diff --git a/config/predict_sim_config.py b/config/predict_sim_config.py
new file mode 100644
index 0000000..d757455
--- /dev/null
+++ b/config/predict_sim_config.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+
+"""
+@Time    :  2023/3/27 10:23
+@Author  :
+@FileName:
+@Software:
+@Describe:
+"""
+import sys
+import os
+
+pre_model_path = {
+    "simbert": {
+        "linux": "/home/zc-nlp-zyp/work_file/ssd_data/模型库/预训练模型集合/keras/chinese_roberta_wwm_ext_L-12_H-768_A-12",
+        "win32": r"E:\pycharm_workspace\premodel\keras\chinese_roberta_wwm_ext_L-12_H-768_A-12"
+    },
+}
+
+
+class DropSimBertConfig:
+    def __init__(self):
+        self.sys_platform = sys.platform
+        self.premodel_path = pre_model_path["simbert"][self.sys_platform]
+        self.config_path = os.path.join(self.premodel_path, 'bert_config.json')
+        self.checkpoint_path = os.path.join(self.premodel_path, 'bert_model.ckpt')
+
+        self.dict_path = './config/vocab_drop.txt'
+        self.savemodel_path = "./output_simbert_yy/best_simbertmodel_dropout_datasim_yinhao.weights"
+
+        self.maxlen = 120
+        self.cuda_id = "1"
\ No newline at end of file
diff --git a/config/predict_t5_config.py b/config/predict_t5_config.py
new file mode 100644
index 0000000..873209b
--- /dev/null
+++ b/config/predict_t5_config.py
@@ -0,0 +1,45 @@
+# -*- coding: utf-8 -*-
+
+"""
+@Time    :  2023/3/27 10:23
+@Author  : 
+@FileName: 
+@Software: 
+@Describe:
+"""
+import sys
+import os
+
+pre_model_path = {
+    "t5": {
+        "linux": "/home/zc-nlp-zyp/work_file/ssd_data/模型库/预训练模型集合/keras/mt5/mt5_base",
+        "win32": r"E:\pycharm_workspace\premodel\keras\mt5\mt5_base"
+    },
+
+}
+
+
+class DropT5Config:
+    def __init__(self):
+        self.sys_platform = sys.platform
+        self.premodel_path = pre_model_path["t5"][self.sys_platform]
+        self.config_path = os.path.join(self.premodel_path, 'mt5_base_config.json')
+        self.checkpoint_path = os.path.join(self.premodel_path, 'model.ckpt-1000000')
+        self.spm_path = os.path.join(self.premodel_path, 'sentencepiece_cn.model')
+        self.keep_tokens_path = os.path.join(self.premodel_path, 'sentencepiece_cn_keep_tokens.json')
+        self.savemodel_path = "./output_t5/best_model_t5_zong_sim_99.weights"
+        self.maxlen = 256
+        self.cuda_id = "1"
+
+
+class MultipleResultsDropT5Config:
+    def __init__(self):
+        self.sys_platform = sys.platform
+        self.premodel_path = pre_model_path["t5"][self.sys_platform]
+        self.config_path = os.path.join(self.premodel_path, 'mt5_base_config.json')
+        self.checkpoint_path = os.path.join(self.premodel_path, 'model.ckpt-1000000')
+        self.spm_path = os.path.join(self.premodel_path, 'sentencepiece_cn.model')
+        self.keep_tokens_path = os.path.join(self.premodel_path, 'sentencepiece_cn_keep_tokens.json')
+        self.savemodel_path = "./output_t5/best_model_t5_dropout_0_3.weights"
+        self.maxlen = 256
+        self.cuda_id = "1"
\ No newline at end of file
diff --git a/flask_predict_no_batch_t5.py b/flask_predict_no_batch_t5.py
index e196151..7fc3105 100644
--- a/flask_predict_no_batch_t5.py
+++ b/flask_predict_no_batch_t5.py
@@ -1,13 +1,13 @@
 import os
-# os.environ["TF_KERAS"] = "1"
-# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+from config.predict_t5_config import DropT5Config
+
+config = DropT5Config()
+os.environ["CUDA_VISIBLE_DEVICES"] = config.cuda_id
 from flask import Flask, jsonify
 from flask import request
 # from linshi import autotitle
 import requests
-from flask import request
-from predict_t5 import autotitle
+from predict_t5 import GenerateModel, AutoTitle
 import redis
 import uuid
 import json
@@ -15,7 +15,6 @@ from threading import Thread
 import time
 import re
 
-
 pool = redis.ConnectionPool(host='localhost', port=6379, max_connections=50, db=1)
 redis_ = redis.Redis(connection_pool=pool, decode_responses=True)
 
@@ -27,13 +26,19 @@ app = Flask(__name__)
 app.config["JSON_AS_ASCII"] = False
 
 import logging
+
 pattern = r"[。]"
 RE_DIALOG = re.compile(r"\".*?\"|\'.*?\'|“.*?”")
-fuhao_end_sentence = ["。","，","？","！","…"]
+fuhao_end_sentence = ["。", "，", "？", "！", "…"]
 
-config = {
-    "batch_szie": 1000
-}
+generatemodel = GenerateModel(config.config_path,
+                              config.checkpoint_path,
+                              config.spm_path,
+                              config.keep_tokens_path,
+                              config.maxlen,
+                              config.savemodel_path)
+encoder, decoder, model, tokenizer = generatemodel.device_setup()
+autotitle = AutoTitle(encoder, decoder, model, tokenizer, start_id=0, end_id=tokenizer._token_end_id, maxlen=120)
 
 
 def get_dialogs_index(line: str):
@@ -56,7 +61,7 @@ def get_dialogs_index(line: str):
 
 
 def chulichangju_1(text, snetence_id, chulipangban_return_list, short_num):
-    fuhao = ["，","？","！","…"]
+    fuhao = ["，", "？", "！", "…"]
     dialogs_text, dialogs_index, other_index = get_dialogs_index(text)
     text_1 = text[:120]
     text_2 = text[120:]
@@ -64,7 +69,7 @@ def chulichangju_1(text, snetence_id, chulipangban_return_list, short_num):
     if text_2 == "":
         chulipangban_return_list.append([text_1, snetence_id, short_num])
         return chulipangban_return_list
-    for i in range(len(text_1)-1, -1, -1):
+    for i in range(len(text_1) - 1, -1, -1):
         if text_1[i] in fuhao:
             if i in dialogs_index:
                 continue
@@ -72,8 +77,8 @@ def chulichangju_1(text, snetence_id, chulipangban_return_list, short_num):
             text_1_new += text_1[i]
             chulipangban_return_list.append([text_1_new, snetence_id, short_num])
             if text_2 != "":
-                if i+1 != 120:
-                    text_2 = text_1[i+1:] + text_2
+                if i + 1 != 120:
+                    text_2 = text_1[i + 1:] + text_2
             break
         # else:
         #     chulipangban_return_list.append(text_1)
@@ -121,18 +126,7 @@ def chulipangban_test_1(snetence_id, text):
     return sentence_batch_list
 
 
-def paragraph_test_(text:list, text_new:list):
-
-    for i in range(len(text)):
-        text = chulipangban_test_1(text, i)
-        text = "。".join(text)
-        text_new.append(text)
-
-    # text_new_str = "".join(text_new)
-    return text_new
-
-def paragraph_test(texts:dict):
-
+def paragraph_test(texts: dict):
     text_new = []
     for i, text in texts.items():
         text_list = chulipangban_test_1(i, text)
@@ -159,6 +153,7 @@ def batch_data_process(text_list):
     sentence_batch_list.append(sentence_batch_one)
     return sentence_batch_list
 
+
 def batch_predict(batch_data_list):
     '''
     一个bacth数据预测
@@ -173,7 +168,7 @@ def batch_predict(batch_data_list):
         batch_data_snetence_id_list.append(i[1:])
     # batch_pre_data_list = autotitle.generate_beam_search_batch(batch_data_text_list)
     batch_pre_data_list = batch_data_text_list
-    for text,sentence_id in zip(batch_pre_data_list,batch_data_snetence_id_list):
+    for text, sentence_id in zip(batch_pre_data_list, batch_data_snetence_id_list):
         batch_data_list_new.append([text] + sentence_id)
 
     return batch_data_list_new
@@ -238,6 +233,7 @@ def main(text: dict):
     return_list = predict_data_post_processing(text_list_new)
     return return_list
 
+
 @app.route('/droprepeat/', methods=['POST'])
 def sentence():
     print(request.remote_addr)
@@ -246,7 +242,6 @@ def sentence():
     print("原始语句" + str(texts))
     # question = question.strip('。、！？?')
 
-
     if isinstance(texts, dict):
         texts_list = []
         y_pred_label_list = []
@@ -264,12 +259,14 @@ def sentence():
                 texts_list = main(texts)
             return_text = {"texts": texts_list, "probabilities": None, "status_code": True}
     else:
-        return_text = {"texts":"输入格式应该为list", "probabilities": None, "status_code":False}
+        return_text = {"texts": "输入格式应该为list", "probabilities": None, "status_code": False}
     return jsonify(return_text)
 
+
 def classify():  # 调用模型，设置最大batch_size
     while True:
         if redis_.llen(db_key_query) == 0:  # 若队列中没有元素就继续获取
+            time.sleep(3)
             continue
         query = redis_.lpop(db_key_query).decode('UTF-8')  # 获取query的text
         data_dict_path = json.loads(query)
@@ -291,8 +288,8 @@ def classify():  # 调用模型，设置最大batch_size
             texts_list = main(texts)
         else:
             texts_list = []
-        return_text = {"texts": texts_list, "probabilities": None, "status_code": 200}
 
+        return_text = {"texts": texts_list, "probabilities": None, "status_code": 200}
         redis_.srem(db_key_querying, query_id)
         load_result_path = "./new_data_logs/{}.json".format(query_id)
         with open(load_result_path, 'w', encoding='utf8') as f2:
@@ -319,9 +316,9 @@ def handle_query():
             # ensure_ascii=False才能输入中文，否则是Unicode字符
             # indent=2 JSON数据的缩进，美观
             json.dump(d, f2, ensure_ascii=False, indent=4)
-        redis_.rpush(db_key_query, json.dumps({"id":id_, "path": load_request_path}))  # 加入redis
+        redis_.rpush(db_key_query, json.dumps({"id": id_, "path": load_request_path}))  # 加入redis
         redis_.sadd(db_key_querying, id_)
-        return_text = {"texts": {'id': id_,}, "probabilities": None, "status_code": 200}
+        return_text = {"texts": {'id': id_, }, "probabilities": None, "status_code": 200}
         print("ok")
     else:
         return_text = {"texts": "输入格式应该为字典", "probabilities": None, "status_code": 401}
diff --git a/predict_drop_weight_sim.py b/predict_drop_weight_sim.py
index b9f6399..e5c5b06 100644
--- a/predict_drop_weight_sim.py
+++ b/predict_drop_weight_sim.py
@@ -765,9 +765,8 @@ def paragraph_test(text, text_new):
 
 
 if __name__ == '__main__':
-
-    text = ["在malpezzi研究的日本租赁市场中[23] , 可以看出日本的租赁住房市场主要以规范化经营为主, 强调轻资产经营, 更加重视经营风险的规避"]
-    print(type(just_show_sentence(text)))
+    text = ["所以对学生对应用仪器分析解决实际问题的能力要求很高。","随着经济的发展，人们生活水平的提高，环境问题也日益突出。"]
+    print(just_show_sentence(text))
 
     # is_novel = False
     # path = "./data/700条论文测试.xlsx"
diff --git a/predict_sim.py b/predict_sim.py
index 2c39701..cea973e 100644
--- a/predict_sim.py
+++ b/predict_sim.py
@@ -1,8 +1,11 @@
 #! -*- coding: utf-8 -*-
 
 import os
+
+from config.predict_sim_config import DropSimBertConfig
+config = DropSimBertConfig()
 # os.environ["TF_KERAS"] = "1"
-os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+os.environ["CUDA_VISIBLE_DEVICES"] = config.cuda_id
 import glob
 import random
 from tqdm import tqdm
@@ -21,9 +24,9 @@ import tensorflow as tf
 
 
 from keras.backend import set_session
-config = tf.ConfigProto()
-config.gpu_options.allow_growth = True
-set_session(tf.Session(config=config)) # 此处不同
+tfconfig = tf.ConfigProto()
+tfconfig.gpu_options.allow_growth = True
+set_session(tf.Session(config=tfconfig)) # 此处不同
 global graph
 graph = tf.get_default_graph()
 sess = tf.Session(graph=graph)
@@ -78,14 +81,12 @@ class TotalLoss(Loss):
 
 
 class GenerateModel(object):
-    def __init__(self):
-
-        self.epoch_acc_vel = 0
-        self.config_path = r'./chinese_roberta_wwm_ext_L-12_H-768_A-12/bert_config.json'
-        self.checkpoint_path = r'./chinese_roberta_wwm_ext_L-12_H-768_A-12/bert_model.ckpt'
-        self.dict_path = r'./chinese_roberta_wwm_ext_L-12_H-768_A-12/vocab_drop.txt'
-        self.maxlen = 120
-        self.novel_maxlen = 60
+    def __init__(self, config_path, checkpoint_path, dict_path, maxlen, savemodel_path):
+        self.config_path = config_path
+        self.checkpoint_path = checkpoint_path
+        self.dict_path = dict_path
+        self.maxlen = maxlen
+        self.savemodel_path = savemodel_path
 
     def device_setup(self):
         token_dict, keep_tokens = load_vocab(
@@ -122,7 +123,7 @@ class GenerateModel(object):
         outputs = TotalLoss([2, 3])(bert.model.inputs + bert.model.outputs)
         model = keras.models.Model(bert.model.inputs, outputs)
 
-        path_model = './output_simbert_yy/best_simbertmodel_datasim_yinhao.weights'
+        path_model = self.savemodel_path
         model.load_weights(path_model)
 
         return encoder,seq2seq, tokenizer
@@ -626,12 +627,6 @@ class AutoTitle(AutoRegressiveDecoder):
         else:
             return sentence_list[0]
 
-generatemodel = GenerateModel()
-encoder,seq2seq, tokenizer = generatemodel.device_setup()
-autotitle = AutoTitle(seq2seq, tokenizer, start_id=None, end_id=tokenizer._token_end_id, maxlen=120)
-
-
-
 
 def just_show(file):
     data = []
@@ -708,27 +703,34 @@ def just_show_csv_beam(file):
 
 if __name__ == '__main__':
 
-    # text = ["强调轻资产“经营”, 更加重视“营风险”的规避", "历史和当下都证明,创新是民族生存、发展的不竭源泉,是是自身发展的必然选择", "是时代对于青年们的深切呼唤"]
-    # print(just_show_sentence(text))
+    generatemodel = GenerateModel(config.config_path,
+                                  config.checkpoint_path,
+                                  config.dict_path,
+                                  config.maxlen,
+                                  config.savemodel_path)
+    encoder, seq2seq, tokenizer = generatemodel.device_setup()
+    autotitle = AutoTitle(seq2seq, tokenizer, start_id=None, end_id=tokenizer._token_end_id, maxlen=120)
+    text = ["随着经济的发展，人们生活水平的提高，环境问题也日益突出。"]
+    print(just_show_sentence(text))
     #
     # print(just_show_sentence_batch(text))
     # print(type(just_show_sentence_batch(text)))
 
 
-    path = "./data/700条论文测试.xlsx"
-    df_list = pd.read_excel(path).values.tolist()
-
-    df_list_new = []
-    print(len(df_list))
-    for i in tqdm(df_list):
-        try:
-            pre = just_show_sentence([i[0]])
-            df_list_new.append([i[0], i[1]] + [pre])
-        except:
-            print(i[0])
-            continue
-    df = pd.DataFrame(df_list_new)
-    df.to_excel("./data/700条论文测试_19.xlsx", index=None)
+    # path = "./data/700条论文测试.xlsx"
+    # df_list = pd.read_excel(path).values.tolist()
+    #
+    # df_list_new = []
+    # print(len(df_list))
+    # for i in tqdm(df_list):
+    #     try:
+    #         pre = just_show_sentence([i[0]])
+    #         df_list_new.append([i[0], i[1]] + [pre])
+    #     except:
+    #         print(i[0])
+    #         continue
+    # df = pd.DataFrame(df_list_new)
+    # df.to_excel("./data/700条论文测试_19.xlsx", index=None)
 
 
 
diff --git a/predict_t5.py b/predict_t5.py
index dce0f94..a326d33 100644
--- a/predict_t5.py
+++ b/predict_t5.py
@@ -9,9 +9,6 @@
 """
 #! -*- coding: utf-8 -*-
 
-import os
-# os.environ["TF_KERAS"] = "1"
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"
 import glob
 from numpy import random
 random.seed(1001)
@@ -19,7 +16,6 @@ from tqdm import tqdm
 import numpy as np
 import pandas as pd
 import json
-import numpy as np
 from tqdm import tqdm
 from bert4keras.backend import keras, K
 from bert4keras.layers import Loss
@@ -32,12 +28,10 @@ from keras.models import Model
 # from rouge import Rouge  # pip install rouge
 # from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
 import tensorflow as tf
-
-
 from keras.backend import set_session
-config = tf.ConfigProto()
-config.gpu_options.allow_growth = True
-set_session(tf.Session(config=config)) # 此处不同
+tfconfig = tf.ConfigProto()
+tfconfig.gpu_options.allow_growth = True
+set_session(tf.Session(config=tfconfig)) # 此处不同
 global graph
 graph = tf.get_default_graph()
 sess = tf.Session(graph=graph)
@@ -52,14 +46,13 @@ set_session(sess)
 # 基本参数
 
 class GenerateModel(object):
-    def __init__(self):
-
-        self.epoch_acc_vel = 0
-        self.config_path = 'mt5/mt5_base/mt5_base_config.json'
-        self.checkpoint_path = 'mt5/mt5_base/model.ckpt-1000000'
-        self.spm_path = 'mt5/mt5_base/sentencepiece_cn.model'
-        self.keep_tokens_path = 'mt5/mt5_base/sentencepiece_cn_keep_tokens.json'
-        self.maxlen = 256
+    def __init__(self, config_path, checkpoint_path, spm_path, keep_tokens_path, maxlen, savemodel_path):
+        self.config_path = config_path
+        self.checkpoint_path = checkpoint_path
+        self.spm_path = spm_path
+        self.keep_tokens_path = keep_tokens_path
+        self.maxlen = maxlen
+        self.savemodel_path = savemodel_path
 
     def device_setup(self):
         tokenizer = SpTokenizer(self.spm_path, token_start=None, token_end='</s>')
@@ -85,7 +78,7 @@ class GenerateModel(object):
         output = CrossEntropy(1)([model.inputs[1], model.outputs[0]])
 
         model = Model(model.inputs, output)
-        path_model = "output_t5/best_model_t5.weights"
+        path_model = self.savemodel_path
         model.load_weights(path_model)
 
         return encoder, decoder, model, tokenizer
@@ -131,7 +124,7 @@ class Beamdataone(object):
         self.inputs_vector = 0
 
     def text_2_textids(self,text):
-        token_ids, segment_ids = self.tokenizer.encode(text[0], maxlen=120)
+        token_ids, segment_ids = self.tokenizer.encode(text[0], maxlen=self.maxlen)
         self.text_ids = [token_ids]
 
     def add_data(self, step, output_scores):
@@ -217,6 +210,11 @@ class AutoTitle(AutoRegressiveDecoder):
         self.end_id = end_id
         self.minlen = minlen
         self.models = {}
+        self.chinese_sign = {
+            ",":"，",
+            ":": "：",
+            ";": "；",
+        }
         if start_id is None:
             self.first_output_ids = np.empty((1, 0), dtype=int)
         else:
@@ -246,7 +244,7 @@ class AutoTitle(AutoRegressiveDecoder):
         c_encoded = inputs[0]
         with graph.as_default():
             K.set_session(sess)
-            nodes = self.last_token(decoder).predict([c_encoded, output_ids])
+            nodes = self.last_token(self.decoder).predict([c_encoded, output_ids])
         return nodes
 
     def predict_batch(self, inputs):
@@ -259,14 +257,6 @@ class AutoTitle(AutoRegressiveDecoder):
             nodes = self.decoder.predict([token_ids, output_ids])
         return nodes
 
-    def data_generator(self, token_ids, output_ids):
-
-        batch_token_ids = []
-        for i,j in zip(token_ids, output_ids):
-
-            batch_token_ids = sequence_padding(token_ids)
-            batch_segment_ids = sequence_padding(output_ids)
-        return batch_token_ids, batch_segment_ids
 
     def beam_search_batch(
         self,
@@ -395,15 +385,17 @@ class AutoTitle(AutoRegressiveDecoder):
 
 
     def generate(self, text, topk=5):
-        c_token_ids, _ = tokenizer.encode(text, maxlen=120)
+        c_token_ids, _ = self.tokenizer.encode(text, maxlen=self.maxlen)
         with graph.as_default():
             K.set_session(sess)
-            c_encoded = encoder.predict(np.array([c_token_ids]))[0]
+            c_encoded = self.encoder.predict(np.array([c_token_ids]))[0]
         output_ids = self.beam_search([c_encoded], topk=topk)  # 基于beam search
-        return tokenizer.decode([int(i) for i in output_ids])
+        return_text = self.tokenizer.decode([int(i) for i in output_ids])
+        return_text = "".join([self.chinese_sign[i] if i in self.chinese_sign else i for i in return_text])
+        return return_text
 
     def generate_random(self, text, n=30, topp=0.9):
-        c_token_ids, _ = self.tokenizer.encode(text, maxlen=120)
+        c_token_ids, _ = self.tokenizer.encode(text, maxlen=self.maxlen)
         with graph.as_default():
             K.set_session(sess)
             c_encoded = self.encoder.predict(np.array([c_token_ids]))[0]
@@ -418,13 +410,6 @@ class AutoTitle(AutoRegressiveDecoder):
         return output_str
 
 
-generatemodel = GenerateModel()
-encoder, decoder, model, tokenizer = generatemodel.device_setup()
-autotitle = AutoTitle(encoder, decoder, model, tokenizer, start_id=0, end_id=tokenizer._token_end_id, maxlen=120)
-
-
-
-
 def just_show_sentence(file):
     """
     @param file:list
@@ -441,6 +426,18 @@ def just_show_sentence_batch(file: list) -> object:
 
 
 if __name__ == '__main__':
+    import os
+    from config.predict_t5_config import DropT5Config
+    config = DropT5Config()
+    os.environ["CUDA_VISIBLE_DEVICES"] = config.cuda_id
+    generatemodel = GenerateModel(config.config_path,
+                                  config.checkpoint_path,
+                                  config.spm_path,
+                                  config.keep_tokens_path,
+                                  config.maxlen,
+                                  config.savemodel_path)
+    encoder, decoder, model, tokenizer = generatemodel.device_setup()
+    autotitle = AutoTitle(encoder, decoder, model, tokenizer, start_id=0, end_id=tokenizer._token_end_id, maxlen=256)
     # file = "train_2842.txt"
     # just_show(file)
     # text = ["历史和当下都证明,创新是民族生存、发展的不竭源泉,是自身发展的必然选择,是时代对于青年们的深切呼唤"]
@@ -493,16 +490,20 @@ if __name__ == '__main__':
 
     #++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-    text = ["'李正旺你真是傻逼讪笑，挥手道：“不不不，你千万别误会",
-            "历史和当下都证明,创新是民族生存、“发展的不竭源泉”,是是自身发展的必然选择",
-            "自身发展的必然选择",
-            "强调轻资产经营, 更加重视经营风险的规避",
-            "历史和当下都证明,创新是民族生存、发展的不竭源泉,是是自身发展的必然选择",
-            "是时代对于青年们的深切呼唤"]
-    # text = ["基本消除“热桥”影响。"]
+    # text = ["'李正旺你真是傻逼讪笑，挥手道：“不不不，你千万别误会",
+    #         "历史和当下都证明,创新是民族生存、“发展的不竭源泉”,是是自身发展的必然选择",
+    #         "自身发展的必然选择",
+    #         "强调轻资产经营, 更加重视经营风险的规避",
+    #         "历史和当下都证明,创新是民族生存、发展的不竭源泉,是是自身发展的必然选择",
+    #         "是时代对于青年们的深切呼唤"]
+    text = ["随着经济的发展，人们生活水平的提高，环境：问题也日益突出。",
+            "环境问题中的化学污染是影响我国居民生活质量不可忽视的重要因素，而仪器分析作为化工专业课程中必不可少的一门课程也不例外。",
+            "所以对学生对应用仪器分析解决实际问题的能力要求很高。",
+            "随着经济的发展，人们生活水平的提高，环境问题也日益突出。"]
     print(just_show_sentence(text))
     # print(just_show_sentence_top(text))
     # print(just_show_chachong_random(text))
 
     # print(tokenizer.encode("\"", maxlen=120))
-    # print(just_show_sentence_batch(text))
\ No newline at end of file
+    # print(just_show_sentence_batch(text))
+    # myout.flask_predict_no_batch_t5.logs
\ No newline at end of file