feat(diarization-ui): add FastAPI UI backend with sqlite storage and Ollama analysis pipeline
This commit is contained in:
@@ -1 +1,3 @@
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API_BASE=http://gx10.aquantico.lan:8093
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OLLAMA_BASE_URL=http://gx10.aquantico.lan:11434
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OLLAMA_MODEL=qwen3.5:9b
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16
Dockerfile
16
Dockerfile
@@ -1,8 +1,12 @@
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FROM docker.io/library/nginx:alpine
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FROM docker.io/library/python:3.12-slim
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COPY index.html.template /usr/share/nginx/html/index.html.template
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COPY docker-entrypoint.sh /docker-entrypoint.sh
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RUN chmod +x /docker-entrypoint.sh
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ENV PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1
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EXPOSE 80
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CMD ["/docker-entrypoint.sh"]
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WORKDIR /app
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COPY requirements.txt /app/requirements.txt
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RUN pip install --no-cache-dir -r /app/requirements.txt
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COPY app.py /app/app.py
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EXPOSE 8094
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CMD ["python", "-m", "uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8094"]
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45
README.md
45
README.md
@@ -1,20 +1,39 @@
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# diarization-ui
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Separate UI container for the diarization/transcription API.
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## Run
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```bash
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docker compose up -d --build
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```
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UI will be available on `http://127.0.0.1:8094/`.
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By default it calls API at `http://diarization-api:8093`.
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Set `API_BASE` in `.env` if needed.
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Eigenes UI-Projekt (separates Repo/Container) für:
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- Upload Audio
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- Aufruf von `transcribe-diarize` API
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- Speichern in SQLite
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- LLM-Auswertung via Ollama (Qwen)
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## .env
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```env
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API_BASE=http://diarization-api:8093
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API_BASE=http://gx10.aquantico.lan:8093
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OLLAMA_BASE_URL=http://gx10.aquantico.lan:11434
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OLLAMA_MODEL=qwen3.5:9b
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```
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## Start (Docker/Compose)
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```bash
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cp .env.example .env
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docker compose up -d --build
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```
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UI: `http://127.0.0.1:8094/`
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## Podman (einzelner Container)
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```bash
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podman build -t localhost/diarization-ui:latest .
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podman rm -f diarization-ui || true
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podman run -d --name diarization-ui -p 18094:8094 \
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-e API_BASE=http://gx10.aquantico.lan:8093 \
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-e OLLAMA_BASE_URL=http://gx10.aquantico.lan:11434 \
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-e OLLAMA_MODEL=qwen3.5:9b \
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-v diarization_ui_data:/data \
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localhost/diarization-ui:latest
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```
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UI dann: `http://127.0.0.1:18094/`
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211
app.py
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211
app.py
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import json
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import os
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import sqlite3
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from datetime import datetime
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from typing import Optional
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import requests
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from fastapi import FastAPI, File, Form, HTTPException, UploadFile
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from fastapi.responses import HTMLResponse
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API_BASE = os.getenv("API_BASE", "http://gx10.aquantico.lan:8093").rstrip("/")
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OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://gx10.aquantico.lan:11434").rstrip("/")
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OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "qwen3.5:9b")
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DB_PATH = os.getenv("DB_PATH", "/data/ui.db")
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app = FastAPI(title="Diarization UI + LLM")
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def db():
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conn = sqlite3.connect(DB_PATH)
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conn.row_factory = sqlite3.Row
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return conn
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def init_db():
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os.makedirs(os.path.dirname(DB_PATH), exist_ok=True)
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with db() as c:
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c.execute(
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"""
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CREATE TABLE IF NOT EXISTS transcripts (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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created_at TEXT NOT NULL,
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filename TEXT,
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formatted_text TEXT NOT NULL,
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raw_json TEXT NOT NULL
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)
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"""
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)
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c.execute(
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"""
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CREATE TABLE IF NOT EXISTS analyses (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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transcript_id INTEGER NOT NULL,
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created_at TEXT NOT NULL,
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prompt TEXT NOT NULL,
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answer TEXT NOT NULL,
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FOREIGN KEY(transcript_id) REFERENCES transcripts(id)
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)
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"""
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)
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@app.on_event("startup")
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def startup():
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init_db()
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@app.get("/healthz")
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def healthz():
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return {
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"ok": True,
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"api_base": API_BASE,
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"ollama_base_url": OLLAMA_BASE_URL,
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"ollama_model": OLLAMA_MODEL,
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"db_path": DB_PATH,
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}
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@app.get("/", response_class=HTMLResponse)
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def index():
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return """
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<!doctype html>
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<html><head><meta charset='utf-8'><meta name='viewport' content='width=device-width, initial-scale=1'>
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<title>Diarization UI</title>
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<style>body{font-family:Arial;max-width:1100px;margin:24px auto;padding:0 12px}.row{display:flex;gap:8px;flex-wrap:wrap}button{padding:8px 12px}pre{white-space:pre-wrap;background:#111;color:#0f0;padding:10px;border-radius:8px;min-height:140px}.card{border:1px solid #ddd;border-radius:8px;padding:10px;margin:10px 0}</style>
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</head>
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<body>
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<h2>Upload -> Transcribe + Diarize -> speichern -> LLM Analyse</h2>
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<div class='row'>
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<input id='f' type='file' accept='audio/*'>
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<button onclick='processFile()'>Verarbeiten</button>
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</div>
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<p id='status'></p>
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<pre id='out'></pre>
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<h3>Analyse</h3>
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<div class='row'>
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<input id='tid' type='number' placeholder='transcript_id'>
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<input id='prompt' style='width:500px' placeholder='z.B. Fasse zusammen und extrahiere Aufgaben mit Verantwortlichen.'>
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<button onclick='analyze()'>Mit Qwen analysieren</button>
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</div>
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<pre id='analysis'></pre>
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<h3>Gespeicherte Transkripte</h3>
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<button onclick='loadTranscripts()'>Neu laden</button>
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<div id='list'></div>
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<script>
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async function processFile(){
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const fi=document.getElementById('f');
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if(!fi.files.length){alert('Datei wählen');return;}
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const fd=new FormData(); fd.append('file',fi.files[0]);
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document.getElementById('status').textContent='Läuft...';
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const r=await fetch('/process',{method:'POST',body:fd});
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const j=await r.json();
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document.getElementById('status').textContent = r.ok ? `OK transcript_id=${j.transcript_id}` : `Fehler ${r.status}`;
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document.getElementById('out').textContent = JSON.stringify(j,null,2);
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if(j.transcript_id){document.getElementById('tid').value=j.transcript_id;}
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loadTranscripts();
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}
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async function analyze(){
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const transcript_id=parseInt(document.getElementById('tid').value||'0');
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const prompt=document.getElementById('prompt').value;
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const r=await fetch('/analyze',{method:'POST',headers:{'Content-Type':'application/x-www-form-urlencoded'},body:new URLSearchParams({transcript_id,prompt})});
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const j=await r.json();
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document.getElementById('analysis').textContent = JSON.stringify(j,null,2);
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}
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async function loadTranscripts(){
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const r=await fetch('/transcripts');
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const j=await r.json();
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const root=document.getElementById('list');
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root.innerHTML='';
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for(const t of j.items){
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const d=document.createElement('div'); d.className='card';
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d.innerHTML=`<b>#${t.id}</b> ${t.created_at} ${t.filename||''}<br><pre>${(t.formatted_text||'').slice(0,1200)}</pre>`;
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root.appendChild(d);
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}
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}
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loadTranscripts();
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</script>
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</body></html>
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"""
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@app.post("/process")
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async def process(file: UploadFile = File(...)):
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data = await file.read()
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if not data:
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raise HTTPException(400, "empty file")
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files = {"file": (file.filename or "audio.bin", data, file.content_type or "application/octet-stream")}
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try:
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r = requests.post(f"{API_BASE}/transcribe-diarize", files=files, timeout=1800)
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except Exception as e:
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raise HTTPException(502, f"API unreachable: {e}")
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if r.status_code >= 400:
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raise HTTPException(r.status_code, r.text)
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payload = r.json()
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formatted = payload.get("formatted_text", "")
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with db() as c:
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cur = c.execute(
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"INSERT INTO transcripts(created_at, filename, formatted_text, raw_json) VALUES (?,?,?,?)",
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(datetime.utcnow().isoformat(), file.filename, formatted, json.dumps(payload, ensure_ascii=False)),
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)
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transcript_id = cur.lastrowid
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return {"ok": True, "transcript_id": transcript_id, **payload}
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@app.get("/transcripts")
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def transcripts(limit: int = 20):
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with db() as c:
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rows = c.execute(
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"SELECT id, created_at, filename, formatted_text FROM transcripts ORDER BY id DESC LIMIT ?",
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(limit,),
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).fetchall()
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return {"items": [dict(r) for r in rows]}
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@app.post("/analyze")
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def analyze(transcript_id: int = Form(...), prompt: str = Form(...)):
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with db() as c:
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row = c.execute("SELECT formatted_text FROM transcripts WHERE id=?", (transcript_id,)).fetchone()
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if not row:
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raise HTTPException(404, "transcript not found")
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transcript_text = row[0]
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llm_prompt = (
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"Du bist ein Meeting-Analyst. Arbeite auf Deutsch.\n"
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"Erzeuge präzise Ausgabe für den folgenden Auftrag.\n\n"
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f"AUFTRAG:\n{prompt}\n\n"
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f"TRANSKRIPT:\n{transcript_text}\n"
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)
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body = {
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"model": OLLAMA_MODEL,
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"prompt": llm_prompt,
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"stream": False,
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}
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try:
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r = requests.post(f"{OLLAMA_BASE_URL}/api/generate", json=body, timeout=600)
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except Exception as e:
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raise HTTPException(502, f"Ollama unreachable: {e}")
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if r.status_code >= 400:
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raise HTTPException(r.status_code, r.text)
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j = r.json()
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answer = j.get("response", "")
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with db() as c:
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cur = c.execute(
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"INSERT INTO analyses(transcript_id, created_at, prompt, answer) VALUES (?,?,?,?)",
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(transcript_id, datetime.utcnow().isoformat(), prompt, answer),
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)
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analysis_id = cur.lastrowid
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return {"ok": True, "analysis_id": analysis_id, "answer": answer}
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@@ -1,11 +1,19 @@
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services:
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diarization-ui:
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build:
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context: ./web-ui
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context: .
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dockerfile: Dockerfile
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container_name: diarization-ui
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restart: unless-stopped
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ports:
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- "8094:80"
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- "8094:8094"
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environment:
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- API_BASE=${API_BASE:-http://gx10.aquantico.lan:8093}
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- OLLAMA_BASE_URL=${OLLAMA_BASE_URL:-http://gx10.aquantico.lan:11434}
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- OLLAMA_MODEL=${OLLAMA_MODEL:-qwen3.5:9b}
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- DB_PATH=/data/ui.db
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volumes:
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- diarization_ui_data:/data
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volumes:
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diarization_ui_data:
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@@ -1,7 +0,0 @@
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#!/usr/bin/env sh
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set -eu
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API_BASE="${API_BASE:-http://diarization-api:8093}"
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sed "s|__API_BASE__|${API_BASE}|g" /usr/share/nginx/html/index.html.template > /usr/share/nginx/html/index.html
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exec nginx -g 'daemon off;'
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@@ -1,50 +0,0 @@
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<!doctype html>
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<html>
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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<title>Diarization + Whisper UI</title>
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<style>
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body { font-family: Arial, sans-serif; max-width: 980px; margin: 24px auto; padding: 0 12px; }
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.row { display:flex; gap:8px; align-items:center; flex-wrap:wrap; }
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input[type=file] { padding:6px; }
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button { padding:8px 14px; cursor:pointer; }
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pre { white-space: pre-wrap; background:#111; color:#0f0; padding:12px; min-height:220px; border-radius:8px; }
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.muted { color:#666; font-size:13px; }
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</style>
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</head>
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<body>
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<h2>Whisper + Sprechertrennung</h2>
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<p class="muted">API: <span id="api"></span></p>
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<div class="row">
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<input id="f" type="file" accept="audio/*" />
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<button onclick="go()">Verarbeiten</button>
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</div>
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<pre id="out"></pre>
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<script>
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const API_BASE = "__API_BASE__";
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document.getElementById("api").textContent = API_BASE;
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async function go() {
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const fi = document.getElementById('f');
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if (!fi.files.length) { alert('Bitte Datei wählen'); return; }
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const fd = new FormData();
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fd.append('file', fi.files[0]);
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const out = document.getElementById('out');
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out.textContent = 'Läuft...';
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try {
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const r = await fetch(`${API_BASE}/transcribe-diarize`, { method: 'POST', body: fd });
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const j = await r.json();
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if (!r.ok) {
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out.textContent = `Fehler (${r.status}):\n` + JSON.stringify(j, null, 2);
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return;
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}
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out.textContent = j.formatted_text || JSON.stringify(j, null, 2);
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} catch (e) {
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out.textContent = `Netzwerkfehler: ${e}`;
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}
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}
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</script>
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</body>
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</html>
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4
requirements.txt
Normal file
4
requirements.txt
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fastapi==0.115.6
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uvicorn[standard]==0.32.1
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requests==2.32.3
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python-multipart==0.0.12
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Reference in New Issue
Block a user