Focus Group Results → Audio Report

import os, requests, time
EL_KEY = os.environ["ELEVENLABS_API_KEY"]
EL_BASE = "https://api.elevenlabs.io/v1"
MV = os.environ["MAVERA_API_KEY"]
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}
os.makedirs("audio_reports", exist_ok=True)
FG_ID, VOICE_ID = "fg_abc123def456", "21m00Tcm4TlvDq8ikWAM"

# 1. Retrieve focus group results
fg = requests.get(f"{MV_BASE}/focus-groups/{FG_ID}", headers=MV_H).json()
questions, responses, personas = fg.get("questions", []), fg.get("responses", []), fg.get("personas", [])

# 2. Build response corpus
corpus = ""
for q in questions:
    corpus += f"\n## {q}\n"
    for r in [r for r in responses if r.get("question") == q]:
        name = next((p["name"] for p in personas if p["id"] == r.get("persona_id")), "Unknown")
        corpus += f"  [{name}]: {r.get('answer', '')}\n"

# 3. Synthesize briefing
synthesis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"Executive briefing writer. Convert focus group results into a 2-3 minute audio script.\n\n"
        f"FOCUS GROUP: {fg.get('name')}\nPERSONAS: {', '.join(p['name'] for p in personas)}\n\n"
        f"RESPONSES:\n{corpus[:8000]}\n\n"
        "Structure: OPENING (context), KEY FINDINGS (top 3-4 with quotes), CONSENSUS POINTS, "
        "DIVERGENCE POINTS, RECOMMENDED ACTIONS (3 ranked), CLOSING.\n"
        "Write conversationally — this will be listened to, not read.",
}).json()
briefing = synthesis.get("content", "")
words = len(briefing.split())
print(f"Briefing: {words} words (~{words * 60 // 150}s audio)")
time.sleep(1)

# 4. Convert to audio
tts = requests.post(f"{EL_BASE}/text-to-speech/{VOICE_ID}",
    headers={"xi-api-key": EL_KEY, "Content-Type": "application/json"},
    json={"text": briefing, "model_id": "eleven_multilingual_v2",
          "voice_settings": {"stability": 0.6, "similarity_boost": 0.7,
                             "style": 0.2, "use_speaker_boost": True}})
if tts.status_code == 200:
    path = f"audio_reports/fg-briefing-{FG_ID}.mp3"
    with open(path, "wb") as f: f.write(tts.content)
    print(f"Audio saved: {path} ({len(tts.content) // 1024} KB, ~{words * 60 // 150}s)")

import fs from "fs";
const EL_KEY = process.env.ELEVENLABS_API_KEY;
const EL_BASE = "https://api.elevenlabs.io/v1";
const MV = process.env.MAVERA_API_KEY;
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
fs.mkdirSync("audio_reports", { recursive: true });
const FG_ID = "fg_abc123def456", VOICE_ID = "21m00Tcm4TlvDq8ikWAM";

const fg = await fetch(`${MV_BASE}/focus-groups/${FG_ID}`, { headers: MV_H }).then(r => r.json());
const { questions = [], responses = [], personas = [] } = fg;
let corpus = "";
for (const q of questions) {
  corpus += `\n## ${q}\n`;
  for (const r of responses.filter(r => r.question === q)) {
    corpus += `  [${personas.find(p => p.id === r.persona_id)?.name || "Unknown"}]: ${r.answer}\n`;
  }
}

const synthesis = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
  body: JSON.stringify({
    message: `Executive briefing writer. 2-3 min audio script.\nFOCUS GROUP: ${fg.name}\n`
      + `PERSONAS: ${personas.map(p => p.name).join(", ")}\nRESPONSES:\n${corpus.slice(0, 8000)}\n`
      + `Structure: OPENING, KEY FINDINGS, CONSENSUS, DIVERGENCE, ACTIONS, CLOSING.`,
  }),
}).then(r => r.json());
const briefing = synthesis.content || "";

const tts = await fetch(`${EL_BASE}/text-to-speech/${VOICE_ID}`, { method: "POST",
  headers: { "xi-api-key": EL_KEY, "Content-Type": "application/json" },
  body: JSON.stringify({ text: briefing, model_id: "eleven_multilingual_v2",
    voice_settings: { stability: 0.6, similarity_boost: 0.7, style: 0.2, use_speaker_boost: true } }),
});
if (tts.ok) {
  const buf = Buffer.from(await tts.arrayBuffer());
  fs.writeFileSync(`audio_reports/fg-briefing-${FG_ID}.mp3`, buf);
  console.log(`Audio: ${Math.round(buf.length / 1024)} KB`);
}