#!/usr/bin/env python3
"""
内容清洗工具集：所有 AI 思考内容/噪音段落的清洗逻辑集中管理
各脚本（writer/outline/compliance_optimizer）统一引用此模块
"""
import re
from typing import List, Tuple

THINKING_PATTERNS: List[str] = [
    r'^(好的|好的，|好[之，]|我来|让我|我将|我这就).*?(?=\n|$)',
    r'^(以下|下面是|这是|为您|根据).*?(?=\n|$)',
    r'^基于.*?(?=\n|$)',
    r'^【.*?】',
    r'^这里.*?(?=\n|$)',
    r'\n+希望[这以].*?$',
    r'\n+如果.*?$',
    r'\n+若有.*?$',
    r'\n+如有.*?$',
    r'\n+\*\*免责.*?$',
    r'^(这是按照要求|我已按|根据您的要求|^首先|^其次|^最后|^补充|^完成后).*?(?=\n|$)',
    r'^(以下是|下面为|这是完整|已按要求|已完成|处理完成).*?(?=\n|$)',
]

AI_PREFACE_PATTERNS: List[str] = [
    r'^(好的[，,]?|好的 |我来|让我|我将|我这就|以下|下面|这是|为您|基于)',
    r'^(这是按照要求|我已按|根据您的要求|^首先|^其次|^最后|^补充|^完成后|以下是|下面为|这是完整|已按要求|已完成)',
]

AI_VERBAL_PATTERNS: List[str] = [
    r'^(首先|其次|最后)(，|,)?',
    r'^总的来说',
    r'^值得注意的是',
    r'^换句话说',
    r'^总而言之',
    r'^简而言之',
    r'^一言以蔽之',
    r'^可以说',
    r'^不难发现',
    r'^由此可见',
    r'^综上所述',
    r'^通过以上',
]

_cached_clean_rules = None

def _load_clean_rules():
    global _cached_clean_rules
    if _cached_clean_rules is not None:
        return _cached_clean_rules

    try:
        from app.core.prompt_loader import _get_session
        from app.models import ContentCleanRule
        session = _get_session()
        try:
            rows = session.query(ContentCleanRule).filter(ContentCleanRule.is_active == True).order_by(ContentCleanRule.sort_order).all()
            if rows:
                result = {"thinking": [], "preface": [], "verbosity": [], "html_thinking": []}
                for r in rows:
                    rule_type = r.rule_type or "thinking"
                    if rule_type in result:
                        result[rule_type].append(r.pattern)
                _cached_clean_rules = result
                return _cached_clean_rules
        finally:
            session.close()
    except Exception:
        pass

    _cached_clean_rules = {
        "thinking": THINKING_PATTERNS,
        "preface": AI_PREFACE_PATTERNS,
        "verbosity": AI_VERBAL_PATTERNS,
        "html_thinking": [],
    }
    return _cached_clean_rules


def _get_thinking_patterns() -> List[str]:
    rules = _load_clean_rules()
    return rules.get("thinking", THINKING_PATTERNS)


def _get_preface_patterns() -> List[str]:
    rules = _load_clean_rules()
    return rules.get("preface", AI_PREFACE_PATTERNS)


def _get_verbal_patterns() -> List[str]:
    rules = _load_clean_rules()
    return rules.get("verbosity", AI_VERBAL_PATTERNS)


def strip_thinking(text: str) -> str:
    """清洗 AI 思考前缀/后缀（正则替换，支持纯文本和 HTML 内联）"""
    for pat in _get_thinking_patterns():
        text = re.sub(pat, '', text, flags=re.MULTILINE)
    return text.strip()

def strip_thinking_html(html: str) -> str:
    """清洗 HTML 中的 AI 思考段落（处理 <p>/<div> 包裹的情况）"""
    rules = _load_clean_rules()
    patterns = rules.get("html_thinking", [])
    if not patterns:
        patterns = [
            r'<p[^>]*>(好的|好的，|好[的，]|我来|让我|我将|我这就|以下|下面|这是|为您|基于|这是按照要求|我已按|根据您的要求|^首先|^其次|^最后|^补充|^完成后|以下是|下面为|这是完整|已按要求|已完成).*?</p>',
            r'<div[^>]*>(好的|好的，|好[的，]|我来|让我|我将|我这就|以下|下面|这是|为您|基于|这是按照要求|我已按|根据您的要求|^首先|^其次|^最后|^补充|^完成后|以下是|下面为|这是完整|已按要求|已完成).*?</div>',
            r'<p[^>]*>首先.*?</p>',
            r'<p[^>]*>其次.*?</p>',
            r'<p[^>]*>最后.*?</p>',
            r'<p[^>]*>(总的来说|值得注意的是|换句话说|总而言之|简而言之|一言以蔽之|可以说|不难发现|由此可见|综上所述).*?</p>',
            r'<div[^>]*>(总的来说|值得注意的是|换句话说|总而言之|简而言之|一言以蔽之|可以说|不难发现|由此可见|综上所述).*?</div>',
        ]
    for pat in patterns:
        html = re.sub(pat, '', html, flags=re.IGNORECASE)
    return html

def strip_ai_preface(text: str) -> str:
    """清洗以 AI 自述开头的整段说明文字（含代码围栏块）"""
    lines = text.split('\n')
    result = []
    skip_mode = False
    code_start = re.compile(r'^```')
    for line in lines:
        stripped = line.strip()
        if skip_mode:
            if code_start.match(stripped):
                skip_mode = False
            continue
        should_skip = False
        for pat in _get_preface_patterns():
            if re.match(pat, stripped):
                should_skip = True
                break
        if should_skip:
            if code_start.match(stripped) or '```' in stripped:
                skip_mode = True
            continue
        result.append(line)
    return '\n'.join(result).strip()

def strip_ai_verbosity(text: str) -> str:
    """清洗正文中常见的 AI 套话段落"""
    lines = text.split('\n')
    result = []
    for line in lines:
        stripped = line.strip()
        skip = False
        for pat in _get_verbal_patterns():
            if re.match(pat, stripped):
                skip = True
                break
        if not skip:
            result.append(line)
    return '\n'.join(result).strip()

def clean_markdown_content(text: str) -> str:
    """清洗 markdown 正文：去思考内容 + 去 AI 套话 + 去格式噪音"""
    text = strip_thinking(text)
    text = strip_ai_preface(text)
    text = strip_ai_verbosity(text)
    lines = text.split('\n')
    cleaned = []
    in_code = False
    for line in lines:
        if line.strip().startswith('```'):
            in_code = not in_code
            continue
        if in_code:
            continue
        line = re.sub(r'^#{1,6}\s+', '', line)
        line = re.sub(r'^[\-\*\+]\s+', '', line)
        line = re.sub(r'^\d+[\.\)]\s+', '', line)
        line = re.sub(r'\*{1,3}([^*]+)\*{1,3}', r'\1', line)
        cleaned.append(line)
    return '\n'.join(cleaned).strip()

def clean_html_content(html: str) -> str:
    """清洗 HTML 输出：去 markdown 代码围栏头尾 + 去 AI 思考注释"""
    html = re.sub(r'^```+\w*\s*\n?', '', html)
    html = html.strip()
    html = re.sub(r'\n?```+\s*$', '', html)
    html = strip_thinking_html(html)
    return html

def clean_full_pipeline(text: str, output_format: str = 'markdown') -> str:
    """
    完整清洗流程：
    - markdown 输入：先去思考前缀 → 再去格式噪音 → 再转 HTML
    - html 输入：直接去代码围栏 + 思考注释
    """
    if output_format == 'html':
        return clean_html_content(text)
    return clean_markdown_content(text)

def get_statistics(text: str) -> dict:
    """返回清洗前后的行数/字数统计（用于日志）"""
    original_lines = len(text.split('\n'))
    original_chars = len(text)
    cleaned = strip_thinking(text)
    cleaned = strip_ai_preface(cleaned)
    cleaned = strip_ai_verbosity(cleaned)
    cleaned_lines = len(cleaned.split('\n'))
    cleaned_chars = len(cleaned)
    return {
        'original_lines': original_lines,
        'cleaned_lines': cleaned_lines,
        'original_chars': original_chars,
        'cleaned_chars': cleaned_chars,
        'dropped_lines': original_lines - cleaned_lines,
        'dropped_chars': original_chars - cleaned_chars,
    }