b876f13fac
- FastAPI backend with JWT auth, roles (admin/moderator/user) - PDF upload (up to 500MB) with streaming, PyMuPDF text extraction - ChromaDB vectorization per page with metadata - LiteLLM AI question extraction from PDF (not generation) - Image extraction from PDF pages, graceful fallback - Quiz modes: timed (countdown timer) + learning (answers shown inline) - Page-by-page question navigation with dot navigator - TTS endpoint using LiteLLM (Google Vertex / OpenAI voices) - Admin dashboard: AI model management per task, user role management - Moderator role: upload PDFs, create sections, generate quizzes - Spaced repetition reminders via SMTP email (SM-2 intervals) - APScheduler daily reminder jobs - Celery + Redis for background PDF processing - React frontend with all pages - Docker Compose deployment (nginx + backend + celery + redis) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
60 lines
1.1 KiB
Python
60 lines
1.1 KiB
Python
from datetime import datetime
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from pydantic import BaseModel
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class AnswerSubmission(BaseModel):
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question_id: int
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user_answer: str
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class AttemptSubmit(BaseModel):
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answers: list[AnswerSubmission]
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class AnswerDetail(BaseModel):
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question_id: int
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question_text: str
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question_type: str
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user_answer: str
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correct_answer: str
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is_correct: bool
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explanation: str | None
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class Config:
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from_attributes = True
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class AttemptResponse(BaseModel):
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id: int
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quiz_id: int
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score: int
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total_questions: int
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percentage: float
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started_at: datetime
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completed_at: datetime | None
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class Config:
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from_attributes = True
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class AttemptDetail(AttemptResponse):
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answers: list[AnswerDetail] = []
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# Stats schemas
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class QuizStats(BaseModel):
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quiz_id: int
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quiz_title: str
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attempts_count: int
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best_score: float
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latest_score: float
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average_score: float
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class DashboardStats(BaseModel):
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total_documents: int
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total_quizzes: int
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total_attempts: int
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average_score: float
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quiz_stats: list[QuizStats] = []
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