters`)
    
    if (argv.format === 'json') {
      console.log(JSON.stringify(clusters, null, 2))
    } else {
      console.log(`\n🎯 ${chalk.cyan(clusters.length)} Semantic Clusters:\n`)
      
      clusters.forEach((cluster, index) => {
        console.log(`${chalk.yellow(`Cluster ${index + 1}:`)} ${cluster.label || cluster.id}`)
        console.log(`  📊 Confidence: ${chalk.green((cluster.confidence * 100).toFixed(1))}%`)
        console.log(`  👥 Members: ${cluster.members.length}`)
        if (cluster.members.length <= 5) {
          cluster.members.forEach(member => {
            console.log(`    • ${member}`)
          })
        } else {
          cluster.members.slice(0, 3).forEach(member => {
            console.log(`    • ${member}`)
          })
          console.log(`    ... and ${cluster.members.length - 3} more`)
        }
        console.log()
      })
    }
    
    if (argv.output) {
      await saveToFile(argv.output, clusters, argv.format!)
    }

  } catch (error) {
    if (spinner) spinner.fail('💥 Failed to find clusters')
    throw error
  }
}

async function handleHierarchyCommand(neural: any, argv: CommandArguments): Promise<void> {
  const spinner = ora('🌳 Building semantic hierarchy...').start()
  
  try {
    const id = argv.id || argv._[1]
    if (!id) {
      spinner.stop()
      const answer = await inquirer.prompt([{
        type: 'input',
        name: 'id',
        message: 'Enter item ID:',
        validate: (input: string) => input.length > 0
      }])
      spinner.start()
      const hierarchy = await neural.hierarchy(answer.id)
      displayHierarchy(hierarchy)
    } else {
      const hierarchy = await neural.hierarchy(id)
      spinner.succeed('✅ Hierarchy built')
      displayHierarchy(hierarchy)
    }
    
    if (argv.output) {
      const hierarchy = await neural.hierarchy(id || argv._[1])
      await saveToFile(argv.output, hierarchy, argv.format!)
    }
    
  } catch (error) {
    spinner.fail('💥 Failed to build hierarchy')
    throw error
  }
}

function displayHierarchy(hierarchy: any): void {
  console.log(`\n🌳 Semantic Hierarchy for ${chalk.cyan(hierarchy.self.id)}:`)
  
  if (hierarchy.root) {
    console.log(`🔝 Root: ${hierarchy.root.id} (${(hierarchy.root.similarity * 100).toFixed(1)}%)`)
  }
  
  if (hierarchy.grandparent) {
    console.log(`👴 Grandparent: ${hierarchy.grandparent.id} (${(hierarchy.grandparent.similarity * 100).toFixed(1)}%)`)
  }
  
  if (hierarchy.parent) {
    console.log(`👨 Parent: ${hierarchy.parent.id} (${(hierarchy.parent.similarity * 100).toFixed(1)}%)`)
  }
  
  console.log(`🎯 ${chalk.bold('Self:')} ${hierarchy.self.id}`)
  
  if (hierarchy.siblings && hierarchy.siblings.length > 0) {
    console.log(`👥 Siblings: ${hierarchy.siblings.length}`)
    hierarchy.siblings.forEach((sibling: any) => {
      console.log(`  • ${sibling.id} (${(sibling.similarity * 100).toFixed(1)}%)`)
    })
  }
  
  if (hierarchy.children && hierarchy.children.length > 0) {
    console.log(`👶 Children: ${hierarchy.children.length}`)
    hierarchy.children.forEach((child: any) => {
      console.log(`  • ${child.id} (${(child.similarity * 100).toFixed(1)}%)`)
    })
  }
}

async function handleNeighborsCommand(neural: any, argv: CommandArguments): Promise<void> {
  const spinner = ora('🕸️ Finding semantic neighbors...').start()
  
  try {
    const id = argv.id || argv._[1]
    if (!id) {
      spinner.stop()
      const answer = await inquirer.prompt([{
        type: 'input',
        name: 'id',
        message: 'Enter item ID:',
        validate: (input: string) => input.length > 0
      }])
      spinner.start()
    }
    
    const targetId = id || (await inquirer.prompt([{
      type: 'input',
      name: 'id',
      message: 'Enter item ID:',
      validate: (input: string) => input.length > 0
    }])).id
    
    const graph = await neural.neighbors(targetId, {
      limit: argv.limit,
      includeEdges: true
    })
    
    spinner.succeed(`✅ Found ${graph.neighbors.length} neighbors`)
    
    console.log(`\n🕸️ Neighbors of ${chalk.cyan(graph.center)}:`)
    
    graph.neighbors.forEach((neighbor, index) => {
      console.log(`${index + 1}. ${neighbor.id} (${(neighbor.similarity * 100).toFixed(1)}%)`)
      if (neighbor.type) {
        console.log(`   Type: ${neighbor.type}`)
      }
      if (neighbor.connections) {
        console.log(`   Connections: ${neighbor.connections}`)
      }
    })
    
    if (graph.edges && graph.edges.length > 0) {
      console.log(`\n🔗 ${graph.edges.length} semantic connections found`)
    }
    
    if (argv.output) {
      await saveToFile(argv.output, graph, argv.format!)
    }
    
  } catch (error) {
    spinner.fail('💥 Failed to find neighbors')
    throw error
  }
}

async function handleOutliersCommand(neural: any, argv: CommandArguments): Promise<void> {
  const spinner = ora('🚨 Detecting semantic outliers...').start()
  
  try {
    const outliers = await neural.outliers(argv.threshold)
    
    spinner.succeed(`✅ Found ${outliers.length} outliers`)
    
    if (outliers.length === 0) {
      console.log('\n🎉 No outliers detected - all items are well connected!')
    } else {
      console.log(`\n🚨 ${chalk.red(outliers.length)} Semantic Outliers:`)
      outliers.forEach((outlier, index) => {
        console.log(`${index + 1}. ${outlier}`)
      })
      
      console.log(`\n💡 These items have similarity < ${argv.threshold} to their nearest neighbors`)
    }
    
    if (argv.output) {
      await saveToFile(argv.output, outliers, argv.format!)
    }
    
  } catch (error) {
    spinner.fail('💥 Failed to detect outliers')
    throw error
  }
}

async function handleVisualizeCommand(neural: any, argv: CommandArguments): Promise<void> {
  const spinner = ora('📊 Generating visualization data...').start()
  
  try {
    const vizData = await neural.visualize({
      dimensions: argv.dimensions as 2 | 3,
      maxNodes: argv.limit
    })
    
    spinner.succeed('✅ Visualization data generated')
    
    console.log(`\n📊 Visualization Data (${vizData.format} layout):`)
    console.log(`📍 Nodes: ${vizData.nodes.length}`)
    console.log(`🔗 Edges: ${vizData.edges.length}`)
    console.log(`🎯 Clusters: ${vizData.clusters?.length || 0}`)
    console.log(`📐 Dimensions: ${vizData.layout?.dimensions}D`)
    
    if (argv.format === 'json') {
      console.log('\nData:')
      console.log(JSON.stringify(vizData, null, 2))
    } else {
      console.log('\n🎨 Style Settings:')
      console.log(`  Node Colors: ${vizData.style?.nodeColors}`)
      console.log(`  Edge Width: ${vizData.style?.edgeWidth}`)
      console.log(`  Labels: ${vizData.style?.labels}`)
    }
    
    if (argv.output) {
      await saveToFile(argv.output, vizData, 'json')
      console.log(`\n💾 Visualization data saved to: ${chalk.green(argv.output)}`)
    } else {
      console.log(`\n💡 Use --output to save visualization data for external tools`)
    }
    
  } catch (error) {
    spinner.fail('💥 Failed to generate visualization')
    throw error
  }
}

async function saveToFile(filepath: string, data: any, format: string): Promise<void> {
  const dir = path.dirname(filepath)
  if (!fs.existsSync(dir)) {
    fs.mkdirSync(dir, { recursive: true })
  }
  
  let output: string
  switch (format) {
    case 'json':
      output = JSON.stringify(data, null, 2)
      break
    case 'table':
      output = formatAsTable(data)
      break
    default:
      output = JSON.stringify(data, null, 2)
  }
  
  fs.writeFileSync(filepath, output, 'utf8')
  console.log(`💾 Saved to: ${chalk.green(filepath)}`)
}

function formatAsTable(data: any): string {
  // Simple table formatting - could be enhanced with a table library
  if (Array.isArray(data)) {
    return data.map((item, index) => `${index + 1}. ${JSON.stringify(item)}`).join('\n')
  }
  return JSON.stringify(data, null, 2)
}

/**
 * @description Run a neural CLI handler with the one-shot lifecycle every
 * Brainy command follows: init → work → `close()` → explicit `process.exit`.
 * close() releases the writer lock and indexes, but global timers
 * (UnifiedCache bookkeeping, PathResolver stats) keep the event loop alive,
 * so one-shot commands must exit explicitly.
 * @param work - The handler body, given the initialized neural API.
 */
async function runNeuralCommand(work: (neural: ReturnType<Brainy['neural']>) => Promise<void>): Promise<void> {
  try {
    const brain = getBrainy()
    await brain.init()

    await work(brain.neural())

    await brain.close()
    process.exit(0)
  } catch (error) {
    console.error(chalk.red('💥 Error:'), error instanceof Error ? error.message : error)
    process.exit(1)
  }
}

// Commander-compatible wrappers
export const neuralCommands = {
  async similar(a?: string, b?: string, options?: any) {
    // Build argv-style object for handler
    const argv: CommandArguments = {
      _: ['neural', 'similar', a || '', b || ''].filter(x => x),
      id: a,
      query: b,
      explain: options?.explain,
      ...options
    }

    await runNeuralCommand((neural) => handleSimilarCommand(neural, argv))
  },

  async cluster(options?: any) {
    const argv: CommandArguments = {
      _: ['neural', 'cluster'],
      algorithm: options?.algorithm || 'hierarchical',
      threshold: options?.threshold ? parseFloat(options.threshold) : 0.7,
      limit: options?.maxClusters ? parseInt(options.maxClusters) : 10,
      query: options?.near,
      ...options
    }

    await runNeuralCommand((neural) => handleClustersCommand(neural, argv))
  },

  async hierarchy(id?: string, options?: any) {
    const argv: CommandArguments = {
      _: ['neural', 'hierarchy', id || ''].filter(x => x),
      id,
      ...options
    }

    await runNeuralCommand((neural) => handleHierarchyCommand(neural, argv))
  },

  async related(id?: string, options?: any) {
    const argv: CommandArguments = {
      _: ['neural', 'related', id || ''].filter(x => x),
      id,
      limit: options?.limit ? parseInt(options.limit) : 10,
      threshold: options?.radius ? parseFloat(options.radius) : 0.3,
      ...options
    }

    await runNeuralCommand((neural) => handleNeighborsCommand(neural, argv))
  },

  async outliers(options?: any) {
    const argv: CommandArguments = {
      _: ['neural', 'outliers'],
      threshold: options?.threshold ? parseFloat(options.threshold) : 0.3,
      explain: options?.explain,
      ...options
    }

    await runNeuralCommand((neural) => handleOutliersCommand(neural, argv))
  },

  async visualize(options?: any) {
    const argv: CommandArguments = {
      _: ['neural', 'visualize'],
      format: options?.format || 'json',
      dimensions: options?.dimensions ? parseInt(options.dimensions) : 2,
      limit: options?.maxNodes ? parseInt(options.maxNodes) : 500,
      output: options?.output,
      ...options
    }

    await runNeuralCommand((neural) => handleVisualizeCommand(neural, argv))
  }
}