import { config } from '../config.ts';

interface BriefAnalysisResult {
  requirements: Array<{ label: string; value: string; confidence: number }>;
  searchCriteria: {
    platforms: string[];
    keywords: string[];
    fanRange: { min: number; max: number };
    budgetRange: { min: number; max: number };
    region?: string;
    regionalBudgetRules?: RegionalBudgetRule[];
    gender?: string;
    contentTags?: string[];
    excludeTags?: string[];
    targetCount?: number;
    evaluationNeeds?: EvaluationNeeds;
  };
}

export interface EvaluationNeeds {
  cpmCpe?: boolean;
  commercialStability?: boolean;
  recentPerformance?: boolean;
  updateFrequency?: boolean;
  commentQuality?: boolean;
  publicSentiment?: boolean;
  audienceGender?: boolean;
  reasons?: string[];
}

export interface RegionalBudgetRule {
  regions: string[];
  min: number;
  max: number;
}

const BRIEF_ANALYSIS_PROMPT = `你是一个专业的媒介策划助手。请分析以下客户Brief文件内容，提取出结构化的投放需求。

要求提取以下信息：
1. 客户/品牌名称
2. 投放目标（种草、转化、品宣等）
3. 平台需求（小红书、抖音、B站等及各平台需求人数）
4. 粉丝数要求范围
5. 预算范围
6. 内容风格要求
7. 地区要求
8. 排除项（竞品、不合适的类型等）
9. 合作形式（图文、视频等）

请以JSON格式输出，包含requirements数组和searchCriteria对象。
requirements格式：[{label: "字段名", value: "提取值", confidence: 0-100}]
searchCriteria格式：{platforms: [], keywords: [], fanRange: {min, max}, budgetRange: {min, max}, region?, regionalBudgetRules?: [{regions: [], min, max}], gender?, contentTags?: [], excludeTags?: [], targetCount?}
如果 Brief 里出现“某些城市/地区对应不同单个预算/平台价格”，请把这些规则放入 regionalBudgetRules，并保留总 budgetRange 为全部规则的最小/最大范围。
如果 Brief 写明达人数量，请额外输出 targetCount，表示最终需求达人总数（不是候选池数量）。

Brief内容：
`;

const EVALUATION_NEEDS_PROMPT = `
请同时在 searchCriteria 中输出 evaluationNeeds，用来控制是否启用额外判断流程。
只有 Brief 明确提到对应维度时才置 true，不要默认开启。
格式：{
  cpmCpe?: boolean,
  commercialStability?: boolean,
  recentPerformance?: boolean,
  updateFrequency?: boolean,
  commentQuality?: boolean,
  publicSentiment?: boolean,
  audienceGender?: boolean,
  reasons?: string[]
}
`;

export async function analyzeBrief(briefText: string): Promise<BriefAnalysisResult> {
  if (!config.llm.apiKey) {
    console.warn('[LLM] No API key configured, using fallback analysis');
    return fallbackAnalysis(briefText);
  }

  try {
    console.log('[LLM] 发起请求:', { url: `${config.llm.baseUrl}/chat/completions`, model: config.llm.model, briefLength: briefText.length });

    const response = await fetch(`${config.llm.baseUrl}/chat/completions`, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        Authorization: `Bearer ${config.llm.apiKey}`,
      },
      body: JSON.stringify({
        model: config.llm.model,
        messages: [
          { role: 'system', content: '你是专业的媒介策划AI助手，擅长从Brief文档中提取结构化投放需求。你的回复必须是纯JSON格式，不要包含markdown代码块标记。' },
          { role: 'user', content: BRIEF_ANALYSIS_PROMPT + EVALUATION_NEEDS_PROMPT + briefText },
        ],
        temperature: 0.3,
      }),
    });

    if (!response.ok) {
      const errorBody = await response.text();
      console.error(`[LLM] API error: ${response.status} ${response.statusText}`, errorBody);
      return fallbackAnalysis(briefText);
    }

    const data = await response.json();
    const content = data.choices?.[0]?.message?.content;

    if (!content) {
      return fallbackAnalysis(briefText);
    }

    // 提取 JSON（可能被 markdown 代码块包裹）
    let jsonStr = content.trim();
    const codeBlockMatch = jsonStr.match(/```(?:json)?\s*([\s\S]*?)```/);
    if (codeBlockMatch) {
      jsonStr = codeBlockMatch[1].trim();
    }

    const parsed = JSON.parse(jsonStr);
    const result = {
      requirements: parsed.requirements || [],
      searchCriteria: parsed.searchCriteria || { platforms: [], keywords: [], fanRange: { min: 10000, max: 500000 }, budgetRange: { min: 2000, max: 20000 } },
    };
    result.searchCriteria.targetCount = normalizeTargetCount(result.searchCriteria.targetCount) || inferTargetCount(briefText, result.requirements);
    result.searchCriteria.regionalBudgetRules = normalizeRegionalBudgetRules(
      result.searchCriteria.regionalBudgetRules,
      briefText,
    );
    result.searchCriteria.evaluationNeeds = normalizeEvaluationNeeds(
      result.searchCriteria.evaluationNeeds,
      briefText,
      result.requirements,
    );
    console.log('[LLM] 解析结果:', JSON.stringify(result, null, 2));
    return result;
  } catch (error) {
    console.error('[LLM] Analysis failed:', error);
    return fallbackAnalysis(briefText);
  }
}

function fallbackAnalysis(briefText: string): BriefAnalysisResult {
  // 简单的关键词匹配作为降级方案
  const platforms: string[] = [];
  if (briefText.includes('小红书') || briefText.includes('红书')) platforms.push('xiaohongshu');
  if (briefText.includes('抖音') || briefText.includes('TikTok')) platforms.push('douyin');
  if (briefText.includes('B站') || briefText.includes('哔哩哔哩')) platforms.push('bilibili');
  if (briefText.includes('微博')) platforms.push('weibo');
  if (briefText.includes('微信') || briefText.includes('公众号')) platforms.push('weixin');
  if (platforms.length === 0) platforms.push('xiaohongshu', 'douyin');

  const keywords: string[] = [];
  const keywordPatterns = ['护肤', '美妆', '母婴', '数码', '穿搭', '美食', '家居', '运动', '旅行', '教育'];
  for (const kw of keywordPatterns) {
    if (briefText.includes(kw)) keywords.push(kw);
  }
  if (keywords.length === 0) keywords.push('生活方式');

  return {
    requirements: [
      { label: '平台需求', value: platforms.join('、'), confidence: 75 },
      { label: '内容关键词', value: keywords.join('、'), confidence: 70 },
      { label: '预算范围', value: '2000-20000', confidence: 60 },
      { label: '粉丝范围', value: '1万-50万', confidence: 60 },
    ],
    searchCriteria: {
      platforms,
      keywords,
      fanRange: { min: 10000, max: 500000 },
      budgetRange: { min: 2000, max: 20000 },
      regionalBudgetRules: inferRegionalBudgetRules(briefText),
      targetCount: inferTargetCount(briefText, []),
      evaluationNeeds: inferEvaluationNeeds(briefText, []),
    },
  };
}

function normalizeRegionalBudgetRules(parsedRules: unknown, briefText: string): RegionalBudgetRule[] | undefined {
  const parsed = Array.isArray(parsedRules)
    ? parsedRules
        .map((item) => {
          if (typeof item !== 'object' || item === null) return null;
          const raw = item as Record<string, unknown>;
          const regions = toStringArray(raw.regions || raw.region || raw.cities || raw.city);
          const min = Number(raw.min || raw.minPrice || raw.budgetMin || 0);
          const max = Number(raw.max || raw.maxPrice || raw.budgetMax || 0);
          return regions.length > 0 && max > 0 ? { regions, min: Math.max(0, min), max } : null;
        })
        .filter((item): item is RegionalBudgetRule => item !== null)
    : [];
  const inferred = inferRegionalBudgetRules(briefText);
  const combined = [...parsed, ...inferred];
  const deduped = new Map<string, RegionalBudgetRule>();
  for (const rule of combined) {
    const regions = [...new Set(rule.regions.map((region) => region.trim()).filter(Boolean))];
    if (regions.length === 0 || rule.max <= 0) continue;
    const key = `${regions.sort().join('|')}:${rule.min}-${rule.max}`;
    deduped.set(key, { regions, min: rule.min, max: rule.max });
  }
  return deduped.size > 0 ? [...deduped.values()] : undefined;
}

function inferRegionalBudgetRules(briefText: string): RegionalBudgetRule[] {
  const cityNames = [
    '北京', '上海', '广州', '深圳', '成都', '杭州', '武汉', '西安', '南京', '重庆',
    '天津', '苏州', '长沙', '郑州', '青岛', '厦门', '宁波', '合肥', '福州', '无锡',
  ];
  const rules: RegionalBudgetRule[] = [];
  const pattern = /([^\n。；;：:]{1,80})[：:][^\n。；;]{0,30}?(\d{3,6})\s*(?:-|~|—|至|到)\s*(\d{3,6})/g;
  for (const match of briefText.matchAll(pattern)) {
    const regions = cityNames.filter((city) => match[1].includes(city));
    if (regions.length === 0) continue;
    const min = Number(match[2]);
    const max = Number(match[3]);
    if (Number.isFinite(min) && Number.isFinite(max) && max > 0) {
      rules.push({ regions, min: Math.min(min, max), max: Math.max(min, max) });
    }
  }
  return rules;
}

function normalizeEvaluationNeeds(
  parsedNeeds: unknown,
  briefText: string,
  requirements: Array<{ label: string; value: string; confidence: number }>
): EvaluationNeeds {
  const inferred = inferEvaluationNeeds(briefText, requirements);
  const parsed = typeof parsedNeeds === 'object' && parsedNeeds !== null
    ? parsedNeeds as Record<string, unknown>
    : {};
  const reasons = [
    ...toStringArray(parsed.reasons),
    ...(inferred.reasons || []),
  ];

  return {
    cpmCpe: Boolean(parsed.cpmCpe) || Boolean(inferred.cpmCpe),
    commercialStability: Boolean(parsed.commercialStability) || Boolean(inferred.commercialStability),
    recentPerformance: Boolean(parsed.recentPerformance) || Boolean(inferred.recentPerformance),
    updateFrequency: Boolean(parsed.updateFrequency) || Boolean(inferred.updateFrequency),
    commentQuality: Boolean(parsed.commentQuality) || Boolean(inferred.commentQuality),
    publicSentiment: Boolean(parsed.publicSentiment) || Boolean(inferred.publicSentiment),
    audienceGender: Boolean(parsed.audienceGender) || Boolean(inferred.audienceGender),
    reasons: [...new Set(reasons)].slice(0, 12),
  };
}

function inferEvaluationNeeds(
  briefText: string,
  requirements: Array<{ label: string; value: string; confidence: number }>
): EvaluationNeeds {
  const text = `${briefText}\n${requirements.map((item) => `${item.label}:${item.value}`).join('\n')}`.toLowerCase();
  const reasons: string[] = [];
  const hasAny = (...terms: string[]) => terms.some((term) => text.includes(term.toLowerCase()));
  const mark = (enabled: boolean, reason: string) => {
    if (enabled) reasons.push(reason);
    return enabled;
  };

  return {
    cpmCpe: mark(hasAny('cpm', 'cpe', '千次曝光', '互动成本'), 'Brief 提到 CPM/CPE 或投放成本效率'),
    commercialStability: mark(hasAny('商单', '蒲公英', '合作数据', '广告数据', '含广'), 'Brief 提到蒲公英/商单/广告数据'),
    recentPerformance: mark(hasAny('近期数据', '近30', '最近30', '数据下降', '互动量落差', '稳定性'), 'Brief 提到近期表现或数据稳定性'),
    updateFrequency: mark(hasAny('更新率', '更新频率', '低更新', '活跃度', 'low active'), 'Brief 提到更新频率或活跃度'),
    commentQuality: mark(hasAny('评论区', '良性互动', '评论质量', '评论'), 'Brief 提到评论区质量'),
    publicSentiment: mark(hasAny('舆情', '负面', '黑料', '争议', '翻车'), 'Brief 提到舆情或负面风险'),
    audienceGender: mark(hasAny('粉丝男女', '男女占比', '女性占比', '粉丝性别', '品牌ta'), 'Brief 提到粉丝性别画像'),
    reasons,
  };
}

function toStringArray(value: unknown): string[] {
  if (Array.isArray(value)) return value.map(String).filter(Boolean);
  if (typeof value === 'string') return value.split(/[、，,\s/]+/).map((item) => item.trim()).filter(Boolean);
  return [];
}

function normalizeTargetCount(value: unknown): number | undefined {
  const count = Number(value || 0);
  return count > 0 ? Math.ceil(count) : undefined;
}

function inferTargetCount(
  briefText: string,
  requirements: Array<{ label: string; value: string; confidence: number }>
): number | undefined {
  const text = `${briefText}\n${requirements.map((item) => `${item.label}:${item.value}`).join('\n')}`;

  const perCityMatch = text.match(/(\d+)\s*(?:个|座)?城市[\s\S]{0,40}?(\d+)\s*位\s*\/\s*城市/);
  if (perCityMatch) {
    return Number(perCityMatch[1]) * Number(perCityMatch[2]);
  }

  const cityCountMatch = text.match(/(\d+)\s*(?:个|座)?城市/);
  const perCityLooseMatch = text.match(/每(?:个)?城市\s*(\d+)\s*位|(\d+)\s*位\s*\/\s*城市/);
  if (cityCountMatch && perCityLooseMatch) {
    return Number(cityCountMatch[1]) * Number(perCityLooseMatch[1] || perCityLooseMatch[2]);
  }

  const directMatch = text.match(/(?:达人|博主|KOL|账号|人数)[^\d]{0,10}(\d+)\s*位|(\d+)\s*位\s*(?:达人|博主|KOL|账号)/i);
  if (directMatch) {
    return Number(directMatch[1] || directMatch[2]);
  }

  return undefined;
}