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343 lines
11 KiB
343 lines
11 KiB
import os
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from config.predict_t5_config import DropT5Config
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config = DropT5Config()
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os.environ["CUDA_VISIBLE_DEVICES"] = config.cuda_id
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from flask import Flask, jsonify
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from flask import request
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# from linshi import autotitle
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import requests
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from predict_t5 import GenerateModel, AutoTitle
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import redis
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import uuid
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import json
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from threading import Thread
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import time
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import re
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import logging
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logging.basicConfig(level=logging.DEBUG, # 控制台打印的日志级别
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filename='rewrite.log',
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filemode='a', ##模式，有w和a，w就是写模式，每次都会重新写日志，覆盖之前的日志
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# a是追加模式，默认如果不写的话，就是追加模式
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format=
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'%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'
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# 日志格式
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)
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pool = redis.ConnectionPool(host='localhost', port=6379, max_connections=100, db=1)
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redis_ = redis.Redis(connection_pool=pool, decode_responses=True)
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db_key_query = 'query'
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db_key_querying = 'querying'
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db_key_queryset = 'queryset'
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batch_size = 32
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app = Flask(__name__)
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app.config["JSON_AS_ASCII"] = False
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import logging
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pattern = r"[。]"
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RE_DIALOG = re.compile(r"\".*?\"|\'.*?\'|“.*?”")
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fuhao_end_sentence = ["。", "，", "？", "！", "…"]
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generatemodel = GenerateModel(config.config_path,
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config.checkpoint_path,
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config.spm_path,
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config.keep_tokens_path,
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config.maxlen,
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config.savemodel_path)
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encoder, decoder, model, tokenizer = generatemodel.device_setup()
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autotitle = AutoTitle(encoder, decoder, model, tokenizer, start_id=0, end_id=tokenizer._token_end_id, maxlen=120)
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class log:
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def __init__(self):
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pass
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def log(*args, **kwargs):
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format = '%Y/%m/%d-%H:%M:%S'
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format_h = '%Y-%m-%d'
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value = time.localtime(int(time.time()))
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dt = time.strftime(format, value)
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dt_log_file = time.strftime(format_h, value)
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log_file = 'log_file/access-%s' % dt_log_file + ".log"
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if not os.path.exists(log_file):
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with open(os.path.join(log_file), 'w', encoding='utf-8') as f:
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print(dt, *args, file=f, **kwargs)
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else:
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with open(os.path.join(log_file), 'a+', encoding='utf-8') as f:
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print(dt, *args, file=f, **kwargs)
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def get_dialogs_index(line: str):
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"""
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获取对话及其索引
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:param line 文本
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:return dialogs 对话内容
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dialogs_index: 对话位置索引
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other_index: 其他内容位置索引
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"""
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dialogs = re.finditer(RE_DIALOG, line)
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dialogs_text = re.findall(RE_DIALOG, line)
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dialogs_index = []
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for dialog in dialogs:
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all_ = [i for i in range(dialog.start(), dialog.end())]
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dialogs_index.extend(all_)
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other_index = [i for i in range(len(line)) if i not in dialogs_index]
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return dialogs_text, dialogs_index, other_index
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def chulichangju_1(text, snetence_id, chulipangban_return_list, short_num):
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fuhao = ["，", "？", "！", "…"]
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dialogs_text, dialogs_index, other_index = get_dialogs_index(text)
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text_1 = text[:120]
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text_2 = text[120:]
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text_1_new = ""
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if text_2 == "":
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chulipangban_return_list.append([text_1, snetence_id, short_num])
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return chulipangban_return_list
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for i in range(len(text_1) - 1, -1, -1):
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if text_1[i] in fuhao:
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if i in dialogs_index:
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continue
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text_1_new = text_1[:i]
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text_1_new += text_1[i]
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chulipangban_return_list.append([text_1_new, snetence_id, short_num])
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if text_2 != "":
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if i + 1 != 120:
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text_2 = text_1[i + 1:] + text_2
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break
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# else:
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# chulipangban_return_list.append(text_1)
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if text_1_new == "":
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chulipangban_return_list.append([text_1, snetence_id, short_num])
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if text_2 != "":
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short_num += 1
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chulipangban_return_list = chulichangju_1(text_2, snetence_id, chulipangban_return_list, short_num)
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return chulipangban_return_list
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def chulipangban_test_1(snetence_id, text):
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# 引号处理
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dialogs_text, dialogs_index, other_index = get_dialogs_index(text)
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for dialogs_text_dan in dialogs_text:
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text_dan_list = text.split(dialogs_text_dan)
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if "。" in dialogs_text_dan:
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dialogs_text_dan = str(dialogs_text_dan).replace("。", "&")
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text = dialogs_text_dan.join(text_dan_list)
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# text_new_str = "".join(text_new)
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sentence_list = text.split("。")
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# sentence_list_new = []
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# for i in sentence_list:
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# if i != "":
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# sentence_list_new.append(i)
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# sentence_list = sentence_list_new
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sentence_batch_list = []
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sentence_batch_one = []
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sentence_batch_length = 0
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return_list = []
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for sentence in sentence_list:
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if len(sentence) < 120:
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sentence_batch_length += len(sentence)
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sentence_batch_list.append([sentence, snetence_id, 0])
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# sentence_pre = autotitle.gen_synonyms_short(sentence)
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# return_list.append(sentence_pre)
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else:
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sentence_split_list = chulichangju_1(sentence, snetence_id, [], 0)
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for sentence_short in sentence_split_list:
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sentence_batch_list.append(sentence_short)
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return sentence_batch_list
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def paragraph_test(texts: dict):
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text_new = []
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for i, text in texts.items():
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text_list = chulipangban_test_1(i, text)
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text_new.extend(text_list)
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# text_new_str = "".join(text_new)
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return text_new
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def batch_data_process(text_list):
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sentence_batch_length = 0
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sentence_batch_one = []
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sentence_batch_list = []
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for sentence in text_list:
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sentence_batch_length += len(sentence[0])
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sentence_batch_one.append(sentence)
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if sentence_batch_length > 500:
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sentence_batch_length = 0
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sentence_ = sentence_batch_one.pop(-1)
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sentence_batch_list.append(sentence_batch_one)
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sentence_batch_one = []
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sentence_batch_one.append(sentence_)
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sentence_batch_list.append(sentence_batch_one)
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return sentence_batch_list
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def batch_predict(batch_data_list):
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'''
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一个bacth数据预测
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@param data_text:
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@return:
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'''
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batch_data_list_new = []
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batch_data_text_list = []
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batch_data_snetence_id_list = []
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for i in batch_data_list:
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batch_data_text_list.append(i[0])
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batch_data_snetence_id_list.append(i[1:])
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# batch_pre_data_list = autotitle.generate_beam_search_batch(batch_data_text_list)
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batch_pre_data_list = batch_data_text_list
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for text, sentence_id in zip(batch_pre_data_list, batch_data_snetence_id_list):
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batch_data_list_new.append([text] + sentence_id)
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return batch_data_list_new
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def one_predict(data_text):
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'''
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一个条数据预测
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@param data_text:
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@return:
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'''
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if data_text[0] != "":
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data_inputs = data_text[0].replace("&", "。")
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pre_data = autotitle.generate(data_inputs)
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else:
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pre_data = ""
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data_new = [pre_data] + data_text[1:]
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return data_new
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def predict_data_post_processing(text_list):
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text_list_sentence = []
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# text_list_sentence.append([text_list[0][0], text_list[0][1]])
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for i in range(len(text_list)):
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if text_list[i][2] != 0:
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text_list_sentence[-1][0] += text_list[i][0]
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else:
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text_list_sentence.append([text_list[i][0], text_list[i][1]])
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return_list = {}
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sentence_one = []
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sentence_id = text_list_sentence[0][1]
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for i in text_list_sentence:
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if i[1] == sentence_id:
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sentence_one.append(i[0])
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else:
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return_list[sentence_id] = "。".join(sentence_one)
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sentence_id = i[1]
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sentence_one = []
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sentence_one.append(i[0])
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if sentence_one != []:
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return_list[sentence_id] = "。".join(sentence_one)
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return return_list
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# def main(text:list):
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# # text_list = paragraph_test(text)
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# # batch_data = batch_data_process(text_list)
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# # text_list = []
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# # for i in batch_data:
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# # text_list.extend(i)
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# # return_list = predict_data_post_processing(text_list)
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# # return return_list
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def main(text: dict):
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text_list = paragraph_test(text)
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text_list_new = []
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for i in text_list:
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pre = one_predict(i)
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text_list_new.append(pre)
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return_list = predict_data_post_processing(text_list_new)
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return return_list
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@app.route('/droprepeat/', methods=['POST'])
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def sentence():
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print(request.remote_addr)
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texts = request.json["texts"]
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text_type = request.json["text_type"]
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print("原始语句" + str(texts))
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# question = question.strip('。、！？?')
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if isinstance(texts, dict):
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texts_list = []
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y_pred_label_list = []
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position_list = []
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# texts = texts.replace('\'', '\"')
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if texts is None:
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return_text = {"texts": "输入了空值", "probabilities": None, "status_code": False}
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return jsonify(return_text)
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else:
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assert text_type in ['focus', 'chapter']
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if text_type == 'focus':
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texts_list = main(texts)
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if text_type == 'chapter':
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texts_list = main(texts)
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return_text = {"texts": texts_list, "probabilities": None, "status_code": True}
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else:
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return_text = {"texts": "输入格式应该为list", "probabilities": None, "status_code": False}
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return jsonify(return_text)
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def classify(): # 调用模型，设置最大batch_size
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while True:
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if redis_.llen(db_key_query) == 0: # 若队列中没有元素就继续获取
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time.sleep(3)
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continue
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query = redis_.lpop(db_key_query).decode('UTF-8') # 获取query的text
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data_dict_path = json.loads(query)
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path = data_dict_path['path']
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# text_type = data_dict["text_type"]
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with open(path, encoding='utf8') as f1:
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# 加载文件的对象
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data_dict = json.load(f1)
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query_id = data_dict['id']
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texts = data_dict["text"]
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text_type = data_dict["text_type"]
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assert text_type in ['focus', 'chapter']
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if text_type == 'focus':
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texts_list = main(texts)
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elif text_type == 'chapter':
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texts_list = main(texts)
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else:
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texts_list = []
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return_text = {"texts": texts_list, "probabilities": None, "status_code": 200}
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load_result_path = "./new_data_logs/{}.json".format(query_id)
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print("query_id: ", query_id)
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print("load_result_path: ", load_result_path)
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with open(load_result_path, 'w', encoding='utf8') as f2:
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# ensure_ascii=False才能输入中文，否则是Unicode字符
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# indent=2 JSON数据的缩进，美观
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json.dump(return_text, f2, ensure_ascii=False, indent=4)
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debug_id_1 = 1
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redis_.set(query_id, load_result_path, 86400)
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debug_id_2 = 2
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redis_.srem(db_key_querying, query_id)
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debug_id_3 = 3
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log.log('start at',
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'query_id:{},load_result_path:{},return_text:{}, debug_id_1:{}, debug_id_2:{}, debug_id_3:{}'.format(
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query_id, load_result_path, return_text, debug_id_1, debug_id_2, debug_id_3))
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if __name__ == '__main__':
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classify()
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