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413 lines
15 KiB
413 lines
15 KiB
import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "2"
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from flask import Flask, jsonify
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from flask import request
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import requests
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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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from vllm import LLM, SamplingParams
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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=63179, max_connections=100, db=7, password="zhicheng123*")
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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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# 加载模型
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sampling_params = SamplingParams(temperature=0.95, top_p=0.7,presence_penalty=1.1,stop="</s>", max_tokens=4096)
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models_path = "/home/majiahui/model-llm/openbuddy-mistral-7b-v13.1"
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llm = LLM(model=models_path, tokenizer_mode="slow")
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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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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[:-1]:
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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[:-1]:
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sentence_batch_list.append(sentence_short)
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sentence_batch_list.append(sentence_split_list[-1] + "。")
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if sentence_list[:-1] != "":
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if len(sentence_list[-1]) < 120:
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sentence_batch_length += len(sentence_list[-1])
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sentence_batch_list.append([sentence_list[-1], 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_list[-1], 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 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 pre_sentence_ulit(sentence):
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if "改写后：" in sentence:
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sentence_lable_index = sentence.index("改写后：")
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sentence = sentence[sentence_lable_index + 4:]
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return sentence
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def main(texts: dict):
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text_list = paragraph_test(texts)
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text_info = []
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text_sentence = []
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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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# vllm预测
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for i in text_list:
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if len(i[0]) > 7:
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text = "You are a helpful assistant.\n\nUser:改写下面这句话，要求意思接近但是改动幅度比较大，字数只能多不能少:\n{}\nAssistant:".format(i[0])
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else:
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text = "You are a helpful assistant.\n\nUser:下面词不做任何变化:\n{}\nAssistant:".format(i[0])
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text_sentence.append(text)
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text_info.append([i[1], i[2]])
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outputs = llm.generate(text_sentence, sampling_params) # 调用模型
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generated_text_list = [""] * len(text_sentence)
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# generated_text_list = ["" if len(i[0]) > 5 else i[0] for i in text_list]
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for i, output in enumerate(outputs):
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index = output.request_id
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generated_text = output.outputs[0].text
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generated_text_list[int(index)] = generated_text
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for i in range(len(text_list)):
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if len(text_list[i][0]) > 7:
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generated_text_list[i] = pre_sentence_ulit(generated_text_list[i])
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else:
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generated_text_list[i] = text_list[i][0]
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for i, j in zip(generated_text_list, text_info):
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text_list_new.append([i] + j)
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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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#
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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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#
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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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@app.route("/predict", methods=["POST"])
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def handle_query():
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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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if texts is None:
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return_text = {"texts": "输入了空值", "probabilities": None, "status_code": 402}
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return jsonify(return_text)
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if isinstance(texts, dict):
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id_ = str(uuid.uuid1()) # 为query生成唯一标识
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print("uuid: ", uuid)
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d = {'id': id_, 'text': texts, "text_type": text_type} # 绑定文本和query id
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load_request_path = './request_data_logs/{}.json'.format(id_)
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with open(load_request_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(d, f2, ensure_ascii=False, indent=4)
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redis_.rpush(db_key_query, json.dumps({"id": id_, "path": load_request_path})) # 加入redis
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redis_.sadd(db_key_querying, id_)
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redis_.sadd(db_key_queryset, id_)
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return_text = {"texts": {'id': id_, }, "probabilities": None, "status_code": 200}
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print("ok")
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else:
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return_text = {"texts": "输入格式应该为字典", "probabilities": None, "status_code": 401}
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return jsonify(return_text) # 返回结果
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t = Thread(target=classify)
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t.start()
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if __name__ == "__main__":
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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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app.run(host="0.0.0.0", port=14002, threaded=True, debug=False)
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