import axios from 'axios';
import * as PerformanceDAO from '../dao/PerformanceDAO';
import * as AIResultDAO from '../dao/AIResultDAO';

// ─── Types ────────────────────────────────────────────────────────────────────

export interface AIScoreData {
  aiScoreDetail: AIResultDAO.AIScoreItem[];
  aiTotalScore: number;
  aiProblems: string[];
  aiSuggestions: string[];
}

// ─── Prompt Builder ───────────────────────────────────────────────────────────

/**
 * Build the prompt sent to FastGPT.
 * Includes employee position, month, performance items, attendance, and scoring rules.
 * Requirements: 13.2
 */
export function buildPrompt(
  performance: PerformanceDAO.PerformanceRow,
  items: PerformanceDAO.PerfItemRow[],
  attendance: PerformanceDAO.AttendanceRow | null,
  position: string
): string {
  const itemsText = items
    .map(
      (item, idx) =>
        `${idx + 1}. 【${item.item_name}】(${item.item_category === 'business' ? '业务素质' : '综合素质'}，权重${item.weight}分)\n` +
        `   员工填写内容：${item.user_content ?? '（未填写）'}\n` +
        `   员工自评分：${item.self_score ?? '（未填写）'}`
    )
    .join('\n');

  const attendanceText = attendance
    ? `事假${attendance.leave_days}天，迟到${attendance.late_times}次，缺卡${attendance.lack_card_times}次，旷工${attendance.absent_days}天`
    : '无考勤数据';

  return `你是一名专业的HR绩效评估专家，请根据以下员工绩效填报内容进行客观评分。

## 员工信息
- 岗位：${position}
- 考核月份：${performance.month}

## 考核项目及填报内容
${itemsText}

## 考勤情况
${attendanceText}

## 工作汇总
${performance.work_summary ?? '（未填写）'}

## 评分规则
- 业务素质考评占总分70%，综合素质考评占总分30%
- 每个考核项按0-100分评分，最终加权计算总分
- 请根据员工填写内容的质量、完整性和实际工作表现进行评分

## 输出要求
请严格按照以下JSON格式输出，不要包含任何其他内容：
{
  "ai_score_detail": [
    {
      "itemName": "考核项名称",
      "weight": 权重分值,
      "aiScore": 评分(0-100),
      "scoreExplanation": "评分说明"
    }
  ],
  "ai_total_score": 加权总分(0-100),
  "ai_problems": ["问题1", "问题2", "问题3"],
  "ai_suggestions": ["建议1", "建议2", "建议3"]
}

注意：
- ai_problems 和 ai_suggestions 各需要3到5条
- ai_total_score 为各项加权分数之和，业务素质项权重之和为70，综合素质项权重之和为30
- 所有字段必须存在且格式正确`;
}

// ─── Response Parser ──────────────────────────────────────────────────────────

/**
 * Parse and validate the AI JSON response.
 * Extracts ai_score_detail, ai_total_score, ai_problems, ai_suggestions.
 * Throws on invalid format, recording the raw response.
 * Requirements: 3.2, 3.3, 3.4, 13.3, 13.4
 */
export function parseAIResponse(rawResponse: string): AIScoreData {
  let parsed: any;

  // Extract JSON from the response (model may wrap it in markdown code blocks)
  const jsonMatch = rawResponse.match(/```(?:json)?\s*([\s\S]*?)```/) ||
    rawResponse.match(/(\{[\s\S]*\})/);

  const jsonStr = jsonMatch ? jsonMatch[1].trim() : rawResponse.trim();

  try {
    parsed = JSON.parse(jsonStr);
  } catch (e) {
    throw new Error(`AI响应JSON解析失败，原始响应：${rawResponse}`);
  }

  // Validate required fields
  if (!Array.isArray(parsed.ai_score_detail)) {
    throw new Error(`AI响应缺少 ai_score_detail 字段，原始响应：${rawResponse}`);
  }
  if (typeof parsed.ai_total_score !== 'number') {
    throw new Error(`AI响应缺少有效的 ai_total_score 字段，原始响应：${rawResponse}`);
  }
  if (!Array.isArray(parsed.ai_problems) || parsed.ai_problems.length < 1) {
    throw new Error(`AI响应缺少 ai_problems 字段，原始响应：${rawResponse}`);
  }
  if (!Array.isArray(parsed.ai_suggestions) || parsed.ai_suggestions.length < 1) {
    throw new Error(`AI响应缺少 ai_suggestions 字段，原始响应：${rawResponse}`);
  }

  // Validate and map score detail items
  const aiScoreDetail: AIResultDAO.AIScoreItem[] = parsed.ai_score_detail.map((item: any, idx: number) => {
    if (typeof item.itemName !== 'string' || typeof item.weight !== 'number' ||
        typeof item.aiScore !== 'number' || typeof item.scoreExplanation !== 'string') {
      throw new Error(`AI响应 ai_score_detail[${idx}] 格式无效，原始响应：${rawResponse}`);
    }
    return {
      itemName: item.itemName,
      weight: item.weight,
      aiScore: item.aiScore,
      scoreExplanation: item.scoreExplanation,
    };
  });

  return {
    aiScoreDetail,
    aiTotalScore: parsed.ai_total_score,
    aiProblems: parsed.ai_problems as string[],
    aiSuggestions: parsed.ai_suggestions as string[],
  };
}

// ─── FastGPT API Call ─────────────────────────────────────────────────────────

const FASTGPT_API_URL = process.env.FASTGPT_API_URL || 'https://cloud.fastgpt.cn/api/v1/chat/completions';
const FASTGPT_API_KEY = process.env.FASTGPT_API_KEY || '';
const AI_TIMEOUT_MS = 60_000; // 60秒超时

// 读取系统代理配置（支持 HTTP_PROXY / HTTPS_PROXY 环境变量）
function getProxyConfig() {
  const proxyUrl = process.env.HTTPS_PROXY || process.env.HTTP_PROXY || process.env.https_proxy || process.env.http_proxy;
  if (proxyUrl) {
    try {
      const url = new URL(proxyUrl);
      return {
        host: url.hostname,
        port: parseInt(url.port, 10),
        protocol: url.protocol.replace(':', '') as 'http' | 'https',
      };
    } catch {
      return undefined;
    }
  }
  return undefined;
}

/**
 * Call FastGPT workflow API with structured variables.
 * FastGPT workflow expects: variables (position, month) + userChatInput (绩效内容)
 */
async function callFastGPT(
  position: string,
  month: string,
  contentText: string
): Promise<AIScoreData> {
  console.log(`[AI] 调用 FastGPT API: ${FASTGPT_API_URL}`);

  const proxyConfig = getProxyConfig();
  if (proxyConfig) {
    console.log(`[AI] 使用代理: ${proxyConfig.host}:${proxyConfig.port}`);
  }

  const response = await axios.post(
    FASTGPT_API_URL,
    {
      messages: [{ role: 'user', content: contentText }],
      variables: {
        zslh34AG: position,  // 员工岗位
        month: month,        // 考核月份
      },
      stream: false,
    },
    {
      headers: {
        Authorization: `Bearer ${FASTGPT_API_KEY}`,
        'Content-Type': 'application/json',
      },
      timeout: AI_TIMEOUT_MS,
      proxy: proxyConfig,
    }
  );

  console.log(`[AI] FastGPT 响应状态: ${response.status}`);

  // FastGPT workflow 返回的最终结果在 choices[0].message.content 里
  // 内容是 system_rawResponse 对象（JSON字符串或对象）
  const content = response.data?.choices?.[0]?.message?.content;
  if (!content) {
    throw new Error(`FastGPT API 返回内容为空，响应: ${JSON.stringify(response.data).substring(0, 300)}`);
  }

  console.log(`[AI] FastGPT 原始返回:`, typeof content === 'string' ? content.substring(0, 300) : JSON.stringify(content).substring(0, 300));

  // content 可能是带 markdown 代码块的字符串，或纯 JSON 字符串，或对象
  let parsed: any;
  if (typeof content === 'object') {
    parsed = content;
  } else {
    // 先尝试去掉 markdown 代码块 ```json ... ```
    let jsonStr = content.trim();
    const codeBlockMatch = jsonStr.match(/```(?:json)?\s*([\s\S]*?)```/);
    if (codeBlockMatch) {
      jsonStr = codeBlockMatch[1].trim();
    } else {
      // 直接提取第一个完整 JSON 对象
      const objMatch = jsonStr.match(/\{[\s\S]*\}/);
      if (objMatch) {
        jsonStr = objMatch[0];
      }
    }
    try {
      parsed = JSON.parse(jsonStr);
    } catch (e: any) {
      throw new Error(`FastGPT 返回内容无法解析为 JSON: ${e.message}，内容片段: ${jsonStr.substring(0, 200)}`);
    }
  }

  // 校验必要字段
  if (!Array.isArray(parsed.ai_score_detail)) {
    throw new Error(`响应缺少 ai_score_detail，内容: ${JSON.stringify(parsed).substring(0, 300)}`);
  }
  if (typeof parsed.ai_total_score !== 'number') {
    throw new Error(`响应缺少有效的 ai_total_score，内容: ${JSON.stringify(parsed).substring(0, 300)}`);
  }

  return {
    aiScoreDetail: parsed.ai_score_detail.map((item: any) => ({
      itemName: item.itemName,
      weight: item.weight,
      aiScore: item.aiScore,
      scoreExplanation: item.scoreExplanation,
    })),
    aiTotalScore: parsed.ai_total_score,
    aiProblems: Array.isArray(parsed.ai_problems) ? parsed.ai_problems : [],
    aiSuggestions: Array.isArray(parsed.ai_suggestions) ? parsed.ai_suggestions : [],
  };
}

// ─── Core Evaluation ──────────────────────────────────────────────────────────

/**
 * Perform a single AI evaluation attempt for the given performance record.
 */
async function evaluateOnce(
  perfId: number,
  performance: PerformanceDAO.PerformanceRow,
  items: PerformanceDAO.PerfItemRow[],
  attendance: PerformanceDAO.AttendanceRow | null,
  position: string
): Promise<AIResultDAO.AIResult> {
  // 构建员工绩效内容文本（作为 userChatInput 传入 workflow）
  const itemsText = items
    .map(
      (item, idx) =>
        `${idx + 1}. 【${item.item_name}】(权重${item.weight}分)\n` +
        `   员工填写：${item.user_content ?? '（未填写）'}\n` +
        `   自评分：${item.self_score ?? '（未填写）'}`
    )
    .join('\n');

  const attendanceText = attendance
    ? `事假${attendance.leave_days}天，迟到${attendance.late_times}次，缺卡${attendance.lack_card_times}次，旷工${attendance.absent_days}天`
    : '无考勤数据';

  const contentText = `考勤情况：${attendanceText}\n\n工作汇总：${performance.work_summary ?? '（未填写）'}\n\n考核项目：\n${itemsText}`;

  const scoreData = await callFastGPT(position, performance.month, contentText);

  const aiId = await AIResultDAO.save({
    perfId,
    aiScoreDetail: scoreData.aiScoreDetail,
    aiTotalScore: scoreData.aiTotalScore,
    aiProblems: scoreData.aiProblems,
    aiSuggestions: scoreData.aiSuggestions,
    apiResponse: JSON.stringify(scoreData),
  });

  return {
    aiId,
    perfId,
    aiScoreDetail: scoreData.aiScoreDetail,
    aiTotalScore: scoreData.aiTotalScore,
    aiProblems: scoreData.aiProblems,
    aiSuggestions: scoreData.aiSuggestions,
    createTime: new Date(),
  };
}

// ─── Retry + Degradation ──────────────────────────────────────────────────────

const MAX_RETRIES = 3;

/**
 * Evaluate performance with up to MAX_RETRIES attempts.
 * On all failures, logs the error and updates the performance status to 'ai_failed' (degradation).
 * Requirements: 3.5
 */
export async function evaluatePerformance(perfId: number): Promise<AIResultDAO.AIResult> {
  // Load full performance detail
  const detail = await PerformanceDAO.findDetailByPerfId(perfId);
  if (!detail) {
    throw new Error(`绩效记录不存在: perfId=${perfId}`);
  }

  // Load employee position from user table
  const pool = (await import('../config/database')).default;
  const [userRows] = await pool.query<any[]>(
    'SELECT position FROM user WHERE user_id = ? LIMIT 1',
    [detail.performance.user_id]
  );
  const position: string = userRows[0]?.position ?? '未知岗位';

  let lastError: Error = new Error('未知错误');

  for (let attempt = 1; attempt <= MAX_RETRIES; attempt++) {
    try {
      const result = await evaluateOnce(
        perfId,
        detail.performance,
        detail.items,
        detail.attendance,
        position
      );

      // Update ai_score on performance_month
      await pool.query(
        'UPDATE performance_month SET ai_score = ? WHERE perf_id = ?',
        [result.aiTotalScore, perfId]
      );

      // 回写 AI 评分到 perf_item 各考核项
      for (const scoreItem of result.aiScoreDetail) {
        await pool.query(
          `UPDATE perf_item SET ai_score = ?, ai_explanation = ?
           WHERE perf_id = ? AND item_name = ?`,
          [scoreItem.aiScore, scoreItem.scoreExplanation, perfId, scoreItem.itemName]
        );
      }

      console.log(`[AI] 评分完成 perfId=${perfId}，总分=${result.aiTotalScore}（第${attempt}次尝试）`);
      return result;
    } catch (err: any) {
      lastError = err;
      console.error(`[AI] 第${attempt}次评分失败 perfId=${perfId}:`, err.message);
      if (attempt < MAX_RETRIES) {
        // Brief delay before retry (exponential backoff: 1s, 2s)
        await new Promise((resolve) => setTimeout(resolve, attempt * 1000));
      }
    }
  }

  // All retries exhausted — degradation: log error, notify admin via console
  console.error(`[AI] 评分最终失败 perfId=${perfId}，已重试${MAX_RETRIES}次。错误：${lastError.message}`);

  // Note: We don't log to operation_log here because there's no user_id context
  // Admin can check console logs for AI evaluation failures

  throw lastError;
}