"""
OpenRouter API 服務
用於進階自然語言處理功能
"""

import httpx
import json
from typing import Dict, Any, Optional
from backend.config import settings
import logging

logger = logging.getLogger(__name__)

class OpenRouterService:
    """OpenRouter API 服務類"""
    
    def __init__(self):
        self.api_key = settings.OPENROUTER_API_KEY
        self.model = settings.OPENROUTER_MODEL
        self.base_url = "https://openrouter.ai/api/v1"
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json",
            "HTTP-Referer": "https://huggingface.co/spaces",
            "X-Title": "LINE Bot NLP Service"
        }
    
    async def analyze_intent_advanced(self, message: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
        """
        使用 OpenRouter 進行進階意圖分析
        
        Args:
            message: 用戶訊息
            context: 上下文資訊
            
        Returns:
            分析結果字典
        """
        if not self.api_key:
            logger.warning("OpenRouter API Key 未設定，使用基礎 NLP 服務")
            return self._fallback_analysis(message)
        
        try:
            prompt = self._build_analysis_prompt(message, context)
            
            async with httpx.AsyncClient(timeout=30.0) as client:
                response = await client.post(
                    f"{self.base_url}/chat/completions",
                    headers=self.headers,
                    json={
                        "model": self.model,
                        "messages": [
                            {
                                "role": "system",
                                "content": "你是一個專業的中文自然語言處理助手，專門分析用戶查詢意圖並提取相關實體。請以 JSON 格式回應。"
                            },
                            {
                                "role": "user",
                                "content": prompt
                            }
                        ],
                        "temperature": 0.1,
                        "max_tokens": 500
                    }
                )
            
            if response.status_code == 200:
                result = response.json()
                content = result["choices"][0]["message"]["content"]
                
                try:
                    # 嘗試解析 JSON 回應
                    analysis = json.loads(content)
                    return self._validate_analysis_result(analysis)
                except json.JSONDecodeError:
                    logger.error(f"無法解析 OpenRouter 回應: {content}")
                    return self._fallback_analysis(message)
            else:
                logger.error(f"OpenRouter API 錯誤: {response.status_code}")
                return self._fallback_analysis(message)
                
        except Exception as e:
            logger.error(f"OpenRouter 服務錯誤: {str(e)}")
            return self._fallback_analysis(message)
    
    def _build_analysis_prompt(self, message: str, context: Dict[str, Any] = None) -> str:
        """建構分析提示詞"""
        context_info = ""
        if context:
            context_info = f"\n上下文資訊: {json.dumps(context, ensure_ascii=False)}"
        
        prompt = f"""
請分析以下中文訊息的意圖和實體：

訊息: "{message}"{context_info}

請以以下 JSON 格式回應：
{{
    "intent": "查詢意圖類型 (search_user/search_product/search_order/create_order/update_profile/analytics/unknown)",
    "confidence": 0.0-1.0之間的信心度,
    "entities": {{
        "user_id": "提取的用戶ID",
        "user_name": "提取的用戶名稱",
        "product_name": "提取的商品名稱",
        "order_id": "提取的訂單ID",
        "min_price": 最低價格數字,
        "max_price": 最高價格數字,
        "category": "商品類別",
        "number": 數量
    }},
    "query_type": "search/create/update/delete/analytics",
    "parameters": {{
        "table": "目標資料表名稱",
        "conditions": "查詢條件",
        "limit": 查詢限制數量
    }}
}}

範例：
- "查詢用戶張三" → intent: "search_user", entities: {{"user_name": "張三"}}
- "找價格1000到5000的手機" → intent: "search_product", entities: {{"min_price": 1000, "max_price": 5000, "category": "手機"}}
- "統計訂單數量" → intent: "analytics", query_type: "analytics"
"""
        return prompt
    
    def _validate_analysis_result(self, analysis: Dict[str, Any]) -> Dict[str, Any]:
        """驗證分析結果"""
        # 確保必要欄位存在
        required_fields = ["intent", "confidence", "entities", "query_type"]
        for field in required_fields:
            if field not in analysis:
                analysis[field] = self._get_default_value(field)
        
        # 驗證信心度範圍
        if not 0 <= analysis.get("confidence", 0) <= 1:
            analysis["confidence"] = 0.5
        
        # 確保實體是字典
        if not isinstance(analysis.get("entities"), dict):
            analysis["entities"] = {}
        
        return analysis
    
    def _get_default_value(self, field: str) -> Any:
        """取得欄位預設值"""
        defaults = {
            "intent": "unknown",
            "confidence": 0.1,
            "entities": {},
            "query_type": "unknown",
            "parameters": {}
        }
        return defaults.get(field, None)
    
    def _fallback_analysis(self, message: str) -> Dict[str, Any]:
        """備用分析方法"""
        return {
            "intent": "unknown",
            "confidence": 0.1,
            "entities": {},
            "query_type": "unknown",
            "parameters": {},
            "fallback": True
        }
    
    async def generate_response(self, query_result: Dict[str, Any], user_message: str) -> str:
        """
        使用 OpenRouter 生成更自然的回應
        
        Args:
            query_result: 資料庫查詢結果
            user_message: 用戶原始訊息
            
        Returns:
            生成的回應文字
        """
        if not self.api_key:
            return self._generate_simple_response(query_result)
        
        try:
            prompt = f"""
請根據以下資訊生成一個友善、自然的中文回應：

用戶訊息: "{user_message}"
查詢結果: {json.dumps(query_result, ensure_ascii=False)}

要求：
1. 回應要簡潔明瞭
2. 使用友善的語調
3. 如果有資料，要清楚呈現重要資訊
4. 如果沒有資料，要給出建議
5. 回應長度控制在 200 字以內

範例格式：
- 找到資料時：「找到 X 筆資料：[列出重要資訊]」
- 沒有資料時：「很抱歉，沒有找到相關資料，您可以嘗試...」
"""
            
            async with httpx.AsyncClient(timeout=30.0) as client:
                response = await client.post(
                    f"{self.base_url}/chat/completions",
                    headers=self.headers,
                    json={
                        "model": self.model,
                        "messages": [
                            {
                                "role": "system",
                                "content": "你是一個友善的客服助手，專門幫助用戶理解查詢結果。"
                            },
                            {
                                "role": "user",
                                "content": prompt
                            }
                        ],
                        "temperature": 0.3,
                        "max_tokens": 300
                    }
                )
            
            if response.status_code == 200:
                result = response.json()
                generated_response = result["choices"][0]["message"]["content"].strip()
                return generated_response
            else:
                logger.error(f"OpenRouter 回應生成錯誤: {response.status_code}")
                return self._generate_simple_response(query_result)
                
        except Exception as e:
            logger.error(f"OpenRouter 回應生成失敗: {str(e)}")
            return self._generate_simple_response(query_result)
    
    def _generate_simple_response(self, query_result: Dict[str, Any]) -> str:
        """簡單回應生成"""
        if query_result.get("success"):
            data_count = len(query_result.get("data", []))
            if data_count > 0:
                return f"找到 {data_count} 筆相關資料。"
            else:
                return "沒有找到相關資料。"
        else:
            return "查詢時發生錯誤，請稍後再試。"