import os
from config.predict_t5_config import MultipleResultsDropT5Config
t5config = MultipleResultsDropT5Config()
from config.predict_sim_config import DropSimBertConfig
simbertconfig = DropSimBertConfig()
os.environ["CUDA_VISIBLE_DEVICES"] = t5config.cuda_id
from flask import Flask, jsonify
from flask import request
from predict_t5 import (GenerateModel as T5GenerateModel,
AutoTitle as T5AutoTitle)
from predict_sim import (GenerateModel as SimBertGenerateModel,
AutoTitle as SimBertT5AutoTitle)
import json
from threading import Thread
import time
import re
import requests
app = Flask(__name__)
app.config["JSON_AS_ASCII"] = False
import logging
pattern = r"[。]"
RE_DIALOG = re.compile(r"\".*?\"|\'.*?\'|“.*?”")
fuhao_end_sentence = ["。","，","？","！","…"]
t5generatemodel = T5GenerateModel(t5config.config_path,
t5config.checkpoint_path,
t5config.spm_path,
t5config.keep_tokens_path,
t5config.maxlen,
t5config.savemodel_path)
encoder, decoder, model, tokenizer = t5generatemodel.device_setup()
t5autotitle = T5AutoTitle(encoder, decoder, model, tokenizer, start_id=0, end_id=tokenizer._token_end_id, maxlen=120)
simbertgeneratemodel = SimBertGenerateModel(simbertconfig.config_path,
simbertconfig.checkpoint_path,
simbertconfig.dict_path,
simbertconfig.maxlen,
simbertconfig.savemodel_path)
encoder, seq2seq, tokenizer = simbertgeneratemodel.device_setup()
simbertautotitle = SimBertT5AutoTitle(seq2seq, tokenizer, start_id=None, end_id=tokenizer._token_end_id, maxlen=120)
def requests_chatGPT(data):
res = requests.post('http://98.142.138.229:9999/chatgpt', data=data)
return res.json()['res']
def get_dialogs_index(line: str):
"""
获取对话及其索引
:param line 文本
:return dialogs 对话内容
dialogs_index: 对话位置索引
other_index: 其他内容位置索引
"""
dialogs = re.finditer(RE_DIALOG, line)
dialogs_text = re.findall(RE_DIALOG, line)
dialogs_index = []
for dialog in dialogs:
all_ = [i for i in range(dialog.start(), dialog.end())]
dialogs_index.extend(all_)
other_index = [i for i in range(len(line)) if i not in dialogs_index]
return dialogs_text, dialogs_index, other_index
def chulichangju_1(text, chulipangban_return_list, short_num):
fuhao = ["，","？","！","…"]
dialogs_text, dialogs_index, other_index = get_dialogs_index(text)
text_1 = text[:120]
text_2 = text[120:]
text_1_new = ""
if text_2 == "":
chulipangban_return_list.append([text_1, short_num])
return chulipangban_return_list
for i in range(len(text_1)-1, -1, -1):
if text_1[i] in fuhao:
if i in dialogs_index:
continue
text_1_new = text_1[:i]
text_1_new += text_1[i]
chulipangban_return_list.append([text_1_new, short_num])
if text_2 != "":
if i+1 != 120:
text_2 = text_1[i+1:] + text_2
break
# else:
# chulipangban_return_list.append(text_1)
if text_1_new == "":
chulipangban_return_list.append([text_1, short_num])
if text_2 != "":
short_num += 1
chulipangban_return_list = chulichangju_1(text_2, chulipangban_return_list, short_num)
return chulipangban_return_list
def chulipangban_test_1(text):
# 引号处理
dialogs_text, dialogs_index, other_index = get_dialogs_index(text)
for dialogs_text_dan in dialogs_text:
text_dan_list = text.split(dialogs_text_dan)
if "。" in dialogs_text_dan:
dialogs_text_dan = str(dialogs_text_dan).replace("。", "&")
text = dialogs_text_dan.join(text_dan_list)
# text_new_str = "".join(text_new)
sentence_list = text.split("。")
# sentence_list_new = []
# for i in sentence_list:
# if i != "":
# sentence_list_new.append(i)
# sentence_list = sentence_list_new
sentence_batch_list = []
sentence_batch_one = []
sentence_batch_length = 0
return_list = []
for sentence in sentence_list:
if len(sentence) < 120:
sentence_batch_length += len(sentence)
sentence_batch_list.append([sentence, 0])
# sentence_pre = autotitle.gen_synonyms_short(sentence)
# return_list.append(sentence_pre)
else:
sentence_split_list = chulichangju_1(sentence,[], 0)
for sentence_short in sentence_split_list:
sentence_batch_list.append(sentence_short)
return sentence_batch_list
def paragraph_test(texts:str):
text_list = chulipangban_test_1(texts)
# text_new_str = "".join(text_new)
return text_list
def batch_data_process(text_list):
sentence_batch_length = 0
sentence_batch_one = []
sentence_batch_list = []
for sentence in text_list:
sentence_batch_length += len(sentence[0])
sentence_batch_one.append(sentence)
if sentence_batch_length > 500:
sentence_batch_length = 0
sentence_ = sentence_batch_one.pop(-1)
sentence_batch_list.append(sentence_batch_one)
sentence_batch_one = []
sentence_batch_one.append(sentence_)
sentence_batch_list.append(sentence_batch_one)
return sentence_batch_list
def batch_predict(batch_data_list):
'''
一个bacth数据预测
@param data_text:
@return:
'''
batch_data_list_new = []
batch_data_text_list = []
batch_data_snetence_id_list = []
for i in batch_data_list:
batch_data_text_list.append(i[0])
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):
batch_data_list_new.append([text] + sentence_id)
return batch_data_list_new
def one_predict(data_text):
'''
一个条数据预测
@param data_text:
@return:
'''
return_data_list = []
if data_text[0] != "":
data_inputs = data_text[0].replace("&", "。")
prompt_list = ["请帮我改写一下这个句子", "请帮美化一下下面句子", "请帮我修改下面句子让这句话更完美"]
pre_data_list = []
for i in prompt_list:
pre_data = requests_chatGPT(
data={
'prompt':i,
'text':data_inputs
}
)
pre_data_list.append(pre_data)
modelclass_list = [t5autotitle, simbertautotitle]
for model in modelclass_list:
pre_data_list.append(model.generate(data_inputs))
else:
pre_data_list = [""] * 5
for pre_data in pre_data_list:
return_data_list.append([pre_data] + data_text[1:])
return return_data_list
def predict_data_post_processing(text_list, index):
text_list_sentence = []
# text_list_sentence.append([text_list[0][0], text_list[0][1]])
for i in range(len(text_list)):
if text_list[i][index][2] != 0:
text_list_sentence[-1][0] += text_list[i][index][0]
else:
text_list_sentence.append([text_list[i][0], text_list[i][1]])
return_list = {}
sentence_one = []
sentence_id = text_list_sentence[0][1]
for i in text_list_sentence:
if i[1] == sentence_id:
sentence_one.append(i[0])
else:
return_list[sentence_id] = "。".join(sentence_one)
sentence_id = i[1]
sentence_one = []
sentence_one.append(i[0])
if sentence_one != []:
return_list[sentence_id] = "。".join(sentence_one)
return return_list
# def main(text:list):
# # text_list = paragraph_test(text)
# # batch_data = batch_data_process(text_list)
# # text_list = []
# # for i in batch_data:
# # text_list.extend(i)
# # return_list = predict_data_post_processing(text_list)
# # return return_list
def main(text: str):
text_list = paragraph_test(text)
text_list_new = []
return_list = []
for i in text_list:
pre_list = one_predict(i)
text_list_new.append(pre_list)
for index in range(len(text_list_new[0])):
return_list.append(predict_data_post_processing(text_list_new, index))
return return_list
@app.route('/multiple_results_droprepeat/', methods=['POST'])
def sentence():
print(request.remote_addr)
texts = request.json["texts"]
print("原始语句" + str(texts))
# question = question.strip('。、！？?')
if isinstance(texts, str):
texts_list = []
y_pred_label_list = []
position_list = []
# texts = texts.replace('\'', '\"')
if texts is None:
return_text = {"texts": "输入了空值", "probabilities": None, "status_code": False}
return jsonify(return_text)
else:
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 jsonify(return_text)
if __name__ == "__main__":
fh = logging.FileHandler(mode='a', encoding='utf-8', filename='chitchat.log')
logging.basicConfig(
handlers=[fh],
level=logging.DEBUG,
format='%(asctime)s - %(levelname)s - %(message)s',
datefmt='%a, %d %b %Y %H:%M:%S',
)
app.run(host="0.0.0.0", port=14000, threaded=True, debug=False)