"""LLM service for AI-powered features."""
from typing import List, Dict, Any, Optional
import httpx

from app.core.config import settings


class LLMService:
    """Service for LLM API interactions."""

    def __init__(self):
        self.api_base = settings.LLM_API_BASE
        self.api_key = settings.LLM_API_KEY
        self.model = settings.LLM_MODEL

    async def chat_completion(
        self,
        messages: List[Dict[str, str]],
        temperature: float = 0.7,
        max_tokens: int = 2000,
    ) -> str:
        """Get chat completion from LLM."""
        if not self.api_key:
            # Return mock response for testing
            return "这是一个模拟的法律回复。"

        async with httpx.AsyncClient() as client:
            response = await client.post(
                f"{self.api_base}/chat/completions",
                headers={
                    "Authorization": f"Bearer {self.api_key}",
                    "Content-Type": "application/json",
                },
                json={
                    "model": self.model,
                    "messages": messages,
                    "temperature": temperature,
                    "max_tokens": max_tokens,
                },
                timeout=60.0,
            )
            response.raise_for_status()
            data = response.json()
            return data["choices"][0]["message"]["content"]

    async def legal_qa(
        self,
        question: str,
        context: Optional[str] = None,
    ) -> str:
        """Answer a legal question."""
        system_prompt = """你是一个专业的法律助手。请根据以下原则回答问题：
1. 准确引用相关法律条文
2. 解释法律条文的含义和适用条件
3. 提供实用的法律建议
4. 如不确定，请明确说明"""

        messages = [
            {"role": "system", "content": system_prompt},
        ]

        if context:
            messages.append({
                "role": "user",
                "content": f"参考以下法律内容：\n{context}\n\n问题：{question}"
            })
        else:
            messages.append({"role": "user", "content": question})

        return await self.chat_completion(messages)

    async def analyze_legal_issue(
        self,
        issue_description: str,
        relevant_laws: Optional[List[str]] = None,
    ) -> str:
        """Analyze a legal issue and provide insights."""
        system_prompt = """你是一个专业的法律分析师。请根据以下结构分析法律问题：
1. 问题定性
2. 相关法律依据
3. 法律分析
4. 风险提示
5. 建议"""

        user_content = f"请分析以下法律问题：\n{issue_description}"

        if relevant_laws:
            user_content += f"\n\n相关法律：\n" + "\n".join(relevant_laws)

        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_content},
        ]

        return await self.chat_completion(messages, temperature=0.5)

    async def review_contract(
        self,
        contract_content: str,
        contract_type: Optional[str] = None,
    ) -> str:
        """Review a contract and identify potential issues."""
        system_prompt = """你是一个专业的合同审查专家。请审查合同并：
1. 识别潜在风险条款
2. 指出不明确的条款
3. 提出修改建议
4. 检查法律合规性"""

        user_content = f"请审查以下合同内容：\n{contract_content}"

        if contract_type:
            user_content = f"合同类型：{contract_type}\n\n{user_content}"

        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_content},
        ]

        return await self.chat_completion(messages, max_tokens=3000)


# Singleton instance
llm_service = LLMService()