Update app.py

app.py

@@ -225,16 +225,12 @@ html_table = """
     with the predictions from the first level serving as contextual 
     input for subsequent second-level classification. The project 
     is conducted with an exclusive focus on academic and research 
-    objectives. 
-
-    For batch prediction we integrated Retriever-Augmented Generator (RAG) 
+    objectives.<br>For batch prediction we integrated Retriever-Augmented Generator (RAG) 
     approach. This approach enriches the prediction process 
     by incorporating contextual information from up to 5 preceding 
     lines in the dataset, significantly enhancing the model's 
     ability to understand and classify each entry in the context 
-    of related data.
-    
-    Detailed metrics of the training process are as follows: 
+    of related data.<br>Detailed metrics of the training process are as follows: 
     <code>TrainOutput(global_step=395, training_loss=1.1497593360611156, 
     metrics={'train_runtime': 650.0119, 'train_samples_per_second':
       9.638, 'train_steps_per_second': 0.608, 'total_flos': 1648509163714560.0,