Update app.py

app.py

@@ -992,7 +992,7 @@ def generate_example_questions(sheet_name, column_headers):
         questions.append(f"What is the price of a specific product in {sheet_name}?")
     
     if any(fnmatch.fnmatch(header, 'Employee*') for header in column_headers):
-        questions.append(f"What are the details of the distinct broker names?")
+        questions.append(f"What are the the distinct broker names?")
         
     return questions
 
@@ -1041,10 +1041,14 @@ def generate_prompt_from_excel_file(df_dict):
             {sample_table}. 
             """
     
-    #prompt += f"- Query: A natural language question (e.g., List all the employees whose names start with 'A'). The question should be sent as 'What are the employee details with name starts with a'."
-    #prompt += f"""Output :  {docstatus}. Here is the sample table:
-     #       {sample_table}. 
-      #      """
+    prompt += f"- Query: A natural language question (e.g., List all the employees whose names start with 'A'). The question should be sent as 'What are the employee details with name starts with a'."
+    prompt += f"""Output :  {docstatus}. Here is the sample table:
+            {sample_table}. 
+          """
+    prompt += f"- Query: A natural language question (e.g., Give me details of sarah webb). The question should be sent as 'What are the employee details with employee name or accouunt name contains sarah webb'"
+    prompt += f"""Output :  {docstatus}. Here is the sample table:
+            {sample_table}. 
+          """
 
     prompt += f"- Query: A natural language question with request to create LOA document (e.g., can you create LOA document for all the employees with broker name XXXX). The question should be sent as 'What are the employee details with broker name XXXX : LOA document'."
     prompt += f"""Output:  {docstatus}. Here is the sample table: