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27cde0c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 | """HTML processing utilities for ScrapeRL backend."""
import re
from typing import Any, Optional
from bs4 import BeautifulSoup, Tag, NavigableString
from app.utils.logging import get_logger
logger = get_logger(__name__)
def parse_html(html: str, parser: str = "html.parser") -> BeautifulSoup:
"""
Parse HTML string into a BeautifulSoup object.
Args:
html: Raw HTML string
parser: Parser to use (html.parser, lxml, html5lib)
Returns:
Parsed BeautifulSoup object
"""
return BeautifulSoup(html, parser)
def clean_html(
html: str,
remove_scripts: bool = True,
remove_styles: bool = True,
remove_comments: bool = True,
remove_tags: Optional[list[str]] = None,
) -> str:
"""
Clean HTML by removing unwanted elements.
Args:
html: Raw HTML string
remove_scripts: Remove <script> tags
remove_styles: Remove <style> tags
remove_comments: Remove HTML comments
remove_tags: Additional tags to remove
Returns:
Cleaned HTML string
"""
soup = parse_html(html)
# Remove script tags
if remove_scripts:
for script in soup.find_all("script"):
script.decompose()
# Remove style tags
if remove_styles:
for style in soup.find_all("style"):
style.decompose()
# Remove comments
if remove_comments:
from bs4 import Comment
for comment in soup.find_all(string=lambda text: isinstance(text, Comment)):
comment.extract()
# Remove additional specified tags
if remove_tags:
for tag_name in remove_tags:
for tag in soup.find_all(tag_name):
tag.decompose()
return str(soup)
def extract_text(
html: str,
separator: str = " ",
strip: bool = True,
) -> str:
"""
Extract plain text from HTML.
Args:
html: Raw HTML string
separator: String to join text segments
strip: Strip whitespace from result
Returns:
Extracted plain text
"""
soup = parse_html(html)
# Remove script and style elements
for element in soup(["script", "style", "noscript"]):
element.decompose()
text = soup.get_text(separator=separator)
if strip:
# Normalize whitespace
text = re.sub(r"\s+", " ", text).strip()
return text
def semantic_chunk(
html: str,
max_chunk_size: int = 4000,
overlap: int = 200,
) -> list[dict[str, Any]]:
"""
Split HTML content into semantic chunks based on structure.
Args:
html: Raw HTML string
max_chunk_size: Maximum characters per chunk
overlap: Number of characters to overlap between chunks
Returns:
List of chunk dictionaries with text and metadata
"""
soup = parse_html(html)
chunks: list[dict[str, Any]] = []
# Remove non-content elements
for element in soup(["script", "style", "noscript", "nav", "footer", "header"]):
element.decompose()
# Find semantic boundaries
semantic_tags = ["article", "section", "div", "p", "h1", "h2", "h3", "h4", "h5", "h6"]
def get_text_content(element: Tag | NavigableString) -> str:
if isinstance(element, NavigableString):
return str(element).strip()
return element.get_text(separator=" ", strip=True)
current_chunk = ""
current_metadata: dict[str, Any] = {"tags": [], "headings": []}
for element in soup.find_all(semantic_tags):
text = get_text_content(element)
if not text:
continue
tag_name = element.name if isinstance(element, Tag) else "text"
# Check if adding this would exceed max size
if len(current_chunk) + len(text) + 1 > max_chunk_size:
if current_chunk:
chunks.append({
"text": current_chunk.strip(),
"metadata": current_metadata.copy(),
"char_count": len(current_chunk),
})
# Start new chunk with overlap
if overlap > 0 and current_chunk:
current_chunk = current_chunk[-overlap:] + " " + text
else:
current_chunk = text
current_metadata = {"tags": [tag_name], "headings": []}
else:
current_chunk += " " + text if current_chunk else text
current_metadata["tags"].append(tag_name)
# Track headings
if tag_name in ["h1", "h2", "h3", "h4", "h5", "h6"]:
current_metadata["headings"].append(text[:100])
# Add remaining content
if current_chunk.strip():
chunks.append({
"text": current_chunk.strip(),
"metadata": current_metadata,
"char_count": len(current_chunk),
})
# If no semantic chunks found, fall back to simple chunking
if not chunks:
text = extract_text(html)
for i in range(0, len(text), max_chunk_size - overlap):
chunk_text = text[i : i + max_chunk_size]
if chunk_text.strip():
chunks.append({
"text": chunk_text.strip(),
"metadata": {"tags": [], "headings": []},
"char_count": len(chunk_text),
})
return chunks
def extract_links(
html: str,
base_url: Optional[str] = None,
include_text: bool = True,
) -> list[dict[str, str]]:
"""
Extract all links from HTML.
Args:
html: Raw HTML string
base_url: Base URL for resolving relative links
include_text: Include link text in results
Returns:
List of link dictionaries with href and optionally text
"""
from urllib.parse import urljoin
soup = parse_html(html)
links: list[dict[str, str]] = []
for anchor in soup.find_all("a", href=True):
href = anchor.get("href", "")
if not href or href.startswith("#") or href.startswith("javascript:"):
continue
# Resolve relative URLs
if base_url and not href.startswith(("http://", "https://", "//")):
href = urljoin(base_url, href)
link_data: dict[str, str] = {"href": href}
if include_text:
link_data["text"] = anchor.get_text(strip=True)
# Include title if present
title = anchor.get("title")
if title:
link_data["title"] = title
links.append(link_data)
return links
def extract_tables(
html: str,
include_headers: bool = True,
) -> list[dict[str, Any]]:
"""
Extract tables from HTML as structured data.
Args:
html: Raw HTML string
include_headers: Try to identify and include header rows
Returns:
List of table dictionaries with headers and rows
"""
soup = parse_html(html)
tables: list[dict[str, Any]] = []
for table in soup.find_all("table"):
table_data: dict[str, Any] = {
"headers": [],
"rows": [],
}
# Extract headers from thead or first row
if include_headers:
thead = table.find("thead")
if thead:
header_row = thead.find("tr")
if header_row:
table_data["headers"] = [
th.get_text(strip=True)
for th in header_row.find_all(["th", "td"])
]
# Extract body rows
tbody = table.find("tbody") or table
for row in tbody.find_all("tr"):
cells = row.find_all(["td", "th"])
row_data = [cell.get_text(strip=True) for cell in cells]
# If no headers yet and this looks like a header row
if include_headers and not table_data["headers"] and row.find("th"):
table_data["headers"] = row_data
else:
if row_data: # Skip empty rows
table_data["rows"].append(row_data)
if table_data["rows"] or table_data["headers"]:
tables.append(table_data)
return tables
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