"""
Intent Classifier - Phase 13.3 #85
===================================
快速意圖分類，用於智能路由

目標: < 100ms 延遲
策略: 關鍵字優先 → 小模型備援

Phase 13.3 (2026-03-26): 初始實作
"""

import re
from enum import Enum

import structlog

logger = structlog.get_logger(__name__)


class IntentType(Enum):
    """意圖類型"""

    ALERT_TRIAGE = "alert_triage"  # 告警分流/處理
    DEPLOYMENT = "deployment"  # 部署操作 (kubectl, rollout)
    QUERY = "query"  # 資訊查詢 (狀態, 日誌)
    MAINTENANCE = "maintenance"  # 維運操作 (重啟, 擴容)
    CODE_REVIEW = "code_review"  # 程式碼審查
    UNKNOWN = "unknown"


# 關鍵字映射 (優先匹配，0ms)
INTENT_KEYWORDS: dict[IntentType, list[str]] = {
    IntentType.ALERT_TRIAGE: [
        "alert", "告警", "警報", "異常", "error", "critical", "warning",
        "高負載", "high cpu", "memory", "oom", "crash", "down",
    ],
    IntentType.DEPLOYMENT: [
        "deploy", "部署", "rollout", "kubectl apply", "helm", "release",
        "版本", "upgrade", "更新", "上線",
    ],
    IntentType.QUERY: [
        "查詢", "狀態", "status", "describe", "get", "list", "日誌", "log",
        "哪個", "什麼", "how many", "多少",
    ],
    IntentType.MAINTENANCE: [
        "restart", "重啟", "scale", "擴容", "縮容", "rollback", "回滾",
        "維護", "maintenance", "patch", "修補",
    ],
    IntentType.CODE_REVIEW: [
        "review", "審查", "pr", "pull request", "commit", "diff",
        "程式碼", "code", "merge",
    ],
}


class IntentClassifier:
    """
    意圖分類器

    使用兩階段分類策略:
    1. 關鍵字快速匹配 (0ms)
    2. 小模型 LLM 分類 (< 100ms) - 備援
    """

    # 小模型，低延遲
    MODEL = "qwen2.5:1b"

    def __init__(self):
        self._keyword_cache: dict[str, IntentType] = {}

    async def classify(self, text: str) -> IntentType:
        """
        分類意圖

        Args:
            text: 用戶輸入或告警內容

        Returns:
            IntentType: 分類結果
        """
        text_lower = text.lower()

        # 階段 1: 關鍵字快速匹配 (0ms)
        intent = self._keyword_match(text_lower)
        if intent != IntentType.UNKNOWN:
            logger.debug(
                "intent_classified_by_keyword",
                intent=intent.value,
                text_preview=text[:50],
            )
            return intent

        # 階段 2: LLM 分類 (< 100ms)
        # 目前先用關鍵字，LLM 整合待 Qwen 1B 部署
        logger.debug(
            "intent_fallback_to_unknown",
            text_preview=text[:50],
        )
        return IntentType.UNKNOWN

    def _keyword_match(self, text: str) -> IntentType:
        """關鍵字匹配"""
        # 檢查快取
        cache_key = text[:100]
        if cache_key in self._keyword_cache:
            return self._keyword_cache[cache_key]

        # 計算每個意圖的匹配分數
        scores: dict[IntentType, int] = {}

        for intent, keywords in INTENT_KEYWORDS.items():
            score = 0
            for keyword in keywords:
                if keyword in text:
                    score += 1
                    # 完整匹配加分
                    if re.search(rf"\b{re.escape(keyword)}\b", text):
                        score += 1
            if score > 0:
                scores[intent] = score

        if not scores:
            return IntentType.UNKNOWN

        # 選擇最高分
        best_intent = max(scores, key=lambda k: scores[k])

        # 快取結果
        self._keyword_cache[cache_key] = best_intent

        return best_intent

    def classify_sync(self, text: str) -> IntentType:
        """同步版本 (僅關鍵字匹配)"""
        return self._keyword_match(text.lower())


# 單例
_classifier: IntentClassifier | None = None


def get_intent_classifier() -> IntentClassifier:
    """取得 IntentClassifier 單例"""
    global _classifier
    if _classifier is None:
        _classifier = IntentClassifier()
    return _classifier