Upload 3 files

app.py

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+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()

model_big.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6bbe4e4fc7c2a54cdaca6d1329cd6ffc298b1c961e6ee7cef0be53fef851fa66
+size 432097082

requirements.txt

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+transformers
+torch