def classify_passage_boson_url():
url = request.form['url']
content = get_content(url)
# print 'top_k=%s' % top_k
classify_result = requests.post('http://bosonnlp.com/analysis/category', {'data': content}).content
keyword_result = json.loads(
requests.post('http://bosonnlp.com/analysis/key?top_k=%s' % 100, {'data': content}).content)
class_dict = {0: u'体育', 1: u'教育', 2: u'财经', 3: u'社会',
4: u'娱乐', 5: u'军事', 6: u'国内',
7: u'科技', 8: '互联网', 9: u'房产', 10: u'国际',
11: u'女人', 12: u'汽车', 13: u'游戏'}
print classify_result
classify_result = int(re.compile('\d+').findall(classify_result)[0])
jieba_textrank = jieba.analyse.textrank(content, topK=15)
jieba_keywords = jieba.analyse.extract_tags(content, allowPOS=['n', 'vn', 'ns', 'v'], topK=15)
topic_list = TaggingUtils.passage_second_level_classify(content)
resp = make_response(
json.dumps({'code': 0, 'class': class_dict[classify_result], 'keyword': keyword_result,
'jieba_textrank': jieba_textrank, 'jieba_keywords': jieba_keywords,
'topic_list': topic_list},
ensure_ascii=False),
200)
return resp
def show_content_topic_prob(text_path):
test_text = open(text_path, 'r').read()
topics = TaggingUtils.passage_second_level_classify(test_text)
for i in topics:
print i['topic_tag'], i['topic_prob']
return topics
def show_content_topic_prob(text_path):
test_text = open(text_path, 'r').read()
topics = TaggingUtils.passage_second_level_classify(test_text)
for i in topics:
print i['topic_tag'], i['topic_prob']
return topics