drop_weight_rewrite/flask_predict_no_batch_t5.py

import os
# os.environ["TF_KERAS"] = "1"
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
from flask import Flask, jsonify
from flask import request
# from linshi import autotitle
import requests
from flask import request
from predict_t5 import autotitle
import redis
import uuid
import json
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)

db_key_query = 'query'
db_key_querying = 'querying'
batch_size = 32

app = Flask(__name__)
app.config["JSON_AS_ASCII"] = False

import logging
pattern = r"[。]"
RE_DIALOG = re.compile(r"\".*?\"|\'.*?\'|“.*?”")
fuhao_end_sentence = ["。","，","？","！","…"]

config = {
    "batch_szie": 1000
}


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, snetence_id, 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, snetence_id, 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, snetence_id, 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, snetence_id, short_num])
    if text_2 != "":
        short_num += 1
        chulipangban_return_list = chulichangju_1(text_2, snetence_id, chulipangban_return_list, short_num)
    return chulipangban_return_list


def chulipangban_test_1(snetence_id, 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, snetence_id, 0])
            # sentence_pre = autotitle.gen_synonyms_short(sentence)
            # return_list.append(sentence_pre)
        else:

            sentence_split_list = chulichangju_1(sentence, snetence_id, [], 0)
            for sentence_short in sentence_split_list:
                sentence_batch_list.append(sentence_short)
    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):

    text_new = []
    for i, text in texts.items():
        text_list = chulipangban_test_1(i, text)
        text_new.extend(text_list)

    # text_new_str = "".join(text_new)
    return text_new


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:
    '''
    if data_text[0] != "":
        data_inputs = data_text[0].replace("&", "。")
        pre_data = autotitle.generate(data_inputs)
    else:
        pre_data = ""
    data_new = [pre_data] + data_text[1:]
    return data_new


def predict_data_post_processing(text_list):
    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][2] != 0:
            text_list_sentence[-1][0] += text_list[i][0]
        else:
            text_list_sentence.append([text_list[i][0], text_list[i][1]])

    return_list = {}
    sentence_one = []
    sentence_id = "0"
    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: dict):
    text_list = paragraph_test(text)
    text_list_new = []
    for i in text_list:
        pre = one_predict(i)
        text_list_new.append(pre)
    return_list = predict_data_post_processing(text_list_new)
    return return_list

@app.route('/droprepeat/', methods=['POST'])
def sentence():
    print(request.remote_addr)
    texts = request.json["texts"]
    text_type = request.json["text_type"]
    print("原始语句" + str(texts))
    # question = question.strip('。、！？?')


    if isinstance(texts, dict):
        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:
            assert text_type in ['focus', 'chapter']
            if text_type == 'focus':
                texts_list = main(texts)
            if text_type == 'chapter':
                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)

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
            continue
        query = redis_.lpop(db_key_query).decode('UTF-8')  # 获取query的text
        data_dict = json.loads(query)
        query_id = data_dict['id']
        texts = data_dict['text']  # 拼接若干text 为batch
        text_type = data_dict["text_type"]
        if isinstance(texts, dict):
            texts_list = []
            if texts is None:
                return_text = {"texts": "输入了空值", "probabilities": None, "status_code": 402}
            else:
                assert text_type in ['focus', 'chapter']
                if text_type == 'focus':
                    texts_list = main(texts)
                if text_type == 'chapter':
                    texts_list = main(texts)
                return_text = {"texts": texts_list, "probabilities": None, "status_code": 200}
        else:
            return_text = {"texts": "输入格式应该为字典", "probabilities": None, "status_code": 401}

        redis_.srem(db_key_querying, query_id)
        redis_.set(query_id, json.dumps(return_text, ensure_ascii=False), 28800)


@app.route("/predict", methods=["POST"])
def handle_query():
    print(request.remote_addr)
    texts = request.json["texts"]
    text_type = request.json["text_type"]
    if texts is None:
        return_text = {"texts": "输入了空值", "probabilities": None, "status_code": False}
        return jsonify(return_text)
    if isinstance(texts, dict):
        id_ = str(uuid.uuid1())  # 为query生成唯一标识
        d = {'id': id_, 'text': texts, "text_type": text_type}  # 绑定文本和query id
        redis_.rpush(db_key_query, json.dumps(d, ensure_ascii=False))  # 加入redis
        redis_.sadd(db_key_querying, id_)
        return_text = d
        print("ok")
    else:
        return_text = {"texts": "输入格式应该为字典", "probabilities": None, "status_code": 401}
    return jsonify(return_text)  # 返回结果


t = Thread(target=classify)
t.start()

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)