Update app.py

app.py

@@ -1044,7 +1044,7 @@ Each plan must include a **single Scratch Hat Block** (e.g., 'event_whenflagclic
 """
 
     try:
-        response = agent_2.invoke({"messages": [{"role": "user", "content": planning_prompt}]})
+        response = agent.invoke({"messages": [{"role": "user", "content": planning_prompt}]})
         print("Raw response from LLM [OverallPlannerNode 1]:",response)
         raw_response = response["messages"][-1].content#strip_noise(response["messages"][-1].content)
         print("Raw response from LLM [OverallPlannerNode 2]:", raw_response) # Uncomment for debugging
@@ -1347,7 +1347,7 @@ Use sprite names exactly as provided in `sprite_names` (e.g., 'Sprite1', 'soccer
     - If feedback is minor, make precise, minimal improvements only.
 """
     try:
-        response = agent_2.invoke({"messages": [{"role": "user", "content": refinement_prompt}]})
+        response = agent.invoke({"messages": [{"role": "user", "content": refinement_prompt}]})
         raw_response = response["messages"][-1].content#strip_noise(response["messages"][-1].content)
         logger.info(f"Raw response from LLM [RefinedPlannerNode]: {raw_response[:500]}...")
         # json debugging and solving
@@ -1536,7 +1536,7 @@ Example output:
 ```
 """                                    
             try:
-                response = agent_2.invoke({"messages": [{"role": "user", "content": refinement_prompt}]})
+                response = agent.invoke({"messages": [{"role": "user", "content": refinement_prompt}]})
                 llm_output = response["messages"][-1].content
                 llm_json = extract_json_from_llm_response(llm_output)                 
                 logger.info(f"Successfully analyze the opcode requirement for {sprite} - {event}.")