tutor/model_loader.py

"""tutor/model_loader.py — generates child-facing feedback (templates + optional LLM)."""
from __future__ import annotations
import os, random
from typing import Optional

_CORRECT = {
    "en":  ["Amazing work! 🎉 You got it!","Superstar! ⭐ That's exactly right!",
             "Brilliant! 🌟 You're on fire!","Perfect! Keep it up! 🚀","You nailed it! 🎊"],
    "fr":  ["Bravo ! 🎉 C'est exactement ça !","Super ! ⭐ Tu es fantastique !",
             "Excellent ! 🌟 Continue comme ça !","Parfait ! 🥳 Tu es une étoile !"],
    "kin": ["Byiza cyane! 🎉 Wabitsinze!","Ntangaza! ⭐ Uravuga ukuri!",
             "Komeza! 🌟 Uri inzobere!","Superbe! 🥳 Urashoboye!"],
    "sw":  ["Vizuri sana! 🎉 Umeshinda!","Hongera! ⭐ Jibu sahihi!",
             "Endelea hivyo! 🌟 Umefaulu!","Bora sana! 🥳 Ushindi!"],
}
_WRONG = {
    "en":  ["Almost! Give it another try 💪","Not quite — you can do it! 🌈",
             "Good try! Think again 🤔","Keep going — try once more! 💫"],
    "fr":  ["Presque ! Essaie encore 💪","Pas tout à fait — tu peux le faire ! 🌈",
             "Bon essai ! Réfléchis encore 🤔"],
    "kin": ["Hafi! Gerageza nanone 💪","Ntabwo ari byo — gerageza! 🌈","Gerageza nanone! 🤔"],
    "sw":  ["Karibu! Jaribu tena 💪","Si sahihi — unaweza! 🌈","Jaribu tena! 🤔"],
}

_sdk_checked = False
_sdk_ok      = False

def _check_sdk():
    global _sdk_checked, _sdk_ok
    if _sdk_checked:
        return _sdk_ok
    _sdk_checked = True
    try:
        import anthropic  # type: ignore  # noqa
        _sdk_ok = bool(os.environ.get("ANTHROPIC_API_KEY"))
    except ImportError:
        pass
    return _sdk_ok

def _llm_feedback(is_correct, answer, lang, child_text) -> Optional[str]:
    if not _check_sdk():
        return None
    try:
        import anthropic  # type: ignore
        client = anthropic.Anthropic()
        outcome = "correct" if is_correct else "incorrect"
        msg = client.messages.create(
            model="claude-haiku-4-5-20251001", max_tokens=60,
            messages=[{"role":"user","content":
                f"You are a warm math tutor for a child aged 5-9. "
                f"Child answered '{child_text}', correct answer is {answer}, "
                f"their answer was {outcome}. Reply in language '{lang}' "
                f"with ONE short encouraging sentence (max 12 words)."}])
        return msg.content[0].text.strip()
    except Exception:
        return None

def generate_feedback(is_correct: bool, answer: int,
                      lang: str = "en", child_text: str = "") -> str:
    llm = _llm_feedback(is_correct, answer, lang, child_text)
    if llm:
        return llm
    bank = _CORRECT if is_correct else _WRONG
    return random.choice(bank.get(lang, bank["en"]))