def get(self):
# Read in global keywords
keywords = LoadKeywordsFromFile('data/SEG_Keywords.txt')
keywords = LoadKeywordsFromFile('data/seg2015.json_keywords.txt',
kw=keywords)
# Populate the keywords in the db
articles = Article.all().fetch(100000)
for article in articles:
if (article.title is None) or (article.abstract is None):
continue
new_kw, prob, ind = GenerateKeywords(article.abstract,
article.title,
article.keywords,
keywords)
article.keywords = list(new_kw)
article.kw_prob = prob
article.kw_ind = ind
article.put()
Keywords(data=list(keywords)).put()
self.response.out.write('worky worky')
def get(self):
self.response.headers['Content-Type'] = 'application/json'
articles = Article.all().fetch(10)
arts = []
kw = []
authors = []
for art in articles:
arts.append(art.json)
kw += art.keywords
authors += art.authors
keywords = [kw[i] for i in randint(0, len(kw), 15)]
authors = [authors[i] for i in randint(0, len(authors), 5)]
keywords = list(set(keywords))
authors = list(set(keywords))
self.response.out.write(json.dumps({'keywords': keywords,
'authors': authors,
'suggestions': arts}))
def get(self):
self.response.headers['Content-Type'] = 'application/json'
AuthorQuery = self.request.get('authors')
kqv = self.request.get('keywords')
W0 = 2.0
W1 = 1.0
W2 = 0.5
articles = Article.all().fetch(100000)
scores = []
for article in articles:
authors = article.authors
kv = article.kw_prob # get the keyword vector
citation = article.citedby
score = (W0 * AuthorScore(AuthorQuery, authors) +
W1 * KeywordScore(kqv, kv) +
W2 * CitationAuthorScore(AuthorQuery, citation))
scores.append(score)
self.request.out.write('success')