"""推荐引擎：找出薄弱知识点并生成“下一步任务”。"""
from __future__ import annotations

import json

from sqlalchemy.orm import Session

from models import (
    AttemptItem,
    Chapter,
    DailyCompletion,
    ErrorEntry,
    Knowledge,
    Question,
    Textbook,
    UserKnowledge,
)
from services.mastery_engine import snapshot
from services.knowledge_service import chapter_knowledge_names


def _error_counts(db: Session, user_id: int) -> dict[str, int]:
    counts: dict[str, int] = {}
    for primary, raw_names in (
        db.query(ErrorEntry.knowledge_name, ErrorEntry.knowledge_names)
        .filter(ErrorEntry.user_id == user_id)
        .all()
    ):
        try:
            names = json.loads(raw_names or "[]")
            if not isinstance(names, list) or not names:
                names = [primary]
        except (TypeError, ValueError):
            names = [primary]
        for item in names:
            counts[str(item)] = counts.get(str(item), 0) + 1
    return counts


def build_plan(db: Session, user_id: int) -> dict:
    knowledge = snapshot(db, user_id)
    counts = _error_counts(db, user_id)
    weak = [k for k in knowledge if k.mastery < 75]
    strong = [k for k in knowledge if k.mastery >= 75]

    focus_names = [k.name for k in weak]
    if not focus_names:
        focus_names = [strong[0].name] if strong else []

    focus_text = " / ".join(focus_names[:2]) or "当前知识点"
    notice = f"系统根据已有答题记录，将“{focus_text}”设为当前强化重点。"

    steps: list[str] = []
    if weak:
        group = weak[:2]
        step1 = " / ".join(k.name for k in group)
        steps.append(f"① 重点强化 {step1}：巩固定义 → 点的对应 → 图像性质判断。")
        if len(weak) > 2:
            steps.append(f"② {weak[2].name}：从表达式判断图像性质，补做针对性小题。")
        else:
            steps.append("② 再完成一次章节测试，检验本轮掌握情况。")
        steps.append("③ 间隔复习：在后续测试中穿插旧知识，防止遗忘。")
    else:
        steps.append("① 保持当前节奏，进入下一章节的学习。")
        steps.append("② 每周做一次章节测试，维持各知识点掌握度。")
        steps.append("③ 错题清零后开始新主题，建立更完整的知识地图。")

    top_error = sorted(knowledge, key=lambda k: (-counts.get(k.name, 0), k.mastery))
    return {
        "notice": notice,
        "focus_names": focus_names,
        "steps": steps,
        "top_error": (
            top_error[0].name
            if top_error and counts.get(top_error[0].name, 0)
            else None
        ),
    }


def build_daily_recommendation(
    db: Session, user_id: int
) -> dict | None:
    """推荐今日章节：无掌握度且未做过的章优先，然后按薄弱反馈/错误加权。"""
    chapters = (
        db.query(Chapter, Textbook)
        .join(Textbook, Textbook.id == Chapter.textbook_id)
        .order_by(Textbook.position.asc(), Chapter.position.asc())
        .all()
    )
    candidates: list[dict] = []
    for chapter, textbook in chapters:
        question_count = (
            db.query(Question.id).filter(Question.chapter_id == chapter.id).count()
        )
        if question_count == 0:
            continue
        tags = chapter_knowledge_names(db, chapter.id)
        masteries = [
            mastery
            for mastery, in db.query(UserKnowledge.mastery)
            .join(Knowledge, Knowledge.id == UserKnowledge.knowledge_id)
            .filter(
                UserKnowledge.user_id == user_id,
                Knowledge.name.in_(tags),
            )
            .all()
        ]
        answered = (
            db.query(AttemptItem.id)
            .join(Question, Question.id == AttemptItem.question_id)
            .filter(
                AttemptItem.user_id == user_id,
                Question.chapter_id == chapter.id,
            )
            .count()
        )
        candidates.append(
            {
                "chapter": chapter,
                "textbook": textbook,
                "tags": tags,
                "mastery_avg": sum(masteries) / len(masteries)
                if masteries
                else None,
                "answered": answered,
                "knowledge_count": len(tags),
            }
        )
    if not candidates:
        return None
    # 未开始章节优先；同等情况按章节顺序
    candidates.sort(
        key=lambda item: (
            0 if item["answered"] == 0 and item["mastery_avg"] is None else 1,
            item["mastery_avg"] if item["mastery_avg"] is not None else 50,
        )
    )
    choice = candidates[0]
    reason = (
        "你还没有练习过这一章，建议从本章开始建立基础"
        if choice["answered"] == 0
        else "该章知识点掌握度较低，需要优先强化"
    )
    return {
        "chapter_id": choice["chapter"].id,
        "chapter_name": choice["chapter"].name,
        "book_name": choice["textbook"].name,
        "knowledge_names": choice["tags"],
        "reason": reason,
    }