def mention():
print("请求方式为:", request.method)
mention = request.args.to_dict().get('mention', '')
if mention == '':
return "你可能忘记提问了, 试试/mention?mention=泡泡糖"
print(mention)
answers = mention2entity(mention)
print(answers)
return json.dumps(answers, ensure_ascii=False)
def show_demo():
print("请求方式为:", request.method)
entity = request.args.to_dict().get('entity', '')
# mention = request.args.to_dict().get('mention','')
if entity == '':
return "请确定要查询的功能!"
elif "ad_social:" in entity:
print(entity)
line = entity.split(':')
params = line[1].split(",")
contents = advogato_data_KG_source('source', params[0], int(params[1]))
print(contents)
with open("static/data/entity_relationships.json", 'w') as data:
json.dump(contents, data)
# name = {"entity":entity, "level":params[1]}
name = {}
name['entity'] = str(params[0])
name['level'] = str(params[1])
print(name)
return render_template('entity_relationships.html', contents=name)
elif "mention:" in entity:
print(entity)
line = entity.strip().split(":")
mention = line[1]
contents = mention2entity(mention)
print(contents)
with open("static/data/entity_mention.json", 'w') as data:
json.dump(contents, data)
urls = {}
urls['mention'] = mention
return render_template('entity_mention.html', contents=urls)
elif "ad_trust:" in entity:
print(entity)
line = entity.strip().split(":")
entities = line[1].split(",")
source = entities[0]
target = entities[1]
cutoff = entities[2]
print(source, target, cutoff)
contents = get_paths(source, target, cutoff, '')
with open("static/data/entity_entity_paths.json", 'w') as data:
json.dump(contents, data)
paths = {}
paths['source'] = str(source)
paths['target'] = str(target)
paths['cutoff'] = str(cutoff)
print(contents)
if int(cutoff) == 1:
paths['trust'] = get_trust_value_1(source, target)
elif len(contents['links']) == 0:
paths['trust'] = 0.0
else:
paths['trust'] = get_trust_value(source, target, cutoff)
print(paths)
return render_template('entity_entity_paths.html', contents=paths)
elif "nlp:" in entity:
print(entity)
line = entity.strip().split(":")
statement = line[1]
contents = lexer(statement)
with open("static/data/nlp.json", 'w') as data:
json.dump(contents, data)
return render_template('nlp.html')
elif 'emotion' in entity:
print(entity)
line = entity.strip().split(":")
statement = line[1]
mydata = emotion(statement)
return render_template('sentiment.html', contents=mydata)
elif 'weibo' in entity:
line = entity.strip().split(":")
username = line[1]
print("username:"******"user_id:", user_id)
if user_id == None:
return "请确保输入的用户名正确无误~"
contents, username = generate_weibo_user_graph(user_id=int(user_id))
print(user_id)
if contents == '':
return "抱歉，因你过于帅气，我自己都凌乱了。"
with open("static/data/weibo.json", 'w') as data:
json.dump(contents, data)
mydata = {}
mydata['user_id'] = user_id
mydata['username'] = username
return render_template('weibo_graph.html', contents=mydata)
elif 'network' in entity:
line = entity.strip().split(":")
entity = line[1]
if entity == "特朗普":
entity = "唐纳德·特朗普"
if entity == '李小勇':
entity = '李小勇[北京邮电大学教授]'
file_name = "entity_attribution_{}.json"
file_path = "static/data/entity_attr/" + file_name.format(entity)
result = os.path.exists(file_path)
baidu = {}
baike_url = get_baike_url(entity) # 百度百科的url
baidu['url'] = baike_url
baidu['entity'] = entity
baidu[
'url'] = "http://47.105.58.24:8989/" + "static/data/links/" + file_name.format(
entity)
if result:
print("已经有这个文件了")
return render_template('entity_attribution.html', contents=baidu)
contents, baike_url_2 = KG_View_2(entity)
if '李小勇' in entity:
contents = generate_lxy_contents()
if len(contents) == 0:
return "输入的实体暂不支持，请检查合法性后重新输入。"
if entity == "唐纳德·特朗普":
entity = "特朗普"
related_urls = []
#related_urls = get_related_urls(entity)
if entity == "特朗普":
entity = "唐纳德·特朗普"
related_urls.append(baike_url)
print("baike_url:", baike_url)
with open("static/data/links/" + file_name.format(entity),
'w') as data:
for url in related_urls:
data.write(url + "\n")
data.close()
with open("static/data/entity_attr/" + file_name.format(entity),
'w') as data:
json.dump(contents, data)
return render_template('entity_attribution.html', contents=baidu)
elif "fb_social" in entity:
print(entity)
line = entity.split(':')
params = line[1].split(",")
entity_id = params[0]
entity_name = get_entity_name(entity_id)
contents = advogato_data_KG_source('freebase', entity_name,
int(params[1]))
print(contents)
with open("static/data/entity_relationships.json", 'w') as data:
json.dump(contents, data)
name = {}
name['entity'] = str(entity_name)
name['level'] = str(params[1])
print(name)
return render_template('entity_relationships.html', contents=name)
elif "fb_relation" in entity:
print(entity)
line = entity.strip().split(":")
entities = line[1].split(",")
source = entities[0]
target = entities[1]
# cutoff = entities[2]
print(source, target)
contents = get_paths(source, target, 1, "freebase")
infos = get_freebase_info(source, target)
with open("static/data/entity_entity_paths.json", 'w') as data:
json.dump(contents, data)
paths = {}
paths['source'] = str(source)
paths['target'] = str(target)
paths['cutoff'] = 1
paths['trust'] = round(float(infos.get('trust', '0')), 3)
paths['max_trust'] = infos.get('max_trust_relation', '')
print(paths)
return render_template('freebase_relation_trust.html', contents=paths)
else:
return "输入格式不合法，请检查后重新输入"
def show_demo():
print("请求方式为:", request.method)
entity = request.args.to_dict().get('entity', '')
# mention = request.args.to_dict().get('mention','')
if entity == '':
return render_template('demo.html')
elif "source:" in entity:
print(entity)
line = entity.split(':')
params = line[1].split(",")
contents = advogato_data_KG_source('source', params[0], int(params[1]))
print(contents)
with open("static/data/entity_relationships.json", 'w') as data:
json.dump(contents, data)
# name = {"entity":entity, "level":params[1]}
name = {}
name['entity'] = str(params[0])
name['level'] = str(params[1])
print(name)
return render_template('entity_relationships.html', contents=name)
elif "target:" in entity:
print(entity)
line = entity.split(':')
contents = advogato_data_KG_target('target', line[1])
print(contents)
return render_template('index.html', contents=contents)
elif "mention:" in entity:
print(entity)
line = entity.strip().split(":")
mention = line[1]
contents = mention2entity(mention)
print(contents)
with open("static/data/entity_mention.json", 'w') as data:
json.dump(contents, data)
urls = {}
urls['mention'] = mention
return render_template('entity_mention.html', contents=urls)
elif "attr:" in entity:
print(entity)
line = entity.strip().split(":")
user = line[1]
contents = get_page_rank(user)
print(contents)
return render_template('index.html', contents=contents)
elif "path:" in entity:
print(entity)
line = entity.strip().split(":")
entities = line[1].split(",")
source = entities[0]
target = entities[1]
cutoff = entities[2]
print(source, target, cutoff)
contents = get_paths(source, target, cutoff)
with open("static/data/entity_entity_paths.json", 'w') as data:
json.dump(contents, data)
paths = {}
paths['source'] = str(source)
paths['target'] = str(target)
paths['cutoff'] = str(cutoff)
print(paths)
return render_template('entity_entity_paths.html', contents=paths)
elif "nlp:" in entity:
print(entity)
line = entity.strip().split(":")
statement = line[1]
contents = lexer(statement) # 百度的api
# slu = Analysis(statement)
# data = slu.analysis() # 解析
# print(data) # 总的结果
# print('--------------------------')
# print("分词：", slu.cws) # 分词
# print("词性标注", slu.pos) # 词性标注
# print("命名实体识别", slu.ner) # 命名实体识别
# print("领域分类", slu.domain) # 领域分类
# print("意图识别", slu.intent) # 意图识别
# print("槽填充", slu.slot) # 槽填充
# contents = {}
# contents["type"] = "force"
# categories = []
# no_repeat_categories = set()
# for category in slu.pos:
# no_repeat_categories.add(category)
# no_repeat_categories = list(no_repeat_categories)
# categories_to_int = {}
# cnt = 0
# for category in no_repeat_categories:
# node = {'name':category, 'keyword':{}, 'base':category}
# categories.append(node)
# categories_to_int[category] = cnt
# cnt += 1
# node = {'name':'文本语句', 'keyword':{}, 'base':'文本语句'}
# categories.append(node)
# categories_to_int['文本语句'] = cnt
# contents['categories'] = categories
# nodes = []
# links = []
# node = {'name': statement,'value': statement,'category': categories_to_int['文本语句']}
# nodes.append(node)
# cnt = 0
# for word in slu.cws:
# node = {'name':word, 'value':str(word), 'category':categories_to_int[slu.pos[cnt]]}
# link = {'source':0, 'target':cnt+1, 'value':slu.pos[cnt]}
# nodes.append(node)
# links.append(link)
# cnt += 1
# contents['nodes'] = nodes
# contents['links'] = links
with open("static/data/nlp.json", 'w') as data:
json.dump(contents, data)
return render_template('nlp.html')
elif 'emotion' in entity:
print(entity)
line = entity.strip().split(":")
statement = line[1]
mydata = emotion(statement)
# results = sentiment(statement)
# positive = []
# negative = []
# print(results)
# for model in models:
# result = results[model_to_name[model]]
# print(result)
# positive.append(result[0][0])
# negative.append(result[0][1])
# mydata = {}
# mydata['positive'] = positive
# mydata['negative'] = negative
# mydata['statement'] = statement
return render_template('sentiment.html', contents=mydata)
elif 'weibo' in entity:
print(entity)
line = entity.strip().split(":")
user_id = line[1]
contents, username = generate_weibo_user_graph(user_id=user_id)
if contents == '':
return "抱歉，因你过于帅气，我自己都凌乱了。"
with open("static/data/weibo.json", 'w') as data:
json.dump(contents, data)
mydata = {}
mydata['user_id'] = user_id
mydata['username'] = username
return render_template('weibo_graph.html', contents=mydata)
else:
file_name = "entity_attribution_{}.json"
file_path = "static/data/entity_attr/" + file_name.format(entity)
result = os.path.exists(file_path)
baidu = {}
baike_url = get_baike_url(entity) # 百度百科的url
baidu['url'] = baike_url
baidu['entity'] = entity
if result:
print("已经有这个文件了")
return render_template('entity_attribution.html', contents=baidu)
contents, baike_url = KG_View_2(entity)
print(contents)
print(baike_url)
with open("static/data/entity_attr/" + file_name.format(entity),
'w') as data:
json.dump(contents, data)
return render_template('entity_attribution.html', contents=baidu)