mirror of
https://github.com/Monadical-SAS/reflector.git
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67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
#!/usr/bin/env python
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"""
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Test multitrack processing with correct S3 bucket configuration
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"""
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import asyncio
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import uuid
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from reflector.db import get_database
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from reflector.db.transcripts import Transcript, transcripts
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from reflector.pipelines.main_multitrack_pipeline import (
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task_pipeline_multitrack_process,
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)
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async def create_and_process():
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"""Create a new transcript and process with correct S3 bucket"""
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# Correct S3 configuration
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bucket_name = "reflector-dailyco-local"
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track_keys = [
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"monadical/daily-20251020193458/1760988935484-52f7f48b-fbab-431f-9a50-87b9abfc8255-cam-audio-1760988935922",
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"monadical/daily-20251020193458/1760988935484-a37c35e3-6f8e-4274-a482-e9d0f102a732-cam-audio-1760988943823",
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]
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# Create a new transcript
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db = get_database()
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await db.connect()
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try:
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transcript_id = str(uuid.uuid4())
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transcript = Transcript(
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id=transcript_id,
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name="Daily Multitrack - Correct S3 Bucket Test",
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source_kind="file",
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source_language="en",
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target_language="en",
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status="idle",
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events=[],
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title="",
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)
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query = transcripts.insert().values(**transcript.model_dump())
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await db.execute(query)
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print(f"Created transcript: {transcript_id}")
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# Trigger processing with Celery
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result = task_pipeline_multitrack_process.delay(
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transcript_id=transcript_id, bucket_name=bucket_name, track_keys=track_keys
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)
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print(f"Task ID: {result.id}")
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print(
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f"Processing started! Check: http://localhost:3000/transcripts/{transcript_id}"
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)
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print(f"API Status: http://localhost:1250/v1/transcripts/{transcript_id}")
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return transcript_id
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finally:
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await db.disconnect()
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if __name__ == "__main__":
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transcript_id = asyncio.run(create_and_process())
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print(f"\n✅ Task submitted successfully!")
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print(f"📍 Transcript ID: {transcript_id}")
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