You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
293 lines
9.7 KiB
293 lines
9.7 KiB
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)
|
|
|