diff --git a/diann_lib_processing.py b/diann_lib_processing.py
index 25d3811fd4c156aeea1d242cf21fc59152a368f4..53594b148989f8dca59429573d5040bf3edee6a1 100644
--- a/diann_lib_processing.py
+++ b/diann_lib_processing.py
@@ -5,7 +5,7 @@ import pyarrow.parquet as pq
 import pyarrow as pa
 import torch
 import matplotlib.pyplot as plt
-from loess.loess_1d import loess_1d
+# from loess.loess_1d import loess_1d
 
 from model.model import ModelTransformer
 from config import load_args
@@ -95,9 +95,9 @@ def predict(data_pred, model, output_path):
 
 
 if __name__ =='__main__':
-    df = load_lib('spectral_lib/1-240711_ident_resistance_idbioriv_fluoroquinolones_conta_human_sang.parquet')
-    df = extract_sequence(df)
-    df.to_csv('spectral_lib/1-240711_ident_resistance_idbioriv_fluoroquinolones_conta_human_sang.csv')
+    # df = load_lib('spectral_lib/1-240711_ident_resistance_idbioriv_fluoroquinolones_conta_human_sang.parquet')
+    # df = extract_sequence(df)
+    # df.to_csv('spectral_lib/1-240711_ident_resistance_idbioriv_fluoroquinolones_conta_human_sang.csv')
     # plt.hist(df['RT'])
     # plt.savefig('test.png')
     #
@@ -174,19 +174,19 @@ if __name__ =='__main__':
     #
 
 
-    # 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, seq_length=30)
-    #
-    # if torch.cuda.is_available():
-    #     model = model.cuda()
-    #
-    # 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)
+    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, seq_length=30)
+
+    if torch.cuda.is_available():
+        model = model.cuda()
+
+    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)