import json
from collections.abc import AsyncGenerator

import httpx
from sqlalchemy.ext.asyncio import AsyncSession

from app.models import Agent, LLMModel, LLMProvider


class LLMService:
    def __init__(self, db: AsyncSession):
        self.db = db

    async def complete(self, agent: Agent, user_message: str, history: list[dict] | None = None) -> str:
        if not agent.model_id:
            return self._fallback_answer(agent, user_message)
        model = await self.db.get(LLMModel, agent.model_id)
        if not model:
            return self._fallback_answer(agent, user_message)
        provider = await self.db.get(LLMProvider, model.provider_id)
        if not provider or provider.status != "enabled" or not provider.api_key or not provider.base_url:
            return self._fallback_answer(agent, user_message)

        messages = [{"role": "system", "content": agent.system_prompt or f"你是 {agent.name}。"}]
        messages.extend(history or [])
        messages.append({"role": "user", "content": user_message})
        url = provider.base_url.rstrip("/") + "/chat/completions"
        payload = {
            "model": model.name,
            "messages": messages,
            "temperature": agent.temperature,
            "max_tokens": agent.max_tokens,
            "stream": False,
        }
        async with httpx.AsyncClient(timeout=60) as client:
            response = await client.post(
                url,
                headers={"Authorization": f"Bearer {provider.api_key}", "Content-Type": "application/json"},
                json=payload,
            )
            response.raise_for_status()
            data = response.json()
            return data.get("choices", [{}])[0].get("message", {}).get("content") or ""

    async def stream(self, agent: Agent, user_message: str, history: list[dict] | None = None) -> AsyncGenerator[str, None]:
        answer = await self.complete(agent, user_message, history)
        for chunk in self._chunk_text(answer):
            yield chunk

    def _fallback_answer(self, agent: Agent, user_message: str) -> str:
        prompt = agent.system_prompt.strip() or "轻量 AI Agent"
        return (
            f"{agent.name} 已收到任务：{user_message}\n\n"
            f"当前使用本地占位响应。配置可用的 LLM Provider 和 Model 后，将自动调用真实模型。\n\n"
            f"系统提示词摘要：{prompt[:160]}"
        )

    def _chunk_text(self, text: str) -> list[str]:
        if not text:
            return [""]
        return [text[i : i + 24] for i in range(0, len(text), 24)]


def sse(data: dict | str) -> str:
    if isinstance(data, str):
        payload = data
    else:
        payload = json.dumps(data, ensure_ascii=False, default=str)
    return f"data: {payload}\n\n"