21a844cb8a
A previous `git pull --rebase origin main` dropped 177 local commits,
losing 3400+ files across admin-v2, backend, studio-v2, website,
klausur-service, and many other services. The partial restore attempt
(660295e2) only recovered some files.
This commit restores all missing files from pre-rebase ref 98933f5e
while preserving post-rebase additions (night-scheduler, night-mode UI,
NightModeWidget dashboard integration).
Restored features include:
- AI Module Sidebar (FAB), OCR Labeling, OCR Compare
- GPU Dashboard, RAG Pipeline, Magic Help
- Klausur-Korrektur (8 files), Abitur-Archiv (5+ files)
- Companion, Zeugnisse-Crawler, Screen Flow
- Full backend, studio-v2, website, klausur-service
- All compliance SDKs, agent-core, voice-service
- CI/CD configs, documentation, scripts
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
130 lines
3.6 KiB
Python
130 lines
3.6 KiB
Python
"""
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BreakPilot Transcription Worker - Main Entry Point
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Runs as an RQ worker, processing transcription jobs from the queue.
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"""
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import os
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import sys
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import signal
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import structlog
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from redis import Redis
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from rq import Worker, Queue, Connection
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# Configure logging
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structlog.configure(
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processors=[
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structlog.stdlib.filter_by_level,
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structlog.stdlib.add_logger_name,
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structlog.stdlib.add_log_level,
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structlog.stdlib.PositionalArgumentsFormatter(),
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structlog.processors.TimeStamper(fmt="iso"),
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structlog.processors.StackInfoRenderer(),
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structlog.processors.format_exc_info,
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structlog.processors.UnicodeDecoder(),
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structlog.processors.JSONRenderer()
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],
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wrapper_class=structlog.stdlib.BoundLogger,
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context_class=dict,
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logger_factory=structlog.stdlib.LoggerFactory(),
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cache_logger_on_first_use=True,
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)
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log = structlog.get_logger(__name__)
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# Configuration
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REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379/1")
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QUEUE_NAME = os.getenv("QUEUE_NAME", "transcription")
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WORKER_NAME = os.getenv("WORKER_NAME", f"transcription-worker-{os.getpid()}")
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def setup_signal_handlers(worker: Worker):
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"""Setup graceful shutdown handlers."""
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def shutdown_handler(signum, frame):
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log.info("shutdown_signal_received", signal=signum)
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worker.request_stop(signum, frame)
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signal.signal(signal.SIGINT, shutdown_handler)
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signal.signal(signal.SIGTERM, shutdown_handler)
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def preload_models():
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"""Preload ML models to reduce first-job latency."""
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log.info("preloading_models")
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try:
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from .transcriber import WhisperTranscriber
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from .diarizer import SpeakerDiarizer
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# Initialize transcriber (downloads model if needed)
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whisper_model = os.getenv("WHISPER_MODEL", "large-v3")
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device = os.getenv("WHISPER_DEVICE", "cpu")
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compute_type = os.getenv("WHISPER_COMPUTE_TYPE", "int8")
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transcriber = WhisperTranscriber(
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model_name=whisper_model,
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device=device,
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compute_type=compute_type
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)
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log.info("whisper_model_loaded", model=whisper_model, device=device)
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# Initialize diarizer (downloads model if needed)
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pyannote_token = os.getenv("PYANNOTE_AUTH_TOKEN")
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if pyannote_token:
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diarizer = SpeakerDiarizer(auth_token=pyannote_token)
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log.info("pyannote_model_loaded")
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else:
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log.warning("pyannote_token_missing", message="Speaker diarization disabled")
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except Exception as e:
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log.error("model_preload_failed", error=str(e))
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# Don't fail startup, models will be loaded on first job
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def main():
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"""Main entry point for the worker."""
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log.info(
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"worker_starting",
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redis_url=REDIS_URL,
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queue=QUEUE_NAME,
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worker_name=WORKER_NAME
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)
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# Connect to Redis
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redis_conn = Redis.from_url(REDIS_URL)
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# Test connection
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try:
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redis_conn.ping()
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log.info("redis_connected")
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except Exception as e:
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log.error("redis_connection_failed", error=str(e))
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sys.exit(1)
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# Preload models
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preload_models()
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# Create queue
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queue = Queue(QUEUE_NAME, connection=redis_conn)
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# Create worker
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worker = Worker(
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queues=[queue],
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connection=redis_conn,
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name=WORKER_NAME
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)
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# Setup signal handlers
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setup_signal_handlers(worker)
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log.info("worker_ready", queues=[QUEUE_NAME])
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# Start processing
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with Connection(redis_conn):
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worker.work(with_scheduler=True)
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if __name__ == "__main__":
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main()
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