Cross-Creative Performance Leaderboard

import os, requests, time, tempfile

TT = os.environ["TIKTOK_ACCESS_TOKEN"]
ADV = os.environ["TIKTOK_ADVERTISER_ID"]
MV = os.environ["MAVERA_API_KEY"]
TT_BASE = "https://business-api.tiktok.com/open_api/v1.3"
MV_BASE = "https://app.mavera.io/api/v1"
TT_H = {"Access-Token": TT}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

# 1. Pull integrated report — creative-level performance
report = requests.post(f"{TT_BASE}/reports/integrated/get/",
    headers=TT_H,
    json={
        "advertiser_id": ADV,
        "report_type": "BASIC",
        "data_level": "AUCTION_AD",
        "dimensions": ["ad_id"],
        "metrics": ["ad_name", "impressions", "clicks", "ctr", "conversion", "cost_per_conversion",
                     "spend", "video_play_actions", "video_watched_2s", "video_watched_6s",
                     "average_video_play_per_user"],
        "start_date": "2025-01-01",
        "end_date": "2025-12-31",
        "page_size": 50,
        "page": 1,
    }).json()

if report.get("code") != 0:
    raise SystemExit(f"TikTok report error: {report.get('message')}")

rows = report.get("data", {}).get("list", [])

# 2. For each creative, fetch video and run analysis
leaderboard = []
for row in rows[:15]:
    metrics = row.get("metrics", {})
    dims = row.get("dimensions", {})
    ad_id = dims.get("ad_id", "")
    impressions = int(metrics.get("impressions", 0))
    if impressions < 500:
        continue

    # Fetch ad details for video URL
    ad_detail = requests.get(f"{TT_BASE}/ad/get/",
        headers=TT_H,
        params={"advertiser_id": ADV, "filtering": f'{{"ad_ids": ["{ad_id}"]}}',
                "fields": '["video_id", "ad_name"]'}).json()
    ad_info = (ad_detail.get("data", {}).get("list") or [{}])[0]
    video_id = ad_info.get("video_id")
    if not video_id:
        continue

    vid_info = requests.get(f"{TT_BASE}/file/video/ad/info/",
        headers=TT_H,
        params={"advertiser_id": ADV, "video_ids": f'["{video_id}"]'}).json()
    video_url = (vid_info.get("data", {}).get("list") or [{}])[0].get("video_url", "")
    if not video_url:
        continue

    vid_bytes = requests.get(video_url).content
    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp:
        tmp.write(vid_bytes); tmp_path = tmp.name

    upload = requests.post(f"{MV_BASE}/assets",
        headers={"Authorization": f"Bearer {MV}"},
        files={"file": (f"ad_{ad_id}.mp4", open(tmp_path, "rb"), "video/mp4")}).json()

    analysis = requests.post(f"{MV_BASE}/video-analysis", headers=MV_H, json={
        "asset_id": upload["id"],
        "analysis_types": ["hook_score", "emotional_arc", "pacing", "cognitive_load"],
    }).json()

    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", {})
    hook = results.get("hook_score", {}).get("score", 0)
    emotion = results.get("emotional_arc", {}).get("intensity_avg", 0)
    ctr = float(metrics.get("ctr", 0))
    behavioral_score = (hook * 0.4 + emotion * 6 * 0.3 + (10 - results.get("cognitive_load", {}).get("average", 5)) * 10 * 0.3)

    leaderboard.append({
        "ad_id": ad_id,
        "ad_name": metrics.get("ad_name", ad_info.get("ad_name", "")),
        "impressions": impressions,
        "ctr": ctr, "spend": float(metrics.get("spend", 0)),
        "hook_score": hook, "emotion_avg": emotion,
        "behavioral_score": round(behavioral_score, 1),
        "verdict": "SCALE" if behavioral_score > 60 and ctr > 1.5 else
                   "TEST" if behavioral_score > 60 else
                   "KILL" if behavioral_score < 40 and ctr < 1.0 else "HOLD",
    })
    os.unlink(tmp_path)
    time.sleep(1)

# 3. Print leaderboard
leaderboard.sort(key=lambda x: -x["behavioral_score"])
print(f"{'Rank':<5} {'Ad Name':<30} {'Hook':<6} {'Behavioral':<12} {'CTR':<8} {'Spend':<10} {'Verdict'}")
print("-" * 85)
for i, lb in enumerate(leaderboard, 1):
    print(f"{i:<5} {lb['ad_name'][:28]:<30} {lb['hook_score']:<6} {lb['behavioral_score']:<12} {lb['ctr']:.2f}%{'':<4} ${lb['spend']:>8,.0f}  {lb['verdict']}")

const TT = process.env.TIKTOK_ACCESS_TOKEN;
const ADV = process.env.TIKTOK_ADVERTISER_ID;
const MV = process.env.MAVERA_API_KEY;
const TT_BASE = "https://business-api.tiktok.com/open_api/v1.3";
const MV_BASE = "https://app.mavera.io/api/v1";
const TT_H = { "Access-Token": TT };
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

// 1. Pull integrated report
const report = await fetch(`${TT_BASE}/reports/integrated/get/`, {
  method: "POST", headers: { ...TT_H, "Content-Type": "application/json" },
  body: JSON.stringify({
    advertiser_id: ADV, report_type: "BASIC", data_level: "AUCTION_AD",
    dimensions: ["ad_id"],
    metrics: ["ad_name", "impressions", "clicks", "ctr", "conversion",
              "cost_per_conversion", "spend", "video_play_actions",
              "video_watched_2s", "video_watched_6s", "average_video_play_per_user"],
    start_date: "2025-01-01", end_date: "2025-12-31", page_size: 50, page: 1,
  }),
}).then(r => r.json());

if (report.code !== 0) throw new Error(`TikTok: ${report.message}`);
const rows = report.data?.list || [];

// 2. Analyze each creative
const leaderboard = [];
for (const row of rows.slice(0, 15)) {
  const metrics = row.metrics || {};
  const adId = row.dimensions?.ad_id || "";
  const impressions = parseInt(metrics.impressions || "0", 10);
  if (impressions < 500) continue;

  const adDetail = await fetch(
    `${TT_BASE}/ad/get/?advertiser_id=${ADV}&filtering=${encodeURIComponent(`{"ad_ids":["${adId}"]}`)}` +
    `&fields=${encodeURIComponent('["video_id","ad_name"]')}`,
    { headers: TT_H }
  ).then(r => r.json());
  const videoId = adDetail.data?.list?.[0]?.video_id;
  if (!videoId) continue;

  const vidInfo = await fetch(
    `${TT_BASE}/file/video/ad/info/?advertiser_id=${ADV}&video_ids=${encodeURIComponent(`["${videoId}"]`)}`,
    { headers: TT_H }
  ).then(r => r.json());
  const videoUrl = vidInfo.data?.list?.[0]?.video_url;
  if (!videoUrl) continue;

  const vidBytes = await fetch(videoUrl).then(r => r.arrayBuffer());
  const formData = new FormData();
  formData.append("file", new Blob([vidBytes], { type: "video/mp4" }), `ad_${adId}.mp4`);
  const upload = await fetch(`${MV_BASE}/assets`, {
    method: "POST", headers: { Authorization: `Bearer ${MV}` }, body: formData,
  }).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: ["hook_score", "emotional_arc", "pacing", "cognitive_load"],
    }),
  }).then(r => r.json());

  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 hook = results.hook_score?.score || 0;
  const emotion = results.emotional_arc?.intensity_avg || 0;
  const cogLoad = results.cognitive_load?.average || 5;
  const ctr = parseFloat(metrics.ctr || "0");
  const behavioral = hook * 0.4 + emotion * 6 * 0.3 + (10 - cogLoad) * 10 * 0.3;

  leaderboard.push({
    ad_id: adId, ad_name: metrics.ad_name || "",
    impressions, ctr, spend: parseFloat(metrics.spend || "0"),
    hook_score: hook, emotion_avg: emotion,
    behavioral_score: parseFloat(behavioral.toFixed(1)),
    verdict: behavioral > 60 && ctr > 1.5 ? "SCALE" :
             behavioral > 60 ? "TEST" :
             behavioral < 40 && ctr < 1.0 ? "KILL" : "HOLD",
  });
  await new Promise(r => setTimeout(r, 1000));
}

// 3. Leaderboard
leaderboard.sort((a, b) => b.behavioral_score - a.behavioral_score);
console.log("Rank  Ad Name                       Hook  Behavioral  CTR      Spend     Verdict");
console.log("-".repeat(85));
leaderboard.forEach((lb, i) =>
  console.log(`${i+1}     ${lb.ad_name.slice(0,28).padEnd(30)} ${String(lb.hook_score).padEnd(6)} ${String(lb.behavioral_score).padEnd(12)} ${lb.ctr.toFixed(2)}%    $${lb.spend.toLocaleString().padStart(8)}  ${lb.verdict}`)
);