Update app.py

app.py

@@ -21,14 +21,22 @@ text_embeddings = model.encode(df["combined_text"].tolist(), convert_to_tensor=F
 results_dict = {}
 
 # Create markdown from results
+
 def format_markdown(top_results):
     markdown_output = ""
     for _, row in top_results.iterrows():
-        markdown_output += f"### [{row['Title']}]({row['Link']})\n"
-        markdown_output += f"**Date**: {row['Date'].strftime('%Y-%m-%d') if pd.notnull(row['Date']) else 'N/A'}\n\n"
-        markdown_output += f"{row['Description']}\n\n---\n"
+        title = row['Title']
+        link = row['Link']
+        desc = row['Description']
+        date_str = row['Date'].strftime('%Y-%m-%d') if pd.notnull(row['Date']) else 'N/A'
+
+        markdown_output += f"### [{title}]({link})\n"
+        markdown_output += f"**Date**: {date_str}\n\n"
+        markdown_output += f"{desc}\n\n---\n"
     return markdown_output
 
+
+
 # Auto-query construction and retrieval
 def process_query_csv(query_file):
     global results_dict