# Use unified LLM router to prefer Groq when available
from ai.llm_router import run_llama
from ai.profile_manager import load_profile


def suggest_refactor(code: str, max_tokens: int = 500) -> str:
    profile = load_profile()
    style = profile.get("coding_style", {})
    # produce a concise style summary
    style_summary = ", ".join(f"{k}: {v}" for k, v in style.items()) or "no explicit style"

    prompt = f"""
System: You are CodeMate, a precise, concise code refactoring assistant.
Constraint: Preserve the user's style where possible ({style_summary}).
Goal: Improve clarity, maintainability, and reduce complexity. Keep behavior identical.

Input code:
{code}

Instructions:
- Show only the refactored code.
- If you make interface changes, explain briefly after the code in a short comment.
- Preserve comments and idioms that match the user's style.

Refactored code:
"""
    # prefer deterministic low-temperature output for accuracy
    return run_llama(prompt, max_tokens=max_tokens, temperature=0.05, top_p=0.9, seed=42)