Delete app_2.py

app_2.py

@@ -1,244 +0,0 @@
-import transformers
-import torch
-import os
-import json
-import random
-import numpy as np
-from torch import nn
-import argparse
-from torch.utils.tensorboard import SummaryWriter
-from datetime import datetime
-from tqdm import tqdm
-from torch.nn import DataParallel
-import logging
-from transformers import BertTokenizerFast
-# from transformers import BertTokenizer
-from os.path import join, exists
-from itertools import zip_longest, chain
-# from chatbot.model import DialogueGPT2Model
-# from dataset import MyDataset
-from torch.utils.data import Dataset, DataLoader
-from torch.nn import CrossEntropyLoss
-from sklearn.model_selection import train_test_split
-import torch.nn.functional as F
-from transformers import AutoTokenizer, AutoConfig, get_linear_schedule_with_warmup, AdamW, BertModel
-
-PAD = '[PAD]'
-pad_id = 0
-
-
-def set_args():
-    """
-    Sets up the arguments.
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', default='0', type=str, required=False, help='生成设备')
-    # parser.add_argument('--model_config', default='config/model_config_dialogue_small.json', type=str, required=False,
-    #                     help='模型参数')
-    parser.add_argument('--log_path', default='interact.log', type=str, required=False, help='interact日志存放位置')
-    parser.add_argument('--model_path', default='./pathology_extra/result/12/model.pth', type=str, required=False, help='对话模型路径')
-    parser.add_argument('--vocab_path', default='./bert-base-zh\\vocab.txt', type=str, required=False,
-                        help='对话模型路径')
-    parser.add_argument('--save_samples_path', default="sample/", type=str, required=False, help="保存聊天记录的文件路径")
-    parser.add_argument('--repetition_penalty', default=1.0, type=float, required=False,
-                        help="重复惩罚参数，若生成的对话重复性较高，可适当提高该参数")
-    # parser.add_argument('--seed', type=int, default=None, help='设置种子用于生成随机数，以使得训练的结果是确定的')
-    parser.add_argument('--max_len', type=int, default=25, help='每个utterance的最大长度,超过指定长度则进行截断')
-    parser.add_argument('--max_history_len', type=int, default=3, help="dialogue history的最大长度")
-    parser.add_argument('--no_cuda', action='store_true', help='不使用GPU进行预测')
-    return parser.parse_args()
-
-
-def create_logger(args):
-    """
-    将日志输出到日志文件和控制台
-    """
-    logger = logging.getLogger(__name__)
-    logger.setLevel(logging.INFO)
-
-    formatter = logging.Formatter(
-        '%(asctime)s - %(levelname)s - %(message)s')
-
-    # 创建一个handler，用于写入日志文件
-    file_handler = logging.FileHandler(
-        filename=args.log_path)
-    file_handler.setFormatter(formatter)
-    file_handler.setLevel(logging.INFO)
-    logger.addHandler(file_handler)
-
-    # 创建一个handler，用于将日志输出到控制台
-    console = logging.StreamHandler()
-    console.setLevel(logging.DEBUG)
-    console.setFormatter(formatter)
-    logger.addHandler(console)
-
-    return logger
-
-class Word_BERT(nn.Module):
-    def __init__(self, seq_label=1,cancer_label=8,transfer_label=2,ly_transfer=2):
-        super(Word_BERT, self).__init__()
-        self.bert = BertModel.from_pretrained('./bert-base-zh')
-        # self.bert_config = self.bert.config
-        self.out = nn.Sequential(
-            # nn.Linear(768,256),
-            # nn.ReLU(),
-            nn.Dropout(0.1),
-            nn.Linear(768, seq_label)
-        )
-        self.cancer = nn.Sequential(
-            nn.Dropout(0.1),
-            nn.Linear(768, cancer_label)
-        )
-        self.transfer = nn.Sequential(
-            nn.Dropout(0.1),
-            nn.Linear(768, transfer_label)
-        )
-        self.ly_transfer = nn.Sequential(
-            nn.Dropout(0.1),
-            nn.Linear(768, ly_transfer)
-        )
-
-    def forward(self, word_input, masks):
-        # print(word_input.size())
-        output = self.bert(word_input, attention_mask=masks)
-        sequence_output = output.last_hidden_state
-        pool = output.pooler_output
-        # print(sequence_output.size())
-        # print(pool.size())
-        out = self.out(sequence_output)
-        cancer = self.cancer(pool)
-        transfer = self.transfer(pool)
-        ly_transfer = self.ly_transfer(pool)
-        return out,cancer,transfer,ly_transfer
-
-def getChat(text: str, userid: int):
-    # while True:
-    #     if True:
-    # text = input("user:")
-    # text = "你好"
-    # if args.save_samples_path:
-    #     samples_file.write("user:{}\n".format(text))
-    text = ['[CLS]']+[i for i in text]+['[SEP]']
-    # print(text)
-    text_ids = tokenizer.convert_tokens_to_ids(text)
-    # print(text_ids)
-
-    input_ids = torch.tensor(text_ids).long().to(device)
-    input_ids = input_ids.unsqueeze(0)
-    mask_input = torch.ones_like(input_ids).long().to(device)
-    # print(input_ids.size())
-    response = []  # 根据context，生成的response
-    # 最多生成max_len个token
-    with torch.no_grad():
-        out, cancer, transfer, ly_transfer = model(input_ids, mask_input)
-        out = F.sigmoid(out).squeeze(2).cpu()
-        out = out.numpy().tolist()
-        cancer = cancer.argmax(dim=-1).cpu().numpy().tolist()
-        transfer = transfer.argmax(dim=-1).cpu().numpy().tolist()
-        ly_transfer = ly_transfer.argmax(dim=-1).cpu().numpy().tolist()
-        # print(out)
-        # print(cancer,transfer,ly_transfer)
-
-    pred_thresold = [[1 if jj > 0.4 else 0 for jj in ii] for ii in out]
-    size_list = []
-    start,end = 0,0
-    for i,j in enumerate(pred_thresold[0]):
-        if j==1 and start==end:
-            start = i
-        elif j!=1 and start!=end:
-            end = i
-            size_list.append((start,end))
-            start = end
-    print(size_list)
-
-    cancer_dict = {'腺癌': 0, '肺良性疾病': 1, '鳞癌': 2, '无法判断组织分型': 3, '复合型': 4, '转移癌': 5, '小细胞癌': 6, '大细胞癌': 7}
-    id_cancer = {j:i for i,j in cancer_dict.items()}
-    transfer_id = {'无': 0, '转移': 1}
-    id_transfer = {j:i for i,j in transfer_id.items()}
-    lymph_transfer_id = {'无': 0, '淋巴转移': 1}
-    id_lymph_transfer = {j: i for i, j in lymph_transfer_id.items()}
-    # print(cancer)
-    cancer = id_cancer[cancer[0]]
-    transfer = id_transfer[transfer[0]]
-    ly_transfer = id_lymph_transfer[ly_transfer[0]]
-    print(cancer,transfer,ly_transfer)
-
-    return size_list,cancer,transfer,ly_transfer
-
-
-import requests
-
-
-def testFunc():
-    url = "http://localhost:7788/getChat"
-    payload = json.dumps({
-        "userid": 602099768,
-        "context": "我就试一下"
-    })
-    headers = {
-        'Content-Type': 'application/json'
-    }
-    response = requests.request("POST", url, headers=headers, data=payload)
-    print(response.text)
-
-
-import uvicorn
-from pydantic import BaseModel
-from fastapi import FastAPI
-
-app = FastAPI()
-# import intel_extension_for_pytorch as ipex
-
-args = set_args()
-logger = create_logger(args)
-# 当用户使用GPU,并且GPU可用时
-args.cuda = torch.cuda.is_available() and not args.no_cuda
-device = 'cuda' if args.cuda else 'cpu'
-logger.info('using device:{}'.format(device))
-os.environ["CUDA_VISIBLE_DEVICES"] = args.device
-tokenizer = BertTokenizerFast(vocab_file=args.vocab_path, sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]")
-# tokenizer = BertTokenizer(vocab_file=args.voca_path)
-model = Word_BERT()
-# model = model.load_state_dict(torch.load(args.model_path))
-model = model.to(device)
-# model = ipex.optimize(model, dtype=torch.float32)
-model.eval()
-# if args.save_samples_path:
-#     if not os.path.exists(args.save_samples_path):
-#         os.makedirs(args.save_samples_path)
-#     samples_file = open(args.save_samples_path + '/samples.txt', 'a', encoding='utf8')
-#     samples_file.write("聊天记录{}:\n".format(datetime.now()))
-# 存储聊天记录，每个utterance以token的id的形式进行存储
-# history = []
-Allhistory = {}
-print('初始化完成')
-
-if __name__ == '__main__':
-    # getChat("测试一下", 0)
-    # main()
-    uvicorn.run(app='main_2:app', host="localhost",
-                port=7788, reload=False)
-    # testFunc()
-
-
-class Items1(BaseModel):
-    context: str
-    userid: int
-    # must: bool
-
-
-import time
-
-lastReplyTime = 0
-
-
-@app.post("/getChat")
-async def get_Chat(item1: Items1):
-    global lastReplyTime
-    tempReplyTime = int(time.time() * 1000)
-    # if tempReplyTime % 10 == 0 or item1.must == True or tempReplyTime - lastReplyTime < 30000:
-    # if item1.must == True:
-    #     lastReplyTime = tempReplyTime
-    result = getChat(
-        item1.context, item1.userid)
-    return {"res": result}