/**
 * LLM API Routes
 * 處理 LLM 相關的 API 請求
 */

const express = require('express');
const router = express.Router();
const llmService = require('../services/llm.service');
const { asyncHandler, createError } = require('../utils/errorHandler');

/**
 * POST /api/llm/test/gemini
 * 測試 Gemini API 連線
 */
router.post('/test/gemini', asyncHandler(async (req, res) => {
  const result = await llmService.testGeminiConnection();
  res.json(result);
}));

/**
 * POST /api/llm/test/deepseek
 * 測試 DeepSeek API 連線
 */
router.post('/test/deepseek', asyncHandler(async (req, res) => {
  const result = await llmService.testDeepSeekConnection();
  res.json(result);
}));

/**
 * POST /api/llm/test/openai
 * 測試 OpenAI API 連線
 */
router.post('/test/openai', asyncHandler(async (req, res) => {
  const result = await llmService.testOpenAIConnection();
  res.json(result);
}));

/**
 * POST /api/llm/test/all
 * 測試所有 LLM API 連線
 */
router.post('/test/all', asyncHandler(async (req, res) => {
  const results = await llmService.testAllConnections();
  res.json(results);
}));

/**
 * POST /api/llm/generate
 * 使用 LLM 生成內容
 *
 * Body:
 * {
 *   "prompt": "你的提示內容",
 *   "provider": "gemini|deepseek|openai", (可選，預設使用 gemini)
 *   "options": {
 *     "temperature": 0.7,
 *     "maxTokens": 2000
 *   }
 * }
 */
router.post('/generate', asyncHandler(async (req, res) => {
  const { prompt, provider, options } = req.body;

  if (!prompt) {
    throw createError('BAD_REQUEST', '缺少必要參數: prompt');
  }

  const result = await llmService.generate(prompt, provider, options);
  res.json(result);
}));

/**
 * POST /api/llm/help-me-fill
 * Help Me AI 智能填寫功能
 *
 * Body:
 * {
 *   "cardType": "role|competency|performance|growth",
 *   "cardId": "卡片ID",
 *   "filledFields": { ... },
 *   "emptyFields": [ ... ],
 *   "context": { ... }
 * }
 */
router.post('/help-me-fill', asyncHandler(async (req, res) => {
  const { cardType, cardId, filledFields, emptyFields, context } = req.body;

  if (!cardType || !emptyFields || emptyFields.length === 0) {
    throw createError('BAD_REQUEST', '缺少必要參數');
  }

  // 建立提示詞
  const prompt = buildHelpMeFillPrompt(cardType, filledFields, emptyFields, context);

  // 使用 LLM 生成建議內容
  const result = await llmService.generate(prompt, 'gemini', {
    temperature: 0.7,
    maxTokens: 2000,
  });

  if (!result.success) {
    throw createError('LLM_API_ERROR', '生成內容失敗');
  }

  // 解析生成的內容
  const suggestions = parseHelpMeFillResponse(result.content, emptyFields);

  res.json({
    success: true,
    filledCount: Object.keys(suggestions).length,
    suggestions,
  });
}));

/**
 * 建立 Help Me Fill 提示詞
 */
function buildHelpMeFillPrompt(cardType, filledFields, emptyFields, context) {
  let prompt = `你是一個 HR 績效評核系統的智能助手。請根據以下資訊，為空白欄位生成合適的內容。\n\n`;

  prompt += `卡片類型: ${cardType}\n\n`;

  if (Object.keys(filledFields).length > 0) {
    prompt += `已填寫的欄位:\n`;
    for (const [key, value] of Object.entries(filledFields)) {
      prompt += `- ${key}: ${value}\n`;
    }
    prompt += `\n`;
  }

  if (context) {
    prompt += `上下文資訊:\n`;
    prompt += JSON.stringify(context, null, 2);
    prompt += `\n\n`;
  }

  prompt += `請為以下空白欄位生成內容:\n`;
  emptyFields.forEach(field => {
    prompt += `- ${field}\n`;
  });

  prompt += `\n請以 JSON 格式回覆，格式如下:\n`;
  prompt += `{\n`;
  emptyFields.forEach(field => {
    prompt += `  "${field}": "生成的內容",\n`;
  });
  prompt += `}\n`;

  return prompt;
}

/**
 * 解析 Help Me Fill 回應
 */
function parseHelpMeFillResponse(content, emptyFields) {
  try {
    // 嘗試直接解析 JSON
    const jsonMatch = content.match(/\{[\s\S]*\}/);
    if (jsonMatch) {
      return JSON.parse(jsonMatch[0]);
    }

    // 如果無法解析 JSON，則返回空物件
    return {};
  } catch (error) {
    console.error('解析 LLM 回應失敗:', error);
    return {};
  }
}

module.exports = router;