"""
===============================================================================
FILE: modules/web_api/routers/chat.py
ROLE: Роутер чата с чистым разделением:
      - Диалог и команды СКУД/1С (через agent.py).
      - Парсинг и извлечение документов без обрезания (через file_parser.py).
===============================================================================
"""

import os
import shutil
import logging
from typing import Optional

from fastapi import APIRouter, Header, HTTPException, UploadFile, File, Form
from pydantic import BaseModel

from llm.agent import process_chat_message
from llm.file_parser import parse_uploaded_file
from config import BASE_DIR

logger = logging.getLogger("CHAT_API")
router = APIRouter(prefix="/api/v1", tags=["Chat"])

UPLOAD_TMP_DIR = os.path.join(BASE_DIR, "data", "uploads")
os.makedirs(UPLOAD_TMP_DIR, exist_ok=True)


class ChatMessageRequest(BaseModel):
    message: str
    session_id: Optional[str] = "web_session_main"
    user_id: Optional[int] = 1


def resolve_user_id(authorization: Optional[str] = None, explicit_user_id: Optional[int] = None) -> int:
    if explicit_user_id and explicit_user_id > 0:
        return explicit_user_id

    if authorization and authorization.startswith("Bearer "):
        token = authorization.replace("Bearer ", "").strip()
        if token.isdigit():
            return int(token)
    return 1


@router.post("/chat")
async def chat_endpoint(payload: ChatMessageRequest, authorization: Optional[str] = Header(None)):
    user_id = resolve_user_id(authorization, payload.user_id)
    session_id = payload.session_id or "web_session_main"
    user_msg = payload.message.strip()

    if not user_msg:
        raise HTTPException(status_code=400, detail="Пустое сообщение")

    reply_text, history, action_payload = process_chat_message(
        user_id=user_id,
        user_message=user_msg,
        session_id=session_id
    )

    return {
        "status": "success",
        "user_id": user_id,
        "session_id": session_id,
        "response": reply_text,
        "action_payload": action_payload
    }


@router.post("/chat/upload")
async def chat_upload_endpoint(
    file: UploadFile = File(...),
    message: Optional[str] = Form(""),
    session_id: Optional[str] = Form("web_session_main"),
    authorization: Optional[str] = Header(None)
):
    user_id = resolve_user_id(authorization, 1)
    file_path = os.path.join(UPLOAD_TMP_DIR, file.filename)
    
    with open(file_path, "wb") as buffer:
        shutil.copyfileobj(file.file, buffer)

    user_msg = (message or "").strip()
    msg_lower = user_msg.lower()
    fn_lower = file.filename.lower()

    # ⭐️ 1. ПРЯМАЯ ИЗОЛИРОВАННАЯ ОБРАБОТКА PDF/СКАНОВ В WORD ЧЕРЕЗ OFFICE МОДУЛЬ
    ocr_keywords = [
        "распознай", "распознать", "в word", "в ворд", "для ворда", "для word", 
        "отформатируй", "текст для вставки", "извлеки текст", "сделай документ", "переведи в ворд"
    ]
    
    if fn_lower.endswith(".pdf") and (any(k in msg_lower for k in ocr_keywords) or not user_msg):
        logger.info(f"[Office] Прямой запуск распознавания PDF в Word: {file.filename}")
        from services.office.service import convert_pdf_to_word_service
        from modules.web_api.llm.db_tools import db_save_chat_message, db_get_chat_history

        # Сохраняем вопрос пользователя в историю
        prompt_text = user_msg or f"Распознать документ {file.filename} для MS Word"
        db_save_chat_message(session_id, "user", prompt_text, is_ephemeral=0)

        # Вызываем офисный сервис постраничного OCR и сборки DOCX
        office_res = convert_pdf_to_word_service(file_path, file.filename)

        reply_text = (
            f"📄 **{office_res['message']}**\n\n"
            f"**Фрагмент первой страницы документа:**\n"
            f"```text\n{office_res['preview_text']}...\n```\n\n"
            f"Файл готов к скачиванию и редактированию в MS Word."
        )
        db_save_chat_message(session_id, "assistant", reply_text, is_ephemeral=0)

        action_payload = {
            "type": "FILE_DOWNLOAD_CARD",
            "filename": office_res["filename"],
            "download_url": office_res["download_url"],
            "tasks_count": f"{office_res['total_pages']} стр."
        }

        return {
            "status": "success",
            "user_id": user_id,
            "session_id": session_id,
            "response": reply_text,
            "action_payload": action_payload
        }

    # 2. Если это не задача OCR в Word — отправляем файл в обычный диалог агента
    parsed = parse_uploaded_file(file_path, file.filename)
    file_context = parsed.get("context_text", "")
    image_b64 = parsed.get("image_b64")

    prompt_for_agent = user_msg or f"Проанализируй документ {file.filename}"
    reply_text, history, action_payload = process_chat_message(
        user_id=user_id,
        user_message=prompt_for_agent,
        file_context=file_context,
        image_b64=image_b64,
        session_id=session_id
    )

    return {
        "status": "success",
        "user_id": user_id,
        "session_id": session_id,
        "response": reply_text,
        "action_payload": action_payload
    }