fixes

src/models/model.py

@@ -7,7 +7,7 @@ from transformers import (
 )
 from torch.utils.data import Dataset, DataLoader
 import pytorch_lightning as pl
-from pytorch_lightning.loggers import MLFlowLogger, WandbLogger
+from pytorch_lightning.loggers import MLFlowLogger
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks.early_stopping import EarlyStopping
 from pytorch_lightning import LightningDataModule
@@ -328,12 +328,12 @@ class Summarization:
         )
 
         MLlogger = MLFlowLogger(experiment_name="Summarization",
-                               tracking_uri="https://dagshub.com/gagan3012/summarization.mlflow",
-                                save_dir='reports/training_metrics.txt')
+                                tracking_uri="https://dagshub.com/gagan3012/summarization.mlflow")
+                                #save_dir="reports/training_metrics.txt"
 
         # WandLogger = WandbLogger(project="summarization-dagshub")
 
-        #logger = DAGsHubLogger(metrics_path='reports/training_metrics.txt')
+        # logger = DAGsHubLogger(metrics_path='reports/training_metrics.txt')
 
         early_stop_callback = (
             [
@@ -352,7 +352,7 @@ class Summarization:
         gpus = -1 if use_gpu and torch.cuda.is_available() else 0
 
         trainer = Trainer(
-            logger=[MLlogger],
+            logger=MLlogger,
             callbacks=early_stop_callback,
             max_epochs=max_epochs,
             gpus=gpus,
@@ -460,10 +460,10 @@ class Summarization:
             num_return_sequences=num_return_sequences,
         )
         preds = self.tokenizer.decode(
-                generated_ids[0],
-                skip_special_tokens=skip_special_tokens,
-                clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            )
+            generated_ids[0],
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+        )
         return preds
 
     def evaluate(