"""Auto recommend/suggest: produce actionable recommendations from lessons and evaluations."""

from typing import Any, Protocol

from fusionagi.schemas.recommendation import Recommendation, RecommendationKind
from fusionagi._logger import logger


class ReflectiveMemoryLike(Protocol):
    """Protocol for reflective memory: get lessons and heuristics."""

    def get_lessons(self, limit: int = 50) -> list[dict[str, Any]]: ...
    def get_all_heuristics(self) -> dict[str, Any]: ...


class AutoRecommender:
    """
    Produces actionable recommendations from reflective memory lessons and
    from post-task evaluations (suggestions, error_analysis).
    """

    def __init__(self, reflective_memory: ReflectiveMemoryLike | None = None) -> None:
        """
        Initialize the auto-recommender.

        Args:
            reflective_memory: Optional reflective memory for lessons/heuristics.
        """
        self._memory = reflective_memory

    def recommend_from_evaluation(
        self,
        task_id: str,
        evaluation: dict[str, Any],
    ) -> list[Recommendation]:
        """Turn a single evaluation (from Critic) into recommendations."""
        recs: list[Recommendation] = []
        suggestions = evaluation.get("suggestions", [])
        error_analysis = evaluation.get("error_analysis", [])
        score = evaluation.get("score", 0.5)
        success = evaluation.get("success", False)

        for i, s in enumerate(suggestions[:10]):
            recs.append(
                Recommendation(
                    kind=RecommendationKind.NEXT_ACTION,
                    title="Suggestion from evaluation",
                    description=s if isinstance(s, str) else str(s),
                    payload={"raw": s, "error_analysis": error_analysis},
                    source_task_id=task_id,
                    priority=8 if not success else 5,
                )
            )
        if error_analysis and not recs:
            recs.append(
                Recommendation(
                    kind=RecommendationKind.STRATEGY_CHANGE,
                    title="Address error analysis",
                    description="; ".join(str(e) for e in error_analysis[:3]),
                    payload={"error_analysis": error_analysis},
                    source_task_id=task_id,
                    priority=7,
                )
            )
        if score < 0.5 and not success:
            recs.append(
                Recommendation(
                    kind=RecommendationKind.TRAINING_TARGET,
                    title="Low score: consider training or prompt tuning",
                    description=f"Task {task_id} scored {score}; add as training target.",
                    payload={"score": score, "task_id": task_id},
                    source_task_id=task_id,
                    priority=6,
                )
            )
        return recs

    def recommend_from_lessons(self, limit_lessons: int = 20) -> list[Recommendation]:
        """Aggregate lessons from reflective memory into recommendations."""
        if not self._memory:
            return []
        lessons = self._memory.get_lessons(limit=limit_lessons)
        recs: list[Recommendation] = []
        failed = [l for l in lessons if l.get("outcome") == "failed"]
        if len(failed) >= 3:
            recs.append(
                Recommendation(
                    kind=RecommendationKind.STRATEGY_CHANGE,
                    title="Multiple failures in recent lessons",
                    description=f"{len(failed)} failed tasks in last {limit_lessons} lessons.",
                    payload={"failed_count": len(failed), "lesson_sample": failed[-3:]},
                    source_task_id=None,
                    priority=6,
                )
            )
        for lesson in lessons[-5:]:
            ev = lesson.get("evaluation", {})
            suggestions = ev.get("suggestions", [])
            for s in suggestions[:2]:
                recs.append(
                    Recommendation(
                        kind=RecommendationKind.NEXT_ACTION,
                        title="From lesson",
                        description=s if isinstance(s, str) else str(s),
                        payload={"lesson": lesson},
                        source_task_id=lesson.get("task_id"),
                        priority=4,
                    )
                )
        return recs

    def recommend(
        self,
        task_id: str | None = None,
        evaluation: dict[str, Any] | None = None,
        include_lessons: bool = True,
    ) -> list[Recommendation]:
        """
        Produce all recommendations: from optional evaluation and optionally
        from reflective memory lessons. Deduplicated by title/description.
        """
        recs: list[Recommendation] = []
        if task_id and evaluation:
            recs.extend(self.recommend_from_evaluation(task_id, evaluation))
        if include_lessons:
            recs.extend(self.recommend_from_lessons())
        seen: set[tuple[str, str]] = set()
        unique: list[Recommendation] = []
        for r in recs:
            key = (r.title, r.description)
            if key not in seen:
                seen.add(key)
                unique.append(r)
        unique.sort(key=lambda x: (-x.priority, x.title))
        logger.debug("AutoRecommender.recommend", extra={"count": len(unique), "task_id": task_id})
        return unique