Update app.py

app.py

@@ -1565,7 +1565,7 @@ def validate_ai_output(ai_output, proof_points):
 prompt_message = """
 **Role**  
 You are an expert copywriter specializing in creating high-quality marketing content that integrates Top-Scoring Statements for Each Trust Bucket into various formats. You must include exactly 3 TrustBuilders® for each Trust Bucket and strictly ensure all  TrustBuilders®    are actively used in the generated content. Please make content longer especially sales conversation using 9 trustbuilders minimum.
-Strictly use google search for copy
+*Strictly use google search for finding features*
 **Listing Top-Scoring Statements**
 - Use the following format to display top-scoring statements:  
   Top-scoring statements
@@ -1830,20 +1830,17 @@ def chatbot_response(message, history):
         else:
             return f"Error: Invalid dataset selection '{selected_dataset_ai}'."
 
-        # If top-scoring statements have not been generated yet, generate them
-        if not last_top_scoring_statements:
-            top_scoring_statements = "### Top Scoring Statements ###\n\n"
-            for bucket, statements in trust_data.items():
-                top_scoring_statements += f"**{bucket}**:\n"
-                for statement in statements:
-                    top_scoring_statements += f"- {statement}\n"
-                top_scoring_statements += "\n"
-            last_top_scoring_statements = top_scoring_statements  # Save this for future use
-        else:
-            top_scoring_statements = ""  # Don't include the top-scoring statements in subsequent messages
-
-        # Combine predefined prompt, top-scoring statements (only the first time), and user input
-        combined_prompt = top_scoring_statements  # Include top-scoring statements only once
+        # Prepare top-scoring statements
+        top_scoring_statements = "### Top Scoring Statements ###\n\n"
+        for bucket, statements in trust_data.items():
+            top_scoring_statements += f"**{bucket}**:\n"
+            for statement in statements:
+                top_scoring_statements += f"- {statement}\n"
+            top_scoring_statements += "\n"
+        last_top_scoring_statements = top_scoring_statements
+
+        # Combine predefined prompt, top-scoring statements, and user input
+        combined_prompt = "\n\n### Top-Scoring Statements for Integration ###\n" + top_scoring_statements
         combined_prompt += "\n\nUser Input:\n" + message
         trust_tip, suggestion = get_trust_tip_and_suggestion()
         trust_tip_and_suggestion = f"\n\n---\n\n**Trust Tip**: {trust_tip}\n\n**Suggestion**: {suggestion}"
@@ -1871,9 +1868,7 @@ def chatbot_response(message, history):
 
         # Prepare the final response
         response = f"**Selected Dataset: {selected_dataset_ai}**\n\n"
-        # Only include top-scoring statements once
-        if top_scoring_statements:
-            response += f"{top_scoring_statements}\n"
+        response += f"{top_scoring_statements}\n"
         response += f"\n{agent_output['output']}"
         response += trust_tip_and_suggestion
 
@@ -1893,119 +1888,6 @@ def chatbot_response(message, history):
         logger.error(f"Unexpected error: {e}")
         return "Error occurred during response generation."
 
-# def chatbot_response(message, history):
-#     """
-#     Generate chatbot response dynamically using selected dataset, user input, and maintaining history.
-#     """
-#     global selected_dataset_ai, last_top_scoring_statements, chat_history
-
-#     try:
-#         # Ensure a dataset is selected
-#         if not selected_dataset_ai:
-#             return "Error: No dataset selected. Please select a dataset and try again."
-
-#         # Define datasets and corresponding trust buckets
-#         datasets = {
-#             "VW Owners.xlsx": {
-#                 "Development": [
-#                     "We bring together the world's best talent in many disciplines to create your cars. (25%)",
-#                     "Building great and affordable cars is our foundation. (22%)",
-#                     "Our beginnings are a unique combination of investors and unions. (18%)",
-#                 ],
-#                 "Benefit": [
-#                     "We bring together the world's best talent in many disciplines to create your cars. (23%)",
-#                     "We strongly focus on keeping and nurturing our team and have a 99.5% retention rate. (18%)",
-#                     "Employees are provided with extensive continuous training. (16%)",
-#                 ],
-#                 "Vision": [
-#                     "Our brands are ranked No. 2 and 5 in the reliability rankings. (27%)",
-#                     "Our technology and manufacturing capabilities are second to none. (22%)",
-#                     "We produce almost 9 million cars per year. (15%)",
-#                 ],
-#             },
-#             "Volkswagen Non Customers.xlsx": {
-#                 "Stability": [
-#                     "We work with our unions in our restructuring and future plans. (21%)",
-#                     "We have learned from our mistakes in the Diesel Affair and we have made fundamental changes. (19%)",
-#                     "Building great and affordable cars is our foundation. (18%)",
-#                 ],
-#                 "Relationship": [
-#                     "We put a lot of emphasis on the interior experience and two of our cars have been ranked in the top 10. (24%)",
-#                     "We are at the forefront of technology to deliver better cars and driving experiences. (17%)",
-#                     "Our beginnings are a unique combination of investors and unions and today 9 of our 20 board members are staff representatives. (17%)",
-#                 ],
-#                 "Competence": [
-#                     "At the heart of our decision-making is the long-term quality of life for all of us. (20%)",
-#                     "We put a lot of emphasis on the interior experience and two of our cars have been ranked in the top 10. (19%)",
-#                     "We are one of the longest-established car companies. (18%)",
-#                 ],
-#             },
-#         }
-
-#         # Retrieve relevant data for the selected dataset
-#         if selected_dataset_ai in datasets:
-#             trust_data = datasets[selected_dataset_ai]
-#         else:
-#             return f"Error: Invalid dataset selection '{selected_dataset_ai}'."
-
-#         # Prepare top-scoring statements
-#         top_scoring_statements = "### Top Scoring Statements ###\n\n"
-#         for bucket, statements in trust_data.items():
-#             top_scoring_statements += f"**{bucket}**:\n"
-#             for statement in statements:
-#                 top_scoring_statements += f"- {statement}\n"
-#             top_scoring_statements += "\n"
-#         last_top_scoring_statements = top_scoring_statements
-
-#         # Combine predefined prompt, top-scoring statements, and user input
-#         combined_prompt = "\n\n### Top-Scoring Statements for Integration ###\n" + top_scoring_statements
-#         combined_prompt += "\n\nUser Input:\n" + message
-#         trust_tip, suggestion = get_trust_tip_and_suggestion()
-#         trust_tip_and_suggestion = f"\n\n---\n\n**Trust Tip**: {trust_tip}\n\n**Suggestion**: {suggestion}"
-
-#         # Validate chat history
-#         validated_chat_history = []
-#         for entry in history:
-#             if isinstance(entry, dict) and "role" in entry and "content" in entry:
-#                 validated_chat_history.append(entry)
-#             else:
-#                 logger.warning(f"Invalid chat history entry skipped: {entry}")
-
-#         # Include validated history in the prompt
-#         for entry in validated_chat_history:
-#             combined_prompt += f"\n{entry['role']}: {entry['content']}"
-
-#         # Structured input for agent execution
-#         structured_input = {
-#             "input": combined_prompt,
-#             "chat_history": validated_chat_history,
-#         }
-
-#         # Generate AI output using the agent pipeline (replace with actual logic)
-#         agent_output = agent_executor.invoke(structured_input)
-
-#         # Prepare the final response
-#         response = f"**Selected Dataset: {selected_dataset_ai}**\n\n"
-#         response += f"{top_scoring_statements}\n"
-#         response += f"\n{agent_output['output']}"
-#         response += trust_tip_and_suggestion
-
-#         # Append interaction to history
-#         validated_chat_history.append({"role": "user", "content": message})
-#         validated_chat_history.append({"role": "assistant", "content": agent_output["output"]})
-
-#         return response
-
-#     except KeyError as ke:
-#         logger.error(f"KeyError encountered: {ke}")
-#         return "An unexpected error occurred. Please try again."
-#     except ValueError as ve:
-#         logger.error(f"ValueError encountered: {ve}")
-#         return "An unexpected value was encountered. Please refine your input."
-#     except Exception as e:
-#         logger.error(f"Unexpected error: {e}")
-#         return "Error occurred during response generation."
-
 def read_ai_dataset_selection():
     global selected_dataset_ai
     return selected_dataset_ai