import os
import logging

from sqlalchemy.orm import Session

from app.config import settings
from app.models.section import Section
from app.models.pdf_document import PDFDocument
from app.models.quiz import Quiz
from app.models.question import Question
from app.services import ai_service, vector_service, pdf_service, embedding_service
from app.utils.quiz_questions import add_questions_to_quiz

logger = logging.getLogger(__name__)


def create_quiz_from_section(
    db: Session,
    user_id: int,
    section_id: int,
    title: str,
    mode: str = "timed",
    time_limit_minutes: int | None = None,
    model_id: str | None = None,
    question_category_id: int | None = None,
) -> Quiz:
    """Extract questions from a section's page range using AI."""
    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")

    # Get text from vector store for this page range
    content = vector_service.get_pages_text(
        document_id=section.document_id,
        start_page=section.start_page,
        end_page=section.end_page,
    )

    if not content:
        raise ValueError("No content found for this section's page range")

    # Get configured model (use override if provided)
    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
    else:
        model_id, api_key = ai_service.get_model_for_task(db, "extraction")

    # Extract questions via AI (not generate — questions already exist in PDF)
    page_info = f"{section.start_page}-{section.end_page}"
    question_data = ai_service.extract_questions(
        content,
        page_info=page_info,
        model_id=model_id,
        api_key=api_key,
    )

    # Extract images for the page range
    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:
            logger.warning(f"Image extraction failed: {e}")

    # Collect skipped questions (those without a correct answer)
    import json
    skipped = []
    if question_data and question_data[0].get("skipped"):
        skipped = question_data[0].pop("skipped")
    valid_questions = [q for q in question_data if q.get("correct_answer")]

    # 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()

    # Create question records, associating images where possible
    for pos, q in enumerate(valid_questions):
        page_ref = q.get("page_reference")
        image_path = None

        # Try to associate an image with this question
        if page_ref and page_ref in page_images and page_images[page_ref]:
            # Take the first unassigned image from this page
            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()
        # Register in junction table
        from app.models.quiz_question_link import QuizQuestionLink
        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 generation failed for question {question.id}: {e}")

    db.commit()
    db.refresh(quiz)
    return quiz