def __init__(self, model_file, sess):
self.sess = sess
if not model_file:
self.bert_model = None
else:
log_server.logging('>>>>>>>> Load Model {}'.format(model_file))
self.bert_model = tf.saved_model.loader.load(
self.sess, [tf.saved_model.tag_constants.SERVING], model_file)
def __init__(self, model_file, tokenizer_file, max_esssay_len):
if not model_file:
self.keras_model = None
else:
log_server.logging('>>>>>>>> Load Model {}'.format(model_file))
self.keras_model = load_model(model_file)
log_server.logging('>>>>>>>> Load Tokenizer {}'.format(tokenizer_file))
self.tokenizer = pickle.load(open(tokenizer_file, 'rb'))
self.max_essay_length = max_esssay_len
def infer(self, data, grade):
"""
机器预测分数接口
:param data: 作文
:param grade: 学生年级
:return: 机器预测分数
"""
if data is None:
return 0
result = self.models[grade].predict(data)
if '初' in grade:
result = result / cfg.total_score_junior
elif '高' in grade:
result = result / cfg.total_score_senior
else:
log_server.logging('>>>>>>>> Invalid Grade !!!!!!!!!!!')
return result
def __init__(self):
self.grade_list = ["初一", "初二", "初三", "初四", "高一", "高二", "高三"]
self.model_map = {
'初一': JuniorOnePredictor,
'初二': JuniorTwoPredictor,
'初三': JuniorThreePredictor,
'初四': JuniorThreePredictor,
'高一': SeniorOneoPredictor,
'高二': SeniorTwoPredictor,
'高三': SeniorThreePredictor
}
self.models = dict()
load_model_start = time.time()
for grade in self.grade_list:
self.models[grade] = self.model_map[grade]()
load_model_end = time.time()
load_model_total = load_model_end - load_model_start
log_server.logging(
'>>>>>>>> Load Model Total Time: {}'.format(load_model_total))
def AES_post():
log_server.logging('============AES Begin!===========')
content = request.values.get('post_content')
grade = request.values.get('grade')
if grade in list(grade_dict.keys()):
start = time.time()
AD = AdvancedWords()
# 模型预测分数方法（predict代表机评分）
try:
# TODO: predict = model.infer(......) (DL predict)
predict = model.infer(content, grade)
except Exception as e:
predict = 0
# 高亮词提取和高亮词数
try:
highlight_site, high_num = AD.find_highlight_site(content, grade)
except Exception as e:
highlight_site = []
predict_ = {'predict': predict, 'highlight': highlight_site}
log_server.logging('>>>>>>>> Time of Predict: {:.2f}'.format(time.time()-start))
log_server.logging('>>>>>>>> Predict Score: {}'.format(predict))
log_server.logging("===============AES Over!!!==============" + "\n")
return jsonify(predict_)
else:
predict, highlight_site = 0, []
predict_ = {'predict': predict, 'highlight': highlight_site}
log_server.logging('>>>>>>>> Invalid Grade')
log_server.logging("===============AES Over!!!==============" + "\n")
return jsonify(predict_)