Update app/main.py

app/main.py

@@ -165,40 +165,46 @@ def get_recommendations(user: schemas.UserProfile):
 
 @app.post("/chat/process", response_model=schemas.ChatResponse)
 async def process_chat(req: schemas.ChatRequest):
-    role_data = skill_manager.get_role_data(req.role)
     available_skill_names = []
-    if role_data:
-        available_skill_names = [s['name'] for s in role_data['sub_skills']]
+    role_data = None
     
-    # --- [Keyword Search Logic Tetap Ada] ---
-    found_keywords = find_keywords_in_text(req.message)
-    if found_keywords:
-        keyword_context = ", ".join(found_keywords)
-        dataset_status = "FOUND"
-    else:
-        keyword_context = "NONE"
-        dataset_status = "NOT_FOUND"
-        
-    # --- [UPDATE BARU: Ektrak Silabus Lengkap] ---
-    # Kita buat string rapi berisi Skill + Topik-topiknya
-    found_keywords = find_keywords_in_text(req.message)
+    # Cek apakah Role ada isinya? (Safety check)
+    if req.role and req.role.strip() != "":
+        role_data = skill_manager.get_role_data(req.role)
+        if role_data:
+             available_skill_names = [s['name'] for s in role_data['sub_skills']]
+
+    found_keywords = find_keywords_in_text(req.message) # Asumsi fungsi ini ada
+    
+    keyword_context = "NONE"
+    dataset_status = "NOT_FOUND"
     
-    # Siapkan context string untuk dikirim ke LLM
     if found_keywords:
-        # Jika ketemu: "User bertanya tentang: Python, SQL"
         keyword_context = ", ".join(found_keywords)
         dataset_status = "FOUND"
-    else:
-        # Jika tidak ketemu
-        keyword_context = "NONE"
-        dataset_status = "NOT_FOUND"
+    
+    # [PENTING] Konversi History ke Dict agar tidak error di LLM
+    history_dicts = [m.model_dump() if hasattr(m, 'model_dump') else m.dict() for m in req.history]
 
-    # 2. Router
-    intent = await llm_engine.process_user_intent(req.message, available_skill_names)
+    # Kirim parameter lengkap ke Router
+    intent = await llm_engine.process_user_intent(
+        user_text=req.message, 
+        available_skills=available_skill_names, 
+        user_role=req.role,
+        history=history_dicts # Tambahan agar AI ingat konteks
+    )
     
-    action = intent.get('action')
-    # PERUBAHAN 1: Ambil List skills, bukan single skill
+    action = intent.get('action', 'CASUAL_CHAT')
     detected_skills_list = intent.get('detected_skills', [])
+
+    user_role_is_empty = not req.role or req.role.strip() == ""
+    restricted_actions = ["START_EXAM", "GET_RECOMMENDATION", "CHECK_PROGRESS"]
+    
+    if action in restricted_actions and user_role_is_empty:
+        print(f"DEBUG: Role Kosong mencoba {action} -> BELOKKAN KE CASUAL_CHAT")
+        action = "CASUAL_CHAT"
+    
+    # ============================================================
     
     final_reply = ""
     response_data = None
@@ -269,12 +275,17 @@ async def process_chat(req: schemas.ChatRequest):
         final_reply = "Siap! Berikut adalah ringkasan progress belajar kamu sejauh ini. Silakan dicek di dashboard ya! 📊🚀"
 
     elif action == "CASUAL_CHAT":
-        final_reply = await llm_engine.casual_chat(
-            req.message, 
-            [m.dict() for m in req.history], 
-            keyword_context, 
-            dataset_status 
+        
+        print(f"DEBUG ROLE STATUS: '{req.role}' -> is_empty={user_role_is_empty}") 
+        history_dicts = [m.model_dump() if hasattr(m, 'model_dump') else m.dict() for m in req.history]
+       
+        reply_text = await llm_engine.casual_chat(
+            user_text=req.message, 
+            history=history_dicts,
+            is_role_empty=user_role_is_empty  
         )
+        
+        final_reply = reply_text
 
     return schemas.ChatResponse(
         reply=final_reply,