"""噪声过滤：识别广告尾注、无关条款等非岗位描述内容。"""
from __future__ import annotations

import logging
import re

from app.models import JDRecord

logger = logging.getLogger(__name__)

# 噪声特征：广告导流、扫码领资料、与岗位无关的商业条款等
NOISE_PATTERNS = [
    r"扫码|点击关注|关注公众号|领取.{0,6}(资料|福利|红包)",
    r"【广告】|推广信息|商务合作请加",
    r"长期回收|代办|加盟代理|刷单",
    r"内部推荐奖.{0,10}扫码",
]
_NOISE_RE = re.compile("|".join(NOISE_PATTERNS))


def is_noise(text: str) -> bool:
    """判断文本是否含有噪声内容。"""
    return bool(_NOISE_RE.search(text))


def strip_noise_lines(text: str) -> str:
    """逐行剔除噪声行，保留正文（用于噪声行与正文混排的情况）。"""
    kept = [ln for ln in text.splitlines() if ln.strip() and not is_noise(ln)]
    return "\n".join(kept)


def filter_noise(records: list[JDRecord]) -> list[JDRecord]:
    """标记噪声记录：整条以噪声为主的记录 is_noise=True 并降权；混排的只剔除噪声行。"""
    marked = 0
    for r in records:
        lines = [ln for ln in r.text.splitlines() if ln.strip()]
        noisy = [ln for ln in lines if is_noise(ln)]
        if not noisy:
            continue
        if len(noisy) >= max(1, len(lines) // 2):
            r.is_noise = True
            r.weight = round(r.weight * 0.1, 4)
            marked += 1
        else:
            r.text = strip_noise_lines(r.text)
    logger.info("噪声过滤：%d 条记录被标记为噪声", marked)
    return records