app (1).py

import torch 
from torch import nn
import torch.nn.functional as F
from transformers import BertModel
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AdamW
import json
import gradio as gr


eg_text = '	酒店的地理位置实在不错，所以从大堂开始就令人惊艳。城景房不但在房间可以看到上海的美景'
model_name = 'bert-base-chinese'

max_len = 128
n_class = 2
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer_cn = AutoTokenizer.from_pretrained(model_name)
voc_size = len(tokenizer_cn.vocab)
name_list = ['Negative review', 'Positive review']

class bertBlock(nn.Module):
    def __init__(self,):
        super().__init__()
        self.model_block = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=100).to(device)

    def forward(self, text_b):
        x = self.model_block(text_b)
        return x.logits


class textCNNblock(nn.Module):
    def __init__(self,):
        super().__init__()
        emb_dim = 100
        # n_class = 4
        kernels=[3,4,5]
        kernel_number=[150,150,150]
        self.embd = nn.Embedding(voc_size, emb_dim)
        self.convs = nn.ModuleList([nn.Conv1d(max_len, number, size,padding=size) for (size,number) in zip(kernels,kernel_number)])
        self.dropout=nn.Dropout(0.1)
        self.out = nn.Linear(sum(kernel_number), 100)

    def forward(self, x):
        x = self.embd(x)

        x = [F.relu(conv(x)) for conv in self.convs]
        x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x]
        x = torch.cat(x, 1)
        x = self.dropout(x)
        x = self.out(x)
        return x


class LSTMModelblock(nn.Module):
    def __init__(self,):
        super().__init__()
        emb_dim = 100
        # n_class = 2
        self.embd = nn.Embedding(voc_size, emb_dim)
        self.lstm = nn.LSTM(emb_dim,50)
        self.out = nn.Linear(6400, 100)
        self.flatten = nn.Flatten()

    def forward(self, x):
        x = self.embd(x)

        x,_ = self.lstm(x)
        x = self.flatten(x)
        x = self.out(x)
        return x


class BERT_CNN_LSTM(nn.Module):
    def __init__(self, ):
        super(BERT_CNN_LSTM, self).__init__()

        self.bert = bert_block
        self.lstm = lstm_model_block
        self.cnn = text_cnn_block
        self.fc1 = nn.Linear(300, 100)
        self.fc2 = nn.Linear(100, n_class)
        self.dropout1 = nn.Dropout(0.2)
        self.dropout2 = nn.Dropout(0.2)
        self.att = nn.TransformerEncoderLayer(d_model=100, nhead=2)
        self.flatten = nn.Flatten()

    def forward(self, input_ids):
        bert_out = self.bert(input_ids)
        lstm_out = self.lstm(input_ids)
        cnn_out = self.cnn(input_ids)

        x = torch.stack((bert_out,lstm_out, cnn_out), dim=1)
        x = self.att(x)
        x = self.flatten(x)
        x = self.fc1(x)
        x = self.dropout1(x)
        x = self.fc2(x)
        return x


bert_block = bertBlock().to(device)
text_cnn_block = textCNNblock().to(device)
lstm_model_block = LSTMModelblock().to(device)
# 创建模型
model_big_load = BERT_CNN_LSTM()
model_big_load.to(device)

model_big_load.load_state_dict(torch.load("model_big.pth",map_location=torch.device('cpu')))
model_big_load.eval()



def predict(one_text ):
  one_result = tokenizer_cn(one_text,padding='max_length', max_length=max_len, truncation=True, return_tensors="pt")
  # print(one_result)
  one_ids = one_result.input_ids[0]
  one_ids = one_ids.unsqueeze(0).to(device)

  # 使用模型进行预测
  with torch.no_grad():
      output = model_big_load(one_ids)
  # print(output)
  # 计算预测概率
  pred_score = nn.functional.softmax(output[0], dim=0)
  pred_score = torch.max(pred_score).cpu().numpy()

  # 获取预测结果
  pred_index = torch.argmax(output, dim=1).item()
  pred_label = name_list[pred_index]

  print(f"predict class name : {pred_label} \npredict score : {pred_score}")
  print(pred_index)
  # 转为json字符串格式
  result_dict = {'pred_score':str(pred_score),'pred_index':str(pred_index),'pred_label':pred_label }
  result_json = json.dumps(result_dict)

  return result_json


demo = gr.Interface(fn=predict, 
             inputs="text",

             outputs="text",
             examples=['酒店的地理位置实在不错，所以从大堂开始就令人惊艳。城景房不但在房间可以看到上海的美景','住了一次，感觉很差。灯光太暗，房间比较旧！' ],
             )
             
# demo.launch(debug=True)
demo.launch()