Update app.py

app.py

@@ -206,7 +206,7 @@ markdown_text = """
 markdown_text_file_upload = """
 - Trained with ~1500 rows of data on bert-base-uncased, English.
 - Upload CSV ONLY and name your column with budget line item as **text**.
-- Added RAG (Retrieval-augmented generation) to feed context into classifier using preceing lines of budget.
+- Using RAG (Retrieval-augmented generation) aproach to feed context into classifier using preceding lines of budget.
 - Accuracy of the model is ~88%.
 """
 html_table = """
@@ -226,50 +226,20 @@ html_table = """
     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) 
+    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: 
     <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, 
       'train_loss': 1.1497593360611156, 'epoch': 5.0})</code>. 
   </p>
-  <table style="margin-left: auto; margin-right: auto;">
-    <tr>
-      <th>Epoch</th>
-      <th>Training Loss</th>
-      <th>Validation Loss</th>
-      <th>Accuracy</th>
-    </tr>
-    <tr>
-      <td>1</td>
-      <td>No log</td>
-      <td>2.095209</td>
-      <td>0.340764</td>
-    </tr>
-    <tr>
-      <td>2</td>
-      <td>No log</td>
-      <td>1.419945</td>
-      <td>0.662420</td>
-    </tr>
-    <tr>
-      <td>3</td>
-      <td>No log</td>
-      <td>0.683810</td>
-      <td>0.850318</td>
-    </tr>
-    <tr>
-      <td>4</td>
-      <td>No log</td>
-      <td>0.460408</td>
-      <td>0.872611</td>
-    </tr>
-    <tr>
-      <td>5</td>
-      <td>No log</td>
-      <td>0.422096</td>
-      <td>0.888535</td>
-    </tr>
-  </table>
 </div>
 """
 # First interface for single line input
@@ -277,7 +247,7 @@ iface1 = gr.Interface(
     fn=predict, 
     inputs=gr.components.Textbox(lines=1, placeholder="Enter Budget line here...", label="Budget Input"), 
     outputs=gr.components.Label(label="Classification Output"), 
-    title="COFOG AutoClassification",
+    title="COFOG AutoClassification - Single Line",
     description=markdown_text,
     article=html_table,
     allow_flagging="manual",  # Enables flagging
@@ -294,7 +264,7 @@ iface2 = gr.Interface(
     outputs=gr.components.DataFrame(label="Classification Results"),
     description=markdown_text_file_upload,
     article=html_table,
-    title="Batch Classification"
+    title="COFOG AutoClassification - Batch Classification"
 )
 
 # Combine the interfaces in a tabbed interface