initial

Author: gfxmonk

.gitignore

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+*.pyc

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

readability/__init__.py

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+from readability import Document, main

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()