Add BiLSTMAttentionBERT model implementation and update import statements

utils/model.py

@@ -0,0 +1,25 @@
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, AutoModel
+
+class BiLSTMAttentionBERT(PreTrainedModel):
+    def __init__(self, hidden_dim, num_classes, num_layers, dropout):
+        super().__init__(PretrainedConfig())
+        self.bert = AutoModel.from_pretrained('dmis-lab/biobert-base-cased-v1.2')
+        self.lstm = nn.LSTM(768, hidden_dim, num_layers, batch_first=True, bidirectional=True)
+        self.dropout = nn.Dropout(dropout)
+        self.fc = nn.Linear(hidden_dim * 2, num_classes)
+        
+    @classmethod
+    def from_pretrained(cls, model_path, hidden_dim, num_classes, num_layers, dropout):
+        model = cls(hidden_dim, num_classes, num_layers, dropout)
+        state_dict = torch.load(model_path, map_location='cpu')
+        model.load_state_dict(state_dict)
+        return model
+        
+    def forward(self, input_ids, attention_mask):
+        bert_output = self.bert(input_ids, attention_mask=attention_mask)[0]
+        lstm_output, _ = self.lstm(bert_output)
+        dropped = self.dropout(lstm_output[:, -1, :])
+        output = self.fc(dropped)
+        return output

utils/prediction.py

@@ -1,7 +1,7 @@
+from utils.model import BiLSTMAttentionBERT
 import torch
 from transformers import AutoTokenizer
 from sklearn.preprocessing import LabelEncoder
-from utils.BiLSTM import BiLSTMAttentionBERT
 import numpy as np
 import streamlit as st
 import requests