update

app.py

@@ -1,5 +1,7 @@
 import gradio as gr
 import transformers
+from model_config import PragFormerConfig
+from model import PragFormer
 from simpletransformers.classification import ClassificationModel, ClassificationArgs
 import torch
 import json
@@ -8,14 +10,10 @@ import json
 deep_scc_model_args = ClassificationArgs(num_train_epochs=10,max_seq_length=300,use_multiprocessing=False)
 deep_scc_model = ClassificationModel("roberta", "NTUYG/DeepSCC-RoBERTa", num_labels=19, args=deep_scc_model_args, use_cuda=False)
 
-pragformer_config = transformers.AutoConfig.from_pretrained("Pragformer/PragFormer", trust_remote_code=True)
-pragformer = transformers.AutoModel.from_pretrained("Pragformer/PragFormer", config=pragformer_config, trust_remote_code=True)
-
-private_config = transformers.AutoConfig.from_pretrained("Pragformer/PragFormer_private", trust_remote_code=True)
-pragformer_private = transformers.AutoModel.from_pretrained("Pragformer/PragFormer_private", config=private_config, trust_remote_code=True)
-
-reduction_config = transformers.AutoConfig.from_pretrained("Pragformer/PragFormer_reduction", trust_remote_code=True)
-pragformer_reduction = transformers.AutoModel.from_pretrained("Pragformer/PragFormer_reduction", config=reduction_config, trust_remote_code=True)
+pragformer_config = PragFormerConfig.from_pretrained("Pragformer/PragFormer", trust_remote_code=True)
+pragformer = PragFormer.from_pretrained("Pragformer/PragFormer", config=pragformer_config, trust_remote_code=True)
+pragformer_private = PragFormerConfig.from_pretrained("Pragformer/PragFormer_private", config=pragformer_config, trust_remote_code=True)
+pragformer_reduction = PragFormerConfig.from_pretrained("Pragformer/PragFormer_reduction", config=pragformer_config, trust_remote_code=True)
 
 
 #Event Listeners