mirror of
https://github.com/handsomezhuzhu/QQuiz.git
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818 lines
29 KiB
Python
818 lines
29 KiB
Python
"""
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Exam Router - Handles exam creation, file upload, and deduplication
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"""
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from fastapi import APIRouter, Depends, HTTPException, status, UploadFile, File, Form, BackgroundTasks, Request
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from fastapi.responses import StreamingResponse
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, func, and_
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from typing import List, Optional
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from datetime import datetime, timedelta
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import os
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import aiofiles
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import json
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import magic
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from database import get_db
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from models import User, Exam, Question, ExamStatus, SystemConfig
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from schemas import (
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ExamCreate, ExamResponse, ExamListResponse,
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ExamUploadResponse, ParseResult, QuizProgressUpdate
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)
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from services.auth_service import get_current_user
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from services.document_parser import document_parser
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from services.llm_service import LLMService
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from services.config_service import load_llm_config
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from services.progress_service import progress_service
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from utils import is_allowed_file, calculate_content_hash
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from dedup_utils import is_duplicate_question
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from rate_limit import limiter
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router = APIRouter()
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ALLOWED_MIME_TYPES = {
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"text/plain",
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"application/pdf",
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"application/msword",
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"application/vnd.openxmlformats-officedocument.wordprocessingml.document",
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"application/vnd.ms-excel",
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"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
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}
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async def validate_upload_file(file: UploadFile) -> None:
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"""Validate uploaded file by extension and MIME type."""
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if not file.filename or not is_allowed_file(file.filename):
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail="Invalid file type. Allowed: txt, pdf, doc, docx, xlsx, xls",
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)
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try:
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sample = await file.read(2048)
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except Exception:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail="Could not read uploaded file for validation",
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)
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if not sample:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail="Uploaded file is empty",
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)
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try:
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mime_type = magic.from_buffer(sample, mime=True)
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except Exception:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail="Could not determine file type",
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)
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finally:
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file.file.seek(0)
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if mime_type not in ALLOWED_MIME_TYPES:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail="Invalid file content type",
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)
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async def check_upload_limits(user_id: int, file_size: int, db: AsyncSession):
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"""Check if user has exceeded upload limits"""
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# Get max upload size config
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result = await db.execute(
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select(SystemConfig).where(SystemConfig.key == "max_upload_size_mb")
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)
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config = result.scalar_one_or_none()
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max_size_mb = int(config.value) if config else 10
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# Check file size
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if file_size > max_size_mb * 1024 * 1024:
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raise HTTPException(
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status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
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detail=f"File size exceeds limit of {max_size_mb}MB"
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)
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# Get max daily uploads config
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result = await db.execute(
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select(SystemConfig).where(SystemConfig.key == "max_daily_uploads")
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)
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config = result.scalar_one_or_none()
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max_daily = int(config.value) if config else 20
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# Check daily upload count
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today_start = datetime.utcnow().replace(hour=0, minute=0, second=0, microsecond=0)
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result = await db.execute(
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select(func.count(Exam.id)).where(
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and_(
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Exam.user_id == user_id,
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Exam.created_at >= today_start
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)
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)
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)
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upload_count = result.scalar()
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if upload_count >= max_daily:
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raise HTTPException(
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status_code=status.HTTP_429_TOO_MANY_REQUESTS,
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detail=f"Daily upload limit of {max_daily} reached"
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)
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async def generate_ai_reference_answer(
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llm_service,
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question_content: str,
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question_type: str,
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options: Optional[List[str]] = None
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) -> str:
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"""
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Generate an AI reference answer for a question without a provided answer.
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Args:
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llm_service: LLM service instance
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question_content: The question text
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question_type: Type of question (single, multiple, judge, short)
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options: Question options (for choice questions)
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Returns:
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Generated answer text
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"""
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# Build prompt based on question type
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if question_type in ["single", "multiple"] and options:
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options_text = "\n".join(options)
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prompt = f"""这是一道{
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'单选题' if question_type == 'single' else '多选题'
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}，但文档中没有提供答案。请根据题目内容，推理出最可能的正确答案。
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题目：{question_content}
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选项：
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{options_text}
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请只返回你认为正确的选项字母（如 A 或 AB），不要有其他解释。如果无法确定，请返回"无法确定"。"""
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elif question_type == "judge":
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prompt = f"""这是一道判断题，但文档中没有提供答案。请根据题目内容，判断正误。
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题目：{question_content}
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请只返回"对"或"错"，不要有其他解释。如果无法确定，请返回"无法确定"。"""
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else: # short answer
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prompt = f"""这是一道简答题，但文档中没有提供答案。请根据题目内容，给出一个简洁的参考答案（50字以内）。
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题目：{question_content}
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请直接返回答案内容，不要有"答案："等前缀。如果无法回答，请返回"无法确定"。"""
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# Generate answer using LLM
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if llm_service.provider == "gemini":
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# Use REST API for Gemini
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url = f"{llm_service.gemini_base_url}/v1beta/models/{llm_service.model}:generateContent"
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headers = {"Content-Type": "application/json"}
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params = {"key": llm_service.gemini_api_key}
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payload = {
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"contents": [{
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"parts": [{"text": prompt}]
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}]
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}
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response = await llm_service.client.post(url, headers=headers, params=params, json=payload)
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response.raise_for_status()
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response_data = response.json()
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return response_data["candidates"][0]["content"]["parts"][0]["text"].strip()
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elif llm_service.provider == "anthropic":
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response = await llm_service.client.messages.create(
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model=llm_service.model,
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max_tokens=256,
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messages=[{"role": "user", "content": prompt}]
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)
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return response.content[0].text.strip()
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else: # OpenAI or Qwen
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response = await llm_service.client.chat.completions.create(
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model=llm_service.model,
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messages=[
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{"role": "system", "content": "You are a helpful assistant that provides concise answers."},
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{"role": "user", "content": prompt}
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],
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temperature=0.7,
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max_tokens=256
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)
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return response.choices[0].message.content.strip()
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async def process_questions_with_dedup(
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exam_id: int,
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questions_data: List[dict],
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db: AsyncSession,
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llm_service=None
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) -> ParseResult:
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"""
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Process parsed questions with fuzzy deduplication logic.
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Uses a two-stage deduplication strategy:
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1. Fast exact hash matching (for 100% identical questions)
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2. Fuzzy similarity matching (for AI-generated variations)
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Args:
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exam_id: Target exam ID
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questions_data: List of question dicts from LLM parsing
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db: Database session
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llm_service: LLM service instance for generating AI answers
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Returns:
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ParseResult with statistics
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"""
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total_parsed = len(questions_data)
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duplicates_removed = 0
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new_added = 0
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ai_answers_generated = 0
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# Get existing questions for this exam (content for fuzzy matching)
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result = await db.execute(
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select(Question.content, Question.content_hash).where(Question.exam_id == exam_id)
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)
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existing_questions_db = result.all()
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existing_hashes = set(row[1] for row in existing_questions_db)
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existing_questions = [{"content": row[0]} for row in existing_questions_db]
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print(f"[Dedup] Checking against {len(existing_questions)} existing questions in database")
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# Insert only new questions
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for q_data in questions_data:
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content_hash = q_data.get("content_hash")
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# Stage 1: Fast exact hash matching
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if content_hash in existing_hashes:
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duplicates_removed += 1
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print(f"[Dedup] Exact hash match - skipping", flush=True)
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continue
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# Stage 2: Fuzzy similarity matching (only if hash didn't match)
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if is_duplicate_question(q_data, existing_questions, threshold=0.85):
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duplicates_removed += 1
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continue
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# Handle missing answers - generate AI reference answer
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answer = q_data.get("answer")
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if (answer is None or answer == "null" or answer == "") and llm_service:
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print(f"[Question] Generating AI reference answer for: {q_data['content'][:50]}...", flush=True)
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try:
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# Convert question type to string if it's not already
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q_type = q_data["type"]
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if hasattr(q_type, 'value'):
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q_type = q_type.value
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elif isinstance(q_type, str):
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q_type = q_type.lower()
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ai_answer = await generate_ai_reference_answer(
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llm_service,
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q_data["content"],
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q_type,
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q_data.get("options")
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)
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answer = f"AI参考答案：{ai_answer}"
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ai_answers_generated += 1
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print(f"[Question] ✅ AI answer generated: {ai_answer[:50]}...", flush=True)
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except Exception as e:
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print(f"[Question] ⚠️ Failed to generate AI answer: {e}", flush=True)
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answer = "（答案未提供）"
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elif answer is None or answer == "null" or answer == "":
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answer = "（答案未提供）"
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# Create new question
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new_question = Question(
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exam_id=exam_id,
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content=q_data["content"],
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type=q_data["type"],
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options=q_data.get("options"),
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answer=answer,
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analysis=q_data.get("analysis"),
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content_hash=content_hash
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)
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db.add(new_question)
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existing_hashes.add(content_hash) # Prevent exact duplicates in current batch
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existing_questions.append({"content": q_data["content"]}) # Prevent fuzzy duplicates in current batch
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new_added += 1
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await db.commit()
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message = f"Parsed {total_parsed} questions, removed {duplicates_removed} duplicates, added {new_added} new questions"
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if ai_answers_generated > 0:
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message += f", generated {ai_answers_generated} AI reference answers"
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return ParseResult(
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total_parsed=total_parsed,
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duplicates_removed=duplicates_removed,
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new_added=new_added,
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message=message
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)
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async def async_parse_and_save(
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exam_id: int,
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file_content: bytes,
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filename: str,
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db_url: str
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):
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"""
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Background task to parse document and save questions with deduplication.
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Sends real-time progress updates via SSE.
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"""
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from database import AsyncSessionLocal
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from sqlalchemy import select
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from services.progress_service import ProgressUpdate, ProgressStatus
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async with AsyncSessionLocal() as db:
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try:
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# Update exam status to processing
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result = await db.execute(select(Exam).where(Exam.id == exam_id))
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exam = result.scalar_one()
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exam.status = ExamStatus.PROCESSING
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await db.commit()
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# Send initial progress
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await progress_service.update_progress(ProgressUpdate(
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exam_id=exam_id,
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status=ProgressStatus.PARSING,
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message="开始解析文档...",
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progress=5.0
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))
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# Load LLM configuration from database
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llm_config = await load_llm_config(db)
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llm_service = LLMService(config=llm_config)
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# Check if file is PDF and provider is Gemini
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is_pdf = filename.lower().endswith('.pdf')
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is_gemini = llm_config.get('ai_provider') == 'gemini'
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print(f"[Exam {exam_id}] Parsing document: {filename}")
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print(f"[Exam {exam_id}] File type: {'PDF' if is_pdf else 'Text-based'}", flush=True)
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print(f"[Exam {exam_id}] AI Provider: {llm_config.get('ai_provider')}", flush=True)
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try:
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if is_pdf and is_gemini:
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# Use Gemini's native PDF processing
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print(f"[Exam {exam_id}] Using Gemini native PDF processing", flush=True)
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print(f"[Exam {exam_id}] PDF file size: {len(file_content)} bytes", flush=True)
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await progress_service.update_progress(ProgressUpdate(
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exam_id=exam_id,
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status=ProgressStatus.PARSING,
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message="使用Gemini解析PDF文档...",
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progress=10.0
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))
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questions_data = await llm_service.parse_document_with_pdf(file_content, filename, exam_id)
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else:
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# Extract text first, then parse
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if is_pdf:
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print(f"[Exam {exam_id}] ⚠️ Warning: Using text extraction for PDF (provider does not support native PDF)", flush=True)
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await progress_service.update_progress(ProgressUpdate(
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exam_id=exam_id,
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status=ProgressStatus.PARSING,
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message="提取文档文本内容...",
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progress=10.0
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))
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print(f"[Exam {exam_id}] Extracting text from document...", flush=True)
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text_content = await document_parser.parse_file(file_content, filename)
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if not text_content or len(text_content.strip()) < 10:
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raise Exception("Document appears to be empty or too short")
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print(f"[Exam {exam_id}] Text content length: {len(text_content)} chars", flush=True)
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# Check if document is too long and needs splitting
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if len(text_content) > 5000:
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text_chunks = document_parser.split_text_with_overlap(text_content, chunk_size=3000, overlap=1000)
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total_chunks = len(text_chunks)
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print(f"[Exam {exam_id}] Document is long, splitting into chunks...", flush=True)
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print(f"[Exam {exam_id}] Split into {total_chunks} chunks", flush=True)
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await progress_service.update_progress(ProgressUpdate(
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exam_id=exam_id,
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status=ProgressStatus.SPLITTING,
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message=f"文档已拆分为 {total_chunks} 个部分",
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progress=15.0,
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total_chunks=total_chunks
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))
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all_questions = []
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for chunk_idx, chunk in enumerate(text_chunks):
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current_chunk = chunk_idx + 1
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chunk_progress = 15.0 + (60.0 * current_chunk / total_chunks)
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await progress_service.update_progress(ProgressUpdate(
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exam_id=exam_id,
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status=ProgressStatus.PROCESSING_CHUNK,
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message=f"正在处理第 {current_chunk}/{total_chunks} 部分...",
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progress=chunk_progress,
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total_chunks=total_chunks,
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current_chunk=current_chunk,
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questions_extracted=len(all_questions)
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))
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print(f"[Exam {exam_id}] Processing chunk {current_chunk}/{total_chunks}...", flush=True)
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try:
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chunk_questions = await llm_service.parse_document(chunk)
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print(f"[Exam {exam_id}] Chunk {current_chunk} extracted {len(chunk_questions)} questions", flush=True)
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# Fuzzy deduplicate across chunks
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for q in chunk_questions:
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# Use fuzzy matching to check for duplicates
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if not is_duplicate_question(q, all_questions, threshold=0.85):
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all_questions.append(q)
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else:
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print(f"[Exam {exam_id}] Skipped fuzzy duplicate from chunk {current_chunk}", flush=True)
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except Exception as chunk_error:
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print(f"[Exam {exam_id}] Chunk {chunk_idx + 1} failed: {str(chunk_error)}", flush=True)
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continue
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questions_data = all_questions
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print(f"[Exam {exam_id}] Total questions after fuzzy deduplication: {len(questions_data)}", flush=True)
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await progress_service.update_progress(ProgressUpdate(
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exam_id=exam_id,
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status=ProgressStatus.DEDUPLICATING,
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message=f"所有部分处理完成，提取了 {len(questions_data)} 个题目",
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progress=75.0,
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total_chunks=total_chunks,
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current_chunk=total_chunks,
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questions_extracted=len(questions_data)
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))
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else:
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print(f"[Exam {exam_id}] Document content preview:\n{text_content[:500]}\n{'...' if len(text_content) > 500 else ''}", flush=True)
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print(f"[Exam {exam_id}] Calling LLM to extract questions...", flush=True)
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await progress_service.update_progress(ProgressUpdate(
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exam_id=exam_id,
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status=ProgressStatus.PARSING,
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message="正在提取题目...",
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progress=30.0
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))
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questions_data = await llm_service.parse_document(text_content)
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await progress_service.update_progress(ProgressUpdate(
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exam_id=exam_id,
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status=ProgressStatus.DEDUPLICATING,
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message=f"提取了 {len(questions_data)} 个题目",
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progress=60.0,
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questions_extracted=len(questions_data)
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))
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except Exception as parse_error:
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print(f"[Exam {exam_id}] ⚠️ Parse error details: {type(parse_error).__name__}", flush=True)
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print(f"[Exam {exam_id}] ⚠️ Parse error message: {str(parse_error)}", flush=True)
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import traceback
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print(f"[Exam {exam_id}] ⚠️ Full traceback:\n{traceback.format_exc()}", flush=True)
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raise
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||
if not questions_data:
|
||
raise Exception("No questions found in document")
|
||
|
||
# Process questions with deduplication and AI answer generation
|
||
await progress_service.update_progress(ProgressUpdate(
|
||
exam_id=exam_id,
|
||
status=ProgressStatus.SAVING,
|
||
message="正在去重并保存题目到数据库...",
|
||
progress=80.0,
|
||
questions_extracted=len(questions_data)
|
||
))
|
||
|
||
print(f"[Exam {exam_id}] Processing questions with deduplication...")
|
||
parse_result = await process_questions_with_dedup(exam_id, questions_data, db, llm_service)
|
||
|
||
# Update exam status and total questions
|
||
result = await db.execute(select(Exam).where(Exam.id == exam_id))
|
||
exam = result.scalar_one()
|
||
|
||
# Get updated question count
|
||
result = await db.execute(
|
||
select(func.count(Question.id)).where(Question.exam_id == exam_id)
|
||
)
|
||
total_questions = result.scalar()
|
||
|
||
exam.status = ExamStatus.READY
|
||
exam.total_questions = total_questions
|
||
await db.commit()
|
||
|
||
print(f"[Exam {exam_id}] ✅ {parse_result.message}")
|
||
|
||
# Send completion progress
|
||
await progress_service.update_progress(ProgressUpdate(
|
||
exam_id=exam_id,
|
||
status=ProgressStatus.COMPLETED,
|
||
message=f"完成！添加了 {parse_result.new_added} 个题目（去重 {parse_result.duplicates_removed} 个）",
|
||
progress=100.0,
|
||
questions_extracted=parse_result.total_parsed,
|
||
questions_added=parse_result.new_added,
|
||
duplicates_removed=parse_result.duplicates_removed
|
||
))
|
||
|
||
except Exception as e:
|
||
print(f"[Exam {exam_id}] ❌ Error: {str(e)}")
|
||
|
||
# Send error progress
|
||
await progress_service.update_progress(ProgressUpdate(
|
||
exam_id=exam_id,
|
||
status=ProgressStatus.FAILED,
|
||
message=f"处理失败：{str(e)}",
|
||
progress=0.0
|
||
))
|
||
|
||
# Update exam status to failed
|
||
result = await db.execute(select(Exam).where(Exam.id == exam_id))
|
||
exam = result.scalar_one()
|
||
exam.status = ExamStatus.FAILED
|
||
await db.commit()
|
||
|
||
|
||
@router.post("/create", response_model=ExamUploadResponse, status_code=status.HTTP_201_CREATED)
|
||
@limiter.limit("10/minute")
|
||
async def create_exam_with_upload(
|
||
request: Request,
|
||
background_tasks: BackgroundTasks,
|
||
title: str = Form(...),
|
||
file: UploadFile = File(...),
|
||
current_user: User = Depends(get_current_user),
|
||
db: AsyncSession = Depends(get_db)
|
||
):
|
||
"""
|
||
Create a new exam and upload the first document.
|
||
Document will be parsed asynchronously in background.
|
||
"""
|
||
|
||
# Validate file
|
||
await validate_upload_file(file)
|
||
|
||
# Read file content
|
||
file_content = await file.read()
|
||
|
||
# Check upload limits
|
||
await check_upload_limits(current_user.id, len(file_content), db)
|
||
|
||
# Create exam
|
||
new_exam = Exam(
|
||
user_id=current_user.id,
|
||
title=title,
|
||
status=ExamStatus.PENDING
|
||
)
|
||
db.add(new_exam)
|
||
await db.commit()
|
||
await db.refresh(new_exam)
|
||
|
||
# Start background parsing
|
||
background_tasks.add_task(
|
||
async_parse_and_save,
|
||
new_exam.id,
|
||
file_content,
|
||
file.filename,
|
||
os.getenv("DATABASE_URL")
|
||
)
|
||
|
||
return ExamUploadResponse(
|
||
exam_id=new_exam.id,
|
||
title=new_exam.title,
|
||
status=new_exam.status.value,
|
||
message="Exam created. Document is being processed in background."
|
||
)
|
||
|
||
|
||
@router.post("/{exam_id}/append", response_model=ExamUploadResponse)
|
||
@limiter.limit("10/minute")
|
||
async def append_document_to_exam(
|
||
request: Request,
|
||
exam_id: int,
|
||
background_tasks: BackgroundTasks,
|
||
file: UploadFile = File(...),
|
||
current_user: User = Depends(get_current_user),
|
||
db: AsyncSession = Depends(get_db)
|
||
):
|
||
"""
|
||
Append a new document to an existing exam.
|
||
Questions will be parsed and deduplicated asynchronously.
|
||
"""
|
||
|
||
# Get exam and verify ownership
|
||
result = await db.execute(
|
||
select(Exam).where(
|
||
and_(Exam.id == exam_id, Exam.user_id == current_user.id)
|
||
)
|
||
)
|
||
exam = result.scalar_one_or_none()
|
||
|
||
if not exam:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="Exam not found"
|
||
)
|
||
|
||
# Don't allow appending while processing
|
||
if exam.status == ExamStatus.PROCESSING:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_409_CONFLICT,
|
||
detail="Exam is currently being processed. Please wait."
|
||
)
|
||
|
||
# Validate file
|
||
await validate_upload_file(file)
|
||
|
||
# Read file content
|
||
file_content = await file.read()
|
||
|
||
# Check upload limits
|
||
await check_upload_limits(current_user.id, len(file_content), db)
|
||
|
||
# Start background parsing (will auto-deduplicate)
|
||
background_tasks.add_task(
|
||
async_parse_and_save,
|
||
exam.id,
|
||
file_content,
|
||
file.filename,
|
||
os.getenv("DATABASE_URL")
|
||
)
|
||
|
||
return ExamUploadResponse(
|
||
exam_id=exam.id,
|
||
title=exam.title,
|
||
status=ExamStatus.PROCESSING.value,
|
||
message=f"Document '{file.filename}' is being processed. Duplicates will be automatically removed."
|
||
)
|
||
|
||
|
||
@router.get("/", response_model=ExamListResponse)
|
||
async def get_user_exams(
|
||
skip: int = 0,
|
||
limit: int = 20,
|
||
current_user: User = Depends(get_current_user),
|
||
db: AsyncSession = Depends(get_db)
|
||
):
|
||
"""Get all exams for current user"""
|
||
|
||
# Get total count
|
||
result = await db.execute(
|
||
select(func.count(Exam.id)).where(Exam.user_id == current_user.id)
|
||
)
|
||
total = result.scalar()
|
||
|
||
# Get exams
|
||
result = await db.execute(
|
||
select(Exam)
|
||
.where(Exam.user_id == current_user.id)
|
||
.order_by(Exam.created_at.desc())
|
||
.offset(skip)
|
||
.limit(limit)
|
||
)
|
||
exams = result.scalars().all()
|
||
|
||
return ExamListResponse(exams=exams, total=total)
|
||
|
||
|
||
@router.get("/{exam_id}", response_model=ExamResponse)
|
||
async def get_exam_detail(
|
||
exam_id: int,
|
||
current_user: User = Depends(get_current_user),
|
||
db: AsyncSession = Depends(get_db)
|
||
):
|
||
"""Get exam details"""
|
||
|
||
result = await db.execute(
|
||
select(Exam).where(
|
||
and_(Exam.id == exam_id, Exam.user_id == current_user.id)
|
||
)
|
||
)
|
||
exam = result.scalar_one_or_none()
|
||
|
||
if not exam:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="Exam not found"
|
||
)
|
||
|
||
return exam
|
||
|
||
|
||
@router.get("/{exam_id}/progress")
|
||
async def get_exam_progress(
|
||
exam_id: int,
|
||
token: Optional[str] = None,
|
||
db: AsyncSession = Depends(get_db)
|
||
):
|
||
"""
|
||
Get real-time progress updates for exam document parsing (SSE endpoint)
|
||
|
||
Returns Server-Sent Events stream with progress updates
|
||
"""
|
||
# Authenticate using token from query parameter (EventSource doesn't support custom headers)
|
||
from services.auth_service import get_current_user_from_token
|
||
|
||
if not token:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||
detail="Token required"
|
||
)
|
||
|
||
try:
|
||
current_user = await get_current_user_from_token(token, db)
|
||
except Exception as e:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||
detail="Invalid token"
|
||
)
|
||
|
||
# Verify exam belongs to user
|
||
result = await db.execute(
|
||
select(Exam).where(
|
||
and_(Exam.id == exam_id, Exam.user_id == current_user.id)
|
||
)
|
||
)
|
||
exam = result.scalar_one_or_none()
|
||
|
||
if not exam:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="Exam not found"
|
||
)
|
||
|
||
async def event_generator():
|
||
"""Generate SSE events"""
|
||
async for update in progress_service.subscribe(exam_id):
|
||
# Format as SSE
|
||
data = json.dumps(update.to_dict())
|
||
yield f"data: {data}\n\n"
|
||
|
||
# Stop if completed or failed
|
||
if update.status in ["completed", "failed"]:
|
||
break
|
||
|
||
return StreamingResponse(
|
||
event_generator(),
|
||
media_type="text/event-stream",
|
||
headers={
|
||
"Cache-Control": "no-cache",
|
||
"Connection": "keep-alive",
|
||
"X-Accel-Buffering": "no" # Disable nginx buffering
|
||
}
|
||
)
|
||
|
||
|
||
@router.delete("/{exam_id}", status_code=status.HTTP_204_NO_CONTENT)
|
||
async def delete_exam(
|
||
exam_id: int,
|
||
current_user: User = Depends(get_current_user),
|
||
db: AsyncSession = Depends(get_db)
|
||
):
|
||
"""Delete an exam and all its questions"""
|
||
|
||
result = await db.execute(
|
||
select(Exam).where(
|
||
and_(Exam.id == exam_id, Exam.user_id == current_user.id)
|
||
)
|
||
)
|
||
exam = result.scalar_one_or_none()
|
||
|
||
if not exam:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="Exam not found"
|
||
)
|
||
|
||
await db.delete(exam)
|
||
await db.commit()
|
||
|
||
|
||
@router.put("/{exam_id}/progress", response_model=ExamResponse)
|
||
async def update_quiz_progress(
|
||
exam_id: int,
|
||
progress: QuizProgressUpdate,
|
||
current_user: User = Depends(get_current_user),
|
||
db: AsyncSession = Depends(get_db)
|
||
):
|
||
"""Update quiz progress (current_index)"""
|
||
|
||
result = await db.execute(
|
||
select(Exam).where(
|
||
and_(Exam.id == exam_id, Exam.user_id == current_user.id)
|
||
)
|
||
)
|
||
exam = result.scalar_one_or_none()
|
||
|
||
if not exam:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="Exam not found"
|
||
)
|
||
|
||
exam.current_index = progress.current_index
|
||
await db.commit()
|
||
await db.refresh(exam)
|
||
|
||
return exam
|