Ad Video Analysis Pipeline

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 active video ads
r = requests.get(f"{TT_BASE}/ad/get/",
    headers=TT_H,
    params={
        "advertiser_id": ADV,
        "filtering": '{"status": "AD_STATUS_DELIVERY_OK", "creative_type": "VIDEO"}',
        "fields": '["ad_id", "ad_name", "video_id", "image_ids", "landing_page_url"]',
        "page_size": 20,
    })
r.raise_for_status()
data = r.json()
if data.get("code") != 0:
    raise SystemExit(f"TikTok API error: {data.get('message')}")
ads = data.get("data", {}).get("list", [])

# 2. Get video URLs for each ad
video_analyses = []
for ad in ads[:10]:
    video_id = ad.get("video_id")
    if not video_id:
        continue

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

    video_url = vid_data[0].get("video_url") or vid_data[0].get("preview_url", "")
    if not video_url:
        continue

    # 3. Download and upload to Mavera
    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_name']}.mp4", open(tmp_path, "rb"), "video/mp4")},
    ).json()

    # 4. Run Video Analysis
    analysis = requests.post(f"{MV_BASE}/video-analysis", headers=MV_H, json={
        "asset_id": upload["id"],
        "analysis_types": ["hook_score", "emotional_arc", "cognitive_load", "visual_complexity", "pacing"],
        "metadata": {"source": "tiktok", "ad_id": ad["ad_id"], "ad_name": ad["ad_name"]},
    }).json()

    # 5. Poll for completion
    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

    video_analyses.append({
        "ad_id": ad["ad_id"],
        "ad_name": ad["ad_name"],
        "hook_score": status.get("results", {}).get("hook_score", {}),
        "emotional_arc": status.get("results", {}).get("emotional_arc", {}),
        "cognitive_load": status.get("results", {}).get("cognitive_load", {}),
    })
    os.unlink(tmp_path)
    time.sleep(1)

# 6. Output scorecard
for va in video_analyses:
    hook = va["hook_score"]
    print(f"\n{'='*50}")
    print(f"AD: {va['ad_name']} ({va['ad_id']})")
    print(f"  Hook Score: {hook.get('score', 'N/A')}/100 — {hook.get('assessment', '')}")
    print(f"  Emotional Peak: {va['emotional_arc'].get('peak_emotion', 'N/A')} at {va['emotional_arc'].get('peak_timestamp', 'N/A')}s")
    print(f"  Avg Cognitive Load: {va['cognitive_load'].get('average', 'N/A')}/10")

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 active video ads
const adsRes = await fetch(
  `${TT_BASE}/ad/get/?advertiser_id=${ADV}` +
  `&filtering=${encodeURIComponent('{"status":"AD_STATUS_DELIVERY_OK","creative_type":"VIDEO"}')}` +
  `&fields=${encodeURIComponent('["ad_id","ad_name","video_id"]')}&page_size=20`,
  { headers: TT_H }
);
const adsData = await adsRes.json();
if (adsData.code !== 0) throw new Error(`TikTok: ${adsData.message}`);
const ads = adsData.data?.list || [];

// 2. Process each ad
const videoAnalyses = [];
for (const ad of ads.slice(0, 10)) {
  if (!ad.video_id) continue;

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

  // 3. Download and upload to Mavera
  const vidBytes = await fetch(videoUrl).then(r => r.arrayBuffer());
  const formData = new FormData();
  formData.append("file", new Blob([vidBytes], { type: "video/mp4" }), `${ad.ad_name}.mp4`);

  const upload = await fetch(`${MV_BASE}/assets`, {
    method: "POST",
    headers: { Authorization: `Bearer ${MV}` },
    body: formData,
  }).then(r => r.json());

  // 4. Run Video Analysis
  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", "cognitive_load", "visual_complexity", "pacing"],
      metadata: { source: "tiktok", ad_id: ad.ad_id, ad_name: ad.ad_name },
    }),
  }).then(r => r.json());

  // 5. 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;
  }

  videoAnalyses.push({
    ad_id: ad.ad_id, ad_name: ad.ad_name,
    hook_score: status.results?.hook_score || {},
    emotional_arc: status.results?.emotional_arc || {},
    cognitive_load: status.results?.cognitive_load || {},
  });
  await new Promise(r => setTimeout(r, 1000));
}

// 6. Scorecard
for (const va of videoAnalyses) {
  console.log(`\n${"=".repeat(50)}`);
  console.log(`AD: ${va.ad_name} (${va.ad_id})`);
  console.log(`  Hook Score: ${va.hook_score.score ?? "N/A"}/100 — ${va.hook_score.assessment || ""}`);
  console.log(`  Emotional Peak: ${va.emotional_arc.peak_emotion ?? "N/A"} at ${va.emotional_arc.peak_timestamp ?? "N/A"}s`);
  console.log(`  Avg Cognitive Load: ${va.cognitive_load.average ?? "N/A"}/10`);
}