def crawl(self, crawlCandidate):
article = Article()
parseCandidate = URLHelper.getCleanedUrl(crawlCandidate.url)
rawHtml = self.getHTML(crawlCandidate, parseCandidate)
if rawHtml is None:
return article
doc = self.getDocument(parseCandidate.url, rawHtml)
extractor = self.getExtractor()
docCleaner = self.getDocCleaner()
outputFormatter = self.getOutputFormatter()
# article
article.finalUrl = parseCandidate.url
article.linkhash = parseCandidate.linkhash
article.rawHtml = rawHtml
article.doc = doc
article.rawDoc = deepcopy(doc)
article.title = extractor.getTitle(article)
# TODO
# article.publishDate = config.publishDateExtractor.extract(doc)
# article.additionalData = config.getAdditionalDataExtractor.extract(doc)
article.metaLang = extractor.getMetaLang(article)
article.metaFavicon = extractor.getMetaFavicon(article)
article.metaDescription = extractor.getMetaDescription(article)
article.metaKeywords = extractor.getMetaKeywords(article)
article.canonicalLink = extractor.getCanonicalLink(article)
article.domain = extractor.getDomain(article.finalUrl)
article.tags = extractor.extractTags(article)
# # before we do any calcs on the body itself let's clean up the document
article.doc = docCleaner.clean(article)
# big stuff
article.topNode = extractor.calculateBestNodeBasedOnClustering(article)
if article.topNode is not None:
# TODO
# movies and images
# article.movies = extractor.extractVideos(article.topNode)
if self.config.enableImageFetching:
imageExtractor = self.getImageExtractor(article)
article.topImage = imageExtractor.getBestImage(article.rawDoc, article.topNode)
article.topNode = extractor.postExtractionCleanup(article.topNode)
article.cleanedArticleText = outputFormatter.getFormattedText(article)
article.topNode.attrib['rel'] = 'topnode' # mark html element
article.h1 = extractor.getH1(article)
# cleanup tmp file
self.releaseResources(article)
return article
def crawl(self, crawl_candidate):
article = Article()
parse_candidate = URLHelper.getCleanedUrl(crawl_candidate.url)
raw_html = self.get_html(crawl_candidate, parse_candidate)
if raw_html is None:
return article
doc = self.get_document(parse_candidate.url, raw_html)
extractor = self.get_extractor()
document_cleaner = self.get_document_cleaner()
output_formatter = self.get_output_formatter()
# article
article.final_url = parse_candidate.url
article.link_hash = parse_candidate.link_hash
article.raw_html = raw_html
article.doc = doc
article.raw_doc = deepcopy(doc)
article.title = extractor.get_title(article)
# TODO
# article.publish_date = config.publishDateExtractor.extract(doc)
# article.additional_data = config.get_additionaldata_extractor.extract(doc)
article.meta_lang = extractor.get_meta_lang(article)
article.meta_favicon = extractor.get_favicon(article)
article.meta_description = extractor.get_meta_description(article)
article.meta_keywords = extractor.get_meta_keywords(article)
article.canonical_link = extractor.get_canonical_link(article)
article.domain = extractor.get_domain(article.final_url)
article.tags = extractor.extract_tags(article)
# # before we do any calcs on the body itself let's clean up the document
article.doc = document_cleaner.clean(article)
# big stuff
article.top_node = extractor.calculate_best_node(article)
if article.top_node is not None:
# TODO
# movies and images
# article.movies = extractor.extractVideos(article.top_node)
if self.config.enable_image_fetching:
image_extractor = self.get_image_extractor(article)
article.top_image = image_extractor.get_best_image(article.raw_doc, article.top_node)
article.top_node = extractor.post_cleanup(article.top_node)
article.cleaned_text = output_formatter.get_formatted_text(article)
# cleanup tmp file
self.relase_resources(article)
return article
def crawl(self, crawlCandidate):
article = Article()
parseCandidate = URLHelper.getCleanedUrl(crawlCandidate.url)
rawHtml = self.getHTML(crawlCandidate, parseCandidate)
if rawHtml is None:
return article
doc = self.getDocument(parseCandidate.url, rawHtml)
extractor = self.getExtractor()
docCleaner = self.getDocCleaner()
outputFormatter = self.getOutputFormatter()
# article
article.finalUrl = parseCandidate.url
article.linkhash = parseCandidate.linkhash
article.rawHtml = rawHtml
article.doc = doc
article.rawDoc = deepcopy(doc)
article.title = extractor.getTitle(article)
# TODO
# article.publishDate = config.publishDateExtractor.extract(doc)
# article.additionalData = config.getAdditionalDataExtractor.extract(doc)
article.metaLang = extractor.getMetaLang(article)
article.metaFavicon = extractor.getMetaFavicon(article)
article.metaDescription = extractor.getMetaDescription(article)
article.metaKeywords = extractor.getMetaKeywords(article)
article.canonicalLink = extractor.getCanonicalLink(article)
article.domain = extractor.getDomain(article.finalUrl)
article.tags = extractor.extractTags(article)
# # before we do any calcs on the body itself let's clean up the document
article.doc = docCleaner.clean(article)
# big stuff
article.topNode = extractor.calculateBestNodeBasedOnClustering(article)
if article.topNode is not None:
# TODO
# movies and images
# article.movies = extractor.extractVideos(article.topNode)
if self.config.enableImageFetching:
imageExtractor = self.getImageExtractor(article)
article.topImage = imageExtractor.getBestImage(
article.rawDoc, article.topNode)
article.topNode = extractor.postExtractionCleanup(article.topNode)
article.cleanedArticleText = outputFormatter.getFormattedText(
article)
return article
def crawl(self, crawlCandidate):
article = Article()
parseCandidate = URLHelper.getCleanedUrl(crawlCandidate.url)
rawHtml = self.getHTML(crawlCandidate, parseCandidate)
if rawHtml is None:
return article
doc = self.getDocument(parseCandidate.url, rawHtml)
extractor = self.getExtractor()
docCleaner = self.getDocCleaner()
outputFormatter = self.getOutputFormatter()
# article
article.finalUrl = parseCandidate.url
article.linkhash = parseCandidate.linkhash
article.rawHtml = rawHtml
article.doc = doc
article.rawDoc = deepcopy(doc)
article.title = extractor.getTitle(article)
article.metaLang = extractor.getMetaLang(article)
article.metaFavicon = extractor.getMetaFavicon(article)
article.metaDescription = extractor.getMetaDescription(article)
article.metaKeywords = extractor.getMetaKeywords(article)
article.canonicalLink = extractor.getCanonicalLink(article)
article.domain = extractor.getDomain(article.finalUrl)
article.tags = extractor.extractTags(article)
# if the user requested a full body response
article.doc = docCleaner.clean(article)
# big stuff
article.topNode = extractor.calculateBestNodeBasedOnClustering(article)
if article.topNode is not None and any(
[self.config.enableImageFetching, self.config.enableBodyAnalysis]):
if self.config.enableImageFetching:
imageExtractor = self.getImageExtractor(article)
article.topImage = imageExtractor.getBestImage(
article.rawDoc, article.topNode)
if self.config.enableBodyAnalysis:
article.topNode = extractor.postExtractionCleanup(
article.topNode)
article.cleanedArticleText = outputFormatter.getFormattedText(
article.topNode)
return article
def crawl(self, crawlCandidate):
article = Article()
parseCandidate = URLHelper.getCleanedUrl(crawlCandidate.url)
rawHtml = self.getHTML(crawlCandidate, parseCandidate)
if rawHtml is None:
return article
doc = self.getDocument(parseCandidate.url, rawHtml)
extractor = self.getExtractor()
docCleaner = self.getDocCleaner()
outputFormatter = self.getOutputFormatter()
# article
article.finalUrl = parseCandidate.url
article.linkhash = parseCandidate.linkhash
article.rawHtml = rawHtml
article.doc = doc
article.rawDoc = deepcopy(doc)
article.title = extractor.getTitle(article)
article.metaLang = extractor.getMetaLang(article)
article.metaFavicon = extractor.getMetaFavicon(article)
article.metaDescription = extractor.getMetaDescription(article)
article.metaKeywords = extractor.getMetaKeywords(article)
article.canonicalLink = extractor.getCanonicalLink(article)
article.domain = extractor.getDomain(article.finalUrl)
article.tags = extractor.extractTags(article)
# if the user requested a full body response
article.doc = docCleaner.clean(article)
# big stuff
article.topNode = extractor.calculateBestNodeBasedOnClustering(article)
if article.topNode is not None and any([self.config.enableImageFetching, self.config.enableBodyAnalysis]):
if self.config.enableImageFetching:
imageExtractor = self.getImageExtractor(article)
article.topImage = imageExtractor.getBestImage(article.rawDoc, article.topNode)
if self.config.enableBodyAnalysis:
article.topNode = extractor.postExtractionCleanup(article.topNode)
article.cleanedArticleText = outputFormatter.getFormattedText(article.topNode)
return article
def get_parse_candidate(self, crawl_candidate):
if crawl_candidate.raw_html:
return RawHelper.get_parsing_candidate(crawl_candidate.url, crawl_candidate.raw_html)
return URLHelper.get_parsing_candidate(crawl_candidate.url)
def get_parse_candidate(self, crawl_candidate):
if crawl_candidate.raw_html:
return RawHelper.get_parsing_candidate(crawl_candidate.url,
crawl_candidate.raw_html)
return URLHelper.get_parsing_candidate(crawl_candidate.url)
