more updates for better ai huan

ai_chat.py

@@ -218,6 +218,17 @@ async def process_message(user_message: str, user_info: dict = None, session_id:
         extracted_persona = await extract_persona_from_message(user_message, persona)
         print(f"Extracted persona from message: {extracted_persona}")
     
+    # Update persona with extracted information for persona_check calculation
+    updated_persona = persona.copy()
+    for field, value in extracted_persona.items():
+        if value is not None:
+            updated_persona[field] = value
+    
+    # Recalculate persona check with updated persona (but only if we extracted something)
+    if extracted_persona:
+        conversation_context = " ".join([msg["content"] for msg in session_messages[-5:]]) + " " + user_message
+        should_ask, field_to_ask = await should_ask_persona_question(updated_persona, conversation_context)
+    
     # Process with AI
     result = await chat_graph.ainvoke({
         "user_message": user_message,
@@ -226,7 +237,7 @@ async def process_message(user_message: str, user_info: dict = None, session_id:
         "wa_id": wa_id,
         "wamid": wamid,
         "session_messages": session_messages,
-        "persona": persona,
+        "persona": updated_persona,  # Use updated persona
         "is_persona_question": is_persona_question,
         "current_field": current_field,
         "parsed_response": parsed_response,

persona_manager.py

@@ -74,8 +74,10 @@ async def update_persona_field(wa_id: str, field: str, value) -> bool:
             "updated_at": datetime.utcnow().isoformat()
         }
         print(f"Updating persona field {field} with value {value} for user {wa_id}")
+        print(f"Update data: {update_data}")
         response = supabase.table("user_personas").update(update_data).eq("wa_id", wa_id).execute()
         print(f"Persona update response: {response.data if response.data else 'No data returned'}")
+        print(f"Response status: {response.status_code if hasattr(response, 'status_code') else 'No status'}")
         return True
     except Exception as e:
         print(f"Error updating persona field {field}: {e}")
@@ -416,9 +418,12 @@ Available persona fields:
 Return ONLY a JSON object with the extracted fields. Only include fields that are clearly mentioned or implied.
 If no relevant information is found for a field, don't include it in the response.
 
+IMPORTANT: For location_preference, be very generous in extraction. If the user mentions ANY location (including "pretoria", "pta", "johannesburg", "jhb", "cape town", "cpt", etc.), extract it.
+
 Examples:
 - "I want a property in cpt" → {"location_preference": "cape town"}
 - "Looking for warehouse in pta around 500k" → {"location_preference": "pretoria", "budget": 500000}
+- "Looking for a property in pretoria for my clothing business" → {"location_preference": "pretoria"}
 - "Need space for clothing business with office" → {"must_have": ["office"]}
 """
 
@@ -429,10 +434,13 @@ Examples:
         
         response = llm.invoke(messages)
         
+        print(f"AI extraction response: {response.content}")  # Debug
+        
         # Parse the JSON response
         import json
         try:
             extracted = json.loads(response.content)
+            print(f"Parsed extraction: {extracted}")  # Debug
             
             # Validate and clean the extracted data
             cleaned_extracted = {}
@@ -442,6 +450,7 @@ Examples:
                 if field in PERSONA_FIELDS and not current_persona.get(field) and value is not None:
                     cleaned_extracted[field] = value
             
+            print(f"Cleaned extraction: {cleaned_extracted}")  # Debug
             return cleaned_extracted
             
         except json.JSONDecodeError: