Cross-Platform Creative Optimization

import os, requests, time, tempfile

META = os.environ["META_ACCESS_TOKEN"]
ACCT = os.environ["META_AD_ACCOUNT_ID"]
MV = os.environ["MAVERA_API_KEY"]
GRAPH = "https://graph.facebook.com/v24.0"
MB = "https://app.mavera.io/api/v1"
MH = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

# 1. Pull performance by placement for recent ads
placement_data = requests.get(
    f"{GRAPH}/{ACCT}/insights",
    params={
        "access_token": META,
        "fields": "ad_id,ad_name,impressions,clicks,ctr,cpc,spend,actions",
        "breakdowns": "publisher_platform,platform_position",
        "level": "ad",
        "date_preset": "last_30d",
        "limit": 200,
    },
).json().get("data", [])

# 2. Group by ad, find ads with multi-placement data
from collections import defaultdict
ad_placements = defaultdict(list)
for row in placement_data:
    ad_placements[row["ad_id"]].append({
        "platform": row.get("publisher_platform", "unknown"),
        "position": row.get("platform_position", "unknown"),
        "impressions": int(row.get("impressions", 0)),
        "clicks": int(row.get("clicks", 0)),
        "ctr": float(row.get("ctr", 0)),
        "spend": float(row.get("spend", 0)),
        "name": row.get("ad_name", ""),
    })

multi_placement = {k: v for k, v in ad_placements.items() if len(v) >= 2}
print(f"Ads with 2+ placements: {len(multi_placement)}")

# 3. Pick the top ad by total spend and analyze
if not multi_placement:
    print("No multi-placement ads found.")
    exit()

target_ad_id = max(multi_placement, key=lambda k: sum(p["spend"] for p in multi_placement[k]))
placements = multi_placement[target_ad_id]
ad_name = placements[0]["name"]
print(f"Analyzing: {ad_name} across {len(placements)} placements")

# 4. Get the creative video
ad_detail = requests.get(
    f"{GRAPH}/{target_ad_id}",
    params={"access_token": META, "fields": "creative{video_id,title,body}"},
).json()
vid = ad_detail.get("creative", {}).get("video_id")
if not vid:
    print("Selected ad has no video creative.")
    exit()

video_info = requests.get(f"{GRAPH}/{vid}",
    params={"access_token": META, "fields": "source,length"}).json()

vid_resp = requests.get(video_info["source"], stream=True)
tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
for chunk in vid_resp.iter_content(8192):
    tmp.write(chunk)
tmp.close()

# 5. Run Video Analysis for each placement context
placement_analyses = []
for pl in placements:
    label = f"{pl['platform']} — {pl['position']}"
    with open(tmp.name, "rb") as f:
        asset = requests.post(f"{MB}/assets",
            headers={"Authorization": f"Bearer {MV}"},
            files={"file": (f"{vid}_{pl['platform']}_{pl['position']}.mp4", f, "video/mp4")}).json()

    analysis = requests.post(f"{MB}/video-analyses", headers=MH, json={
        "asset_id": asset["id"],
        "name": f"{ad_name} — {label}",
    }).json()

    for _ in range(30):
        time.sleep(10)
        status = requests.get(f"{MB}/video-analyses/{analysis['id']}",
            headers={"Authorization": f"Bearer {MV}"}).json()
        if status.get("status") in ("completed", "failed"):
            break

    if status.get("status") == "completed":
        placement_analyses.append({
            "placement": label,
            "platform": pl["platform"],
            "position": pl["position"],
            "ctr": pl["ctr"],
            "spend": pl["spend"],
            "scores": status.get("scores", {}),
        })
    time.sleep(1)

os.unlink(tmp.name)

# 6. Mave comparison
analysis_summary = "\n".join(
    f"- {pa['placement']}: CTR={pa['ctr']:.2f}%, spend=${pa['spend']:.0f}, "
    f"emotional={pa['scores'].get('emotional','?')}, cognitive={pa['scores'].get('cognitive','?')}, "
    f"behavioral={pa['scores'].get('behavioral','?')}"
    for pa in placement_analyses
)

comparison = requests.post(f"{MB}/mave/chat", headers=MH, json={
    "message": f"""Compare this video creative's performance across Meta placements.

CREATIVE: "{ad_name}" ({video_info.get('length',0)}s video)

PLACEMENT ANALYSIS:
{analysis_summary}

For each placement:
1. Why does performance differ? (viewing context, user intent, format fit)
2. Specific edits for this placement (aspect ratio, hook timing, CTA overlay position)
3. Budget reallocation recommendation based on efficiency
4. Which placement should get the most budget and why
5. Should any placement get a dedicated creative variant?"""
}).json()

print("\n=== Cross-Platform Creative Analysis ===")
print(comparison.get("content", ""))

const META = process.env.META_ACCESS_TOKEN;
const ACCT = process.env.META_AD_ACCOUNT_ID;
const MV = process.env.MAVERA_API_KEY;
const GRAPH = "https://graph.facebook.com/v24.0";
const MB = "https://app.mavera.io/api/v1";
const MH = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

// 1. Placement breakdown
const placementData = await fetch(
  `${GRAPH}/${ACCT}/insights?access_token=${META}&fields=ad_id,ad_name,impressions,clicks,ctr,cpc,spend,actions&breakdowns=publisher_platform,platform_position&level=ad&date_preset=last_30d&limit=200`
).then(r => r.json()).then(d => d.data || []);

// 2. Group by ad
const adPlacements = {};
for (const row of placementData) {
  (adPlacements[row.ad_id] ??= []).push({
    platform: row.publisher_platform || "unknown",
    position: row.platform_position || "unknown",
    impressions: parseInt(row.impressions || "0"),
    clicks: parseInt(row.clicks || "0"),
    ctr: parseFloat(row.ctr || "0"),
    spend: parseFloat(row.spend || "0"),
    name: row.ad_name || "",
  });
}

const multiPlacement = Object.entries(adPlacements).filter(([, v]) => v.length >= 2);
console.log(`Ads with 2+ placements: ${multiPlacement.length}`);
if (!multiPlacement.length) { console.log("No multi-placement ads."); process.exit(); }

// 3. Pick top ad
const [targetAdId, placements] = multiPlacement.sort(
  ([, a], [, b]) => b.reduce((s, p) => s + p.spend, 0) - a.reduce((s, p) => s + p.spend, 0)
)[0];
const adName = placements[0].name;
console.log(`Analyzing: ${adName} across ${placements.length} placements`);

// 4. Get video
const adDetail = await fetch(
  `${GRAPH}/${targetAdId}?access_token=${META}&fields=creative{video_id,title,body}`
).then(r => r.json());
const vid = adDetail.creative?.video_id;
if (!vid) { console.log("No video creative."); process.exit(); }

const videoInfo = await fetch(
  `${GRAPH}/${vid}?access_token=${META}&fields=source,length`
).then(r => r.json());
const vidBuffer = Buffer.from(await fetch(videoInfo.source).then(r => r.arrayBuffer()));

// 5. Analyze per placement
const placementAnalyses = [];
for (const pl of placements) {
  const label = `${pl.platform} — ${pl.position}`;
  const form = new FormData();
  form.append("file", new Blob([vidBuffer], { type: "video/mp4" }), `${vid}_${pl.platform}_${pl.position}.mp4`);
  const asset = await fetch(`${MB}/assets`, {
    method: "POST", headers: { Authorization: `Bearer ${MV}` }, body: form,
  }).then(r => r.json());

  const analysis = await fetch(`${MB}/video-analyses`, {
    method: "POST", headers: MH,
    body: JSON.stringify({ asset_id: asset.id, name: `${adName} — ${label}` }),
  }).then(r => r.json());

  let status;
  for (let i = 0; i < 30; i++) {
    await new Promise(r => setTimeout(r, 10000));
    status = await fetch(`${MB}/video-analyses/${analysis.id}`,
      { headers: { Authorization: `Bearer ${MV}` } }).then(r => r.json());
    if (status.status === "completed" || status.status === "failed") break;
  }

  if (status?.status === "completed") {
    placementAnalyses.push({
      placement: label, platform: pl.platform, position: pl.position,
      ctr: pl.ctr, spend: pl.spend, scores: status.scores || {},
    });
  }
  await new Promise(r => setTimeout(r, 1000));
}

// 6. Mave comparison
const summary = placementAnalyses.map(pa =>
  `- ${pa.placement}: CTR=${pa.ctr.toFixed(2)}%, spend=$${pa.spend.toFixed(0)}, emotional=${pa.scores.emotional ?? "?"}, cognitive=${pa.scores.cognitive ?? "?"}, behavioral=${pa.scores.behavioral ?? "?"}`
).join("\n");

const comparison = await fetch(`${MB}/mave/chat`, {
  method: "POST", headers: MH,
  body: JSON.stringify({
    message: `Compare "${adName}" across placements:\n\n${summary}\n\nFor each: 1) Why different 2) Specific edits 3) Budget reallocation 4) Best placement 5) Need dedicated variant?`,
  }),
}).then(r => r.json());

console.log("\n=== Cross-Platform Analysis ===");
console.log(comparison.content || "");