"""
招标文件解析模块
流程：提取文本 → 生成摘要 → 提取评分要求 → 结构化JSON
"""
import json
import logging
import re
import sqlite3
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime
from functools import partial

from utils import ai_client, prompts as P
from utils.file_utils import extract_text, truncate_text
from utils.tender_kind_sections import (
    get_tender_kind_classify_prompt,
    parse_tender_kind_response,
)

logger = logging.getLogger(__name__)


def parse_boq_file(db_path: str, project_id: int, file_path: str, file_name: str) -> None:
    """
    后台线程：解析工程量清单文件 → 本地结构化分析 → AI 摘要 → 写库。
    boq_status: none → parsing → done / error
    """
    from utils.bill_analysis import analyze_boq_pages, categories_to_prompt_appendix
    from utils.boq_parser import extract_boq_pages

    conn = sqlite3.connect(db_path)
    try:
        _set_boq_status(conn, project_id, 'parsing', '正在提取工程量清单文本...')

        page_texts = extract_boq_pages(file_path)
        boq_text = '\n'.join(page_texts).strip()
        if not boq_text:
            raise ValueError('未能从文件中提取到有效内容，请检查文件格式')

        _set_boq_status(conn, project_id, 'parsing', '正在本地解析清单结构...')
        analysis = analyze_boq_pages(page_texts)
        boq_analysis_json = json.dumps(analysis, ensure_ascii=False)

        structured = ''
        if not analysis.get('scanned') and not analysis.get('no_bill_pages'):
            structured = categories_to_prompt_appendix(analysis)

        _set_boq_status(conn, project_id, 'parsing', '正在生成工程量清单摘要...')

        summary_prompt = P.get_boq_summary_prompt(boq_text[:10000], structured)
        boq_summary = ai_client.chat(summary_prompt, temperature=0.2, max_tokens=2048)

        cur = conn.cursor()
        cur.execute('''
            UPDATE tender_data
            SET boq_file_name=?, boq_text=?, boq_summary=?, boq_analysis_json=?,
                boq_status='done', boq_error='', updated_at=?
            WHERE project_id=?
        ''', (file_name, boq_text[:12000], boq_summary, boq_analysis_json, datetime.now(), project_id))
        conn.commit()
        logger.info(f'项目 {project_id} 工程量清单解析完成')

    except Exception as e:
        logger.exception(f'工程量清单解析失败 project_id={project_id}')
        _set_boq_status(conn, project_id, 'error', str(e))
    finally:
        conn.close()


def _set_boq_status(conn, project_id, status, message=''):
    cur = conn.cursor()
    cur.execute('''
        UPDATE tender_data SET boq_status=?, boq_error=?, updated_at=?
        WHERE project_id=?
    ''', (status, message, datetime.now(), project_id))
    conn.commit()


def parse_tender_file(db_path: str, project_id: int, file_path: str, file_name: str) -> None:
    """
    后台线程中运行：解析招标文件并将结果写入数据库。
    极速优化：提取文本后并行执行3个独立AI任务（摘要、评分要求、类型识别），
    然后顺序执行依赖的JSON结构化。全局 LLM 信号量限制总并发（默认 40，与 config 一致）。
    status 字段：pending → parsing → done / error
    """
    conn = sqlite3.connect(db_path)
    try:
        _set_status(conn, project_id, 'parsing', '正在提取文件文本...')

        # 1. 提取原始文本（I/O/CPU，可进一步并行但当前足够快）
        raw_text = extract_text(file_path)
        raw_text = truncate_text(raw_text, 60000)
        excerpt = (raw_text or '')[:15000]

        _set_status(conn, project_id, 'parsing', '并行生成摘要、评分要求和类型识别...')

        # 并行准备3个独立AI任务（大幅加速解析环节）
        def _run_summary():
            prompt = P.get_project_summary_prompt(raw_text)
            return ai_client.chat(prompt, temperature=0.3, max_tokens=4096)

        def _run_rating():
            prompt = P.get_rating_requirements_prompt(raw_text)
            return ai_client.chat(prompt, temperature=0.2, max_tokens=4096)

        def _run_kind():
            prompt = get_tender_kind_classify_prompt(excerpt)
            raw = ai_client.chat(prompt, temperature=0.1, max_tokens=32)
            return parse_tender_kind_response(raw)

        # 使用有限线程池；单次解析内 LLM 调用仍受全局信号量约束
        with ThreadPoolExecutor(max_workers=3, thread_name_prefix='parse') as executor:
            future_summary = executor.submit(_run_summary)
            future_rating = executor.submit(_run_rating)
            future_kind = executor.submit(_run_kind)

            summary = future_summary.result()
            rating_md = future_rating.result()
            tender_kind = future_kind.result()

        logger.info(f'项目 {project_id} 招标文件类型识别为: {tender_kind}')

        _set_status(conn, project_id, 'parsing', '正在结构化评分数据...')

        # 4. 依赖rating_md的JSON结构化（顺序执行）
        rating_json_prompt = P.get_rating_json_prompt(rating_md)
        rating_json_raw = ai_client.chat(rating_json_prompt, temperature=0.1, max_tokens=2048)
        rating_json_str = _clean_json(rating_json_raw)

        # 写入数据库
        _upsert_tender_data(conn, project_id, file_name, raw_text,
                            summary, rating_md, rating_json_str, tender_kind)

        # Deep integration: persist diagram/anon settings to projects table (auto-defaults)
        # Future: add AI extraction prompt for diagram intent and anon rules from raw_text
        cur = conn.cursor()
        cur.execute('''
            UPDATE projects SET 
                enable_figure = COALESCE(enable_figure, 1),
                enable_table = COALESCE(enable_table, 1),
                anon_requirements = COALESCE(anon_requirements, '不得出现投标人身份信息')
            WHERE id = ?
        ''', (project_id,))
        conn.commit()

        _set_status(conn, project_id, 'done', '解析完成（已同步生成设置）')
        logger.info(f'项目 {project_id} 招标文件解析完成（并行加速完成，生成设置已打通）')

    except Exception as e:
        logger.exception(f'解析失败 project_id={project_id}')
        _set_status(conn, project_id, 'error', str(e))
    finally:
        conn.close()


# ─── 内部工具 ──────────────────────────────────────────────────────────────

def _set_status(conn, project_id, status, message=''):
    cur = conn.cursor()
    cur.execute('''
        INSERT INTO tender_data (project_id, status, error_message)
        VALUES (?, ?, ?)
        ON CONFLICT(project_id) DO UPDATE SET status=?, error_message=?, updated_at=?
    ''', (project_id, status, message, status, message, datetime.now()))
    conn.commit()


def _upsert_tender_data(conn, project_id, file_name, raw_text,
                        summary, rating_md, rating_json_str,
                        tender_kind: str = 'engineering'):
    cur = conn.cursor()
    cur.execute('''
        INSERT INTO tender_data
            (project_id, file_name, raw_text, summary, rating_requirements, rating_json,
             tender_kind, status, error_message)
        VALUES (?, ?, ?, ?, ?, ?, ?, 'done', '')
        ON CONFLICT(project_id) DO UPDATE SET
            file_name=?, raw_text=?, summary=?, rating_requirements=?,
            rating_json=?, tender_kind=?, status='done', error_message='', updated_at=?
    ''', (
        project_id, file_name, raw_text, summary, rating_md, rating_json_str, tender_kind,
        file_name, raw_text, summary, rating_md, rating_json_str, tender_kind, datetime.now()
    ))
    conn.commit()


def _clean_json(raw: str) -> str:
    """尝试从 AI 返回中提取 JSON 字符串"""
    # 去除 markdown 代码块
    raw = re.sub(r'```(?:json)?\s*', '', raw)
    raw = raw.replace('```', '').strip()
    # 验证是否是有效 JSON
    try:
        json.loads(raw)
        return raw
    except json.JSONDecodeError:
        # 尝试提取 { ... } 部分
        m = re.search(r'\{[\s\S]*\}', raw)
        if m:
            candidate = m.group(0)
            try:
                json.loads(candidate)
                return candidate
            except Exception:
                pass
    return raw