feat(diarization-ui): add FastAPI UI backend with sqlite storage and Ollama analysis pipeline
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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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