from typing import List, Dict, Any, Optional
from datetime import datetime
from pathlib import Path
import json
import os
import logging

from fastapi import APIRouter, HTTPException
from pydantic import BaseModel

from learning_units import (
    LearningUnit,
    LearningUnitCreate,
    LearningUnitUpdate,
    list_learning_units,
    get_learning_unit,
    create_learning_unit,
    update_learning_unit,
    delete_learning_unit,
)

logger = logging.getLogger(__name__)


router = APIRouter(
    prefix="/learning-units",
    tags=["learning-units"],
)


# ---------- Payload-Modelle für das Frontend ----------


class LearningUnitCreatePayload(BaseModel):
    """
    Payload so, wie er aus dem Frontend kommt:
    {
      "student": "...",
      "subject": "...",
      "title": "...",
      "grade": "7a"
    }
    """
    student: Optional[str] = None
    subject: Optional[str] = None
    title: Optional[str] = None
    grade: Optional[str] = None


class AttachWorksheetsPayload(BaseModel):
    worksheet_files: List[str]


class RemoveWorksheetPayload(BaseModel):
    worksheet_file: str


class GenerateFromAnalysisPayload(BaseModel):
    analysis_data: Dict[str, Any]
    num_questions: int = 8


# ---------- Hilfsfunktion: Backend-Modell -> Frontend-Objekt ----------


def unit_to_frontend_dict(lu: LearningUnit) -> Dict[str, Any]:
    """
    Wandelt eine LearningUnit in das Format um, das das Frontend erwartet.
    Wichtig sind:
    - id
    - label  (sichtbarer Name)
    - meta   (Untertitelzeile)
    - worksheet_files (Liste von Dateinamen)
    """
    label = lu.title or "Lerneinheit"

    # Meta-Text: z.B. "Thema: Auge · Klasse: 7a · angelegt am 10.12.2025"
    meta_parts: List[str] = []
    if lu.topic:
        meta_parts.append(f"Thema: {lu.topic}")
    if lu.grade_level:
        meta_parts.append(f"Klasse: {lu.grade_level}")
    created_str = lu.created_at.strftime("%d.%m.%Y")
    meta_parts.append(f"angelegt am {created_str}")

    meta = " · ".join(meta_parts)

    return {
        "id": lu.id,
        "label": label,
        "meta": meta,
        "title": lu.title,
        "topic": lu.topic,
        "grade_level": lu.grade_level,
        "language": lu.language,
        "status": lu.status,
        "worksheet_files": lu.worksheet_files,
        "created_at": lu.created_at.isoformat(),
        "updated_at": lu.updated_at.isoformat(),
    }


# ---------- Endpunkte ----------


@router.get("/", response_model=List[Dict[str, Any]])
def api_list_learning_units():
    """Alle Lerneinheiten für das Frontend auflisten."""
    units = list_learning_units()
    return [unit_to_frontend_dict(u) for u in units]


@router.post("/", response_model=Dict[str, Any])
def api_create_learning_unit(payload: LearningUnitCreatePayload):
    """
    Neue Lerneinheit anlegen.
    Mapped das Frontend-Payload (student/subject/title/grade)
    auf das generische LearningUnit-Modell.
    """

    # Mindestens eines der Felder muss gesetzt sein
    if not (payload.student or payload.subject or payload.title):
        raise HTTPException(
            status_code=400,
            detail="Bitte mindestens Schüler/in, Fach oder Thema angeben.",
        )

    # Titel/Topic bestimmen
    # sichtbarer Titel: bevorzugt Thema (title), sonst Kombination
    if payload.title:
        title = payload.title
    else:
        parts = []
        if payload.subject:
            parts.append(payload.subject)
        if payload.student:
            parts.append(payload.student)
        title = " – ".join(parts) if parts else "Lerneinheit"

    topic = payload.title or payload.subject or None
    grade_level = payload.grade or None

    lu_create = LearningUnitCreate(
        title=title,
        description=None,
        topic=topic,
        grade_level=grade_level,
        language="de",
        worksheet_files=[],
        status="raw",
    )

    lu = create_learning_unit(lu_create)
    return unit_to_frontend_dict(lu)


@router.post("/{unit_id}/attach-worksheets", response_model=Dict[str, Any])
def api_attach_worksheets(unit_id: str, payload: AttachWorksheetsPayload):
    """
    Fügt der Lerneinheit eine oder mehrere Arbeitsblätter hinzu.
    """
    lu = get_learning_unit(unit_id)
    if not lu:
        raise HTTPException(status_code=404, detail="Lerneinheit nicht gefunden.")

    files_to_add = [f for f in payload.worksheet_files if f not in lu.worksheet_files]
    if files_to_add:
        new_list = lu.worksheet_files + files_to_add
        update = LearningUnitUpdate(worksheet_files=new_list)
        lu = update_learning_unit(unit_id, update)
        if not lu:
            raise HTTPException(status_code=500, detail="Lerneinheit konnte nicht aktualisiert werden.")

    return unit_to_frontend_dict(lu)


@router.post("/{unit_id}/remove-worksheet", response_model=Dict[str, Any])
def api_remove_worksheet(unit_id: str, payload: RemoveWorksheetPayload):
    """
    Entfernt genau ein Arbeitsblatt aus der Lerneinheit.
    """
    lu = get_learning_unit(unit_id)
    if not lu:
        raise HTTPException(status_code=404, detail="Lerneinheit nicht gefunden.")

    if payload.worksheet_file not in lu.worksheet_files:
        # Nichts zu tun, aber kein Fehler – einfach unverändert zurückgeben
        return unit_to_frontend_dict(lu)

    new_list = [f for f in lu.worksheet_files if f != payload.worksheet_file]
    update = LearningUnitUpdate(worksheet_files=new_list)
    lu = update_learning_unit(unit_id, update)
    if not lu:
        raise HTTPException(status_code=500, detail="Lerneinheit konnte nicht aktualisiert werden.")

    return unit_to_frontend_dict(lu)


@router.delete("/{unit_id}")
def api_delete_learning_unit(unit_id: str):
    """
    Lerneinheit komplett löschen (aktuell vom Frontend noch nicht verwendet).
    """
    ok = delete_learning_unit(unit_id)
    if not ok:
        raise HTTPException(status_code=404, detail="Lerneinheit nicht gefunden.")
    return {"status": "deleted", "id": unit_id}


# ---------- Generator-Endpunkte ----------

LERNEINHEITEN_DIR = os.path.expanduser("~/Arbeitsblaetter/Lerneinheiten")


def _save_analysis_and_get_path(unit_id: str, analysis_data: Dict[str, Any]) -> Path:
    """Save analysis_data to disk and return the path."""
    os.makedirs(LERNEINHEITEN_DIR, exist_ok=True)
    path = Path(LERNEINHEITEN_DIR) / f"{unit_id}_analyse.json"
    with open(path, "w", encoding="utf-8") as f:
        json.dump(analysis_data, f, ensure_ascii=False, indent=2)
    return path


@router.post("/{unit_id}/generate-qa")
def api_generate_qa(unit_id: str, payload: GenerateFromAnalysisPayload):
    """Generate Q&A items with Leitner fields from analysis data."""
    lu = get_learning_unit(unit_id)
    if not lu:
        raise HTTPException(status_code=404, detail="Lerneinheit nicht gefunden.")

    analysis_path = _save_analysis_and_get_path(unit_id, payload.analysis_data)

    try:
        from ai_processing.qa_generator import generate_qa_from_analysis
        qa_path = generate_qa_from_analysis(analysis_path, num_questions=payload.num_questions)
        with open(qa_path, "r", encoding="utf-8") as f:
            qa_data = json.load(f)

        # Update unit status
        update_learning_unit(unit_id, LearningUnitUpdate(status="qa_generated"))
        logger.info(f"Generated QA for unit {unit_id}: {len(qa_data.get('qa_items', []))} items")
        return qa_data
    except Exception as e:
        logger.error(f"QA generation failed for {unit_id}: {e}")
        raise HTTPException(status_code=500, detail=f"QA-Generierung fehlgeschlagen: {e}")


@router.post("/{unit_id}/generate-mc")
def api_generate_mc(unit_id: str, payload: GenerateFromAnalysisPayload):
    """Generate multiple choice questions from analysis data."""
    lu = get_learning_unit(unit_id)
    if not lu:
        raise HTTPException(status_code=404, detail="Lerneinheit nicht gefunden.")

    analysis_path = _save_analysis_and_get_path(unit_id, payload.analysis_data)

    try:
        from ai_processing.mc_generator import generate_mc_from_analysis
        mc_path = generate_mc_from_analysis(analysis_path, num_questions=payload.num_questions)
        with open(mc_path, "r", encoding="utf-8") as f:
            mc_data = json.load(f)

        update_learning_unit(unit_id, LearningUnitUpdate(status="mc_generated"))
        logger.info(f"Generated MC for unit {unit_id}: {len(mc_data.get('questions', []))} questions")
        return mc_data
    except Exception as e:
        logger.error(f"MC generation failed for {unit_id}: {e}")
        raise HTTPException(status_code=500, detail=f"MC-Generierung fehlgeschlagen: {e}")


@router.post("/{unit_id}/generate-cloze")
def api_generate_cloze(unit_id: str, payload: GenerateFromAnalysisPayload):
    """Generate cloze (fill-in-the-blank) items from analysis data."""
    lu = get_learning_unit(unit_id)
    if not lu:
        raise HTTPException(status_code=404, detail="Lerneinheit nicht gefunden.")

    analysis_path = _save_analysis_and_get_path(unit_id, payload.analysis_data)

    try:
        from ai_processing.cloze_generator import generate_cloze_from_analysis
        cloze_path = generate_cloze_from_analysis(analysis_path)
        with open(cloze_path, "r", encoding="utf-8") as f:
            cloze_data = json.load(f)

        update_learning_unit(unit_id, LearningUnitUpdate(status="cloze_generated"))
        logger.info(f"Generated Cloze for unit {unit_id}: {len(cloze_data.get('cloze_items', []))} items")
        return cloze_data
    except Exception as e:
        logger.error(f"Cloze generation failed for {unit_id}: {e}")
        raise HTTPException(status_code=500, detail=f"Cloze-Generierung fehlgeschlagen: {e}")


@router.get("/{unit_id}/qa")
def api_get_qa(unit_id: str):
    """Get generated QA items for a unit."""
    qa_path = Path(LERNEINHEITEN_DIR) / f"{unit_id}_qa.json"
    if not qa_path.exists():
        raise HTTPException(status_code=404, detail="Keine QA-Daten gefunden.")
    with open(qa_path, "r", encoding="utf-8") as f:
        return json.load(f)


@router.get("/{unit_id}/mc")
def api_get_mc(unit_id: str):
    """Get generated MC questions for a unit."""
    mc_path = Path(LERNEINHEITEN_DIR) / f"{unit_id}_mc.json"
    if not mc_path.exists():
        raise HTTPException(status_code=404, detail="Keine MC-Daten gefunden.")
    with open(mc_path, "r", encoding="utf-8") as f:
        return json.load(f)


@router.get("/{unit_id}/cloze")
def api_get_cloze(unit_id: str):
    """Get generated cloze items for a unit."""
    cloze_path = Path(LERNEINHEITEN_DIR) / f"{unit_id}_cloze.json"
    if not cloze_path.exists():
        raise HTTPException(status_code=404, detail="Keine Cloze-Daten gefunden.")
    with open(cloze_path, "r", encoding="utf-8") as f:
        return json.load(f)


@router.post("/{unit_id}/leitner/update")
def api_update_leitner(unit_id: str, item_id: str, correct: bool):
    """Update Leitner progress for a QA item."""
    qa_path = Path(LERNEINHEITEN_DIR) / f"{unit_id}_qa.json"
    if not qa_path.exists():
        raise HTTPException(status_code=404, detail="Keine QA-Daten gefunden.")
    try:
        from ai_processing.qa_generator import update_leitner_progress
        result = update_leitner_progress(qa_path, item_id, correct)
        return result
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@router.get("/{unit_id}/leitner/next")
def api_get_next_review(unit_id: str, limit: int = 5):
    """Get next Leitner review items."""
    qa_path = Path(LERNEINHEITEN_DIR) / f"{unit_id}_qa.json"
    if not qa_path.exists():
        raise HTTPException(status_code=404, detail="Keine QA-Daten gefunden.")
    try:
        from ai_processing.qa_generator import get_next_review_items
        items = get_next_review_items(qa_path, limit=limit)
        return {"items": items, "count": len(items)}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


class StoryGeneratePayload(BaseModel):
    vocabulary: List[Dict[str, Any]]
    language: str = "en"
    grade_level: str = "5-8"


@router.post("/{unit_id}/generate-story")
def api_generate_story(unit_id: str, payload: StoryGeneratePayload):
    """Generate a short story using vocabulary words."""
    lu = get_learning_unit(unit_id)
    if not lu:
        raise HTTPException(status_code=404, detail="Lerneinheit nicht gefunden.")

    try:
        from story_generator import generate_story
        result = generate_story(
            vocabulary=payload.vocabulary,
            language=payload.language,
            grade_level=payload.grade_level,
        )
        return result
    except Exception as e:
        logger.error(f"Story generation failed for {unit_id}: {e}")
        raise HTTPException(status_code=500, detail=f"Story-Generierung fehlgeschlagen: {e}")