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from __future__ import annotations
import asyncio
import os
import sys
import time
from dataclasses import asdict
from pathlib import Path
from typing import Any
REPO_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(REPO_ROOT))
def load_env_file(path: Path) -> None:
if not path.exists():
return
for raw_line in path.read_text(encoding="utf-8").splitlines():
line = raw_line.strip()
if not line or line.startswith("#") or "=" not in line:
continue
key, value = line.split("=", 1)
os.environ.setdefault(key.strip(), value.strip().strip('"').strip("'"))
load_env_file(REPO_ROOT / ".env.local")
load_env_file(REPO_ROOT / ".env")
from src.core.logging import configure_logging
from src.nexus_ai.core.paths import ensure_runtime_dirs
from src.nexus_ai.repositories.sqlite import JobRepository, init_sqlite
POLL_SECONDS = float(os.getenv("WORKER_POLL_SECONDS", "1.0"))
JOB_REPOSITORY = JobRepository()
_AUDIO_TRANSCRIBER: Any | None = None
def utc_now() -> str:
from datetime import datetime, timezone
return datetime.now(timezone.utc).isoformat().replace("+00:00", "Z")
def _update(job_id: str, **kwargs: Any) -> None:
JOB_REPOSITORY.update(job_id, updated_at=utc_now(), **kwargs)
def _serialize_diarization(diarization: Any) -> dict[str, Any]:
return {
"turns": [asdict(turn) for turn in diarization.turns],
"speaker_map": diarization.speaker_map,
"provider": diarization.provider,
"speaker_confidence": diarization.speaker_confidence,
"warnings": diarization.warnings,
}
def _deserialize_diarization(payload: Any) -> Any:
from src.aspect_sentiment.diarization import DiarizationResult, TranscriptTurn
if isinstance(payload, list):
turns = []
for turn in payload:
if not isinstance(turn, dict):
continue
turns.append(TranscriptTurn(
speaker=turn.get("speaker", "Unknown"),
raw_speaker=turn.get("rawSpeaker") or turn.get("speaker", "Unknown"),
text=turn.get("text", ""),
start=turn.get("start"),
end=turn.get("end"),
confidence=turn.get("confidence", 1.0),
overlap=turn.get("overlap", False),
warnings=turn.get("warnings") or [],
))
return DiarizationResult(
turns=turns,
speaker_map={},
provider="frontend-provided",
speaker_confidence={},
warnings=[],
)
if isinstance(payload, dict):
return DiarizationResult(
turns=[TranscriptTurn(**turn) for turn in payload.get("turns", [])],
speaker_map=payload.get("speaker_map") or {},
provider=payload.get("provider") or "worker-payload",
speaker_confidence=payload.get("speaker_confidence") or {},
warnings=payload.get("warnings") or [],
)
return None
def _get_audio_transcriber() -> Any:
global _AUDIO_TRANSCRIBER
if _AUDIO_TRANSCRIBER is None:
if os.getenv("USE_GROQ_WHISPER", "true").lower() == "true":
from src.aspect_sentiment.groq_audio import GroqCloudTranscriber
_AUDIO_TRANSCRIBER = GroqCloudTranscriber()
else:
from src.aspect_sentiment.audio import WhisperTranscriber
_AUDIO_TRANSCRIBER = WhisperTranscriber()
return _AUDIO_TRANSCRIBER
async def process_audio_job(job: dict[str, Any]) -> None:
from src.aspect_sentiment.diarization import diarize_audio_segments
job_id = str(job["id"])
storage_path = Path(str(job["storage_path"]))
payload = job.get("payload") or {}
_update(job_id, status="processing", progress_stage="Transcribing", progress_percent=20)
transcriber = _get_audio_transcriber()
transcription = await asyncio.to_thread(transcriber.transcribe, storage_path)
_update(job_id, status="processing", progress_stage="Speaker Diarization", progress_percent=45)
diarization = await asyncio.to_thread(diarize_audio_segments, storage_path, transcription.segments)
next_payload = {
**payload,
"text": transcription.text,
"source_name": job.get("filename") or storage_path.name,
"language": transcription.language,
"transcription_confidence": transcription.confidence,
"duration_seconds": transcription.duration_seconds,
"whisper_model": getattr(transcriber, "model_size", None),
"diarization": _serialize_diarization(diarization),
}
_update(
job_id,
status="awaiting_ml",
payload=next_payload,
progress_stage="Waiting for ML worker",
progress_percent=70,
)
async def process_ml_job(job: dict[str, Any]) -> None:
from src.services.analysis_service import analysis_service
job_id = str(job["id"])
payload = job.get("payload") or {}
source_type = str(job.get("source_type") or "text")
source_name = str(payload.get("source_name") or job.get("filename") or f"{source_type}-{job_id}")
text = str(payload.get("text") or "")
if not text.strip():
raise ValueError("Job payload is missing transcript text")
_update(job_id, status="processing", progress_stage="Feature Extraction", progress_percent=75)
started = float(payload.get("started_at") or time.perf_counter())
diarization_payload = payload.get("diarization") or payload.get("diarizedTranscript")
diarization = _deserialize_diarization(diarization_payload) if diarization_payload else None
result = await analysis_service.run_pipeline(
text,
source_name=source_name,
source_type=source_type,
started=started,
diarization=diarization,
transcription_confidence=payload.get("transcription_confidence"),
whisper_model=payload.get("whisper_model"),
language=payload.get("language"),
)
from src.services.sap_lead_service import sap_lead_service
result["sapLead"] = await sap_lead_service.create_lead_from_analysis(result)
safe_text = analysis_service.privacy_safe_csv_text(result, text)
analysis_service.append_transcript_csv(
source_name=source_name,
text=safe_text,
result=result,
language=result.get("metadata", {}).get("language"),
duration_s=payload.get("duration_seconds"),
)
analysis_service.append_transcript_sqlite(
source_name=source_name,
source_type=source_type,
text=safe_text,
result=result,
language=result.get("metadata", {}).get("language"),
duration_s=payload.get("duration_seconds"),
)
_update(
job_id,
status="completed",
result=result,
completed_at=utc_now(),
progress_stage="Completed",
progress_percent=100,
)
def claim_audio_job() -> dict[str, Any] | None:
return JOB_REPOSITORY.claim_next(
statuses=("pending",),
source_types=("audio",),
claimed_status="processing",
updated_at=utc_now(),
progress_stage="Transcribing",
progress_percent=10,
)
def claim_ml_job() -> dict[str, Any] | None:
text_job = JOB_REPOSITORY.claim_next(
statuses=("pending",),
source_types=("text",),
claimed_status="processing",
updated_at=utc_now(),
progress_stage="Feature Extraction",
progress_percent=10,
)
if text_job:
return text_job
return JOB_REPOSITORY.claim_next(
statuses=("awaiting_ml",),
source_types=("audio",),
claimed_status="processing",
updated_at=utc_now(),
progress_stage="Feature Extraction",
progress_percent=75,
)
async def run_forever(worker_type: str) -> None:
print(f"Starting {worker_type} worker with SQLite queue polling.")
while True:
job = claim_audio_job() if worker_type == "audio" else claim_ml_job()
if not job:
await asyncio.sleep(POLL_SECONDS)
continue
job_id = str(job["id"])
try:
if worker_type == "audio":
await process_audio_job(job)
else:
await process_ml_job(job)
except Exception as exc:
_update(
job_id,
status="failed",
error=str(exc),
completed_at=utc_now(),
progress_stage="Failed",
)
def main() -> None:
configure_logging()
ensure_runtime_dirs()
init_sqlite()
worker_type = os.getenv("WORKER_TYPE", "audio").lower()
if worker_type not in {"audio", "ml"}:
raise ValueError(f"Unsupported WORKER_TYPE: {worker_type}")
asyncio.run(run_forever(worker_type))
if __name__ == "__main__":
main()
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