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>
38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
from fastapi import APIRouter, Depends, HTTPException
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from fastapi.responses import Response
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from pydantic import BaseModel
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from sqlalchemy.orm import Session
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from app.database import get_db
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from app.models.user import User
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from app.services import ai_service
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from app.utils.auth import get_current_user
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router = APIRouter()
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class TTSRequest(BaseModel):
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text: str
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@router.post("/speak")
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def text_to_speech(
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request: TTSRequest,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user),
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):
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"""Convert text to speech using configured TTS model."""
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if not request.text.strip():
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raise HTTPException(status_code=400, detail="Text cannot be empty")
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# Limit text length
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text = request.text[:2000]
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# Get TTS model config
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model_id, api_key = ai_service.get_model_for_task(db, "tts")
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audio = ai_service.generate_tts_audio(text, model_id=model_id, api_key=api_key)
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if audio is None:
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raise HTTPException(status_code=500, detail="TTS generation failed. Check model configuration.")
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return Response(content=audio, media_type="audio/mpeg")
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