diff --git a/diann_lib_processing.py b/diann_lib_processing.py
index 419754dd075fe26e9b17636ac7aaf70af98f13c6..5228eeaa9e7fe0c17ef8e862d19794ab68b36fee 100644
--- a/diann_lib_processing.py
+++ b/diann_lib_processing.py
@@ -54,20 +54,20 @@ if __name__ =='__main__':
     # df_2 = extract_sequence(df).reset_index(drop=True)
     # df_2.to_csv('./data/spectral_lib/data_uniprot_base.csv', index=False)
 
-    df = pd.read_csv('spectral_lib/data_uniprot_base.csv')
-    df['sequence']=df['sequence'].map(lambda x:x.replace('M(UniMod:35)','-OxM-'))
-    df.to_csv('spectral_lib/data_uniprot_base.csv')
-
-    # # args = load_args()
-    #
-    # model = ModelTransformer(encoder_ff=args.encoder_ff, decoder_rt_ff=args.decoder_rt_ff,
-    #                          n_head=args.n_head, encoder_num_layer=args.encoder_num_layer,
-    #                          decoder_rt_num_layer=args.decoder_rt_num_layer, drop_rate=args.drop_rate,
-    #                          embedding_dim=args.embedding_dim, acti=args.activation, norm=args.norm_first)
-    #
-    # model.load_state_dict(torch.load(args.model_weigh, weights_only=True))
-    #
-    # data_test = load_data(data_source=args.dataset_test, batch_size=args.batch_size, length=30, mode=args.split_test,
-    #                       seq_col=args.seq_test)
-    #
-    # predict(data_test, model, args.output)
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+    # df = pd.read_csv('spectral_lib/data_uniprot_base.csv')
+    # df['sequence']=df['sequence'].map(lambda x:x.replace('M(UniMod:35)','-OxM-'))
+    # df.to_csv('spectral_lib/data_uniprot_base.csv')
+
+    args = load_args()
+
+    model = ModelTransformer(encoder_ff=args.encoder_ff, decoder_rt_ff=args.decoder_rt_ff,
+                             n_head=args.n_head, encoder_num_layer=args.encoder_num_layer,
+                             decoder_rt_num_layer=args.decoder_rt_num_layer, drop_rate=args.drop_rate,
+                             embedding_dim=args.embedding_dim, acti=args.activation, norm=args.norm_first)
+
+    model.load_state_dict(torch.load(args.model_weigh, weights_only=True))
+
+    data_test = load_data(data_source=args.dataset_test, batch_size=args.batch_size, length=30, mode=args.split_test,
+                          seq_col=args.seq_test)
+
+    predict(data_test, model, args.output)
\ No newline at end of file