382 lines
13 KiB
Python
382 lines
13 KiB
Python
# Copyright 2021 The Matrix.org Foundation C.I.C.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import codecs
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import itertools
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import logging
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import re
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from typing import TYPE_CHECKING, Dict, Generator, Iterable, Optional, Set, Union
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if TYPE_CHECKING:
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from lxml import etree
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logger = logging.getLogger(__name__)
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_charset_match = re.compile(
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rb'<\s*meta[^>]*charset\s*=\s*"?([a-z0-9_-]+)"?', flags=re.I
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)
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_xml_encoding_match = re.compile(
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rb'\s*<\s*\?\s*xml[^>]*encoding="([a-z0-9_-]+)"', flags=re.I
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)
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_content_type_match = re.compile(r'.*; *charset="?(.*?)"?(;|$)', flags=re.I)
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def _normalise_encoding(encoding: str) -> Optional[str]:
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"""Use the Python codec's name as the normalised entry."""
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try:
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return codecs.lookup(encoding).name
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except LookupError:
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return None
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def _get_html_media_encodings(
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body: bytes, content_type: Optional[str]
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) -> Iterable[str]:
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"""
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Get potential encoding of the body based on the (presumably) HTML body or the content-type header.
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The precedence used for finding a character encoding is:
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1. <meta> tag with a charset declared.
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2. The XML document's character encoding attribute.
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3. The Content-Type header.
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4. Fallback to utf-8.
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5. Fallback to windows-1252.
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This roughly follows the algorithm used by BeautifulSoup's bs4.dammit.EncodingDetector.
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Args:
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body: The HTML document, as bytes.
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content_type: The Content-Type header.
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Returns:
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The character encoding of the body, as a string.
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"""
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# There's no point in returning an encoding more than once.
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attempted_encodings: Set[str] = set()
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# Limit searches to the first 1kb, since it ought to be at the top.
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body_start = body[:1024]
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# Check if it has an encoding set in a meta tag.
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match = _charset_match.search(body_start)
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if match:
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encoding = _normalise_encoding(match.group(1).decode("ascii"))
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if encoding:
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attempted_encodings.add(encoding)
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yield encoding
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# TODO Support <meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
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# Check if it has an XML document with an encoding.
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match = _xml_encoding_match.match(body_start)
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if match:
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encoding = _normalise_encoding(match.group(1).decode("ascii"))
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if encoding and encoding not in attempted_encodings:
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attempted_encodings.add(encoding)
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yield encoding
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# Check the HTTP Content-Type header for a character set.
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if content_type:
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content_match = _content_type_match.match(content_type)
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if content_match:
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encoding = _normalise_encoding(content_match.group(1))
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if encoding and encoding not in attempted_encodings:
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attempted_encodings.add(encoding)
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yield encoding
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# Finally, fallback to UTF-8, then windows-1252.
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for fallback in ("utf-8", "cp1252"):
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if fallback not in attempted_encodings:
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yield fallback
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def decode_body(
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body: bytes, uri: str, content_type: Optional[str] = None
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) -> Optional["etree.Element"]:
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"""
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This uses lxml to parse the HTML document.
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Args:
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body: The HTML document, as bytes.
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uri: The URI used to download the body.
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content_type: The Content-Type header.
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Returns:
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The parsed HTML body, or None if an error occurred during processed.
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"""
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# If there's no body, nothing useful is going to be found.
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if not body:
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return None
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# The idea here is that multiple encodings are tried until one works.
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# Unfortunately the result is never used and then LXML will decode the string
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# again with the found encoding.
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for encoding in _get_html_media_encodings(body, content_type):
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try:
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body.decode(encoding)
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except Exception:
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pass
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else:
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break
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else:
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logger.warning("Unable to decode HTML body for %s", uri)
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return None
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from lxml import etree
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# Create an HTML parser.
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parser = etree.HTMLParser(recover=True, encoding=encoding)
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# Attempt to parse the body. Returns None if the body was successfully
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# parsed, but no tree was found.
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return etree.fromstring(body, parser)
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def parse_html_to_open_graph(tree: "etree.Element") -> Dict[str, Optional[str]]:
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"""
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Parse the HTML document into an Open Graph response.
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This uses lxml to search the HTML document for Open Graph data (or
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synthesizes it from the document).
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Args:
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tree: The parsed HTML document.
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Returns:
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The Open Graph response as a dictionary.
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"""
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# if we see any image URLs in the OG response, then spider them
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# (although the client could choose to do this by asking for previews of those
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# URLs to avoid DoSing the server)
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# "og:type" : "video",
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# "og:url" : "https://www.youtube.com/watch?v=LXDBoHyjmtw",
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# "og:site_name" : "YouTube",
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# "og:video:type" : "application/x-shockwave-flash",
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# "og:description" : "Fun stuff happening here",
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# "og:title" : "RemoteJam - Matrix team hack for Disrupt Europe Hackathon",
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# "og:image" : "https://i.ytimg.com/vi/LXDBoHyjmtw/maxresdefault.jpg",
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# "og:video:url" : "http://www.youtube.com/v/LXDBoHyjmtw?version=3&autohide=1",
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# "og:video:width" : "1280"
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# "og:video:height" : "720",
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# "og:video:secure_url": "https://www.youtube.com/v/LXDBoHyjmtw?version=3",
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og: Dict[str, Optional[str]] = {}
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for tag in tree.xpath("//*/meta[starts-with(@property, 'og:')]"):
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if "content" in tag.attrib:
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# if we've got more than 50 tags, someone is taking the piss
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if len(og) >= 50:
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logger.warning("Skipping OG for page with too many 'og:' tags")
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return {}
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og[tag.attrib["property"]] = tag.attrib["content"]
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# TODO: grab article: meta tags too, e.g.:
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# "article:publisher" : "https://www.facebook.com/thethudonline" />
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# "article:author" content="https://www.facebook.com/thethudonline" />
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# "article:tag" content="baby" />
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# "article:section" content="Breaking News" />
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# "article:published_time" content="2016-03-31T19:58:24+00:00" />
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# "article:modified_time" content="2016-04-01T18:31:53+00:00" />
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if "og:title" not in og:
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# do some basic spidering of the HTML
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title = tree.xpath("(//title)[1] | (//h1)[1] | (//h2)[1] | (//h3)[1]")
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if title and title[0].text is not None:
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og["og:title"] = title[0].text.strip()
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else:
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og["og:title"] = None
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if "og:image" not in og:
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# TODO: extract a favicon failing all else
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meta_image = tree.xpath(
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"//*/meta[translate(@itemprop, 'IMAGE', 'image')='image']/@content"
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)
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if meta_image:
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og["og:image"] = meta_image[0]
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else:
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# TODO: consider inlined CSS styles as well as width & height attribs
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images = tree.xpath("//img[@src][number(@width)>10][number(@height)>10]")
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images = sorted(
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images,
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key=lambda i: (
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-1 * float(i.attrib["width"]) * float(i.attrib["height"])
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),
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)
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if not images:
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images = tree.xpath("//img[@src]")
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if images:
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og["og:image"] = images[0].attrib["src"]
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if "og:description" not in og:
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meta_description = tree.xpath(
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"//*/meta"
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"[translate(@name, 'DESCRIPTION', 'description')='description']"
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"/@content"
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)
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if meta_description:
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og["og:description"] = meta_description[0]
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else:
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og["og:description"] = parse_html_description(tree)
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elif og["og:description"]:
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# This must be a non-empty string at this point.
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assert isinstance(og["og:description"], str)
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og["og:description"] = summarize_paragraphs([og["og:description"]])
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# TODO: delete the url downloads to stop diskfilling,
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# as we only ever cared about its OG
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return og
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def parse_html_description(tree: "etree.Element") -> Optional[str]:
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"""
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Calculate a text description based on an HTML document.
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Grabs any text nodes which are inside the <body/> tag, unless they are within
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an HTML5 semantic markup tag (<header/>, <nav/>, <aside/>, <footer/>), or
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if they are within a <script/> or <style/> tag.
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This is a very very very coarse approximation to a plain text render of the page.
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Args:
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tree: The parsed HTML document.
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Returns:
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The plain text description, or None if one cannot be generated.
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"""
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# We don't just use XPATH here as that is slow on some machines.
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from lxml import etree
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TAGS_TO_REMOVE = (
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"header",
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"nav",
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"aside",
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"footer",
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"script",
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"noscript",
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"style",
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etree.Comment,
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)
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# Split all the text nodes into paragraphs (by splitting on new
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# lines)
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text_nodes = (
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re.sub(r"\s+", "\n", el).strip()
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for el in _iterate_over_text(tree.find("body"), *TAGS_TO_REMOVE)
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)
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return summarize_paragraphs(text_nodes)
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def _iterate_over_text(
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tree: "etree.Element", *tags_to_ignore: Iterable[Union[str, "etree.Comment"]]
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) -> Generator[str, None, None]:
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"""Iterate over the tree returning text nodes in a depth first fashion,
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skipping text nodes inside certain tags.
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"""
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# This is basically a stack that we extend using itertools.chain.
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# This will either consist of an element to iterate over *or* a string
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# to be returned.
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elements = iter([tree])
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while True:
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el = next(elements, None)
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if el is None:
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return
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if isinstance(el, str):
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yield el
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elif el.tag not in tags_to_ignore:
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# el.text is the text before the first child, so we can immediately
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# return it if the text exists.
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if el.text:
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yield el.text
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# We add to the stack all the elements children, interspersed with
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# each child's tail text (if it exists). The tail text of a node
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# is text that comes *after* the node, so we always include it even
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# if we ignore the child node.
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elements = itertools.chain(
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itertools.chain.from_iterable( # Basically a flatmap
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[child, child.tail] if child.tail else [child]
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for child in el.iterchildren()
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),
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elements,
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)
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def summarize_paragraphs(
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text_nodes: Iterable[str], min_size: int = 200, max_size: int = 500
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) -> Optional[str]:
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"""
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Try to get a summary respecting first paragraph and then word boundaries.
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Args:
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text_nodes: The paragraphs to summarize.
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min_size: The minimum number of words to include.
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max_size: The maximum number of words to include.
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Returns:
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A summary of the text nodes, or None if that was not possible.
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"""
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# TODO: Respect sentences?
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description = ""
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# Keep adding paragraphs until we get to the MIN_SIZE.
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for text_node in text_nodes:
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if len(description) < min_size:
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text_node = re.sub(r"[\t \r\n]+", " ", text_node)
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description += text_node + "\n\n"
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else:
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break
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description = description.strip()
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description = re.sub(r"[\t ]+", " ", description)
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description = re.sub(r"[\t \r\n]*[\r\n]+", "\n\n", description)
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# If the concatenation of paragraphs to get above MIN_SIZE
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# took us over MAX_SIZE, then we need to truncate mid paragraph
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if len(description) > max_size:
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new_desc = ""
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# This splits the paragraph into words, but keeping the
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# (preceding) whitespace intact so we can easily concat
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# words back together.
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for match in re.finditer(r"\s*\S+", description):
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word = match.group()
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# Keep adding words while the total length is less than
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# MAX_SIZE.
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if len(word) + len(new_desc) < max_size:
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new_desc += word
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else:
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# At this point the next word *will* take us over
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# MAX_SIZE, but we also want to ensure that its not
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# a huge word. If it is add it anyway and we'll
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# truncate later.
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if len(new_desc) < min_size:
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new_desc += word
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break
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# Double check that we're not over the limit
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if len(new_desc) > max_size:
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new_desc = new_desc[:max_size]
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# We always add an ellipsis because at the very least
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# we chopped mid paragraph.
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description = new_desc.strip() + "…"
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return description if description else None
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