8a220bb12e
Extraction fixes: - OCR normalization: 'Pref erred' → 'Preferred', 'ltem' → 'Item' applied to boundary scan, Phase 1 questions, and Phase 2 answer key content before AI processing - Chunk boundary: Phase 1 chunks now capped at (answer_section_start - 1) so no chunk bleeds into the answer section — (51, 100) becomes (51, 55) for PREP 2013 - Result: Phase 1 gets 2 clean chunks (1-50 and 51-55), Phase 2 gets pages 56-227 Category creation in DocumentDetailPage: - Replaced window.prompt() with inline input form (more reliable, no browser quirks) - Fixed option value type: String(c.id) ensures consistent string comparison with selectedQuestionCategoryId state (prevents type mismatch in controlled select) - "+ New" button toggles inline form; Enter key or Add button submits Deletion safety (confirmed): - Deleting a quiz: questions detached to bank if exclusive, kept if shared — NEVER deleted - Deleting a question category: questions uncategorized or moved — NEVER deleted Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
310 lines
14 KiB
Python
310 lines
14 KiB
Python
"""Async quiz extraction task with step-by-step progress reporting via Redis."""
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import json
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import logging
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import time
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from app.tasks import celery_app
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from app.database import SessionLocal
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logger = logging.getLogger(__name__)
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EXPIRE_SECONDS = 3600 # keep progress for 1 hour
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def _redis():
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import redis
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from app.config import settings
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return redis.from_url(settings.REDIS_URL, decode_responses=True)
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def _push_step(r, job_id: str, step: str, message: str):
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key = f"extraction:steps:{job_id}"
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entry = json.dumps({"step": step, "message": message, "ts": time.time()})
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r.rpush(key, entry)
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r.expire(key, EXPIRE_SECONDS)
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@celery_app.task(name="extract_quiz", bind=True)
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def extract_quiz(
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self,
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job_id: str,
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user_id: int,
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section_id: int,
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title: str,
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mode: str,
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time_limit_minutes: int | None,
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model_id: str | None,
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question_category_id: int | None,
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):
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"""Background quiz extraction task with live progress reporting."""
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r = _redis()
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r.set(f"extraction:status:{job_id}", "running", ex=EXPIRE_SECONDS)
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# Ensure user job index exists (in case set before task ran)
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r.lpush(f"extraction:user_jobs:{user_id}", job_id)
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r.expire(f"extraction:user_jobs:{user_id}", 86400)
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db = SessionLocal()
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try:
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from app.models.section import Section
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from app.models.pdf_document import PDFDocument
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from app.services import ai_service, vector_service, pdf_service, embedding_service
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from app.models.quiz import Quiz
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from app.models.question import Question
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from app.models.quiz_question_link import QuizQuestionLink
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from app.config import settings
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import os
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_push_step(r, job_id, "start", "Starting extraction…")
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section = db.query(Section).filter(Section.id == section_id).first()
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if not section:
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raise ValueError("Section not found")
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document = db.query(PDFDocument).filter(PDFDocument.id == section.document_id).first()
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if not document:
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raise ValueError("Document not found")
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total_pages = section.end_page - section.start_page + 1
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_push_step(r, job_id, "text", f"Loading text from pages {section.start_page}–{section.end_page} ({total_pages} pages)…")
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# Determine model first (used for chunk logging)
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if model_id:
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from app.models.ai_model_config import AIModelConfig
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config = db.query(AIModelConfig).filter(AIModelConfig.model_id == model_id).first()
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api_key = config.api_key if config and config.api_key else None
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model_name = config.name if config else model_id
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else:
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model_id, api_key = ai_service.get_model_for_task(db, "extraction")
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model_name = model_id
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# For large page ranges, process in chunks of 50 pages to avoid truncation
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CHUNK_PAGES = 50
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import json as _json
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if total_pages <= CHUNK_PAGES:
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chunks = [(section.start_page, section.end_page)]
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else:
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chunks = []
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p = section.start_page
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while p <= section.end_page:
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end = min(p + CHUNK_PAGES - 1, section.end_page)
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chunks.append((p, end))
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p = end + 1
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n_chunks = len(chunks)
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if n_chunks > 1:
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_push_step(r, job_id, "text", f"Large section: splitting into {n_chunks} chunks of up to {CHUNK_PAGES} pages each.")
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def _normalize_ocr(text: str) -> str:
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"""Normalize common OCR artifacts in PREP PDFs."""
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return (text
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.replace("Pref erred", "Preferred")
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.replace("Pre ferred", "Preferred")
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.replace("Prefer red", "Preferred")
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.replace("ltem", "Item")
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.replace("ltcm", "Item"))
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# --- Detect separate answer key section (e.g. PREP 2013) ---
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# Scan document in 10-page steps to find where "Preferred Response:" first appears
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answer_section_start = None
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scan_step = 10
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for scan_p in range(section.start_page, section.end_page, scan_step):
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scan_end = min(scan_p + scan_step - 1, section.end_page)
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raw_chunk = vector_service.get_pages_text(
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document_id=section.document_id, start_page=scan_p, end_page=scan_end,
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)
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if not raw_chunk:
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continue
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scan_chunk = _normalize_ocr(raw_chunk)
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if "Preferred Response:" in scan_chunk or "preferred response:" in scan_chunk.lower():
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answer_section_start = max(section.start_page, scan_p - 5)
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break
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has_end_answer_key = answer_section_start is not None
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if has_end_answer_key:
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_push_step(r, job_id, "ai", f"Detected separate answer key section starting around page {answer_section_start}. Using two-phase extraction.")
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# Restrict question chunks to pages strictly BEFORE the answer section
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# Cap the end page so no chunk bleeds into the answer section
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q_end = answer_section_start - 1
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chunks = [(s, min(e, q_end)) for s, e in chunks if s <= q_end]
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if not chunks:
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chunks = [(section.start_page, q_end)]
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all_valid_questions = []
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all_skipped = []
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if has_end_answer_key:
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# === PHASE 1: Extract questions (allow null correct_answer) ===
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raw_questions = []
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for chunk_idx, (start_p, end_p) in enumerate(chunks, 1):
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_push_step(r, job_id, "ai", f"Phase 1 – Chunk {chunk_idx}/{n_chunks}: pages {start_p}–{end_p} (questions)…")
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chunk_content = vector_service.get_pages_text(
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document_id=section.document_id, start_page=start_p, end_page=end_p,
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)
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if not chunk_content:
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continue
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try:
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chunk_qs = ai_service.extract_questions_no_answers(
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_normalize_ocr(chunk_content), page_info=f"{start_p}-{end_p}",
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page_ref=start_p, model_id=model_id, api_key=api_key,
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)
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raw_questions.extend(chunk_qs)
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_push_step(r, job_id, "ai", f" Pages {start_p}–{end_p}: {len(chunk_qs)} questions found. Total: {len(raw_questions)}.")
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except Exception as e:
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_push_step(r, job_id, "ai", f" Pages {start_p}–{end_p} failed: {e}. Continuing…")
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# === PHASE 2: Extract answer key from the answer section ===
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_push_step(r, job_id, "ai", f"Phase 2 – Extracting answer key from pages {answer_section_start}–{section.end_page}…")
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# Process answer section in chunks too (it may be large)
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answer_key: dict = {}
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for ans_start in range(answer_section_start, section.end_page + 1, CHUNK_PAGES):
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ans_end = min(ans_start + CHUNK_PAGES - 1, section.end_page)
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answer_content = vector_service.get_pages_text(
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document_id=section.document_id,
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start_page=ans_start,
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end_page=ans_end,
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)
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if not answer_content:
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continue
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chunk_key = ai_service.extract_answer_key(
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_normalize_ocr(answer_content), page_info=f"{ans_start}-{ans_end}",
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model_id=model_id, api_key=api_key,
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)
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answer_key.update(chunk_key)
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_push_step(r, job_id, "ai", f" Answer pages {ans_start}–{ans_end}: +{len(chunk_key)} answers. Total: {len(answer_key)}.")
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_push_step(r, job_id, "ai", f" Answer key complete: {len(answer_key)} items.")
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# === PHASE 3: Match questions to answers ===
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_push_step(r, job_id, "ai", "Phase 3 – Matching questions to answers…")
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for q in raw_questions:
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item_num = q.get("item_number")
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if not item_num:
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# No item number — skip
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all_skipped.append(q.get("question_text", "")[:120])
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continue
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letter = answer_key.get(item_num)
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if not letter:
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all_skipped.append(q.get("question_text", "")[:120])
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continue
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# Resolve letter → full option text
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options = q.get("options") or []
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letter_idx = ord(letter.upper()) - ord("A")
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if 0 <= letter_idx < len(options):
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q["correct_answer"] = options[letter_idx]
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all_valid_questions.append(q)
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else:
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all_skipped.append(q.get("question_text", "")[:120])
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_push_step(r, job_id, "ai", f"Matching complete: {len(all_valid_questions)} matched, {len(all_skipped)} unmatched.")
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else:
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# === STANDARD: inline correct answer format ===
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for chunk_idx, (start_p, end_p) in enumerate(chunks, 1):
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if n_chunks > 1:
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_push_step(r, job_id, "ai", f"Chunk {chunk_idx}/{n_chunks}: pages {start_p}–{end_p} → {model_name}…")
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else:
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_push_step(r, job_id, "ai", f"Sending pages {start_p}–{end_p} to {model_name}…")
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chunk_content = vector_service.get_pages_text(
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document_id=section.document_id, start_page=start_p, end_page=end_p,
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)
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if not chunk_content:
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_push_step(r, job_id, "ai", f" No text found for pages {start_p}–{end_p}, skipping.")
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continue
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try:
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chunk_data = ai_service.extract_questions(
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chunk_content, page_info=f"{start_p}-{end_p}",
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page_ref=start_p, model_id=model_id, api_key=api_key,
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)
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chunk_skipped = []
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if chunk_data and chunk_data[0].get("skipped"):
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chunk_skipped = chunk_data[0].pop("skipped")
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chunk_valid = [q for q in chunk_data if q.get("correct_answer")]
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all_valid_questions.extend(chunk_valid)
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all_skipped.extend(chunk_skipped)
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_push_step(r, job_id, "ai", f" Pages {start_p}–{end_p}: {len(chunk_valid)} questions extracted{f', {len(chunk_skipped)} skipped' if chunk_skipped else ''}. Total so far: {len(all_valid_questions)}.")
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except Exception as e:
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_push_step(r, job_id, "ai", f" Pages {start_p}–{end_p} failed: {e}. Continuing…")
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valid_questions = all_valid_questions
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skipped = all_skipped
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_push_step(r, job_id, "ai", f"Extraction complete: {len(valid_questions)} total valid questions{f', {len(skipped)} skipped' if skipped else ''}.")
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if not valid_questions:
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raise ValueError("No valid questions extracted. The AI could not find questions with correct answers in this page range.")
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# Extract images
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_push_step(r, job_id, "images", "Extracting question images…")
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file_path = os.path.join(settings.UPLOAD_DIR, document.filename)
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page_images = {}
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if os.path.exists(file_path):
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try:
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page_images = pdf_service.extract_all_images(
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file_path, document.id, section.start_page, section.end_page
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)
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except Exception as e:
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_push_step(r, job_id, "images", f"Image extraction skipped: {e}")
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# Create quiz
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quiz = Quiz(
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section_id=section_id,
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user_id=user_id,
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title=title,
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questions_count=len(valid_questions),
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mode=mode,
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time_limit_minutes=time_limit_minutes,
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skipped_questions=_json.dumps(skipped) if skipped else None,
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)
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db.add(quiz)
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db.flush()
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_push_step(r, job_id, "save", f"Saving {len(valid_questions)} questions and generating embeddings…")
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for pos, q in enumerate(valid_questions):
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page_ref = q.get("page_reference")
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image_path = None
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if page_ref and page_ref in page_images and page_images[page_ref]:
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image_path = page_images[page_ref].pop(0)
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if not page_images[page_ref]:
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del page_images[page_ref]
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question = Question(
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source_quiz_id=quiz.id,
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question_category_id=question_category_id,
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question_text=q["question_text"],
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question_type=q["question_type"],
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options=q.get("options"),
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correct_answer=q["correct_answer"],
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explanation=q.get("explanation", ""),
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page_reference=page_ref,
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image_path=image_path,
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)
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db.add(question)
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db.flush()
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db.add(QuizQuestionLink(quiz_id=quiz.id, question_id=question.id, position=pos))
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try:
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embedding_service.embed_question(question)
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except Exception:
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pass
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db.commit()
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db.refresh(quiz)
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_push_step(r, job_id, "done", f"Quiz ready! {len(valid_questions)} questions extracted and saved.")
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r.set(f"extraction:status:{job_id}", "completed", ex=EXPIRE_SECONDS)
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r.set(f"extraction:quiz_id:{job_id}", str(quiz.id), ex=EXPIRE_SECONDS)
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return quiz.id
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except Exception as e:
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logger.exception(f"Quiz extraction failed for job {job_id}")
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_push_step(r, job_id, "error", f"Extraction failed: {e}")
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r.set(f"extraction:status:{job_id}", "failed", ex=EXPIRE_SECONDS)
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r.set(f"extraction:error:{job_id}", str(e)[:500], ex=EXPIRE_SECONDS)
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raise
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finally:
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db.close()
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