/**
 * 使用 Cursor TypeScript SDK 一键调用大模型，按 Skill 约定生成 lab-eval-ai.json
 * 依赖：CURSOR_API_KEY；可选 LAB_EVAL_MODEL（默认 composer-2）
 */
import fs from 'fs';
import path from 'path';
import { fileURLToPath } from 'url';
import { createRequire } from 'module';
import { Agent, CursorAgentError } from '@cursor/sdk';

const __dirname = path.dirname(fileURLToPath(import.meta.url));
const ROOT = path.join(__dirname, '..');
const require = createRequire(import.meta.url);
const { analyzeChatLogsFile } = require(path.join(ROOT, 'lib/analyzeChatLogs.js'));
const { resolveChatLogPath } = require(path.join(ROOT, 'lib/resolveChatLogPath.cjs'));

function readText(p) {
  return fs.readFileSync(p, 'utf8');
}

function extractJson(text) {
  if (text == null) throw new Error('empty model output');
  const s = typeof text === 'string' ? text : JSON.stringify(text);
  const fence = s.match(/```(?:json)?\s*([\s\S]*?)```/i);
  if (fence) return JSON.parse(fence[1].trim());
  const start = s.indexOf('{');
  const end = s.lastIndexOf('}');
  if (start >= 0 && end > start) return JSON.parse(s.slice(start, end + 1));
  throw new Error('Could not parse JSON from model output');
}

function slimReport(full) {
  const max = 48000;
  const str = JSON.stringify(full, null, 2);
  if (str.length <= max) return str;
  return `${str.slice(0, max)}\n…(truncated)…\n`;
}

async function main() {
  const apiKey = process.env.CURSOR_API_KEY;
  if (!apiKey) {
    console.error('缺少环境变量 CURSOR_API_KEY。获取方式：https://cursor.com/dashboard/cloud-agents');
    process.exit(1);
  }

  const chatPath = resolveChatLogPath(ROOT);
  if (!fs.existsSync(chatPath)) {
    console.error(`缺少聊天日志文件: ${chatPath}（可设置环境变量 CHAT_LOG_PATH）`);
    process.exit(1);
  }

  const taskMd = readText(path.join(ROOT, 'config/lab-task-description.md'));
  const dims = readText(path.join(ROOT, 'config/evaluation-dimensions.yaml'));
  const skillPath = path.join(ROOT, '.cursor/skills/student-lab-ai-evaluation/SKILL.md');
  const skill = fs.existsSync(skillPath) ? readText(skillPath) : '';

  let heuristic;
  try {
    heuristic = analyzeChatLogsFile(chatPath);
  } catch (e) {
    console.error(e);
    process.exit(1);
  }
  if (!heuristic.ok) {
    console.error(heuristic.error || '解析 chat_logs 失败');
    process.exit(1);
  }

  const prompt = `你是课程助教。请严格遵守下列 SKILL 中的输出约束（只输出一个 JSON 对象，不要其它文字）。\n\n--- SKILL 开始 ---\n${skill}\n--- SKILL 结束 ---\n\n--- 实训任务描述 ---\n${taskMd}\n\n--- 评价维度配置（YAML）---\n${dims}\n\n--- 启发式解析 JSON（来自 chat_logs，仅供参考）---\n${slimReport(
    heuristic
  )}\n\n请根据以上材料生成评价 JSON。abilities 必须与 evaluation-dimensions.yaml 中的维度 id 对齐。`;

  const modelId = process.env.LAB_EVAL_MODEL || 'composer-2';

  let result;
  try {
    result = await Agent.prompt(prompt, {
      apiKey,
      model: { id: modelId },
      local: { cwd: ROOT },
    });
  } catch (err) {
    if (err instanceof CursorAgentError) {
      console.error('Agent 启动失败:', err.message, 'retryable=', err.isRetryable);
      process.exit(1);
    }
    throw err;
  }

  if (result.status === 'error') {
    console.error('Agent 运行结束为 error:', result);
    process.exit(2);
  }

  const raw = result.result;
  let json;
  try {
    json = extractJson(raw);
  } catch (e) {
    console.error('解析 JSON 失败:', e.message);
    console.error('模型原始输出（前 2500 字符）:\n', String(raw).slice(0, 2500));
    process.exit(3);
  }

  const outPath = path.join(ROOT, 'lab-eval-ai.json');
  fs.writeFileSync(outPath, JSON.stringify(json, null, 2), 'utf8');
  console.log('已写入', outPath);
  console.log('刷新 http://127.0.0.1:53780/ 的作业评价区即可看到合并后的内容。');
}

main().catch((e) => {
  console.error(e);
  process.exit(1);
});