import { generateObject, generateText } from "ai";
import { z } from "zod";
import {
  coerceResearchMapRaw,
  extractJsonFromText,
  researchMapLooseSchema,
} from "./coerce-research-map";
import type { ProviderApiKeys } from "./keys";
import { getModel } from "./provider";
import {
  buildResearchMapSystemPrompt,
  buildResearchMapUserPrompt,
} from "./prompts/research-map";
import {
  buildPlacementResearchMapSystemPrompt,
  buildPlacementResearchMapUserPrompt,
} from "./prompts/research-map-placement";
import type { ResearchMap, SuggestedTag } from "@/lib/types/research";
import type { TopicGoal } from "@/lib/types/topic-goal";
import { isPlacementGoal } from "@/lib/types/topic-goal";
import {
  explainProviderApiError,
  getOpenCodeGenerationSettings,
  prefersOpenCodeTextFirst,
} from "./opencode-go-settings";
import { formatProductContextForPrompt } from "@/lib/types/product-context";
import { filterSuggestedTags } from "./normalize-suggested-tags";
import { generateStructuredObject } from "./generate-structured";

export interface AnalyzeTopicInput {
  label: string;
  query: string;
  brief?: string | null;
  productContext?: string | null;
  topicGoal?: TopicGoal | string | null;
  persona?: string | null;
  aiProvider: string;
  aiModel: string;
  apiKeys?: ProviderApiKeys;
}

const DEFAULT_QUESTIONS = [
  "這個主題下，受眾最常感到焦慮的是什麼？",
  "有哪些實用資訊是大家都在找的？",
  "同溫層想聽什麼樣的親身經驗？",
  "哪些迷思需要被釐清？",
  "什麼情境下會特別想搜這個主題？",
];

const DEFAULT_PILLARS = ["實用知識", "同溫經驗", "語錄故事", "迷思破解"];

const DEFAULT_EXCLUSIONS = ["業配硬銷", "未查證偏方", "純情緒宣洩"];

function padStringList(items: string[], minimum: number, maximum: number, defaults: string[]) {
  const out = items.map((s) => s.trim()).filter(Boolean);
  let i = 0;
  while (out.length < minimum) {
    out.push(defaults[i] ?? `待補充項目 ${out.length + 1}`);
    i += 1;
  }
  return out.slice(0, maximum);
}

function padSuggestedTags(
  tags: SuggestedTag[],
  input: AnalyzeTopicInput
): SuggestedTag[] {
  return filterSuggestedTags(tags, {
    label: input.label,
    query: input.query,
    brief: input.brief,
    topicGoal: input.topicGoal,
  });

}

function normalizeResearchMap(
  raw: z.infer<typeof researchMapLooseSchema>,
  input: AnalyzeTopicInput
): Omit<ResearchMap, "similarAccounts"> {
  const questions = padStringList(raw.questions, 5, 8, DEFAULT_QUESTIONS);
  const pillars = padStringList(raw.pillars, 4, 6, DEFAULT_PILLARS);
  return {
    audienceSummary: raw.audienceSummary.trim(),
    contentGoal: raw.contentGoal.trim(),
    questions,
    pillars,
    suggestedTags: padSuggestedTags(raw.suggestedTags, input),
    exclusions: padStringList(raw.exclusions, 3, 8, DEFAULT_EXCLUSIONS),
  };
}

const searchTagBatchSchema = z.object({
  tags: z.array(z.object({
    tag: z.string().min(2).max(10),
    reason: z.string().min(1).max(60),
    searchIntent: z.enum(["痛點", "知識", "經驗", "對比", "工具", "語錄", "需求", "求助"]),
    searchType: z.enum(["短詞", "情境", "語錄"]),
  })).min(6).max(8),
});

const UNNATURAL_SEARCH_TAG =
  /[，。！？、：；（）()「」【】]|一步步|過程中的|記錄改|的搞|舒適圈的|怎麼交到新|搜尋詞\d|^(這個|哪些|什麼情境)/;

function naturalSearchTags(tags: SuggestedTag[]): SuggestedTag[] {
  const seen = new Set<string>();
  return tags.filter((item) => {
    const tag = item.tag.replace(/^#/, "").trim();
    const key = tag.toLowerCase();
    if (tag.length < 2 || tag.length > 10 || UNNATURAL_SEARCH_TAG.test(tag)) return false;
    if (/的$|才$|要$|會$|怎麼$|如何$/.test(tag)) return false;
    if (seen.has(key)) return false;
    seen.add(key);
    item.tag = tag;
    return true;
  });
}

async function generateNaturalSearchTags(
  model: ReturnType<typeof getModel>,
  input: AnalyzeTopicInput,
  map: Omit<ResearchMap, "similarAccounts">
): Promise<SuggestedTag[]> {
  const placementMode = isPlacementGoal(input.topicGoal);
  const modeRules = placementMode
    ? `你正在替「找 TA」生成需求型搜尋詞：
- 搜尋目標是正在遇到問題、求助、求推薦或準備選購的人，不是泛知識流量
- 至少 5 個詞要帶具體痛點、症狀、使用情境或選購意圖
- 好例子：狗狗皮膚癢、洗毛精推薦、敏感肌洗毛精、在家洗狗、洗完還是臭
- 壞例子：寵物生活、毛孩日常、產品需求、解決方案、內容分享
- 不產語錄、口號或品牌視角文案；每個詞都要能對應一個當下需求
- searchIntent 優先使用「痛點、需求、求助」，不可把品牌行銷詞當成受眾用語`
    : `你正在替「拷貝忍者」生成內容型搜尋詞：
- 組合：2 個核心主題、2 個具體痛點、2 個經驗／行動、1 個受眾用語、最多 1 個語錄詞
- 以「宅男變身」為例，好詞是：宅男改造、男生穿搭、體味改善、內向社交、變帥過程、宅男自救`;
  const result = await generateStructuredObject({
    model,
    provider: input.aiProvider,
    modelId: input.aiModel,
    schema: searchTagBatchSchema,
    system: `你是台灣 Threads 站內搜尋策略師。你只負責把研究地圖改寫成真人會輸入搜尋框的詞。

硬性規則：
- 產出 8 個不重複搜尋詞，每個 2～8 個中文字為佳，最長 10 字
- 它是「搜尋詞」，不是文章標題、內容企劃、完整問句或 hashtag
- 禁止把內容支柱直接截斷；必須先理解語意，再壓縮成自然詞組
- 禁止「一步步記錄改」「穿搭的搞」「過程中的黑色」這類殘句
- 除非種子關鍵字或 Brief 明確指定品牌，否則搜尋詞不可自行加入品牌名
- 不要只換同義字湊數；每個詞應能找到不同但仍相關的貼文
- 想像一位台灣使用者真的會在 Threads 搜尋框輸入它

${modeRules}

壞詞範例：變身實戰記錄一步步記錄改。`,
    prompt: `主題：${input.label}
種子關鍵字：${input.query}
Brief：${input.brief ?? ""}
受眾：${map.audienceSummary}
受眾問題：${map.questions.join("｜")}
內容支柱：${map.pillars.join("｜")}
排除：${map.exclusions.join("｜")}

請重新產出自然、可搜尋的 Threads 搜尋詞。`,
    jsonPromptSuffix: `\n\n只回傳 JSON，不要 markdown。tags 必須恰好 8 筆且 tag 不可重複：\n{"tags":[{"tag":"2到8字搜尋詞1","reason":"搜尋意圖","searchIntent":"經驗","searchType":"短詞"},{"tag":"2到8字搜尋詞2","reason":"搜尋意圖","searchIntent":"痛點","searchType":"情境"},{"tag":"2到8字搜尋詞3","reason":"搜尋意圖","searchIntent":"知識","searchType":"短詞"},{"tag":"2到8字搜尋詞4","reason":"搜尋意圖","searchIntent":"工具","searchType":"情境"},{"tag":"2到8字搜尋詞5","reason":"搜尋意圖","searchIntent":"經驗","searchType":"情境"},{"tag":"2到8字搜尋詞6","reason":"搜尋意圖","searchIntent":"對比","searchType":"短詞"},{"tag":"2到8字搜尋詞7","reason":"搜尋意圖","searchIntent":"痛點","searchType":"情境"},{"tag":"2到8字搜尋詞8","reason":"搜尋意圖","searchIntent":"語錄","searchType":"語錄"}]}`,
  });
  const natural = naturalSearchTags(result.tags);
  return placementMode
    ? natural.filter((tag) => tag.searchIntent !== "語錄" && tag.searchType !== "語錄")
    : natural;
}

export async function regenerateSearchTags(
  input: AnalyzeTopicInput,
  map: Omit<ResearchMap, "similarAccounts">
): Promise<SuggestedTag[]> {
  const model = getModel(input.aiProvider, input.aiModel, input.apiKeys ?? {});
  const tags = await generateNaturalSearchTags(model, input, map);
  if (tags.length < 6) {
    throw new Error(`AI 只產出 ${tags.length} 個可用搜尋詞，未達 6 個，請重試或更換研究模型`);
  }
  return tags.slice(0, 8);
}

export function formatAnalyzeError(error: unknown): string {
  if (typeof error === "string") return error;
  if (error instanceof z.ZodError) {
    const detail = error.issues
      .slice(0, 4)
      .map((issue) => {
        const field = issue.path.join(".") || "回傳內容";
        return `${field}：${issue.message}`;
      })
      .join("；");
    return `AI 回傳的研究地圖格式不完整（${detail}）。請再按一次「AI 分析主題」重試。`;
  }
  if (error instanceof Error) {
    const extra = error as Error & { responseBody?: string };
    const explained = explainProviderApiError(
      error.message,
      typeof extra.responseBody === "string" ? extra.responseBody : undefined
    );
    if (explained) return explained;
    return error.message;
  }
  return "AI 分析失敗";
}

function buildJsonPromptSuffix(): string {
  return `

請只回傳 JSON（不要 markdown），格式：
{
  "audienceSummary": "string",
  "contentGoal": "string",
  "questions": ["string", "..."],
  "pillars": ["string", "..."],
  "suggestedTags": [
    {"tag":"2-4字短詞","reason":"為什麼搜","searchIntent":"痛點|知識|經驗|對比|工具|語錄","searchType":"短詞|情境|語錄"}
  ],
  "exclusions": ["string", "..."]
}
questions 至少 5 個、pillars 至少 4 個、suggestedTags 10～14 個、exclusions 至少 3 個。不要輸出 similarAccounts。`;
}

function resolvePrompts(input: AnalyzeTopicInput) {
  if (isPlacementGoal(input.topicGoal)) {
    const brief = input.brief?.trim() || `主題是「${input.query}」。`;
    const productContext = formatProductContextForPrompt(input.productContext);
    const persona =
      input.persona?.trim() ||
      "專業、願意提供實用建議的品牌代表，語氣自然不硬銷。";
    return {
      system: buildPlacementResearchMapSystemPrompt(),
      prompt: buildPlacementResearchMapUserPrompt({
        label: input.label,
        query: input.query,
        brief,
        productContext,
        persona,
      }),
    };
  }

  const brief = input.brief?.trim()
    ? input.brief.trim()
    : `主題是「${input.query}」。請假設受眾是台灣 25～40 歲、會在 Threads 找資訊與同溫感的一般使用者。`;
  const persona = input.persona?.trim()
    ? input.persona.trim()
    : "專業、有觀點、願意分享實用資訊的 Threads 創作者，語氣自然不說教。";

  return {
    system: buildResearchMapSystemPrompt(),
    prompt: buildResearchMapUserPrompt({
      label: input.label,
      query: input.query,
      brief,
      persona,
    }),
  };
}

async function generateWithText(
  model: ReturnType<typeof getModel>,
  prompt: string,
  system: string,
  fallback: { label: string; query: string; brief: string },
  provider: string,
  modelId: string
) {
  const settings = getOpenCodeGenerationSettings(provider, modelId);
  const { text } = await generateText({
    model,
    system: `${system}${buildJsonPromptSuffix()}`,
    prompt,
    ...settings,
  });
  const parsed = extractJsonFromText(text);
  return coerceResearchMapRaw(parsed, fallback);
}

async function generateWithObject(
  model: ReturnType<typeof getModel>,
  prompt: string,
  system: string,
  fallback: { label: string; query: string; brief: string },
  provider: string,
  modelId: string
) {
  const settings = getOpenCodeGenerationSettings(provider, modelId);
  const result = await generateObject({
    model,
    schema: researchMapLooseSchema,
    system,
    prompt: `${prompt}${buildJsonPromptSuffix()}`,
    ...settings,
  });
  return coerceResearchMapRaw(result.object, fallback);
}

export async function analyzeTopicIntent(
  input: AnalyzeTopicInput
): Promise<Omit<ResearchMap, "similarAccounts">> {
  const model = getModel(input.aiProvider, input.aiModel, input.apiKeys ?? {});

  const { system, prompt } = resolvePrompts(input);
  const brief = input.brief?.trim() || `主題是「${input.query}」。`;
  const fallback = { label: input.label, query: input.query, brief };
  const preferText = prefersOpenCodeTextFirst(input.aiProvider, input.aiModel);

  let raw: z.infer<typeof researchMapLooseSchema> | null = null;
  let lastError: unknown;

  const attempts: Array<() => Promise<z.infer<typeof researchMapLooseSchema>>> = preferText
    ? [
        () => generateWithText(model, prompt, system, fallback, input.aiProvider, input.aiModel),
        () => generateWithObject(model, prompt, system, fallback, input.aiProvider, input.aiModel),
        () =>
          generateWithText(
            model,
            `${prompt}\n\n上次格式不完整，請務必補齊所有欄位。`,
            system,
            fallback,
            input.aiProvider,
            input.aiModel
          ),
      ]
    : [
        () => generateWithObject(model, prompt, system, fallback, input.aiProvider, input.aiModel),
        () => generateWithText(model, prompt, system, fallback, input.aiProvider, input.aiModel),
        () =>
          generateWithText(
            model,
            `${prompt}\n\n上次格式不完整，請務必補齊所有欄位。`,
            system,
            fallback,
            input.aiProvider,
            input.aiModel
          ),
      ];

  for (const attempt of attempts) {
    try {
      raw = await attempt();
      break;
    } catch (error) {
      lastError = error;
    }
  }

  if (!raw) {
    throw formatAnalyzeError(lastError);
  }

  try {
    let normalized = normalizeResearchMap(raw, input);
    {
      try {
        const rewritten = await generateNaturalSearchTags(model, input, normalized);
        if (rewritten.length >= 6) {
          normalized = { ...normalized, suggestedTags: rewritten.slice(0, 8) };
        }
      } catch (error) {
        console.warn("[analyze-topic] natural search tag rewrite failed:", error);
      }
    }
    return normalized;
  } catch (error) {
    throw formatAnalyzeError(error);
  }
}