from fastapi import APIRouter, Depends, HTTPException
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from typing import Optional, Literal
import aiosqlite, json, logging
import httpx
from database import get_db
from config import settings
from services.ai_client import get_openai_client
from services.runtime_settings import get_ai_settings
from services.media_parser import parse_media

router = APIRouter(prefix="/api", tags=["ai"])
log = logging.getLogger(__name__)


async def _get_ai_client():
    return await get_openai_client()


class SummarizeRequest(BaseModel):
    context: str          # 已组装好的对话文本（含媒体描述）
    room_name: Optional[str] = ""
    messages: Optional[list] = None   # 兼容旧调用，忽略


class ParseRequest(BaseModel):
    type: Literal["voice", "image", "video"]
    key: str   # voice: ServerID string; image/video: md5


@router.post("/ai/parse")
async def ai_parse(body: ParseRequest):
    """
    通过 FastAPI 代理 AI 媒体解析：
    - voice: 从 chatlog 下载音频 → DashScope Paraformer ASR 转文字
    - image/video: 从 chatlog 下载媒体 → base64 → 视觉模型描述
    """
    try:
        return await parse_media(body.type, body.key)
    except HTTPException:
        raise
    except Exception as e:
        log.error(f"[ai/parse] 媒体解析失败: {e}", exc_info=True)
        raise HTTPException(500, f"媒体解析失败: {e}")


@router.post("/ai/summarize/stream")
async def summarize_stream(body: SummarizeRequest):
    """
    接收前端已处理好的对话上下文，调用 AI 模型流式输出总结。
    前端负责先把媒体（图片/语音/视频）解析成文字再拼进 context。
    """
    _ai = await get_ai_settings()
    if not _ai.get("ai_api_key"):
        async def err_gen():
            yield 'data: {"error": "AI 服务未配置，请在「设置」页面填入 AI API Key"}\n\n'
        return StreamingResponse(err_gen(), media_type="text/event-stream")
    if not _ai.get("ai_model"):
        async def err_gen():
            yield 'data: {"error": "知识总结模型未配置，请在「设置」页面填入模型名称（如 qwen-max）"}\n\n'
        return StreamingResponse(err_gen(), media_type="text/event-stream")

    context = body.context.strip()
    if not context:
        async def err_gen():
            yield 'data: {"error": "对话内容为空"}\n\n'
        return StreamingResponse(err_gen(), media_type="text/event-stream")

    room = body.room_name or "会话"

    system_prompt = (
        "你是一位专业的对话分析助手。"
        "请根据提供的聊天记录（可能包含图片描述、语音转文字、视频描述等多媒体内容）"
        "生成一份结构清晰的 Markdown 总结。"
        "总结应包含：主要话题、关键信息点、媒体内容要点、待办事项（如有）。"
        "只输出 Markdown 格式内容，不要有任何额外说明。"
    )
    user_prompt = (
        f"群聊：{room}\n\n"
        f"以下是聊天记录（含多媒体内容描述）：\n\n"
        f"{context[:12000]}\n\n"  # 限制 token 数
        f"请生成总结："
    )

    async def generate():
        try:
            _client, _ai = await _get_ai_client()
            stream = await _client.chat.completions.create(
                model=_ai["ai_model"],
                messages=[
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": user_prompt},
                ],
                stream=True,
                temperature=0.3,
            )
            async for chunk in stream:
                delta = chunk.choices[0].delta.content if chunk.choices else None
                if delta:
                    yield f"data: {json.dumps({'delta': delta}, ensure_ascii=False)}\n\n"
            yield 'data: {"done": true}\n\n'
        except Exception as e:
            log.error(f"[summarize/stream] LLM 调用失败: {e}", exc_info=True)
            yield f"data: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"

    return StreamingResponse(generate(), media_type="text/event-stream")


@router.get("/tasks/{task_id}")
async def get_task(task_id: int, db: aiosqlite.Connection = Depends(get_db)):
    async with db.execute("SELECT * FROM ai_tasks WHERE id=?", (task_id,)) as cur:
        row = await cur.fetchone()
    if not row:
        raise HTTPException(404, "not found")
    return dict(row)