const http = require("http");

const SYSTEM_PROMPT = `你是一个场景信息提取助手。根据场景目录的内容，提取 scene-id 和 scene-name。

scene-id 规则：
- 使用英文短横线连接，如 tq-lineloss-report
- 全小写，有业务含义

scene-name 规则：
- 使用中文，简短描述性名称
- 如 "台区线损报表"、"知乎热榜导出"

请以 JSON 格式返回：{"sceneId": "...", "sceneName": "..."}`;

function buildAnalyzePrompt(sourceDir, dirContents) {
  const parts = [];

  parts.push(`=== 目录结构 ===`);
  parts.push(dirContents.tree || "(empty)");

  if (dirContents["scene.toml"]) {
    parts.push(`\n=== scene.toml ===`);
    parts.push(dirContents["scene.toml"]);
  }

  if (dirContents["SKILL.toml"]) {
    parts.push(`\n=== SKILL.toml ===`);
    parts.push(dirContents["SKILL.toml"]);
  }

  if (dirContents["SKILL.md"]) {
    parts.push(`\n=== SKILL.md ===`);
    parts.push(dirContents["SKILL.md"]);
  }

  if (dirContents.scripts && Object.keys(dirContents.scripts).length > 0) {
    parts.push(`\n=== 脚本文件 ===`);
    for (const [name, content] of Object.entries(dirContents.scripts)) {
      parts.push(`\n--- ${name} ---`);
      parts.push(content.substring(0, 2000));
    }
  }

  return `以下是场景目录 "${sourceDir}" 的内容：\n\n${parts.join("\n")}\n\n请以 JSON 格式返回：{"sceneId": "...", "sceneName": "..."}`;
}

function extractJsonFromResponse(text) {
  const codeBlockMatch = text.match(/```(?:json)?\s*\n([\s\S]*?)\n```/);
  if (codeBlockMatch) return JSON.parse(codeBlockMatch[1]);

  const jsonMatch = text.match(
    /\{[\s\S]*"sceneId"[\s\S]*"sceneName"[\s\S]*\}/
  );
  if (jsonMatch) return JSON.parse(jsonMatch[0]);

  return JSON.parse(text);
}

function analyzeScene(sourceDir, dirContents, { apiKey, baseUrl, model }) {
  const userPrompt = buildAnalyzePrompt(sourceDir, dirContents);

  const requestBody = JSON.stringify({
    model,
    messages: [
      { role: "system", content: SYSTEM_PROMPT },
      { role: "user", content: userPrompt },
    ],
    temperature: 0.1,
    max_tokens: 256,
  });

  return new Promise((resolve, reject) => {
    const url = new URL(baseUrl.replace(/\/v1\/?$/, "") + "/v1/chat/completions");
    const options = {
      hostname: url.hostname,
      port: url.port || (url.protocol === "https:" ? 443 : 80),
      path: url.pathname,
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        Authorization: `Bearer ${apiKey}`,
        "Content-Length": Buffer.byteLength(requestBody),
      },
    };

    const req = http.request(options, (res) => {
      let data = "";
      res.on("data", (chunk) => (data += chunk));
      res.on("end", () => {
        if (res.statusCode !== 200) {
          return reject(new Error(`LLM API error ${res.statusCode}: ${data}`));
        }

        try {
          const parsed = JSON.parse(data);
          const content = parsed.choices?.[0]?.message?.content;
          if (!content) return reject(new Error("LLM returned empty response"));
          const result = extractJsonFromResponse(content);
          if (!result.sceneId || !result.sceneName) {
            return reject(
              new Error(`LLM response missing sceneId/sceneName: ${content}`)
            );
          }
          resolve(result);
        } catch (err) {
          reject(new Error(`Failed to parse LLM response: ${err.message}`));
        }
      });
    });

    req.on("error", reject);
    req.setTimeout(30000, () => {
      req.destroy(new Error("LLM API request timed out"));
    });

    req.write(requestBody);
    req.end();
  });
}

module.exports = { buildAnalyzePrompt, extractJsonFromResponse, analyzeScene };