"""Async quiz extraction task with step-by-step progress reporting via Redis."""
import json
import logging
import time
import os

from app.tasks import celery_app
from app.database import SessionLocal

logger = logging.getLogger(__name__)

EXPIRE_SECONDS = 3600
CHUNK_PAGES = 50


def _redis():
    import redis
    from app.config import settings
    return redis.from_url(settings.REDIS_URL, decode_responses=True)


def _push_step(r, job_id: str, step: str, message: str):
    key = f"extraction:steps:{job_id}"
    entry = json.dumps({"step": step, "message": message, "ts": time.time()})
    r.rpush(key, entry)
    r.expire(key, EXPIRE_SECONDS)


def _normalize_ocr(text: str) -> str:
    """Fix common OCR artifacts in PREP PDFs."""
    return (text
            .replace("Pref erred", "Preferred")
            .replace("Pre ferred", "Preferred")
            .replace("Prefer red", "Preferred")
            .replace("ltem", "Item")
            .replace("ltcm", "Item"))


@celery_app.task(name="extract_quiz", bind=True)
def extract_quiz(
    self,
    job_id: str,
    user_id: int,
    section_id: int,
    title: str,
    mode: str,
    time_limit_minutes: int | None,
    model_id: str | None,
    question_category_id: int | None,
    extraction_mode: str = "standard",
):
    r = _redis()
    r.set(f"extraction:status:{job_id}", "running", ex=EXPIRE_SECONDS)

    db = SessionLocal()
    try:
        from app.models.section import Section
        from app.models.pdf_document import PDFDocument
        from app.services import ai_service, vector_service, pdf_service, embedding_service
        from app.models.quiz import Quiz
        from app.models.question import Question
        from app.models.quiz_question_link import QuizQuestionLink
        from app.config import settings

        _push_step(r, job_id, "start", "Starting extraction…")

        section = db.query(Section).filter(Section.id == section_id).first()
        if not section:
            raise ValueError("Section not found")
        document = db.query(PDFDocument).filter(PDFDocument.id == section.document_id).first()
        if not document:
            raise ValueError("Document not found")

        total_pages = section.end_page - section.start_page + 1
        _push_step(r, job_id, "text", f"Loading text from pages {section.start_page}–{section.end_page} ({total_pages} pages)…")

        # Determine model
        if model_id:
            from app.models.ai_model_config import AIModelConfig
            config = db.query(AIModelConfig).filter(AIModelConfig.model_id == model_id).first()
            api_key = config.api_key if config and config.api_key else None
            model_name = config.name if config else model_id
        else:
            model_id, api_key = ai_service.get_model_for_task(db, "extraction")
            model_name = model_id

        # Split into 50-page chunks
        if total_pages <= CHUNK_PAGES:
            chunks = [(section.start_page, section.end_page)]
        else:
            chunks = []
            p = section.start_page
            while p <= section.end_page:
                end = min(p + CHUNK_PAGES - 1, section.end_page)
                chunks.append((p, end))
                p = end + 1

        n_chunks = len(chunks)
        if n_chunks > 1:
            _push_step(r, job_id, "text", f"Large section: splitting into {n_chunks} chunks of up to {CHUNK_PAGES} pages each.")

        all_valid_questions = []
        all_skipped = []

        # ── Non-standard extraction modes ─────────────────────────────────────
        if extraction_mode != "standard":
            from app.services.extraction_modes import (
                extract_questions_only, extract_two_step,
                extract_with_regex, ai_decide_strategy, generate_from_text,
                ai_answer_questions,
            )

            resolved_mode = extraction_mode

            if extraction_mode == "ai_decide":
                _push_step(r, job_id, "ai", "AI is analysing the document to choose the best strategy…")
                resolved_mode, reasoning = ai_decide_strategy(
                    section.document_id, section.start_page, section.end_page,
                    model_id, api_key,
                )
                _push_step(r, job_id, "ai", f"AI chose: {resolved_mode} — {reasoning}")

            if resolved_mode == "questions_only":
                _push_step(r, job_id, "ai", "Mode: Questions Only — extracting questions without answers.")
                for chunk_idx, (start_p, end_p) in enumerate(chunks, 1):
                    if r.get(f"extraction:status:{job_id}") == "cancelled":
                        _push_step(r, job_id, "cancelled", "Job cancelled.")
                        return
                    _push_step(r, job_id, "ai", f"Chunk {chunk_idx}/{n_chunks}: pages {start_p}–{end_p}…")
                    chunk_content = vector_service.get_pages_text(
                        document_id=section.document_id, start_page=start_p, end_page=end_p)
                    if not chunk_content:
                        continue
                    try:
                        qs = extract_questions_only(_normalize_ocr(chunk_content),
                            f"{start_p}-{end_p}", start_p, model_id, api_key)
                        all_valid_questions.extend(qs)
                        _push_step(r, job_id, "ai", f"  Pages {start_p}–{end_p}: {len(qs)} questions. Total: {len(all_valid_questions)}.")
                    except Exception as e:
                        _push_step(r, job_id, "ai", f"  Pages {start_p}–{end_p} failed: {e}")

            elif resolved_mode == "ai_answer":
                _push_step(r, job_id, "ai", "Mode: AI Answer — extracting questions and using AI to determine correct answers.")
                for chunk_idx, (start_p, end_p) in enumerate(chunks, 1):
                    if r.get(f"extraction:status:{job_id}") == "cancelled":
                        _push_step(r, job_id, "cancelled", "Job cancelled.")
                        return
                    _push_step(r, job_id, "ai", f"Chunk {chunk_idx}/{n_chunks}: pages {start_p}–{end_p} — extracting questions…")
                    chunk_content = vector_service.get_pages_text(
                        document_id=section.document_id, start_page=start_p, end_page=end_p)
                    if not chunk_content:
                        continue
                    try:
                        normalized = _normalize_ocr(chunk_content)
                        qs = extract_questions_only(normalized, f"{start_p}-{end_p}", start_p, model_id, api_key)
                        if qs:
                            _push_step(r, job_id, "ai", f"  Pages {start_p}–{end_p}: {len(qs)} questions found, AI determining answers…")
                            qs = ai_answer_questions(qs, normalized, f"{start_p}-{end_p}", model_id, api_key)
                        answered = sum(1 for q in qs if q.get("correct_answer") and q["correct_answer"] != "PENDING")
                        all_valid_questions.extend(qs)
                        _push_step(r, job_id, "ai", f"  Pages {start_p}–{end_p}: {answered}/{len(qs)} answered. Total: {len(all_valid_questions)}.")
                    except Exception as e:
                        _push_step(r, job_id, "ai", f"  Pages {start_p}–{end_p} failed: {e}")

            elif resolved_mode == "two_step":
                _push_step(r, job_id, "ai", "Mode: Two-Step (separate answer key section).")
                all_valid_questions, all_skipped = extract_two_step(
                    section.document_id, section.start_page, section.end_page,
                    model_id, api_key,
                    push_step=lambda step, msg: _push_step(r, job_id, step, msg),
                    chunk_pages=CHUNK_PAGES,
                )

            elif resolved_mode == "regex":
                _push_step(r, job_id, "ai", "Mode: AI+Regex — analysing format then applying regex.")
                all_valid_questions, all_skipped = extract_with_regex(
                    section.document_id, section.start_page, section.end_page,
                    model_id, api_key,
                    push_step=lambda step, msg: _push_step(r, job_id, step, msg),
                    chunk_pages=CHUNK_PAGES,
                )

            elif resolved_mode == "generate":
                _push_step(r, job_id, "ai", "Mode: Generate — AI creates questions from the text.")
                for chunk_idx, (start_p, end_p) in enumerate(chunks, 1):
                    if r.get(f"extraction:status:{job_id}") == "cancelled":
                        _push_step(r, job_id, "cancelled", "Job cancelled.")
                        return
                    _push_step(r, job_id, "ai", f"Chunk {chunk_idx}/{n_chunks}: pages {start_p}–{end_p}…")
                    chunk_content = vector_service.get_pages_text(
                        document_id=section.document_id, start_page=start_p, end_page=end_p)
                    if not chunk_content:
                        continue
                    try:
                        qs = generate_from_text(_normalize_ocr(chunk_content),
                            f"{start_p}-{end_p}", start_p, model_id, api_key)
                        all_valid_questions.extend(qs)
                        _push_step(r, job_id, "ai", f"  Pages {start_p}–{end_p}: {len(qs)} questions generated. Total: {len(all_valid_questions)}.")
                    except Exception as e:
                        _push_step(r, job_id, "ai", f"  Pages {start_p}–{end_p} failed: {e}")

            else:
                # ai_decide resolved to standard — fall through to standard loop below
                extraction_mode = "standard"

        if extraction_mode == "standard":
            # ── STANDARD: existing working extraction (unchanged) ──────────────
            for chunk_idx, (start_p, end_p) in enumerate(chunks, 1):
                if r.get(f"extraction:status:{job_id}") == "cancelled":
                    _push_step(r, job_id, "cancelled", "Job cancelled.")
                    return
                if n_chunks > 1:
                    _push_step(r, job_id, "ai", f"Chunk {chunk_idx}/{n_chunks}: pages {start_p}–{end_p} → {model_name}…")
                else:
                    _push_step(r, job_id, "ai", f"Sending pages {start_p}–{end_p} to {model_name}…")
                chunk_content = vector_service.get_pages_text(
                    document_id=section.document_id, start_page=start_p, end_page=end_p,
                )
                if not chunk_content:
                    _push_step(r, job_id, "ai", f"  No text found for pages {start_p}–{end_p}, skipping.")
                    continue
                try:
                    chunk_data = ai_service.extract_questions(
                        _normalize_ocr(chunk_content),
                        page_info=f"{start_p}-{end_p}",
                        page_ref=start_p,
                        model_id=model_id,
                        api_key=api_key,
                    )
                    chunk_skipped = chunk_data[0].pop("skipped", []) if chunk_data else []
                    chunk_valid = [q for q in chunk_data if q.get("correct_answer")]
                    all_valid_questions.extend(chunk_valid)
                    all_skipped.extend(chunk_skipped)
                    _push_step(r, job_id, "ai",
                               f"  Pages {start_p}–{end_p}: {len(chunk_valid)} questions"
                               f"{f', {len(chunk_skipped)} skipped' if chunk_skipped else ''}. "
                               f"Total: {len(all_valid_questions)}.")
                except Exception as e:
                    _push_step(r, job_id, "ai", f"  Pages {start_p}–{end_p} failed: {e}. Continuing…")

        valid_questions = all_valid_questions
        skipped = all_skipped

        _push_step(r, job_id, "ai", f"Extraction complete: {len(valid_questions)} valid questions{f', {len(skipped)} skipped' if skipped else ''}.")

        if not valid_questions:
            raise ValueError("No valid questions extracted. The AI could not find questions with correct answers in this page range.")

        # Refresh DB connection — it may have gone stale during long LLM extraction
        from sqlalchemy import text as _text
        try:
            db.execute(_text("SELECT 1"))
        except Exception:
            db.rollback()
            db.close()
            db = SessionLocal()
            # Re-fetch objects that were bound to the old session
            section = db.query(Section).filter(Section.id == section_id).first()
            document = db.query(PDFDocument).filter(PDFDocument.id == section.document_id).first()

        # Extract images
        _push_step(r, job_id, "images", "Extracting question images…")
        file_path = os.path.join(settings.UPLOAD_DIR, document.filename)
        page_images = {}
        if os.path.exists(file_path):
            try:
                page_images = pdf_service.extract_all_images(
                    file_path, document.id, section.start_page, section.end_page
                )
            except Exception as e:
                _push_step(r, job_id, "images", f"Image extraction skipped: {e}")

        # Create quiz
        quiz = Quiz(
            section_id=section_id,
            user_id=user_id,
            title=title,
            questions_count=len(valid_questions),
            mode=mode,
            time_limit_minutes=time_limit_minutes,
            skipped_questions=json.dumps(skipped) if skipped else None,
        )
        db.add(quiz)
        db.flush()

        _push_step(r, job_id, "save", f"Saving {len(valid_questions)} questions and generating embeddings…")

        for pos, q in enumerate(valid_questions):
            page_ref = q.get("page_reference")
            image_path = None
            # Only link an image if the AI flagged the question as having a figure
            if q.get("has_figure") and page_ref and page_ref in page_images and page_images[page_ref]:
                image_path = page_images[page_ref].pop(0)
                if not page_images[page_ref]:
                    del page_images[page_ref]

            question = Question(
                source_quiz_id=quiz.id,
                question_category_id=question_category_id,
                question_text=q["question_text"],
                question_type=q["question_type"],
                options=q.get("options"),
                correct_answer=q["correct_answer"],
                explanation=q.get("explanation", ""),
                page_reference=page_ref,
                image_path=image_path,
            )
            db.add(question)
            db.flush()
            db.add(QuizQuestionLink(quiz_id=quiz.id, question_id=question.id, position=pos))
            try:
                embedding_service.embed_question(question)
            except Exception as e:
                logger.warning(f"Embedding failed for question {question.id}: {e}")

        db.commit()
        db.refresh(quiz)

        _push_step(r, job_id, "done", f"Quiz ready! {len(valid_questions)} questions extracted and saved.")
        r.set(f"extraction:status:{job_id}", "completed", ex=EXPIRE_SECONDS)
        r.set(f"extraction:quiz_id:{job_id}", str(quiz.id), ex=EXPIRE_SECONDS)
        return quiz.id

    except Exception as e:
        logger.exception(f"Quiz extraction failed for job {job_id}")
        _push_step(r, job_id, "error", f"Extraction failed: {e}")
        r.set(f"extraction:status:{job_id}", "failed", ex=EXPIRE_SECONDS)
        r.set(f"extraction:error:{job_id}", str(e)[:500], ex=EXPIRE_SECONDS)
        raise
    finally:
        db.close()


CLASSIFY_EXPIRE = 3600


def _push_classify_step(r, job_id: str, step: str, message: str):
    key = f"classify:steps:{job_id}"
    entry = json.dumps({"step": step, "message": message, "ts": time.time()})
    r.rpush(key, entry)
    r.expire(key, CLASSIFY_EXPIRE)


@celery_app.task(name="classify_questions", bind=True)
def classify_questions(self, job_id: str, user_id: int):
    """Classify untagged questions using AI — subjects, diseases, keywords."""
    r = _redis()
    r.set(f"classify:status:{job_id}", "running", ex=CLASSIFY_EXPIRE)

    db = SessionLocal()
    try:
        from app.models.question import Question
        from app.services import ai_service
        from sqlalchemy import text as sa_text

        _push_classify_step(r, job_id, "start", "Finding untagged questions...")

        # Get IDs of questions that already have tags
        tagged_ids_rows = db.execute(sa_text(
            "SELECT DISTINCT question_id FROM question_tag_links"
        )).fetchall()
        tagged_ids = {row[0] for row in tagged_ids_rows}

        # Get all questions not yet tagged
        all_questions = db.query(Question).all()
        untagged = [q for q in all_questions if q.id not in tagged_ids]

        if not untagged:
            _push_classify_step(r, job_id, "done", "All questions are already tagged.")
            r.set(f"classify:status:{job_id}", "completed", ex=CLASSIFY_EXPIRE)
            return

        total = len(untagged)
        _push_classify_step(r, job_id, "start", f"Found {total} untagged questions. Starting classification...")

        # Get AI model for keyword task
        model_id, api_key = ai_service.get_model_for_task(db, "keyword")

        batch_size = 10
        classified = 0

        for i in range(0, total, batch_size):
            if r.get(f"classify:status:{job_id}") == "cancelled":
                _push_classify_step(r, job_id, "cancelled", "Job cancelled.")
                return

            batch = untagged[i:i + batch_size]
            batch_num = (i // batch_size) + 1
            total_batches = (total + batch_size - 1) // batch_size
            _push_classify_step(r, job_id, "progress",
                                f"Batch {batch_num}/{total_batches}: classifying {len(batch)} questions...")

            # Build questions JSON for prompt
            questions_json = json.dumps([
                {"id": q.id, "question_text": q.question_text[:500]}
                for q in batch
            ], indent=2)

            prompt = f"""Classify each medical question below. For each question, provide:
- subjects: 1-3 medical subjects/specialties (e.g., "Cardiology", "Infectious Disease", "Neonatology")
- diseases: 1-3 specific diseases/conditions mentioned (e.g., "Kawasaki Disease", "Pneumonia", "Type 1 Diabetes")
- keywords: 2-4 key clinical concepts (e.g., "fever workup", "antibiotic resistance", "fluid management")

Return ONLY JSON:
{{"classifications": [
  {{"id": <question_id>, "subjects": [...], "diseases": [...], "keywords": [...]}}
]}}

Questions:
{questions_json}"""

            try:
                raw = ai_service._call_model(prompt, model_id, api_key)
                # Parse JSON from response
                text = raw.strip()
                if text.startswith("```"):
                    text = text.split("\n", 1)[1] if "\n" in text else text[3:]
                    if text.endswith("```"):
                        text = text[:-3]
                    text = text.strip()
                data = json.loads(text)
                classifications = data.get("classifications", [])

                # Store tags
                for cls in classifications:
                    q_id = cls.get("id")
                    if not q_id:
                        continue

                    for tag_type, tag_list in [("subject", cls.get("subjects", [])),
                                                ("disease", cls.get("diseases", [])),
                                                ("keyword", cls.get("keywords", []))]:
                        for tag_name in tag_list:
                            if not tag_name or not isinstance(tag_name, str):
                                continue
                            normalized = tag_name.strip().title()
                            if not normalized:
                                continue

                            # Insert tag (ON CONFLICT DO NOTHING for case-insensitive uniqueness)
                            db.execute(sa_text("""
                                INSERT INTO question_tags (name, type)
                                VALUES (:name, :type)
                                ON CONFLICT (LOWER(name), type) DO NOTHING
                            """), {"name": normalized, "type": tag_type})
                            db.flush()

                            # Get the tag ID
                            tag_row = db.execute(sa_text("""
                                SELECT id FROM question_tags
                                WHERE LOWER(name) = LOWER(:name) AND type = :type
                            """), {"name": normalized, "type": tag_type}).fetchone()

                            if tag_row:
                                db.execute(sa_text("""
                                    INSERT INTO question_tag_links (question_id, tag_id)
                                    VALUES (:qid, :tid)
                                    ON CONFLICT DO NOTHING
                                """), {"qid": q_id, "tid": tag_row[0]})

                db.commit()
                classified += len(batch)
                _push_classify_step(r, job_id, "progress",
                                    f"Batch {batch_num}/{total_batches} done. {classified}/{total} classified.")

            except Exception as e:
                logger.warning(f"Classification batch {batch_num} failed: {e}")
                _push_classify_step(r, job_id, "progress",
                                    f"Batch {batch_num} failed: {e}. Continuing...")
                continue

        _push_classify_step(r, job_id, "done", f"Classification complete. {classified}/{total} questions classified.")
        r.set(f"classify:status:{job_id}", "completed", ex=CLASSIFY_EXPIRE)

    except Exception as e:
        logger.exception(f"Classification failed for job {job_id}")
        _push_classify_step(r, job_id, "error", f"Failed: {e}")
        r.set(f"classify:status:{job_id}", "failed", ex=CLASSIFY_EXPIRE)
        r.set(f"classify:error:{job_id}", str(e)[:500], ex=CLASSIFY_EXPIRE)
        raise
    finally:
        db.close()


@celery_app.task(name="regenerate_embeddings", bind=True)
def regenerate_embeddings(self, job_id: str, user_id: int):
    """Regenerate embeddings for all questions using the current embedding model."""
    r = _redis()
    r.set(f"extraction:status:{job_id}", "running", ex=EXPIRE_SECONDS)
    r.set(f"extraction:job_title:{job_id}", "Regenerate Embeddings", ex=EXPIRE_SECONDS)
    r.lpush(f"extraction:user_jobs:{user_id}", job_id)
    r.expire(f"extraction:user_jobs:{user_id}", 86400)

    db = SessionLocal()
    try:
        from app.models.question import Question
        from app.services import embedding_service

        questions = db.query(Question).all()
        total = len(questions)
        _push_step(r, job_id, "start", f"Regenerating embeddings for {total} questions…")

        ok = 0
        for i, q in enumerate(questions):
            try:
                if embedding_service.embed_question(q):
                    ok += 1
                if (i + 1) % 50 == 0:
                    db.commit()
                    _push_step(r, job_id, "progress", f"{i + 1}/{total} processed ({ok} embedded)")
            except Exception as e:
                logger.warning(f"Embedding failed for question {q.id}: {e}")

        db.commit()
        _push_step(r, job_id, "done", f"Done — {ok}/{total} questions re-embedded.")
        r.set(f"extraction:status:{job_id}", "completed", ex=EXPIRE_SECONDS)
    except Exception as e:
        logger.exception(f"Embedding regeneration failed for job {job_id}")
        _push_step(r, job_id, "error", f"Failed: {e}")
        r.set(f"extraction:status:{job_id}", "failed", ex=EXPIRE_SECONDS)
        raise
    finally:
        db.close()


@celery_app.task(name="generate_flashcard_deck", bind=True)
def generate_flashcard_deck(self, job_id: str, section_id: int, user_id: int,
                            title: str, model_id: str | None = None):
    """Generate flashcards from a document section using AI."""
    r = _redis()
    r.set(f"extraction:status:{job_id}", "running", ex=EXPIRE_SECONDS)
    db = SessionLocal()
    try:
        from app.models.section import Section
        from app.models.pdf_document import PDFDocument
        from app.services import vector_service
        from app.services import extraction_modes

        section = db.query(Section).filter(Section.id == section_id).first()
        if not section:
            r.set(f"extraction:status:{job_id}", "failed", ex=EXPIRE_SECONDS)
            _push_step(r, job_id, "error", "Section not found")
            return
        document = db.query(PDFDocument).filter(PDFDocument.id == section.document_id).first()

        from app.services.ai_service import get_model_for_task
        ai_model_id, ai_api_key = get_model_for_task(db, "flashcard")
        if model_id:
            ai_model_id = model_id

        total_pages = section.end_page - section.start_page + 1
        _push_step(r, job_id, "start", f"Generating flashcards from {total_pages} pages…")

        all_cards = []

        if total_pages <= CHUNK_PAGES:
            content = vector_service.get_pages_text(section.document_id, section.start_page, section.end_page)
            if content:
                _push_step(r, job_id, "ai", f"Generating flashcards from pages {section.start_page}–{section.end_page}…")
                cards = extraction_modes.generate_flashcards(
                    content, f"{section.start_page}–{section.end_page}",
                    section.start_page, ai_model_id, ai_api_key,
                )
                all_cards.extend(cards)
                _push_step(r, job_id, "ai", f"Generated {len(cards)} cards")
        else:
            n_chunks = (total_pages + CHUNK_PAGES - 1) // CHUNK_PAGES
            _push_step(r, job_id, "ai", f"Large section: splitting into {n_chunks} chunks")
            for chunk_idx in range(1, n_chunks + 1):
                start_p = section.start_page + (chunk_idx - 1) * CHUNK_PAGES
                end_p = min(start_p + CHUNK_PAGES - 1, section.end_page)
                content = vector_service.get_pages_text(section.document_id, start_p, end_p)
                if not content or len(content.strip()) < 100:
                    _push_step(r, job_id, "ai", f"Chunk {chunk_idx}/{n_chunks}: no text, skipping")
                    continue
                _push_step(r, job_id, "ai", f"Chunk {chunk_idx}/{n_chunks}: pages {start_p}–{end_p}…")
                cards = extraction_modes.generate_flashcards(
                    content, f"{start_p}–{end_p}", start_p, ai_model_id, ai_api_key,
                )
                all_cards.extend(cards)
                _push_step(r, job_id, "ai", f"Chunk {chunk_idx}/{n_chunks}: {len(cards)} cards")

        if not all_cards:
            r.set(f"extraction:status:{job_id}", "failed", ex=EXPIRE_SECONDS)
            _push_step(r, job_id, "error", "No flashcards could be generated")
            return

        # Refresh DB connection for save phase
        from sqlalchemy import text as _text
        try:
            db.execute(_text("SELECT 1"))
        except Exception:
            db.rollback()
            db.close()
            db = SessionLocal()

        _push_step(r, job_id, "save", f"Saving {len(all_cards)} flashcards…")

        from app.models.flashcard import FlashcardDeck, Flashcard
        deck = FlashcardDeck(
            title=title,
            section_id=section_id,
            user_id=user_id,
            card_count=len(all_cards),
        )
        db.add(deck)
        db.flush()

        for c in all_cards:
            card = Flashcard(
                deck_id=deck.id,
                front=c["front"],
                back=c["back"],
                page_reference=c.get("page_reference"),
            )
            db.add(card)

        db.commit()
        r.set(f"extraction:status:{job_id}", "completed", ex=EXPIRE_SECONDS)
        r.set(f"extraction:deck_id:{job_id}", str(deck.id), ex=EXPIRE_SECONDS)
        _push_step(r, job_id, "done", f"Created deck '{title}' with {len(all_cards)} cards")

    except Exception as e:
        logger.exception(f"Flashcard generation failed: {e}")
        r.set(f"extraction:status:{job_id}", "failed", ex=EXPIRE_SECONDS)
        r.set(f"extraction:error:{job_id}", str(e)[:500], ex=EXPIRE_SECONDS)
        _push_step(r, job_id, "error", f"Failed: {str(e)[:200]}")
        try:
            db.rollback()
        except Exception:
            pass
    finally:
        db.close()