"""
CuriosityEngine — proactive vocabulary gap analysis.

Every N interactions (default: 5), sends the last 10 vocabulary entries to
the LLM and asks it to identify one related agricultural / everyday term that
is missing from the learner's vocabulary, then formulate a question asking the
user how to say that word in their language.

Usage in app_lab.py:
    _curiosity = CuriosityEngine(interval=5)

    # Inside _run_llm_and_tts, after the main LLM call:
    question = _curiosity.maybe_ask(_memory, _gemma)
    if question:
        history.append({"role": "assistant", "content": f"🌱 {question}"})
"""
from __future__ import annotations

import logging
from typing import TYPE_CHECKING, Optional

if TYPE_CHECKING:
    from src.memory.memory_manager import MemoryManager
    from src.llm.gemma_client import GemmaClient

logger = logging.getLogger(__name__)

_CURIOSITY_SYSTEM = """\
You are a language-learning assistant that notices gaps in a West African vocabulary list.
Reply with a single valid JSON object and nothing else.\
"""

_CURIOSITY_USER_TEMPLATE = """\
Here are the {n} most recent words I have learned so far:
{vocab_list}

Based on these words, what is ONE related agricultural or common everyday term \
I am likely missing?  Formulate a short, warm question asking the user how to say \
that missing word in their language.

Reply only with this JSON:
{{
  "word_suggestion": "<the English word you think is missing>",
  "question": "<one friendly sentence asking the user>"
}}
"""


class CuriosityEngine:
    """Fires a vocabulary-gap prompt every `interval` user interactions."""

    def __init__(self, interval: int = 5) -> None:
        self._interval    = interval
        self._interaction = 0

    def maybe_ask(
        self,
        memory: "MemoryManager",
        gemma: "GemmaClient",
    ) -> Optional[str]:
        """
        Increment the interaction counter.  On every `interval`-th call, query
        the LLM for a missing vocabulary term and return the question string.
        Returns None on all other calls, or if vocabulary is too sparse, or if
        the LLM call fails.
        """
        self._interaction += 1
        if self._interaction % self._interval != 0:
            return None

        entries = memory.get_all()
        if len(entries) < 3:
            logger.debug("CuriosityEngine: vocabulary too sparse (%d entries)", len(entries))
            return None

        recent = entries[-10:]
        lines  = [
            f"  [{e.get('language','?')}] {e.get('word','')} = {e.get('translation','')}"
            for e in recent
        ]
        prompt = _CURIOSITY_USER_TEMPLATE.format(
            n=len(lines),
            vocab_list="\n".join(lines),
        )

        try:
            # Pass the curiosity prompt as user text; empty vocab context to avoid
            # duplicating the word list inside the system prompt.
            result = gemma.chat(prompt, vocabulary_context="(see above)")
            question = result.get("question") or result.get("response")
            if question:
                word = result.get("word_suggestion", "")
                logger.info(
                    "CuriosityEngine: suggesting '%s' — %s",
                    word,
                    question[:80],
                )
                return question.strip()
        except Exception as exc:
            logger.warning("CuriosityEngine: LLM call failed: %s", exc)

        return None