Update app.py

app.py

@@ -33,8 +33,27 @@ def get_top95(y_predict, convert_target):
             break
     return lst_labels
 #
-# model = MyModel()
-model = torch.load("model.pt", map_location='cpu')
+# Creating the customized model, by adding a drop out and a dense layer on top of distil bert to get the final output for the model. 
+from transformers import DistilBertModel, DistilBertTokenizer
+
+class DistillBERTClass(torch.nn.Module):
+    def __init__(self):
+        super(DistillBERTClass, self).__init__()
+        self.l1 = DistilBertModel.from_pretrained("distilbert-base-uncased")
+        self.pre_classifier = torch.nn.Linear(768, 768)
+        self.dropout = torch.nn.Dropout(0.3)
+        self.classifier = torch.nn.Linear(768, 8)
+
+    def forward(self, input_ids, attention_mask):
+        output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask)
+        hidden_state = output_1[0]
+        pooler = hidden_state[:, 0]
+        pooler = self.pre_classifier(pooler)
+        pooler = torch.nn.ReLU()(pooler)
+        pooler = self.dropout(pooler)
+        output = self.classifier(pooler)
+        return output
+model = torch.load("model.pt", map_location='cpu').eval()
 
 # print(model)
 # model = DistilBertForSequenceClassification.from_pretrained("model/distilbert-model1.pt", local_files_only=True)