"""
调试端点：POST /debug/chat, GET /debug/rag
"""

import json
import time

from fastapi import APIRouter, Query
from fastapi.responses import StreamingResponse

from schemas.chat import DebugChatRequest
from services.local_llm_service import local_llm_service
from services.rag_service import rag_service

router = APIRouter(prefix="/debug", tags=["调试"])


@router.post(
    "/chat",
    summary="调试 LLM 对话",
    description=(
        "直接向 LLM 发送消息，响应为纯文本流（非 SSE）。\n\n"
        "完成后会在**服务端终端**输出本次对话完整的 `history` JSON，"
        "可复制后粘贴到下次请求的 `history` 字段继续对话。\n\n"
        "⚠️ 仅用于本地开发调试，无鉴权。"
    ),
)
async def debug_chat(request: DebugChatRequest):
    current_messages = [
        {"role": message.role, "content": message.content} for message in request.history
    ]
    current_messages.append({"role": "user", "content": request.question})

    start_time = time.time()
    rag_context = await rag_service.retrieve(request.question)
    stream_iterator = local_llm_service.chat_stream(
        history_messages=current_messages,
        rag_context=rag_context,
    )

    def generate_text():
        full_ai_response = ""
        total_usage = None

        for chunk in stream_iterator:
            if chunk is None:
                continue

            choices = getattr(chunk, "choices", None) or []
            if choices:
                delta = getattr(choices[0], "delta", None)
                content = getattr(delta, "content", None)
                if content:
                    full_ai_response += content
                    yield content

            usage = getattr(chunk, "usage", None)
            if usage:
                total_usage = usage

        print(f"DEBUG: LLM 调用耗时: {time.time() - start_time:.2f}s")
        if total_usage:
            print(
                "🎫 Token 统计: "
                f"Total={total_usage.total_tokens} "
                f"(P:{total_usage.prompt_tokens}, C:{total_usage.completion_tokens})"
            )

        new_history = [
            {"role": message.role, "content": message.content}
            for message in request.history
        ]
        new_history.append({"role": "user", "content": request.question})
        new_history.append({"role": "assistant", "content": full_ai_response})

        print("\n" + "=" * 50)
        print("🐞 调试完成！以下是可用于下次请求的 history 结构：")
        print(json.dumps({"history": new_history}, ensure_ascii=False, indent=2))
        print("=" * 50 + "\n")

    return StreamingResponse(generate_text(), media_type="text/plain")


@router.get(
    "/rag",
    summary="调试 RAG 知识库检索",
    description=(
        "对知识库执行一次检索，返回原始检索上下文内容，用于验证 RAG 效果。\n\n"
        "⚠️ 仅用于本地开发调试，无鉴权。"
    ),
)
async def debug_rag(query: str = Query(..., description="检索问题")):
    if not query:
        return {"error": "请提供 query 参数"}

    print(f"🔍 [Debug] 正在检索知识库: {query}")
    context = await rag_service.retrieve(query)
    return {
        "query": query,
        "retrieved_context": context,
        "length": len(context) if context else 0,
        "status": "success" if context else "no_results_or_error",
    }