Add debug statements to persona and intent extraction functions

- Introduced debug print statements in `extract_and_update_persona`, `extract_and_update_intent`, and `classify_user_intent` to log the start of processing and responses generated, enhancing traceability during execution.
- Updated `extract_and_search_properties` to include a debug statement for classification checks, improving visibility into the property search process.
- These changes aim to facilitate debugging and provide better insights into the state of the application during user interactions.

ai_chat.py

@@ -117,6 +117,7 @@ class ChatState(TypedDict):
     classification: str
 
 async def extract_and_update_persona(state):
+    print("DEBUG - Starting extract_and_update_persona")
     # a. Define which persona fields to track
     persona_fields = ["language", "tone"]
     user_message = state["user_message"]
@@ -165,12 +166,15 @@ async def extract_and_update_persona(state):
     missing = [f for f in persona_fields if state["persona"].get(f) is None]
     if missing:
         state["response"] = f"Hi there! What is your {missing[0]} preference?"
+        print(f"DEBUG - Persona update returning response: {state['response']}")
         return state
 
     # f. All persona fields present—proceed to chat
+    print("DEBUG - Persona update returning None")
     return {"response": None}
 
 async def extract_and_update_intent(state):
+    print("DEBUG - Starting extract_and_update_intent")
     intent_fields = ["location_preference", "budget", "size_preference_sqm", "must_have"]
     user_message = state["user_message"]
     session_id = state["session_id"]
@@ -272,14 +276,17 @@ async def extract_and_update_intent(state):
             "must_have": "Hi there! What features are must-haves for you?"
         }
         state["response"] = questions.get(missing[0], f"Hi there! Could you tell me your {missing[0]}?")
+        print(f"DEBUG - Intent update returning response: {state['response']}")
         return state
 
+    print("DEBUG - Intent update returning None")
     return {"response": None}
 
 async def classify_user_intent(state):
     """
     Classify the user's message to determine if they want to search for properties.
     """
+    print("DEBUG - Starting classify_user_intent")
     user_message = state["user_message"]
     prompt = f"""
 Classify the user's message into exactly one of:
@@ -299,6 +306,7 @@ async def extract_and_search_properties(state):
     """
     Search for properties based on user intent and store results in state.
     """
+    print("DEBUG - Starting extract_and_search_properties")
     # Only search when the LLM tagged this as a listings request
     classification = state.get("classification")
     print(f"DEBUG - Property search classification check: '{classification}'")