import { z } from "zod";
import type { MemoryCategory, MemorySourceType } from "@adventureos/shared";
import { MEMORY_CATEGORIES, SENSITIVE_MEMORY_CATEGORIES } from "@adventureos/shared";
import { parseAiJson } from "@/lib/ai/parse-json";
import { getAiBehaviorConfig, getAiProviderConfig, generateTextWithFallback, buildSystemPrompt } from "./ai-config";
import { createSuggestion, getMemoryLearningSettings } from "./ai-memory";

const candidateSchema = z.object({
  candidates: z
    .array(
      z.object({
        category: z.string(),
        title: z.string(),
        content: z.string(),
        confidence: z.coerce.number(),
        sensitivity: z.enum(["normal", "private", "sensitive"]).optional(),
      })
    )
    .max(2),
});

export type MemorySuggestionCandidate = {
  category: MemoryCategory;
  title: string;
  content: string;
  confidence: number;
};

type PatternRule = {
  category: MemoryCategory;
  confidence: number;
  patterns: RegExp[];
  titlePrefix?: string;
  content: (value: string) => string;
};

const FILLER_PREFIX = /^(?:that\s+|to\s+|i\s+|i'm\s+|i am\s+|my\s+)/i;

const RULES: PatternRule[] = [
  {
    category: "current_goals",
    confidence: 0.9,
    patterns: [
      /^(?:one of my goals|my current goal|my goal|a goal of mine)(?:\s+for\s+.+?)?\s+is\s+(.+)$/i,
      /^i want to\s+(.+)$/i,
      /^i'm trying to\s+(.+)$/i,
      /^i am trying to\s+(.+)$/i,
      /^i want to be more\s+(.+)$/i,
      /^my current focus is\s+(.+)$/i,
    ],
    content: (value) => `One of the user's current goals is to ${ensureVerbPhrase(value)}.`,
  },
  {
    category: "likes",
    confidence: 0.85,
    patterns: [/^i like\s+(.+)$/i],
    content: (value) => `The user likes ${lowerFirst(value)}.`,
  },
  {
    category: "dislikes",
    confidence: 0.85,
    patterns: [/^i dislike\s+(.+)$/i, /^i don't like\s+(.+)$/i, /^i do not like\s+(.+)$/i],
    content: (value) => `The user dislikes ${lowerFirst(value)}.`,
  },
  {
    category: "ai_tone",
    confidence: 0.9,
    patterns: [/^i prefer\s+(short explanations|direct explanations|concise explanations|brief explanations|.+\s+explanations)$/i, /^i want the ai to\s+(.+)$/i],
    content: (value) =>
      /\bexplanations\b/i.test(value)
        ? `The user prefers ${lowerFirst(value)}.`
        : `The user wants the AI to ${ensureVerbPhrase(value)}.`,
  },
  {
    category: "worries",
    confidence: 0.8,
    patterns: [/^i get worried when\s+(.+)$/i, /^i'm worried about\s+(.+)$/i, /^i am worried about\s+(.+)$/i],
    content: (value) => `The user has a worry or concern about ${lowerFirst(value)}.`,
  },
  {
    category: "personal_context",
    confidence: 0.75,
    patterns: [/^i struggle with\s+(.+)$/i, /^i need help with\s+(.+)$/i],
    content: (value) => `The user needs support with ${lowerFirst(value)}.`,
  },
  {
    category: "personal_context",
    confidence: 0.95,
    patterns: [/^(?:remember that|save this:?|add this to memory:?)(.+)$/i],
    titlePrefix: "Remember",
    content: (value) => `The user explicitly asked the app to remember that ${stripTrailingPunctuation(value)}.`,
  },
];

const LEARNING_HINTS = /\b(learn|learning|examples?|explanations?|teach|lesson)\b/i;
const EXERCISE_HINTS = /\b(exercise|work out|workout|run|gym|walk|train)\b/i;
const UNSPECIFIC_MESSAGES = /^(hello|hi|hey|thanks|thank you|what can you do\??|that's interesting|that is interesting)$/i;

export function extractDeterministicMemoryCandidates(sourceText: string): MemorySuggestionCandidate[] {
  const normalized = normalizeInput(sourceText);
  if (!normalized || normalized.length < 8 || UNSPECIFIC_MESSAGES.test(normalized)) return [];

  for (const rule of RULES) {
    for (const pattern of rule.patterns) {
      const match = normalized.match(pattern);
      const rawValue = match?.[1]?.trim();
      if (!rawValue) continue;

      const value = cleanValue(rawValue);
      if (!isDurableEnough(value)) return [];

      const category = refineCategory(rule.category, normalized, value);
      return [
        {
          category,
          title: buildTitle(value, rule.titlePrefix),
          content: refineContent(rule.content(value), normalized),
          confidence: rule.confidence,
        },
      ];
    }
  }

  return [];
}

export async function extractMemoryCandidates(
  userId: string,
  sourceType: MemorySourceType,
  sourceText: string,
  sourceRef?: { type: string; id?: string; date?: string }
) {
  const settings = await getMemoryLearningSettings(userId);
  if (
    !settings.learningEnabled ||
    !settings.autoSuggestEnabled ||
    (sourceType === "chat" && !settings.suggestAfterChat)
  ) {
    return [];
  }

  const results = [];
  const deterministic = extractDeterministicMemoryCandidates(sourceText);
  for (const c of deterministic) {
    if (!settings.allowedCategories.includes(c.category)) continue;
    if (
      SENSITIVE_MEMORY_CATEGORIES.includes(c.category) &&
      !settings.allowSensitiveCategories
    ) {
      continue;
    }
    const row = await createSuggestion(userId, {
      category: c.category,
      title: c.title,
      content: c.content,
      sourceType,
      sourceRef,
      confidence: c.confidence,
    });
    if (row) results.push(row);
  }

  let behavior: Awaited<ReturnType<typeof getAiBehaviorConfig>>;
  let providerConfig: Awaited<ReturnType<typeof getAiProviderConfig>>;
  try {
    behavior = await getAiBehaviorConfig(userId);
    providerConfig = await getAiProviderConfig(userId);
  } catch {
    return results;
  }
  if (!behavior.enabled || !providerConfig.enabled) return results;

  const prompt = `From this user text, suggest 0-2 personal memory facts the app could remember (only clear patterns, not guesses).
User text:
"""
${sourceText.slice(0, 1500)}
"""
Use only these categories: ${Object.keys(MEMORY_CATEGORIES).join(", ")}.
Return JSON: { "candidates": [{ "category": "likes|motivators|current_goals|reading_preferences|learning_interests|worries|...", "title": "short label", "content": "one sentence fact about the user", "confidence": 0.0-1.0, "sensitivity": "normal|private|sensitive" }] }
If nothing clear, return { "candidates": [] }.`;

  try {
    const coreSystem = await getTemplateBody(userId);
    const res = await generateTextWithFallback(
      userId,
      {
        model: providerConfig.model,
        prompt,
        system: buildSystemPrompt(behavior, coreSystem),
        format: "json",
        temperature: 0.3,
        maxTokens: 400,
        timeoutMs: providerConfig.timeoutMs,
      },
      behavior
    );

    const parsed = candidateSchema.safeParse(parseAiJson(res.text));
    if (!parsed.success) return results;

    for (const c of parsed.data.candidates) {
      if (c.confidence < 0.6) continue;
      if (!settings.allowedCategories.includes(c.category as MemoryCategory)) continue;
      if (
        SENSITIVE_MEMORY_CATEGORIES.includes(c.category as MemoryCategory) &&
        !settings.allowSensitiveCategories
      ) {
        continue;
      }
      const row = await createSuggestion(userId, {
        category: c.category as MemoryCategory,
        title: c.title,
        content: c.content,
        sourceType,
        sourceRef,
        confidence: c.confidence,
      });
      if (row) results.push(row);
    }
    return results;
  } catch {
    return results;
  }
}

async function getTemplateBody(userId: string) {
  const { getTemplateBody: getTpl } = await import("./ai-templates");
  return getTpl(userId, "system_core");
}

function normalizeInput(value: string): string {
  return value.trim().replace(/[’‘]/g, "'").replace(/\s+/g, " ");
}

function cleanValue(value: string): string {
  return stripTrailingPunctuation(value.replace(FILLER_PREFIX, "").trim());
}

function stripTrailingPunctuation(value: string): string {
  return value.trim().replace(/[.!?]+$/g, "").trim();
}

function isDurableEnough(value: string): boolean {
  const lower = value.toLowerCase();
  if (value.length < 4) return false;
  if (/^(this|that|it|stuff|things|more|better)$/i.test(lower)) return false;
  return true;
}

function refineCategory(category: MemoryCategory, fullText: string, value: string): MemoryCategory {
  const combined = `${fullText} ${value}`;
  if (category === "likes" && LEARNING_HINTS.test(combined)) return "learning_interests";
  if (category === "current_goals" && EXERCISE_HINTS.test(combined)) return "exercise_preferences";
  return category;
}

function refineContent(content: string, fullText: string): string {
  if (/one of my goals/i.test(fullText) && /next coming weeks|next few weeks/i.test(fullText)) {
    return content.replace("current goals is to", "goals for the next few weeks is to");
  }
  return content;
}

function ensureVerbPhrase(value: string): string {
  const cleaned = lowerFirst(stripTrailingPunctuation(value));
  if (/^(be|build|wake|read|exercise|work|study|learn|sleep|get|start|stop|finish|practice|focus)\b/i.test(cleaned)) {
    return cleaned;
  }
  return cleaned;
}

function lowerFirst(value: string): string {
  return value.charAt(0).toLowerCase() + value.slice(1);
}

function buildTitle(value: string, prefix?: string): string {
  const clean = stripTrailingPunctuation(value)
    .replace(/\b7\s*a\.?\s*m\.?\b/gi, "7am")
    .replace(/\s+/g, " ")
    .trim();
  const title = clean
    .replace(/^(?:to\s+)/i, "")
    .split(" ")
    .slice(0, 8)
    .map((word, index) => {
      if (/^\d/.test(word)) return word;
      if (index > 0 && /^(at|to|with|for|and|or|the|a|an|of)$/i.test(word)) {
        return word.toLowerCase();
      }
      return word.charAt(0).toUpperCase() + word.slice(1).toLowerCase();
    })
    .join(" ");
  return prefix ? `${prefix}: ${title}` : title;
}