from langchain_community.llms import HuggingFaceHub

def llm_node(question):
    # Initialize the Hugging Face model
    llm = HuggingFaceHub(
        repo_id="HuggingFaceH4/zephyr-7b-beta",  # You can replace with e.g., mistralai/Mistral-7B-Instruct-v0.2
        model_kwargs={
            "temperature": 0.1,          # Keep responses deterministic
            "max_new_tokens": 500        # Allow for longer outputs if needed
        }
    )

    # Craft the prompt carefully for exact-match outputs
    prompt = f"""You are solving a GAIA benchmark evaluation question.

⚠️ VERY IMPORTANT:
- ONLY return the final answer, exactly as required.
- DO NOT include explanations, prefixes, or notes.
- Format the answer exactly as asked (e.g., comma-separated, plural, in requested order).
- If the question asks for a list, give only the list, no intro.

Here’s the question:

{question}

Your direct answer:"""

    # Run the model
    response = llm.invoke(prompt)
    
    # Clean up whitespace or stray characters
    return response.strip()