mxivideo/src/hooks/useProgressCommand.ts

import { useState, useCallback, useRef } from 'react'
import { executeCommandWithProgress, ProgressListener } from '../services/tauri'

export interface ProgressState {
  step: string
  progress: number  // -1 for indeterminate, 0-100 for percentage
  message: string
  details?: any
  timestamp: number
}

export interface UseProgressCommandOptions {
  onProgress?: (progress: ProgressState) => void
  onSuccess?: (result: any) => void
  onError?: (error: Error) => void
  autoReset?: boolean  // Auto reset state after completion
}

export interface UseProgressCommandReturn<T> {
  // State
  isExecuting: boolean
  progress: ProgressState | null
  result: T
  error: Error | null
  logs: string[]

  // Actions
  execute: (command: string, params: any, eventName: string) => Promise<any>
  reset: () => void
  addLog: (message: string) => void
  clearLogs: () => void
}

/**
 * React Hook for executing Tauri commands with progress monitoring
 * 
 * @param options - Configuration options
 * @returns Hook state and actions
 */
export function useProgressCommand<T>(options: UseProgressCommandOptions = {}): UseProgressCommandReturn<T> {
  const [isExecuting, setIsExecuting] = useState(false)
  const [progress, setProgress] = useState<ProgressState | null>(null)
  const [result, setResult] = useState<any>(null)
  const [error, setError] = useState<Error | null>(null)
  const [logs, setLogs] = useState<string[]>([])

  const listenerRef = useRef<ProgressListener | null>(null)

  const addLog = useCallback((message: string) => {
    const timestamp = new Date().toLocaleTimeString()
    setLogs(prev => [...prev, `[${timestamp}] ${message}`])
  }, [])

  const clearLogs = useCallback(() => {
    setLogs([])
  }, [])

  const reset = useCallback(() => {
    setIsExecuting(false)
    setProgress(null)
    setResult(null)
    setError(null)
    if (options.autoReset !== false) {
      setLogs([])
    }
  }, [options.autoReset])

  const execute = useCallback(async (
    command: string,
    params: any,
    eventName: string
  ): Promise<any> => {
    try {
      setIsExecuting(true)
      setError(null)
      setResult(null)

      if (options.autoReset !== false) {
        setProgress(null)
        setLogs([])
      }

      addLog(`开始执行命令: ${command}`)

      const onProgress = (progressData: ProgressState) => {
        setProgress(progressData)
        addLog(progressData.message)
        options.onProgress?.(progressData)
      }

      const commandResult = await executeCommandWithProgress(
        command,
        params,
        eventName,
        onProgress
      )

      setResult(commandResult)
      addLog('命令执行成功')
      options.onSuccess?.(commandResult)

      return commandResult
    } catch (err) {
      const error = err instanceof Error ? err : new Error(String(err))
      setError(error)
      addLog(`命令执行失败: ${error.message}`)
      options.onError?.(error)
      throw error
    } finally {
      setIsExecuting(false)
    }
  }, [options, addLog])

  return {
    // State
    isExecuting,
    progress,
    result,
    error,
    logs,

    // Actions
    execute,
    reset,
    addLog,
    clearLogs,
  }
}

/**
 * Specialized hook for template operations
 */

export interface TemplateInfo {
  id: string
  name: string
  description: string
  thumbnail_path: string
  draft_content_path: string
  resources_path: string
  created_at: string
  updated_at: string
  canvas_config: any
  duration: number
  material_count: number
  track_count: number
  tags: string[]
}
export interface ImportResult {
  status: boolean
  msg: string
  imported_count: number
  failed_count: number
  imported_templates: TemplateInfo[]
  failed_templates: Array<{
    name: string
    error: string
  }>
}

export function useTemplateProgress(options: UseProgressCommandOptions = {}) {
  const hook = useProgressCommand<ImportResult>(options)

  const batchImport = useCallback(async (sourceFolder: string) => {
    return hook.execute(
      'batch_import_templates_with_progress',
      { request: { source_folder: sourceFolder } },
      'template-import-progress'
    )
  }, [hook])

  return {
    ...hook,
    batchImport,
  }
}

/**
 * Specialized hook for AI video operations
 */
export function useVideoProgress(options: UseProgressCommandOptions = {}) {
  const hook = useProgressCommand(options)

  const generateVideo = useCallback(async (
    imagePath: string,
    prompt: string,
    outputPath: string
  ) => {
    return hook.execute(
      'generate_video_with_progress',
      { imagePath, prompt, outputPath },
      'video-generation-progress'
    )
  }, [hook])

  return {
    ...hook,
    generateVideo,
  }
}

/**
 * Specialized hook for data processing operations
 */
export function useDataProcessingProgress(options: UseProgressCommandOptions = {}) {
  const hook = useProgressCommand(options)

  const processData = useCallback(async (
    inputFile: string,
    outputFile: string,
    processingType: string
  ) => {
    return hook.execute(
      'process_data_with_progress',
      { request: { input_file: inputFile, output_file: outputFile, processing_type: processingType } },
      'data-processing-progress'
    )
  }, [hook])

  const batchConvertFiles = useCallback(async (
    filePaths: string[],
    outputFormat: string
  ) => {
    return hook.execute(
      'batch_convert_files_with_progress',
      { filePaths, outputFormat },
      'file-conversion-progress'
    )
  }, [hook])

  return {
    ...hook,
    processData,
    batchConvertFiles,
  }
}

/**
 * Specialized hook for machine learning operations
 */
export function useMLProgress(options: UseProgressCommandOptions = {}) {
  const hook = useProgressCommand(options)

  const trainModel = useCallback(async (
    datasetPath: string,
    modelType: string,
    epochs: number,
    learningRate: number
  ) => {
    return hook.execute(
      'train_model_with_progress',
      { request: { dataset_path: datasetPath, model_type: modelType, epochs, learning_rate: learningRate } },
      'model-training-progress'
    )
  }, [hook])

  return {
    ...hook,
    trainModel,
  }
}