def retry():
global choice_bodies, candidates, top
top += 1
if top + 1 >= len(candidates):
r = HTTPResponse(status=500)
r.set_header('Content-Type', 'application/json')
r.set_header('Access-Control-Allow-Origin', '*')
return r
restaurant_db = DBModel('gourmet', 'localhost', 'foo', 'bar')
name_result = restaurant_db.select('SELECT name FROM restaurants WHERE id={}'.format(
choice_bodies[top]['_source']['restaurant_id']
))
address_result = restaurant_db.select(
'SELECT address FROM restaurants WHERE id={}'.format(
choice_bodies[top]['_source']['restaurant_id']
)
)
restaurant = 'こちらのお店はいかがでしょうか？\n\n店名 : {}'.format(name_result[0])
if address_result[0] != 'nan':
restaurant += '\n住所 : {}'.format(address_result[0])
if len(candidates[top]) == 0 or candidates[top] is None:
recommend = 'このお店に行ったことがある人は，残念ながら見つかりませんでした．'
else:
recommend = 'このお店に行ったことがある人は次のような感想を述べています :'
for candidate in candidates[top]:
recommend += '\n\n・{}'.format(candidate)
dicts = [
{'restaurant': restaurant, 'recommend': recommend}
]
r = HTTPResponse(status=200, body=json.dumps(dicts, ensure_ascii=False))
r.set_header('Content-Type', 'application/json')
r.set_header('Access-Control-Allow-Origin', '*')
return r
def get_reply(query):
global candidates, choice_bodies, top
candidates = []
choice_bodies = []
top = 0
parser.drop_morph(query)
word_pairs = word2vec_model.similar_words(parser.words)
word_pairs = word2vec_model.most_significant_word_pairs(word_pairs)
terms = [word for word, _ in word_pairs]
elastic_results = elastic_model.search_terms(terms)
bodies = elastic_results[0]
analyst = Analyst(word2vec_model, specific_parts=['普通名詞', '地名', '固有名詞', '組織名'])
all_sum_scores = []
all_scores = []
all_candidates = []
for body in elastic_results[0]:
analyst.parse(body['_source']['body'])
candidate_scores = np.array(analyst.calc_candidate_score())
query_base_scores = np.array(analyst.calc_query_base_score(parser.words))
scores = list(candidate_scores + query_base_scores)
all_sum_scores.append(sum(scores) / (len(scores) if len(scores) != 0 else 1))
all_scores.append(scores)
all_candidates.append(analyst.candidates)
indices = np.argsort(all_sum_scores)[::-1]
for index in indices:
choice_bodies.append(bodies[index])
candidates_ = analyst.most_significant_candidates(
all_scores[index],
all_candidates[index]
)
candidates.append(candidates_)
restaurant_db = DBModel('gourmet', 'localhost', 'foo', 'bar')
name_result = restaurant_db.select('SELECT name FROM restaurants WHERE id={};'.format(
choice_bodies[top]['_source']['restaurant_id']
))
address_result = restaurant_db.select(
'SELECT address FROM restaurants WHERE id={}'.format(
choice_bodies[top]['_source']['restaurant_id']
)
)
restaurant = 'こちらのお店はいかがでしょうか？\n\n店名 : {}'.format(name_result[0])
if address_result[0] != 'nan':
restaurant += '\n住所 : {}'.format(address_result[0])
if len(candidates[top]) == 0 or candidates[top] is None:
recommend = 'このお店に行ったことがある人は，残念ながら見つかりませんでした．'
else:
recommend = 'このお店に行った人は次のような感想を述べています :'
for candidate in candidates[top]:
recommend += '\n\n・{}'.format(candidate)
dicts = [
{'restaurant': restaurant, 'recommend': recommend}
]
return json.dumps(dicts, ensure_ascii=False)
