from langchain_core.messages import HumanMessage, SystemMessage

def route_query(query, llm) -> str:
    """
    Analyzes the user query and determines if it requires official documents 
    or general college/student life advice.
    """
    # Keep the system instructions separate to guide the model's behavior explicitly
    system_instruction = (
        "You are a strict backend query router for a college chatbot. "
        "Classify queries into exactly one of two categories: 'CAMPUS_DOCS' or 'GENERAL_ADVICE'. "
        "Output ONLY the category name. Do not include any punctuation, conversational filler, or extra words."
    )

    user_prompt = f"""Rules:
    - Choose 'CAMPUS_DOCS' if the query asks for specific facts, official rules, dates, ordinances, syllabus details, or event schedules that MUST be looked up in university documents.
    - Choose 'GENERAL_ADVICE' if the query asks for subjective opinions, tips, strategies, general student guidance, study habits, motivation, or career paths.

    User Query: {query}
    Category:"""
    
    # CRITICAL FIX: Pass structured messages, NOT a raw string array
    messages = [
        SystemMessage(content=system_instruction),
        HumanMessage(content=user_prompt)
    ]
    
    try:
        response = llm2.invoke(messages)
        # Clean the output string
        cleaned_route = response.content.strip().upper()
        
        # Defensive check in case the LLM spits out conversational garbage anyway
        if "CAMPUS_DOCS" in cleaned_route:
            return "CAMPUS_DOCS"
        else:
            return "GENERAL_ADVICE"
            
    except Exception as e:
        print(f"[Router Error] LLM routing failed due to: {e}. Falling back to CAMPUS_DOCS.")
        return "CAMPUS_DOCS"