poturaevpetr
265 lines
8.8 KiB
Python
265 lines
8.8 KiB
Python
from fastapi import APIRouter, Depends, HTTPException, BackgroundTasks, status
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from sqlalchemy.orm import Session
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from typing import Dict, Any
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import uuid
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import os
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import asyncio
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from apiApp.database import get_db, Audio
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from apiApp.schemas import (
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RecognitionStartResponse,
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RecognitionStatus,
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ErrorResponse
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)
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from apiApp.services import AudioCRUD, AiConclusionCRUD
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from apiApp.config import UPLOAD_FOLDER
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router = APIRouter()
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# Глобальное хранилище статусов задач (в продакшене лучше использовать Redis)
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recognition_tasks: Dict[str, Dict[str, Any]] = {}
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async def process_recognition(audio_id: uuid.UUID, file_path: str, task_id: str):
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"""
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Фоновая задача для распознавания аудио
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"""
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try:
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# Обновляем статус на processing
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recognition_tasks[task_id] = {
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'audio_id': audio_id,
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'status': 'processing',
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'result': None,
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'error': None
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}
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# Проверяем существование файла
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if not os.path.exists(file_path):
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recognition_tasks[task_id]['status'] = 'error'
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recognition_tasks[task_id]['error'] = 'File not found on disk'
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return
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# Здесь должна быть реальная логика распознавания
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# Например, вызов внешнего API или локальной модели
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# result = await your_recognize_function(file_path)
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# Симуляция обработки (в реальном коде убрать)
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await asyncio.sleep(2)
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# Пример результата (заменить на реальный)
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result = {
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'text': 'Распознанный текст из аудио',
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'confidence': 0.95,
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'duration': 120.5,
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'segments': [
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{
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'start': 0.0,
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'end': 5.2,
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'text': 'Привет, чем могу помочь?',
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'speaker': 'SPEAKER_00'
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},
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{
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'start': 5.5,
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'end': 10.8,
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'text': 'Мне нужна информация о услугах',
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'speaker': 'SPEAKER_01'
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}
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]
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}
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# Обновляем статус на completed
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recognition_tasks[task_id]['status'] = 'completed'
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recognition_tasks[task_id]['result'] = result
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# Получаем сессию БД (для фоновой задачи)
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from apiApp.database import SessionLocal
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db = SessionLocal()
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try:
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# Создаем или обновляем AI Conclusion
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conclusion = AiConclusionCRUD.get_by_audio_id(db, audio_id)
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if conclusion:
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AiConclusionCRUD.update(
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db=db,
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conclusion_id=conclusion.id,
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conclusion_data={
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"transcription": result.get('segments', []),
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"ai_transcription": [result.get('text', '')],
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"conclusion": {
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"confidence": result.get('confidence', 0.0),
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"duration": result.get('duration', 0.0)
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}
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},
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end_date=True
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)
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else:
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AiConclusionCRUD.create(
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db=db,
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audio_id=audio_id,
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conclusion={
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"transcription": result.get('segments', []),
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"ai_transcription": [result.get('text', '')],
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"conclusion": {
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"confidence": result.get('confidence', 0.0),
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"duration": result.get('duration', 0.0)
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}
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}
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)
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# Обновляем запись аудио
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AudioCRUD.update_recognition_result(db, audio_id, result)
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finally:
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db.close()
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except Exception as e:
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recognition_tasks[task_id]['status'] = 'error'
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recognition_tasks[task_id]['error'] = str(e)
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@router.post("/recognize/{audio_id}", response_model=RecognitionStartResponse, status_code=status.HTTP_202_ACCEPTED)
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async def start_recognition(
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audio_id: uuid.UUID,
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background_tasks: BackgroundTasks,
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db: Session = Depends(get_db)
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):
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"""
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Запуск распознавания аудиофайла
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"""
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# Проверяем существование аудио
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audio = AudioCRUD.get_by_id(db, audio_id)
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if not audio:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="Audio not found in database"
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)
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# Проверяем, нет ли уже активной задачи
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for task_id, task in recognition_tasks.items():
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if task['audio_id'] == audio_id and task['status'] == 'processing':
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return RecognitionStartResponse(
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status="info",
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message="Recognition already in progress",
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task_id=task_id,
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audio_id=audio_id
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)
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# Проверяем наличие файла
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if not audio.file_path or not os.path.exists(audio.file_path):
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="Audio file not found on disk"
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)
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# Создаем task_id
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task_id = str(uuid.uuid4())
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# Добавляем фоновую задачу
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background_tasks.add_task(process_recognition, audio_id, audio.file_path, task_id)
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return RecognitionStartResponse(
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status="success",
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message="Recognition started",
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task_id=task_id,
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audio_id=audio_id
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)
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@router.get("/recognize/{audio_id}", response_model=RecognitionStatus)
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async def get_recognition_status(
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audio_id: uuid.UUID,
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db: Session = Depends(get_db)
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):
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"""
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Получение статуса распознавания по audio_id
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"""
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# Проверяем существование аудио
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audio = AudioCRUD.get_by_id(db, audio_id)
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if not audio:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="Audio not found"
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)
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# Ищем задачу для данного audio_id
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task_info = None
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for task_id, task in recognition_tasks.items():
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if task['audio_id'] == audio_id:
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task_info = {
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'task_id': task_id,
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**task
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}
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break
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if not task_info:
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# Проверяем, есть ли сохраненный результат
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conclusion = AiConclusionCRUD.get_by_audio_id(db, audio_id)
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if conclusion and conclusion.end_date:
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return RecognitionStatus(
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task_id="",
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audio_id=audio_id,
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status="completed",
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result=conclusion.conclusion
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)
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return RecognitionStatus(
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task_id="",
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audio_id=audio_id,
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status="not_started"
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)
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return RecognitionStatus(**task_info)
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@router.get("/recognize/task/{task_id}", response_model=RecognitionStatus)
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async def get_recognition_task(task_id: str):
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"""
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Получение статуса задачи по task_id
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"""
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if task_id not in recognition_tasks:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="Task not found"
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)
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task = recognition_tasks[task_id]
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return RecognitionStatus(
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task_id=task_id,
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audio_id=task['audio_id'],
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status=task['status'],
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result=task.get('result'),
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error=task.get('error')
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)
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@router.get("/recognize/{audio_id}/result")
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async def get_recognition_result(
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audio_id: uuid.UUID,
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db: Session = Depends(get_db)
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):
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"""
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Получение результата распознавания из базы данных
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"""
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audio = AudioCRUD.get_by_id(db, audio_id)
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if not audio:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="Audio not found"
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)
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conclusion = AiConclusionCRUD.get_by_audio_id(db, audio_id)
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if not conclusion or not conclusion.end_date:
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return {
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"status": "not_available",
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"message": "Recognition result not available yet",
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"audio_id": str(audio_id)
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}
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return {
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"status": "success",
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"audio_id": str(audio_id),
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"result": conclusion.conclusion,
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"index_date": conclusion.index_date,
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"end_date": conclusion.end_date
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}
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