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commit ad3d52ade4ad2af305b81a5d2bc18b305e513815 0 parents
@gfxmonk authored
1  .gitignore
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+*.pyc
10 README
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+This code is under the Apache License 2.0. http://www.apache.org/licenses/LICENSE-2.0
+
+This is a python port of a ruby port of arc90's readability project
+
+http://lab.arc90.com/experiments/readability/
+
+Given a html document, it pulls out the main body text and cleans it up.
+
+Ruby port by starrhorne and iterationlabs
+Python port by gfxmonk
1  readability/__init__.py
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+from readability import Document, main
320 readability/readability.py
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+#!/usr/bin/env python
+from BeautifulSoup import BeautifulSoup, NavigableString
+import re
+
+REGEXES = { 'unlikelyCandidatesRe': re.compile('combx|comment|disqus|foot|header|menu|meta|nav|rss|shoutbox|sidebar|sponsor',re.I),
+ 'okMaybeItsACandidateRe': re.compile('and|article|body|column|main',re.I),
+ 'positiveRe': re.compile('article|body|content|entry|hentry|page|pagination|post|text',re.I),
+ 'negativeRe': re.compile('combx|comment|contact|foot|footer|footnote|link|media|meta|promo|related|scroll|shoutbox|sponsor|tags|widget',re.I),
+ 'divToPElementsRe': re.compile('<(a|blockquote|dl|div|img|ol|p|pre|table|ul)',re.I),
+ 'replaceBrsRe': re.compile('(<br[^>]*>[ \n\r\t]*){2,}',re.I),
+ 'replaceFontsRe': re.compile('<(\/?)font[^>]*>',re.I),
+ 'trimRe': re.compile('^\s+|\s+$/'),
+ 'normalizeRe': re.compile('\s{2,}/'),
+ 'killBreaksRe': re.compile('(<br\s*\/?>(\s|&nbsp;?)*){1,}/'),
+ 'videoRe': re.compile('http:\/\/(www\.)?(youtube|vimeo)\.com', re.I),
+}
+
+from collections import defaultdict
+def describe(node):
+ if not hasattr(node, 'name'):
+ return "[text]"
+ return "%s#%s.%s" % (
+ node.name, node.get('id', ''), node.get('class',''))
+
+class Document:
+ TEXT_LENGTH_THRESHOLD = 25
+ RETRY_LENGTH = 250
+
+ def __init__(self, input, **options):
+ self.input = input
+ self.options = defaultdict(lambda: None)
+ for k, v in options.items():
+ self.options[k] = v
+ self.make_html()
+
+ def make_html(self):
+ self.html = BeautifulSoup(self.input)
+
+
+ def content(self, remove_unlikely_candidates = True):
+ def remove(tag): [i.extract() for i in self.html.findAll(tag)]
+ remove('script')
+ remove('style')
+
+ if remove_unlikely_candidates: self.remove_unlikely_candidates()
+ self.transform_misused_divs_into_paragraphs()
+ candidates = self.score_paragraphs(self.options.get('min_text_length', self.TEXT_LENGTH_THRESHOLD))
+ best_candidate = self.select_best_candidate(candidates)
+ article = self.get_article(candidates, best_candidate)
+
+ cleaned_article = self.sanitize(article, candidates)
+ if remove_unlikely_candidates and len(cleaned_article or '') < (self.options['retry_length'] or self.RETRY_LENGTH):
+ self.make_html()
+ return self.content(False)
+ else:
+ return cleaned_article
+
+ def get_article(self, candidates, best_candidate):
+ # Now that we have the top candidate, look through its siblings for content that might also be related.
+ # Things like preambles, content split by ads that we removed, etc.
+
+ sibling_score_threshold = max([10, best_candidate['content_score'] * 0.2])
+ output = BeautifulSoup("<div/>")
+ for sibling in best_candidate['elem'].parent.contents:
+ if isinstance(sibling, NavigableString): continue
+ append = False
+ if sibling is best_candidate['elem']:
+ append = True
+ sibling_key = HashableElement(sibling)
+ if sibling_key in candidates and candidates[sibling_key]['content_score'] >= sibling_score_threshold:
+ append = True
+
+ if sibling.name == "p":
+ link_density = self.get_link_density(sibling)
+ node_content = sibling.string or ""
+ node_length = len(node_content)
+
+ if node_length > 80 and link_density < 0.25:
+ append = True
+ elif node_length < 80 and link_density == 0 and re.search('\.( |$)', node_content):
+ append = True
+
+ if append:
+ output.append(sibling)
+
+ return output
+
+ def select_best_candidate(self, candidates):
+ sorted_candidates = sorted(candidates.values(), key=lambda x: x['content_score'], reverse=True)
+
+ self.debug("Top 5 canidates:")
+ for candidate in sorted_candidates[:5]:
+ elem = candidate['elem']
+ self.debug("Candidate %s with score %s" % (
+ describe(elem), candidate['content_score']))
+
+ best_candidate = sorted_candidates[0] if len(sorted_candidates) > 1 else { 'elem': self.html.find("body"), 'content_score': 0 }
+ elem = best_candidate['elem']
+ self.debug("Best candidate %s#%s.%s with score %s" % (
+ elem.name, elem.get('id',''), elem.get('class',''), best_candidate['content_score']))
+
+ return best_candidate
+
+ def get_link_density(self, elem):
+ link_length = len("".join([i.text or "" for i in elem.findAll("a")]))
+ text_length = len(elem.text or "")
+ return float(link_length) / max(text_length, 1)
+
+ def score_paragraphs(self, min_text_length):
+ candidates = {}
+ elems = self.html.findAll("p") + self.html.findAll("td")
+
+ for elem in elems:
+ parent_node = elem.parent
+ grand_parent_node = parent_node.parent
+ parent_key = HashableElement(parent_node)
+ grand_parent_key = HashableElement(grand_parent_node)
+
+ inner_text = elem.string
+
+ # If this paragraph is less than 25 characters, don't even count it.
+ if (not inner_text) or len(inner_text) < min_text_length:
+ continue
+
+ if parent_key not in candidates:
+ candidates[parent_key] = self.score_node(parent_node)
+ if grand_parent_node and grand_parent_key not in candidates:
+ candidates[grand_parent_key] = self.score_node(grand_parent_node)
+
+ content_score = 1
+ content_score += len(inner_text.split(','))
+ content_score += min([(len(inner_text) / 100), 3])
+
+ candidates[parent_key]['content_score'] += content_score
+ if grand_parent_node:
+ candidates[grand_parent_key]['content_score'] += content_score / 2.0
+
+ # Scale the final candidates score based on link density. Good content should have a
+ # relatively small link density (5% or less) and be mostly unaffected by this operation.
+ for elem, candidate in candidates.items():
+ candidate['content_score'] = candidate['content_score'] * (1 - self.get_link_density(elem))
+
+ return candidates
+
+ def class_weight(self, e):
+ weight = 0
+ if e.get('class', None):
+ if REGEXES['negativeRe'].search(e['class']):
+ weight -= 25
+
+ if REGEXES['positiveRe'].search(e['class']):
+ weight += 25
+
+ if e.get('id', None):
+ if REGEXES['negativeRe'].search(e['id']):
+ weight -= 25
+
+ if REGEXES['positiveRe'].search(e['id']):
+ weight += 25
+
+ return weight
+
+ def score_node(self, elem):
+ content_score = self.class_weight(elem)
+ name = elem.name.lower()
+ if name == "div":
+ content_score += 5
+ elif name == "blockquote":
+ content_score += 3
+ elif name == "form":
+ content_score -= 3
+ elif name == "th":
+ content_score -= 5
+ return { 'content_score': content_score, 'elem': elem }
+
+ def debug(self, str):
+ if self.options['debug']:
+ print(str)
+
+ def remove_unlikely_candidates(self):
+ for elem in self.html.findAll():
+ s = "%s%s" % (elem.get('class', ''), elem.get('id'))
+ if REGEXES['unlikelyCandidatesRe'].search(s) and (not REGEXES['okMaybeItsACandidateRe'].search(s)) and elem.name != 'body':
+ self.debug("Removing unlikely candidate - %s" % (s,))
+ elem.extract()
+
+ def transform_misused_divs_into_paragraphs(self):
+ for elem in self.html.findAll():
+ if elem.name.lower() == "div":
+ # transform <div>s that do not contain other block elements into <p>s
+ if REGEXES['divToPElementsRe'].search(''.join(map(str, elem.contents))):
+ self.debug("Altering div(#%s.%s) to p" % (elem.get('id', ''), elem.get('class', '')))
+ elem.name = "p"
+
+ def tags(self, node, *tag_names):
+ for tag_name in tag_names:
+ for e in node.findAll(tag_name):
+ yield e
+
+ def sanitize(self, node, candidates):
+ for header in self.tags(node, "h1", "h2", "h3", "h4", "h5", "h6"):
+ if self.class_weight(header) < 0 or self.get_link_density(header) > 0.33: header.extract()
+
+ for elem in self.tags(node, "form", "object", "iframe", "embed"):
+ elem.extract()
+
+ # remove empty <p> tags
+ for elem in node.findAll("p"):
+ if not (elem.string or elem.contents):
+ elem.extract()
+
+ # Conditionally clean <table>s, <ul>s, and <div>s
+ for el in self.tags(node, "table", "ul", "div"):
+ weight = self.class_weight(el)
+ el_key = HashableElement(el)
+ if el_key in candidates:
+ content_score = candidates[el_key]['content_score']
+ else:
+ content_score = 0
+ name = el.name
+
+ if weight + content_score < 0:
+ el.extract()
+ self.debug("Conditionally cleaned %s with weight %s and content score %s because score + content score was less than zero." %
+ (describe(el), weight, content_score))
+ elif len((el.text or "").split(",")) < 10:
+ counts = {}
+ for kind in ['p', 'img', 'li', 'a', 'embed', 'input']:
+ counts[kind] = len(el.findAll(kind))
+ counts["li"] -= 100
+
+ content_length = len(el.text or "") # Count the text length excluding any surrounding whitespace
+ link_density = self.get_link_density(el)
+ to_remove = False
+ reason = ""
+
+ if counts["img"] > counts["p"]:
+ reason = "too many images"
+ to_remove = True
+ elif counts["li"] > counts["p"] and name != "ul" and name != "ol":
+ reason = "more <li>s than <p>s"
+ to_remove = True
+ elif counts["input"] > (counts["p"] / 3):
+ reason = "less than 3x <p>s than <input>s"
+ to_remove = True
+ elif content_length < (self.options.get('min_text_length', self.TEXT_LENGTH_THRESHOLD)) and (counts["img"] == 0 or counts["img"] > 2):
+ reason = "too short a content length without a single image"
+ to_remove = True
+ elif weight < 25 and link_density > 0.2:
+ reason = "too many links for its weight (#{weight})"
+ to_remove = True
+ elif weight >= 25 and link_density > 0.5:
+ reason = "too many links for its weight (#{weight})"
+ to_remove = True
+ elif (counts["embed"] == 1 and content_length < 75) or counts["embed"] > 1:
+ reason = "<embed>s with too short a content length, or too many <embed>s"
+ to_remove = True
+
+ if to_remove:
+ self.debug("Conditionally cleaned %s#%s.%s with weight %s and content score %s because it has %s." %
+ (el.name, el.get('id',''), el.get('class', ''), weight, content_score, reason))
+ el.extract()
+
+ for el in ([node] + node.findAll()):
+ if not (self.options['attributes']):
+ el.attrMap = {}
+
+ return str(node)
+
+class HashableElement():
+ def __init__(self, node):
+ self.node = node
+ self._path = None
+
+ def _get_path(self):
+ if self._path is None:
+ reverse_path = []
+ node = self.node
+ while node:
+ node_id = (node.name, tuple(node.attrs), node.string)
+ reverse_path.append(node_id)
+ node = node.parent
+ self._path = tuple(reverse_path)
+ return self._path
+ path = property(_get_path)
+
+ def __hash__(self):
+ return hash(self.path)
+
+ def __eq__(self, other):
+ return self.path == other.path
+
+ def __getattr__(self, name):
+ return getattr(self.node, name)
+
+def main():
+ import sys
+ from optparse import OptionParser
+ parser = OptionParser(usage="%prog: [options] [file]")
+ parser.add_option('-v', '--verbose', action='store_true')
+ parser.add_option('-u', '--url', help="use URL instead of a local file")
+ (options, args) = parser.parse_args()
+
+ if not (len(args) == 1 or options.url):
+ parser.print_help()
+ sys.exit(1)
+
+ file = None
+ if options.url:
+ import urllib
+ file = urllib.urlopen(options.url)
+ else:
+ file = open(args[0])
+ try:
+ print Document(file.read(), debug=options.verbose).content()
+ finally:
+ file.close()
+
+if __name__ == '__main__':
+ main()

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