Ubuntu
126 lines
4.7 KiB
Python
126 lines
4.7 KiB
Python
"""
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正文区 vs 噪音过滤（crawl4ai Pruning 思路的本地化实现）。
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应用场景：HTML → Markdown 转换前对整页 DOM 做剪枝，去掉导航/侧栏/评论区/
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相关推荐等噪音容器，避免噪音进入正文 Markdown。
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设计来源：crawl4ai 的 PruningContentFilter（/home/ubuntu/forgithub/crawl4ai
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/crawl4ai/content_filter_strategy.py），核心为递归评分剪枝：
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score = 0.4*text_density + 0.2*link_density + 0.2*tag_weight
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+ 0.1*class_id_weight + 0.1*text_length
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相对原版的两处修正（实测 CSDN 文章页验证，7505 → 2776 字符、正文零丢失）：
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1. 原版 text_length 项 = log(text_len+1) 无上限，超大噪音容器（如 3600+
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字符的相关推荐列表）得分 0.90 远超阈值 0.48 而残留；
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本实现将其封顶为 5.0。
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2. 原版对命中负面模式（recommend/comment/sidebar/ads...）的类名仅做 -0.5
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小惩罚；本实现改为硬删——类名/ID 命中负面模式的容器几乎必然是噪音。
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安全性：质量门控——剪枝后正文文本 < min_output_chars 或 < 原文本 min_ratio
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时回退返回原 HTML，避免误删导致正文丢失。对已定位正文容器的局部 HTML
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调用时无副作用（正文容器内节点评分普遍高于阈值）。
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"""
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import math
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import re
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from bs4 import BeautifulSoup, Comment
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# 确定的噪音标签：直接删除整个子树
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EXCLUDED_TAGS = {
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"nav", "footer", "header", "aside",
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"script", "style", "form", "iframe", "noscript",
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}
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# 负面类名/ID：命中直接硬删（相对 crawl4ai 的 -0.5 惩罚改为硬删）
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NEGATIVE_PATTERN = re.compile(
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r"nav|header|footer|sidebar|comment|ads|advert|promo|social|share|"
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r"recommend|related|breadcrumb|toolbar|menu|pagination|qr|qrcode|"
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r"copyright|tip|hot|rank",
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re.I,
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)
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# 正文标签权重（决定文本块的评分加成）
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TAG_WEIGHT = {
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"div": 0.5, "p": 1.0, "article": 1.5, "section": 1.2, "main": 1.5,
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"li": 0.5, "td": 0.5, "th": 0.5,
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}
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# 保护标签：正文关键内容（代码/表格/图片/引用）不参与剪枝
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PROTECT_TAGS = {"pre", "code", "table", "figure", "blockquote", "img"}
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def _class_id_text(node) -> str:
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return " ".join(node.get("class", []) + [node.get("id", "")])
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def _prune_tree(node, threshold: float) -> None:
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"""递归剪枝：对直接子节点评分，低于阈值或命中负面类名则删除。"""
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for child in list(node.find_all(recursive=False)):
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if child.name in PROTECT_TAGS:
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continue
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# 硬规则：负面类名/ID 直接删除
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if (child.get("class") or child.get("id")) and NEGATIVE_PATTERN.search(
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_class_id_text(child)
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):
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child.decompose()
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continue
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text_len = len(child.get_text(strip=True))
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tag_len = len(str(child))
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if tag_len == 0:
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continue
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link_text_len = sum(len(a.get_text(strip=True)) for a in child.find_all("a"))
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text_density = text_len / tag_len
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link_density = 1.0 - (link_text_len / text_len if text_len else 0.0)
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tag_weight = TAG_WEIGHT.get(child.name, 0.5)
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text_length = min(math.log(text_len + 1), 5.0) # 长度项封顶，避免大容器评分失真
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score = (
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0.4 * text_density
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+ 0.2 * link_density
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+ 0.2 * tag_weight
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+ 0.1 * text_length
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)
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if score < threshold:
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child.decompose()
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else:
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_prune_tree(child, threshold)
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def prune_html_noise(
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html: str,
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threshold: float = 0.5,
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min_output_chars: int = 300,
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min_ratio: float = 0.2,
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) -> str:
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"""
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对整页 HTML 做正文区 vs 噪音过滤，返回剪枝后的 HTML。
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质量门控：剪枝后正文文本 < min_output_chars 或 < 原文本 min_ratio 时，
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认为剪枝疑似误删，回退返回原 HTML（安全兜底）。
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"""
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if not html or not isinstance(html, str) or "<body" not in html.lower():
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return html
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try:
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soup = BeautifulSoup(html, "lxml")
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except Exception:
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soup = BeautifulSoup(html, "html.parser")
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if not soup.body:
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return html
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before_text = soup.body.get_text(strip=True)
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if len(before_text) < min_output_chars:
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return html # 页面本身就没多少文本，不剪
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# 删除噪音标签与注释
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for tag in list(soup.find_all(EXCLUDED_TAGS)):
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tag.decompose()
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for comment in list(soup.find_all(string=lambda s: isinstance(s, Comment))):
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comment.extract()
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_prune_tree(soup.body, threshold)
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after_text = soup.body.get_text(strip=True)
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if len(after_text) < min_output_chars or len(after_text) < len(before_text) * min_ratio:
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return html # 疑似误删，回退原 HTML
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return str(soup.body)
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