Pre-Publish Creative Testing

import os, requests, time

VM = os.environ["VIMEO_ACCESS_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
VM_BASE = "https://api.vimeo.com"
MV_BASE = "https://app.mavera.io/api/v1"
VM_H = {"Authorization": f"Bearer {VM}", "Accept": "application/vnd.vimeo.*+json;version=3.4"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

DRAFT_VIDEO_ID = "123456789"
THRESHOLDS = {
    "message_clarity": 70,
    "emotional_impact": 65,
    "hook_score": 60,
    "behavioral_effectiveness": 60,
}

# 1. Fetch the draft video from Vimeo
draft = requests.get(f"{VM_BASE}/videos/{DRAFT_VIDEO_ID}", headers=VM_H, params={
    "fields": "uri,name,link,duration,privacy,status,pictures.sizes",
}).json()

if "error" in draft:
    raise SystemExit(f"Vimeo API error: {draft.get('developer_message', draft.get('error', ''))}")

print(f"Draft: \"{draft['name']}\" | Duration: {draft.get('duration', 0)}s | Privacy: {draft.get('privacy', {}).get('view', 'unknown')}")

# 2. Upload to Mavera and run Video Analysis
upload = requests.post(f"{MV_BASE}/assets", headers=MV_H, json={
    "url": draft["link"], "name": f"[DRAFT] {draft['name'][:70]}", "type": "video",
}).json()

analysis = requests.post(f"{MV_BASE}/video-analysis", headers=MV_H, json={
    "asset_id": upload["id"],
    "analysis_types": list(THRESHOLDS.keys()) + ["pacing", "cognitive_load", "emotional_arc"],
    "metadata": {"vimeo_id": DRAFT_VIDEO_ID, "stage": "pre-publish"},
}).json()

# 3. Poll for results
for _ in range(30):
    time.sleep(3)
    status = requests.get(
        f"{MV_BASE}/video-analysis/{analysis['id']}", headers=MV_H
    ).json()
    if status.get("status") == "completed":
        break

results = status.get("results", {})
scores = {
    metric: results.get(metric, {}).get("score", 0)
    for metric in THRESHOLDS
}

# 4. Check thresholds
failures = {
    metric: {"score": scores[metric], "threshold": threshold}
    for metric, threshold in THRESHOLDS.items()
    if scores[metric] < threshold
}

print(f"\nSCORECARD:")
for metric, threshold in THRESHOLDS.items():
    score = scores[metric]
    verdict = "PASS" if score >= threshold else "FAIL"
    print(f"  {metric:<30} {score:>3}/100  (threshold: {threshold})  [{verdict}]")

if not failures:
    print("\n✓ ALL THRESHOLDS MET — Ready to publish")
else:
    print(f"\n✗ {len(failures)} THRESHOLD(S) FAILED — Triggering Focus Group review")

    # 5. Create Focus Group for improvement feedback
    persona_ids = []
    for name, desc in [
        ("Target Customer", "35-year-old marketing director evaluating SaaS tools. Watches product videos to assess vendor quality. Judges professionalism, clarity, and whether the video respects their time."),
        ("Creative Director", "Senior creative with 15 years in video production. Evaluates pacing, visual storytelling, emotional arc, and hook effectiveness. Gives specific, actionable feedback."),
        ("Skeptical Buyer", "CFO who dislikes marketing fluff. Wants data, proof points, and clear ROI messaging. Will disengage immediately if the video feels like a hard sell."),
    ]:
        p = requests.post(f"{MV_BASE}/personas", headers=MV_H, json={
            "name": name, "description": desc,
        }).json()
        persona_ids.append(p["id"])
        time.sleep(0.3)

    failure_summary = "\n".join(
        f"- {m}: scored {f['score']}/100 (needs {f['threshold']}+)"
        for m, f in failures.items()
    )
    arc_summary = results.get("emotional_arc", {}).get("summary", "No arc data available")

    fg = requests.post(f"{MV_BASE}/focus-groups", headers=MV_H, json={
        "name": f"Pre-Publish Review — {draft['name'][:40]}",
        "persona_ids": persona_ids,
        "questions": [
            f"This video failed quality thresholds:\n{failure_summary}\n\nEmotional arc: {arc_summary}\n\nWhat specifically could make this video more compelling?",
            "If you could change only ONE thing about the first 5 seconds, what would it be?",
            "What is this video trying to make you feel? Is it working? What emotion is missing?",
            "Would you share this video with a colleague? Why or why not? What would change your answer?",
        ],
        "responses_per_persona": 2,
    }).json()

    for _ in range(20):
        time.sleep(5)
        fg_data = requests.get(f"{MV_BASE}/focus-groups/{fg['id']}", headers=MV_H).json()
        if fg_data.get("status") == "completed":
            break

    print("\nFOCUS GROUP FEEDBACK:")
    for resp in fg_data.get("responses", []):
        print(f"\n[{resp.get('persona_name', '?')}] {resp.get('question', '')[:60]}...")
        print(f"  → {resp.get('answer', '')[:400]}")

const VM = process.env.VIMEO_ACCESS_TOKEN;
const MV = process.env.MAVERA_API_KEY;
const VM_BASE = "https://api.vimeo.com";
const MV_BASE = "https://app.mavera.io/api/v1";
const VM_H = { Authorization: `Bearer ${VM}`, Accept: "application/vnd.vimeo.*+json;version=3.4" };
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const DRAFT_VIDEO_ID = "123456789";
const THRESHOLDS = {
  message_clarity: 70, emotional_impact: 65,
  hook_score: 60, behavioral_effectiveness: 60,
};

// 1. Fetch draft
const draft = await fetch(
  `${VM_BASE}/videos/${DRAFT_VIDEO_ID}?fields=uri,name,link,duration,privacy,status`,
  { headers: VM_H }
).then(r => r.json());

if (draft.error) throw new Error(draft.developer_message || draft.error);
console.log(`Draft: "${draft.name}" | ${draft.duration}s | ${draft.privacy?.view}`);

// 2. Mavera analysis
const upload = await fetch(`${MV_BASE}/assets`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({ url: draft.link, name: `[DRAFT] ${draft.name.slice(0, 70)}`, type: "video" }),
}).then(r => r.json());

const analysis = await fetch(`${MV_BASE}/video-analysis`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    asset_id: upload.id,
    analysis_types: [...Object.keys(THRESHOLDS), "pacing", "cognitive_load", "emotional_arc"],
    metadata: { vimeo_id: DRAFT_VIDEO_ID, stage: "pre-publish" },
  }),
}).then(r => r.json());

// 3. Poll
let status;
for (let i = 0; i < 30; i++) {
  await new Promise(r => setTimeout(r, 3000));
  status = await fetch(
    `${MV_BASE}/video-analysis/${analysis.id}`, { headers: MV_H }
  ).then(r => r.json());
  if (status.status === "completed") break;
}

const results = status.results || {};
const scores = Object.fromEntries(
  Object.keys(THRESHOLDS).map(m => [m, results[m]?.score || 0])
);

// 4. Threshold check
const failures = {};
for (const [metric, threshold] of Object.entries(THRESHOLDS)) {
  if (scores[metric] < threshold) {
    failures[metric] = { score: scores[metric], threshold };
  }
}

console.log("\nSCORECARD:");
for (const [metric, threshold] of Object.entries(THRESHOLDS)) {
  const verdict = scores[metric] >= threshold ? "PASS" : "FAIL";
  console.log(`  ${metric.padEnd(30)} ${String(scores[metric]).padStart(3)}/100  (threshold: ${threshold})  [${verdict}]`);
}

if (Object.keys(failures).length === 0) {
  console.log("\nALL THRESHOLDS MET — Ready to publish");
} else {
  console.log(`\n${Object.keys(failures).length} THRESHOLD(S) FAILED — Triggering Focus Group`);

  // 5. Focus Group
  const personaIds = [];
  for (const [name, desc] of [
    ["Target Customer", "35yo marketing director evaluating SaaS. Judges professionalism, clarity, time-respect."],
    ["Creative Director", "15yr video production veteran. Evaluates pacing, storytelling, arc, hooks. Gives specific fixes."],
    ["Skeptical Buyer", "CFO who hates fluff. Wants data, proof, ROI. Disengages at hard sells."],
  ]) {
    const p = await fetch(`${MV_BASE}/personas`, {
      method: "POST", headers: MV_H,
      body: JSON.stringify({ name, description: desc }),
    }).then(r => r.json());
    personaIds.push(p.id);
    await new Promise(r => setTimeout(r, 300));
  }

  const failureSummary = Object.entries(failures)
    .map(([m, f]) => `- ${m}: scored ${f.score}/100 (needs ${f.threshold}+)`).join("\n");
  const arcSummary = results.emotional_arc?.summary || "No arc data";

  const fg = await fetch(`${MV_BASE}/focus-groups`, {
    method: "POST", headers: MV_H,
    body: JSON.stringify({
      name: `Pre-Publish Review — ${draft.name.slice(0, 40)}`,
      persona_ids: personaIds,
      questions: [
        `Failed thresholds:\n${failureSummary}\n\nArc: ${arcSummary}\n\nWhat could make this more compelling?`,
        "Change ONE thing about the first 5 seconds — what?",
        "What is this video trying to make you feel? Is it working?",
        "Would you share this with a colleague? What would change your answer?",
      ],
      responses_per_persona: 2,
    }),
  }).then(r => r.json());

  let fgData;
  for (let i = 0; i < 20; i++) {
    await new Promise(r => setTimeout(r, 5000));
    fgData = await fetch(`${MV_BASE}/focus-groups/${fg.id}`, { headers: MV_H }).then(r => r.json());
    if (fgData.status === "completed") break;
  }

  console.log("\nFOCUS GROUP FEEDBACK:");
  for (const resp of fgData.responses || []) {
    console.log(`\n[${resp.persona_name || "?"}] ${(resp.question || "").slice(0, 60)}...`);
    console.log(`  → ${(resp.answer || "").slice(0, 400)}`);
  }
}