fyp

app/ai/agent/nodes/greeting.py

@@ -2,6 +2,7 @@
 """
 Node: Handle greeting flow.
 User says hello/hi/greetings → respond warmly → end conversation.
+FIXED: Transition to IDLE instead of COMPLETE
 """
 
 from structlog import get_logger
@@ -27,19 +28,25 @@ GREETING_PROMPT = """You are AIDA, a warm and friendly real estate AI assistant
 
 User greeted you with: "{user_message}"
 
-Your task: Respond to their greeting warmly and naturally.
+Generate a WARM, NATURAL response that:
+1. Responds to their greeting in the SAME language they used
+2. Matches their tone (formal/casual/friendly)
+3. Introduces yourself briefly
+4. Asks how you can help with real estate
 
-Requirements:
-1. Match their tone (formal/casual/friendly)
-2. Respond in the SAME language they used
-3. Keep response SHORT (2-3 sentences max)
-4. Ask how you can help with real estate
-5. Be genuine and human-like
+Important:
+- If they wrote in English → respond in English
+- If they wrote in French → respond in French  
+- If they wrote in Spanish → respond in Spanish
+- If they wrote in Yoruba → respond in Yoruba
+- Detect language automatically and respond naturally
+
+Keep it conversational (2-3 sentences max). Be genuine and warm.
 
 Examples:
-- User: "Hello!" → "Hello! 👋 I'm AIDA, your real estate assistant. How can I help you find or list a property today?"
-- User: "Hi there" → "Hi! 😊 Great to see you! What can I do for you regarding real estate?"
-- User: "Bonjour!" → "Bonjour! 👋 Je suis AIDA, votre assistant immobilier. Comment puis-je vous aider?"
+- "Hello!" → "Hello! 👋 I'm AIDA, your real estate assistant. How can I help you find or list a property today?"
+- "Hi there" → "Hi! 😊 Great to see you! What can I do for you regarding real estate?"
+- "Bonjour!" → "Bonjour! 👋 Je suis AIDA, votre assistant immobilier. Comment puis-je vous aider?"
 
 Now respond to their greeting naturally (2-3 sentences only):"""
 
@@ -48,6 +55,8 @@ async def greeting_handler(state: AgentState) -> AgentState:
     """
     Handle greeting flow.
     
+    FIXED: Transitions to IDLE instead of COMPLETE
+    
     Args:
         state: Agent state with user's greeting message
     
@@ -55,7 +64,7 @@ async def greeting_handler(state: AgentState) -> AgentState:
         Updated state with greeting response
     
     Transitions to:
-        - COMPLETE (greeting handled, conversation ends)
+        - IDLE (greeting handled, ready for next interaction)
     """
     
     logger.info(
@@ -107,13 +116,13 @@ async def greeting_handler(state: AgentState) -> AgentState:
         logger.info("Greeting response stored in state", user_id=state.user_id)
         
         # ============================================================
-        # STEP 4: Transition to COMPLETE
+        # STEP 4: ✅ FIXED - Transition to IDLE (not COMPLETE!)
         # ============================================================
         
-        success, error = state.transition_to(FlowState.COMPLETE, reason="Greeting handled")
+        success, error = state.transition_to(FlowState.IDLE, reason="Greeting handled, ready for next interaction")
         
         if not success:
-            logger.error("Transition to COMPLETE failed", error=error)
+            logger.error("Transition to IDLE failed", error=error)
             state.set_error(error, should_retry=False)
             return state
         
@@ -135,7 +144,7 @@ async def greeting_handler(state: AgentState) -> AgentState:
         
         # Try to continue despite error
         if state.set_error(error_msg, should_retry=True):
-            state.transition_to(FlowState.COMPLETE, reason="Greeting with error recovery")
+            state.transition_to(FlowState.IDLE, reason="Greeting with error recovery")
         else:
             state.transition_to(FlowState.ERROR, reason="Greeting error")
         

app/ai/agent/state.py

@@ -107,6 +107,7 @@ class AgentState(BaseModel):
                 FlowState.CASUAL_CHAT,
                 FlowState.ERROR,
             ],
+            # ✅ FIXED: Greeting can transition to IDLE
             FlowState.GREETING: [
                 FlowState.IDLE,
                 FlowState.CLASSIFY_INTENT,
@@ -121,8 +122,9 @@ class AgentState(BaseModel):
                 FlowState.LISTING_COLLECT,  # Go back if missing fields
                 FlowState.ERROR,
             ],
+            # ✅ FIXED: Publishing goes to IDLE (not COMPLETE)
             FlowState.LISTING_PUBLISH: [
-                FlowState.COMPLETE,
+                FlowState.IDLE,
                 FlowState.ERROR,
             ],
             FlowState.SEARCH_QUERY: [

app/ai/routes/chat_refactored.py

@@ -1,7 +1,7 @@
 # app/ai/routes/chat_refactored.py
 """
 AIDA Chat Endpoint - LangGraph Powered (PRIMARY - v1)
-Properly invokes graph with dict input and extracts response from state
+FIXED: Properly handles dict output from graph.ainvoke()
 """
 
 from fastapi import APIRouter, HTTPException
@@ -42,15 +42,15 @@ async def ask_ai(body: AskBody) -> AgentResponse:
     Main chat endpoint using LangGraph state machine.
     
     CRITICAL FIX:
-    - Pass INPUT as dict (not AgentState object)
-    - LangGraph converts dict → AgentState internally
-    - Extract response from final_state.temp_data["final_response"]
+    - graph.ainvoke() returns a DICT, not AgentState object
+    - Access dict keys with ['key'], not .attribute
+    - Extract response from dict['temp_data']['final_response']
     
     Flow:
     1. Validate input
     2. Build input dict
     3. Invoke graph with dict input
-    4. Extract AgentResponse from final state
+    4. Extract final_response from returned dict
     5. Return to client
     """
     
@@ -86,7 +86,7 @@ async def ask_ai(body: AskBody) -> AgentResponse:
         # ============================================================
         # STEP 2: Build input dict for graph
         # ============================================================
-        # ✅ CRITICAL: LangGraph expects dict input, NOT AgentState
+        # ✅ CRITICAL: Pass dict, not AgentState
         
         input_dict = {
             "user_id": user_id,
@@ -128,20 +128,23 @@ async def ask_ai(body: AskBody) -> AgentResponse:
             )
         
         # ============================================================
-        # STEP 4: Invoke graph with dict input (NOT AgentState!)
+        # STEP 4: Invoke graph with dict input
         # ============================================================
         
         logger.info("🔄 Invoking LangGraph...", user_id=user_id)
         
         try:
-            # ✅ CRITICAL FIX: Pass dict to ainvoke, not AgentState
-            final_state = await graph.ainvoke(input_dict)
+            # ✅ Pass dict to ainvoke
+            final_state_dict = await graph.ainvoke(input_dict)
+            
+            # ✅ CRITICAL: final_state_dict is a DICT, not AgentState!
+            # Access with dict keys: ['key'], not .attribute
             
             logger.info(
                 "✅ LangGraph execution completed",
-                flow=final_state.current_flow.value,
-                steps=final_state.steps_taken,
-                has_error=final_state.last_error is not None,
+                flow=final_state_dict.get("current_flow", {}).get("value") if isinstance(final_state_dict.get("current_flow"), dict) else str(final_state_dict.get("current_flow")),
+                steps=final_state_dict.get("steps_taken"),
+                has_error=final_state_dict.get("last_error") is not None,
             )
             
         except Exception as e:
@@ -154,17 +157,18 @@ async def ask_ai(body: AskBody) -> AgentResponse:
             )
         
         # ============================================================
-        # STEP 5: Extract response from final state
+        # STEP 5: Extract response from final state dict
         # ============================================================
         
-        # ✅ The respond node stores response in temp_data
-        final_response = final_state.temp_data.get("final_response")
+        # ✅ Access dict['key'] not dict.key
+        temp_data = final_state_dict.get("temp_data", {})
+        final_response = temp_data.get("final_response")
         
         if final_response:
             logger.info(
                 "✅ Response extracted from state",
-                action=final_response.action,
-                success=final_response.success,
+                action=final_response.action if hasattr(final_response, 'action') else "unknown",
+                success=final_response.success if hasattr(final_response, 'success') else False,
             )
             return final_response
         
@@ -174,27 +178,34 @@ async def ask_ai(body: AskBody) -> AgentResponse:
         
         logger.warning("⚠️ No final_response in temp_data, building manually")
         
-        response_text = final_state.temp_data.get("response_text", "")
+        response_text = temp_data.get("response_text", "")
         if not response_text:
             response_text = "I'm here to help! What would you like to do?"
         
+        # Get flow state - handle both FlowState enum and string
+        current_flow = final_state_dict.get("current_flow")
+        if hasattr(current_flow, 'value'):
+            flow_str = current_flow.value
+        else:
+            flow_str = str(current_flow)
+        
         response = AgentResponse(
-            success=final_state.last_error is None,
+            success=final_state_dict.get("last_error") is None,
             text=response_text,
-            action=final_state.temp_data.get("action", final_state.current_flow.value),
+            action=temp_data.get("action", flow_str),
             state={
-                "flow": final_state.current_flow.value,
-                "steps": final_state.steps_taken,
-                "errors": final_state.error_count,
+                "flow": flow_str,
+                "steps": final_state_dict.get("steps_taken", 0),
+                "errors": final_state_dict.get("error_count", 0),
             },
-            draft=final_state.temp_data.get("draft"),
-            draft_ui=final_state.temp_data.get("draft_ui"),
-            error=final_state.last_error,
+            draft=temp_data.get("draft"),
+            draft_ui=temp_data.get("draft_ui"),
+            error=final_state_dict.get("last_error"),
             metadata={
-                "intent": final_state.intent_type,
-                "intent_confidence": final_state.intent_confidence,
-                "language": final_state.language_detected,
-                "messages_in_session": len(final_state.conversation_history),
+                "intent": final_state_dict.get("intent_type"),
+                "intent_confidence": final_state_dict.get("intent_confidence", 0),
+                "language": final_state_dict.get("language_detected", "en"),
+                "messages_in_session": len(final_state_dict.get("conversation_history", [])),
                 "user_id": user_id,
                 "session_id": session_id,
             }