def query_doc_sim():
category = request.form['category']
in_type = request.form['type']
if in_type == 'doc':
f1 = request.files['text1']
f2 = request.files['text2']
if save_file(f1) and save_file(f2):
f_text1 = read_file(f1)
f_text2 = read_file(f2)
else:
f_text1 = request.form['text1'].encode('utf-8').strip()
f_text2 = request.form['text2'].encode('utf-8').strip()
inference_engine_wrapper = InferenceEngineWrapper(get_model_dir(category),
get_lda_conf(),
get_emb_file(category))
doc1_seg = inference_engine_wrapper.tokenize(f_text1)
doc2_seg = inference_engine_wrapper.tokenize(f_text2)
distances = inference_engine_wrapper.cal_query_doc_similarity(
doc1_seg, doc2_seg)
return json.dumps(
{
"LDA Similarity": distances[0],
"TWE Similarity": distances[1]
},
ensure_ascii=False)
import sys
from familia_wrapper import InferenceEngineWrapper
if sys.version_info < (3, 0):
input = raw_input
if __name__ == '__main__':
if len(sys.argv) < 4:
sys.stderr.write("Usage:python {} {} {} {}.\n".format(
sys.argv[0], "model_dir", "conf_file", "emb_file"))
exit(-1)
# 获取参数
model_dir = sys.argv[1]
conf_file = sys.argv[2]
emb_file = sys.argv[3]
# 创建InferenceEngineWrapper对象
inference_engine_wrapper = InferenceEngineWrapper(model_dir, conf_file,
emb_file)
while True:
# 输入短文本和长文本
query = input("Enter Query: ").strip()
doc = input("Enter Document: ").strip()
query_seg = inference_engine_wrapper.tokenize(query)
doc_seg = inference_engine_wrapper.tokenize(doc)
distances = inference_engine_wrapper.cal_query_doc_similarity(
query_seg, doc_seg)
# 打印结果
print("LDA Similarity = {}".format(distances[0]))
print("TWE similarity = {}".format(distances[1]))
# found in the LICENSE file. # # Author: [email protected] import sys from familia_wrapper import InferenceEngineWrapper if sys.version_info < (3,0): input = raw_input if __name__ == '__main__': if len(sys.argv) < 4: sys.stderr.write("Usage:python {} {} {} {}.\n".format( sys.argv[0], "model_dir", "conf_file", "emb_file")) exit(-1) # 获取参数 model_dir = sys.argv[1] conf_file = sys.argv[2] emb_file = sys.argv[3] # 创建InferenceEngineWrapper对象 inference_engine_wrapper = InferenceEngineWrapper(model_dir, conf_file, emb_file) while True: # 输入短文本和长文本 query = input("Enter Query: ").strip() doc = input("Enter Document: ").strip() distances = inference_engine_wrapper.cal_query_doc_similarity(query, doc) # 打印结果 print("LDA Similarity = {}".format(distances[0])) print("TWE similarity = {}".format(distances[1]))