summary refs log tree commit diff
path: root/synapse/rest/media/v1/preview_html.py
blob: afe4e29758c6deb6e65b7094289dbaa8e4d84824 (plain) (blame)
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
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
# Copyright 2021 The Matrix.org Foundation C.I.C.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import codecs
import logging
import re
from typing import (
    TYPE_CHECKING,
    Callable,
    Dict,
    Generator,
    Iterable,
   List, Optional,
    Set,
    Union,
)

if TYPE_CHECKING:
    from lxml import etree

logger = logging.getLogger(__name__)

_charset_match = re.compile(
    rb'<\s*meta[^>]*charset\s*=\s*"?([a-z0-9_-]+)"?', flags=re.I
)
_xml_encoding_match = re.compile(
    rb'\s*<\s*\?\s*xml[^>]*encoding="([a-z0-9_-]+)"', flags=re.I
)
_content_type_match = re.compile(r'.*; *charset="?(.*?)"?(;|$)', flags=re.I)

# Certain elements aren't meant for display.
ARIA_ROLES_TO_IGNORE = {"directory", "menu", "menubar", "toolbar"}


def _normalise_encoding(encoding: str) -> Optional[str]:
    """Use the Python codec's name as the normalised entry."""
    try:
        return codecs.lookup(encoding).name
    except LookupError:
        return None


def _get_html_media_encodings(
    body: bytes, content_type: Optional[str]
) -> Iterable[str]:
    """
    Get potential encoding of the body based on the (presumably) HTML body or the content-type header.

    The precedence used for finding a character encoding is:

    1. <meta> tag with a charset declared.
    2. The XML document's character encoding attribute.
    3. The Content-Type header.
    4. Fallback to utf-8.
    5. Fallback to windows-1252.

    This roughly follows the algorithm used by BeautifulSoup's bs4.dammit.EncodingDetector.

    Args:
        body: The HTML document, as bytes.
        content_type: The Content-Type header.

    Returns:
        The character encoding of the body, as a string.
    """
    # There's no point in returning an encoding more than once.
    attempted_encodings: Set[str] = set()

    # Limit searches to the first 1kb, since it ought to be at the top.
    body_start = body[:1024]

    # Check if it has an encoding set in a meta tag.
    match = _charset_match.search(body_start)
    if match:
        encoding = _normalise_encoding(match.group(1).decode("ascii"))
        if encoding:
            attempted_encodings.add(encoding)
            yield encoding

    # TODO Support <meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>

    # Check if it has an XML document with an encoding.
    match = _xml_encoding_match.match(body_start)
    if match:
        encoding = _normalise_encoding(match.group(1).decode("ascii"))
        if encoding and encoding not in attempted_encodings:
            attempted_encodings.add(encoding)
            yield encoding

    # Check the HTTP Content-Type header for a character set.
    if content_type:
        content_match = _content_type_match.match(content_type)
        if content_match:
            encoding = _normalise_encoding(content_match.group(1))
            if encoding and encoding not in attempted_encodings:
                attempted_encodings.add(encoding)
                yield encoding

    # Finally, fallback to UTF-8, then windows-1252.
    for fallback in ("utf-8", "cp1252"):
        if fallback not in attempted_encodings:
            yield fallback


def decode_body(
    body: bytes, uri: str, content_type: Optional[str] = None
) -> Optional["etree.Element"]:
    """
    This uses lxml to parse the HTML document.

    Args:
        body: The HTML document, as bytes.
        uri: The URI used to download the body.
        content_type: The Content-Type header.

    Returns:
        The parsed HTML body, or None if an error occurred during processed.
    """
    # If there's no body, nothing useful is going to be found.
    if not body:
        return None

    # The idea here is that multiple encodings are tried until one works.
    # Unfortunately the result is never used and then LXML will decode the string
    # again with the found encoding.
    for encoding in _get_html_media_encodings(body, content_type):
        try:
            body.decode(encoding)
        except Exception:
            pass
        else:
            break
    else:
        logger.warning("Unable to decode HTML body for %s", uri)
        return None

    from lxml import etree

    # Create an HTML parser.
    parser = etree.HTMLParser(recover=True, encoding=encoding)

    # Attempt to parse the body. Returns None if the body was successfully
    # parsed, but no tree was found.
    return etree.fromstring(body, parser)


def _get_meta_tags(
    tree: "etree.Element",
    property: str,
    prefix: str,
    property_mapper: Optional[Callable[[str], Optional[str]]] = None,
) -> Dict[str, Optional[str]]:
    """
    Search for meta tags prefixed with a particular string.

    Args:
        tree: The parsed HTML document.
        property: The name of the property which contains the tag name, e.g.
            "property" for Open Graph.
        prefix: The prefix on the property to search for, e.g. "og" for Open Graph.
        property_mapper: An optional callable to map the property to the Open Graph
            form. Can return None for a key to ignore that key.

    Returns:
        A map of tag name to value.
    """
    results: Dict[str, Optional[str]] = {}
    for tag in tree.xpath(
        f"//*/meta[starts-with(@{property}, '{prefix}:')][@content][not(@content='')]"
    ):
        # if we've got more than 50 tags, someone is taking the piss
        if len(results) >= 50:
            logger.warning(
                "Skipping parsing of Open Graph for page with too many '%s:' tags",
                prefix,
            )
            return {}

        key = tag.attrib[property]
        if property_mapper:
            key = property_mapper(key)
            # None is a special value used to ignore a value.
            if key is None:
                continue

        results[key] = tag.attrib["content"]

    return results


def _map_twitter_to_open_graph(key: str) -> Optional[str]:
    """
    Map a Twitter card property to the analogous Open Graph property.

    Args:
        key: The Twitter card property (starts with "twitter:").

    Returns:
        The Open Graph property (starts with "og:") or None to have this property
        be ignored.
    """
    # Twitter card properties with no analogous Open Graph property.
    if key == "twitter:card" or key == "twitter:creator":
        return None
    if key == "twitter:site":
        return "og:site_name"
    # Otherwise, swap twitter to og.
    return "og" + key[7:]


def parse_html_to_open_graph(tree: "etree.Element") -> Dict[str, Optional[str]]:
    """
    Parse the HTML document into an Open Graph response.

    This uses lxml to search the HTML document for Open Graph data (or
    synthesizes it from the document).

    Args:
        tree: The parsed HTML document.

    Returns:
        The Open Graph response as a dictionary.
    """

    # Search for Open Graph (og:) meta tags, e.g.:
    #
    # "og:type"         : "video",
    # "og:url"          : "https://www.youtube.com/watch?v=LXDBoHyjmtw",
    # "og:site_name"    : "YouTube",
    # "og:video:type"   : "application/x-shockwave-flash",
    # "og:description"  : "Fun stuff happening here",
    # "og:title"        : "RemoteJam - Matrix team hack for Disrupt Europe Hackathon",
    # "og:image"        : "https://i.ytimg.com/vi/LXDBoHyjmtw/maxresdefault.jpg",
    # "og:video:url"    : "http://www.youtube.com/v/LXDBoHyjmtw?version=3&autohide=1",
    # "og:video:width"  : "1280"
    # "og:video:height" : "720",
    # "og:video:secure_url": "https://www.youtube.com/v/LXDBoHyjmtw?version=3",

    og = _get_meta_tags(tree, "property", "og")

    # TODO: Search for properties specific to the different Open Graph types,
    # such as article: meta tags, e.g.:
    #
    # "article:publisher" : "https://www.facebook.com/thethudonline" />
    # "article:author" content="https://www.facebook.com/thethudonline" />
    # "article:tag" content="baby" />
    # "article:section" content="Breaking News" />
    # "article:published_time" content="2016-03-31T19:58:24+00:00" />
    # "article:modified_time" content="2016-04-01T18:31:53+00:00" />

    # Search for Twitter Card (twitter:) meta tags, e.g.:
    #
    # "twitter:site"    : "@matrixdotorg"
    # "twitter:creator" : "@matrixdotorg"
    #
    # Twitter cards tags also duplicate Open Graph tags.
    #
    # See https://developer.twitter.com/en/docs/twitter-for-websites/cards/guides/getting-started
    twitter = _get_meta_tags(tree, "name", "twitter", _map_twitter_to_open_graph)
    # Merge the Twitter values with the Open Graph values, but do not overwrite
    # information from Open Graph tags.
    for key, value in twitter.items():
        if key not in og:
            og[key] = value

    if "og:title" not in og:
        # Attempt to find a title from the title tag, or the biggest header on the page.
        title = tree.xpath("((//title)[1] | (//h1)[1] | (//h2)[1] | (//h3)[1])/text()")
        if title:
            og["og:title"] = title[0].strip()
        else:
            og["og:title"] = None

    if "og:image" not in og:
        meta_image = tree.xpath(
            "//*/meta[translate(@itemprop, 'IMAGE', 'image')='image'][not(@content='')]/@content[1]"
        )
        # If a meta image is found, use it.
        if meta_image:
            og["og:image"] = meta_image[0]
        else:
            # Try to find images which are larger than 10px by 10px.
            #
            # TODO: consider inlined CSS styles as well as width & height attribs
            images = tree.xpath("//img[@src][number(@width)>10][number(@height)>10]")
            images = sorted(
                images,
                key=lambda i: (
                    -1 * float(i.attrib["width"]) * float(i.attrib["height"])
                ),
            )
            # If no images were found, try to find *any* images.
            if not images:
                images = tree.xpath("//img[@src][1]")
            if images:
                og["og:image"] = images[0].attrib["src"]

            # Finally, fallback to the favicon if nothing else.
            else:
                favicons = tree.xpath("//link[@href][contains(@rel, 'icon')]/@href[1]")
                if favicons:
                    og["og:image"] = favicons[0]

    if "og:description" not in og:
        # Check the first meta description tag for content.
        meta_description = tree.xpath(
            "//*/meta[translate(@name, 'DESCRIPTION', 'description')='description'][not(@content='')]/@content[1]"
        )
        # If a meta description is found with content, use it.
        if meta_description:
            og["og:description"] = meta_description[0]
        else:
            og["og:description"] = parse_html_description(tree)
    elif og["og:description"]:
        # This must be a non-empty string at this point.
        assert isinstance(og["og:description"], str)
        og["og:description"] = summarize_paragraphs([og["og:description"]])

    # TODO: delete the url downloads to stop diskfilling,
    # as we only ever cared about its OG
    return og


def parse_html_description(tree: "etree.Element") -> Optional[str]:
    """
    Calculate a text description based on an HTML document.

    Grabs any text nodes which are inside the <body/> tag, unless they are within
    an HTML5 semantic markup tag (<header/>, <nav/>, <aside/>, <footer/>), or
    if they are within a <script/>, <svg/> or <style/> tag, or if they are within
    a tag whose content is usually only shown to old browsers
    (<iframe/>, <video/>, <canvas/>, <picture/>).

    This is a very very very coarse approximation to a plain text render of the page.

    Args:
        tree: The parsed HTML document.

    Returns:
        The plain text description, or None if one cannot be generated.
    """
    # We don't just use XPATH here as that is slow on some machines.

    from lxml import etree

    TAGS_TO_REMOVE = {
        "header",
        "nav",
        "aside",
        "footer",
        "script",
        "noscript",
        "style",
        "svg",
        "iframe",
        "video",
        "canvas",
        "img",
        "picture",
        etree.Comment,
    }

    # Split all the text nodes into paragraphs (by splitting on new
    # lines)
    text_nodes = (
        re.sub(r"\s+", "\n", el).strip()
        for el in _iterate_over_text(tree.find("body"), TAGS_TO_REMOVE)
    )
    return summarize_paragraphs(text_nodes)


def _iterate_over_text(
    tree: Optional["etree.Element"],
    tags_to_ignore: Set[Union[str, "etree.Comment"]],
    stack_limit: int = 1024,
) -> Generator[str, None, None]:
    """Iterate over the tree returning text nodes in a depth first fashion,
    skipping text nodes inside certain tags.

    Args:
        tree: The parent element to iterate. Can be None if there isn't one.
        tags_to_ignore: Set of tags to ignore
        stack_limit: Maximum stack size limit for depth-first traversal.
            Nodes will be dropped if this limit is hit, which may truncate the
            textual result.
            Intended to limit the maximum working memory when generating a preview.
    """

    if tree is None:
        return

    # This is a stack whose items are elements to iterate over *or* strings
    # to be returned.
    elements: List[Union[str, "etree.Element"]] = [tree]
    while elements:
        el = elements.pop()

        if isinstance(el, str):
            yield el
        elif el.tag not in tags_to_ignore:
            # If the element isn't meant for display, ignore it.
            if el.get("role") in ARIA_ROLES_TO_IGNORE:
                continue

            # el.text is the text before the first child, so we can immediately
            # return it if the text exists.
            if el.text:
                yield el.text

            # We add to the stack all the element's children, interspersed with
            # each child's tail text (if it exists).
            #
            # We iterate in reverse order so that earlier pieces of text appear
            # closer to the top of the stack.
            for child in el.iterchildren(reversed=True):
                if len(elements) > stack_limit:
                    # We've hit our limit for working memory
                    break

                if child.tail:
                    # The tail text of a node is text that comes *after* the node,
                    # so we always include it even if we ignore the child node.
                    elements.append(child.tail)

                elements.append(child)


def summarize_paragraphs(
    text_nodes: Iterable[str], min_size: int = 200, max_size: int = 500
) -> Optional[str]:
    """
    Try to get a summary respecting first paragraph and then word boundaries.

    Args:
        text_nodes: The paragraphs to summarize.
        min_size: The minimum number of words to include.
        max_size: The maximum number of words to include.

    Returns:
        A summary of the text nodes, or None if that was not possible.
    """

    # TODO: Respect sentences?

    description = ""

    # Keep adding paragraphs until we get to the MIN_SIZE.
    for text_node in text_nodes:
        if len(description) < min_size:
            text_node = re.sub(r"[\t \r\n]+", " ", text_node)
            description += text_node + "\n\n"
        else:
            break

    description = description.strip()
    description = re.sub(r"[\t ]+", " ", description)
    description = re.sub(r"[\t \r\n]*[\r\n]+", "\n\n", description)

    # If the concatenation of paragraphs to get above MIN_SIZE
    # took us over MAX_SIZE, then we need to truncate mid paragraph
    if len(description) > max_size:
        new_desc = ""

        # This splits the paragraph into words, but keeping the
        # (preceding) whitespace intact so we can easily concat
        # words back together.
        for match in re.finditer(r"\s*\S+", description):
            word = match.group()

            # Keep adding words while the total length is less than
            # MAX_SIZE.
            if len(word) + len(new_desc) < max_size:
                new_desc += word
            else:
                # At this point the next word *will* take us over
                # MAX_SIZE, but we also want to ensure that its not
                # a huge word. If it is add it anyway and we'll
                # truncate later.
                if len(new_desc) < min_size:
                    new_desc += word
                break

        # Double check that we're not over the limit
        if len(new_desc) > max_size:
            new_desc = new_desc[:max_size]

        # We always add an ellipsis because at the very least
        # we chopped mid paragraph.
        description = new_desc.strip() + "…"
    return description if description else None