Update app.py

app.py

@@ -1167,41 +1167,70 @@ def orchestrate_turn(user_input: str, session_id: str = "default") -> str:
         log_step("Step 7: Prompt assembly", step_start)
         
         # ====================================================================
-        # STEP 8: FINAL PROMPT CONSTRUCTION
+        # STEP 8: PREPARE RESPONSE AGENT INPUT
         # ====================================================================
-        step_start = log_step("Step 8: Final prompt construction")
+        step_start = log_step("Step 8: Prepare response input")
         
-        # Knowledge cutoff
-        knowledge_cutoff = f"""
-
-The current year is {CURRENT_YEAR}. Your knowledge cutoff date is October 2023. If the user asks about recent events or dynamic facts, inform them you may not have the most up-to-date information and suggest referencing direct sources."""
+        # Get active response prompts
+        response_prompt_names = prompt_state.get_active_response_prompts()
+        logger.info(f"Active prompts: {response_prompt_names}")
         
-        complete_prompt = f"""
-{prompt_segments_text}
-
-If tools were used, context and output will be here. Ignore if empty:
-Image output: {tool_img_output}
-Image context: {tool_context}
-
-Conversation history, if available:
-{conversation_history_formatted}
-
-Consider any context available to you:
-{thinking_context}
-
-Here is the user's current query:
-{user_input}
-
-{knowledge_cutoff}
-"""
+        # Combine tool outputs for context
+        # If we have tool_img_output, mention it in tool_context
+        combined_tool_context = tool_context
+        if tool_img_output:
+            # Note: tool_img_output is HTML that will be embedded separately
+            # Just note its presence in the context
+            if combined_tool_context:
+                combined_tool_context += "\n\nNote: A visualization has been generated for the user."
+            else:
+                combined_tool_context = "A visualization has been generated for the user."
+        
+        # Build input dictionary for ResponseAgent
+        # CRITICAL: Must be a Dict, NOT a string!
+        input_data = {
+            'user_query': user_input,
+            'conversation_history': recent_history,  # List[Dict], not formatted string!
+            'active_prompts': response_prompt_names,  # List[str]
+            'thinking_context': thinking_context,  # str (from thinking agents)
+            'tool_context': combined_tool_context,  # str (tool usage info)
+        }
+        
+        logger.info(f"Response input prepared:")
+        logger.info(f"  - User query: {len(user_input)} chars")
+        logger.info(f"  - History: {len(recent_history)} messages")
+        logger.info(f"  - Active prompts: {len(response_prompt_names)} prompts")
+        logger.info(f"  - Thinking context: {len(thinking_context)} chars")
+        logger.info(f"  - Tool context: {len(combined_tool_context)} chars")
         
-        log_step("Step 8: Final prompt construction", step_start)
+        log_step("Step 8: Prepare response input", step_start)
         
         # ====================================================================
-        # STEP 9: RESPONSE GENERATION (Phi3)
+        # STEP 9: RESPONSE GENERATION (Llama-3.2-3B)
         # ====================================================================
         step_start = log_step("Step 9: Response generation")
-        raw_response = response_agent.invoke(complete_prompt)
+        
+        try:
+            result = response_agent.invoke(input_data)
+            
+            # Extract response from result dict
+            raw_response = result.get('response', '')
+            metadata = result.get('metadata', {})
+            
+            if not raw_response:
+                logger.warning("ResponseAgent returned empty response")
+                raw_response = "I apologize, but I wasn't able to generate a response. Please try again."
+            
+            logger.info(f"✓ Generated {len(raw_response)} chars")
+            if metadata:
+                logger.info(f"  Metadata: {metadata}")
+            
+        except Exception as e:
+            logger.error(f"Response generation failed: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+            raw_response = "I apologize, but I encountered an error while generating a response. Please try rephrasing your question or try again."
+        
         log_step("Step 9: Response generation", step_start)
         
         # ====================================================================