Floke
4e52e194f1
- Added FastAPI backend with FFmpeg and Gemini 2.0 integration - Added React frontend with upload and meeting list - Integrated into main docker-compose stack and dashboard
59 lines
2.0 KiB
Python
59 lines
2.0 KiB
Python
import os
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import time
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import logging
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from google import genai
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from google.genai import types
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from ..config import settings
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logger = logging.getLogger(__name__)
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class TranscriptionService:
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def __init__(self):
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if not settings.GEMINI_API_KEY:
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raise Exception("Gemini API Key missing.")
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self.client = genai.Client(api_key=settings.GEMINI_API_KEY)
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def transcribe_chunk(self, file_path: str, offset_seconds: int = 0) -> dict:
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"""
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Uploads a chunk to Gemini and returns the transcription with timestamps.
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"""
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logger.info(f"Uploading chunk {file_path} to Gemini...")
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# 1. Upload file
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media_file = self.client.files.upload(path=file_path)
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# 2. Wait for processing (usually fast for audio)
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while media_file.state == "PROCESSING":
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time.sleep(2)
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media_file = self.client.files.get(name=media_file.name)
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if media_file.state == "FAILED":
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raise Exception("File processing failed at Gemini.")
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# 3. Transcribe with Diarization and Timestamps
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prompt = """
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Transkribiere dieses Audio wortgetreu.
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Identifiziere die Sprecher (Sprecher A, Sprecher B, etc.).
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Gib das Ergebnis als strukturierte Liste mit Timestamps aus.
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Wichtig: Das Audio ist ein Teil eines größeren Gesprächs.
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Antworte NUR mit dem Transkript im Format:
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[MM:SS] Sprecher X: Text
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"""
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logger.info(f"Generating transcription for {file_path}...")
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response = self.client.models.generate_content(
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model="gemini-2.0-flash",
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contents=[media_file, prompt],
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config=types.GenerateContentConfig(
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temperature=0.1, # Low temp for accuracy
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)
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)
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# Cleanup: Delete file from Gemini storage
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self.client.files.delete(name=media_file.name)
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return {
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"raw_text": response.text,
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"offset": offset_seconds
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}
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