Update main.py

main.py

@@ -748,7 +748,7 @@ dataset_sources = pd.DataFrame(
             "1.96B",
             "8.37B",
         ],
-        "Cut-Off Date": [
+        "Information Cut-Off Date": [
             "2024-30",
             "Q4 2023",
             "-",
@@ -776,7 +776,7 @@ styled_table = dataset_sources.style.apply(
 
 table_html_data = styled_table._repr_html_()
 # table_html_data = dataset_sources.to_html(index=False, border=0)
-table_div_data = Div(NotStr(table_html_data), style="margin: 40px;"; align="center")
+table_div_data = Div(NotStr(table_html_data), style="margin: 40px;")
 
 
 @app.get("/intro")
@@ -822,7 +822,7 @@ def intro():
                 "Web datasets are inherently noisy and varied. The TxT360 pipeline implements sophisticated filtering and deduplication techniques to clean and remove redundancies while preserving data integrity."
             ),
             P(
-                "Curated datasets are typically structured and consistently formatted. TxT360 filters these sources with selective steps to maintain their integrity while providing seamless integration into the larger dataset. Both data source types are globally deduplicated together resulting in 5.7T tokens of high-quality data. The table below shows the final source distribution of TxT360 tokens."
+                "Curated datasets are typically structured and consistently formatted. TxT360 filters these sources with selective steps to maintain their integrity while providing seamless integration into the larger dataset. Both data source types are globally deduplicated together resulting in 5.7T tokens of high-quality data. The table below shows the source distribution of TxT360 tokens."
             ),
             table_div_data,
             P(