feat: implement chatbot engine with input validation, command handling, and project generation utilities

src/recommendation_engine/chatbot_engine.py

@@ -18,7 +18,7 @@ from src.recommendation_engine.llm_client import generate_text
 from src.recommendation_engine.prompt_builder import build_chat_prompt
 from src.recommendation_engine.response_formatter import format_response
 from src.recommendation_engine.state_manager import update_state
-from src.recommendation_engine.context_builder import extract_domain
+from src.recommendation_engine.context_builder import extract_domain, DOMAIN_KEYWORDS
 
 from src.recommendation_engine.full_project_generator import (
     generate_full_project
@@ -1230,19 +1230,9 @@ Choose what you want:
         is_question = any(q in text_lower for q in question_words) or "?" in text_lower
         
         if is_question and "other" not in text_lower:
-            # Answer conversational question directly via LLM
-            history_text = "\n".join(
-                f"User: {h.get('user', '')}\nBot: {h.get('bot', '')}"
-                for h in history[-3:]
-            )
-            prompt = build_chat_prompt(state)
-            full_prompt = (
-                f"{prompt}\n\n"
-                f"User History:\n{history_text}\n\n"
-                f"User Input: {user_input}\n\n"
-                "AI Assistant:"
-            )
-            response = generate_text(full_prompt, task="chat")
+            # Return hardcoded domain list instead of using LLM
+            domain_list = "\n".join(f"- {d}" for d in DOMAIN_KEYWORDS.keys())
+            response = f"Here are the available domains you can choose from:\n\n{domain_list}"
             
             save_user_memory(user_id, {"history": history, "state": state})
             return finalize_response(user_input, response, history, state, user_id)