LinkedIn Ad Copy Generation

import os, requests, time

LI = os.environ["LINKEDIN_ACCESS_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
LI_BASE = "https://api.linkedin.com/rest"
MV_BASE = "https://app.mavera.io/api/v1"
LI_H = {"Authorization": f"Bearer {LI}", "LinkedIn-Version": "202401", "X-Restli-Protocol-Version": "2.0.0"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

AD_ACCOUNT_ID = "508000001"

# 1. Pull all creatives
creatives_resp = requests.get(f"{LI_BASE}/adAccounts/{AD_ACCOUNT_ID}/creatives",
    headers=LI_H,
    params={"q": "search", "count": 50})
if creatives_resp.status_code == 429:
    time.sleep(int(creatives_resp.headers.get("Retry-After", 60)))
    creatives_resp = requests.get(f"{LI_BASE}/adAccounts/{AD_ACCOUNT_ID}/creatives",
        headers=LI_H, params={"q": "search", "count": 50})
creatives_resp.raise_for_status()
creatives = creatives_resp.json().get("elements", [])

# 2. Pull analytics for each creative
creative_data = []
for cr in creatives:
    cr_urn = f"urn:li:sponsoredCreative:{cr.get('id', '')}"
    r = requests.get(f"{LI_BASE}/adAnalytics",
        headers=LI_H,
        params={
            "q": "analytics", "pivot": "CREATIVE",
            "timeGranularity": "ALL",
            "creatives[0]": cr_urn,
            "dateRange.start.year": 2025, "dateRange.start.month": 1, "dateRange.start.day": 1,
            "dateRange.end.year": 2025, "dateRange.end.month": 12, "dateRange.end.day": 31,
            "fields": "impressions,clicks,likes,comments,shares",
        })
    if r.status_code == 429:
        time.sleep(int(r.headers.get("Retry-After", 60)))
        continue
    metrics = r.json().get("elements", [{}])[0] if r.ok else {}

    copy = cr.get("commentary", "") or cr.get("content", {}).get("textAd", {}).get("text", "")
    headline = cr.get("content", {}).get("textAd", {}).get("headline", "")
    impressions = metrics.get("impressions", 0)
    clicks = metrics.get("clicks", 0)

    if copy and impressions > 0:
        creative_data.append({
            "copy": copy, "headline": headline,
            "impressions": impressions, "clicks": clicks,
            "ctr": round((clicks / impressions) * 100, 2) if impressions else 0,
            "engagement": metrics.get("likes", 0) + metrics.get("comments", 0) + metrics.get("shares", 0),
        })
    time.sleep(0.3)

# 3. Create Brand Voice from top performers
top_ads = sorted(creative_data, key=lambda x: -x["ctr"])[:10]
samples = "\n\n---\n\n".join(
    f"Headline: {a['headline']}\nCopy: {a['copy'][:500]}\nCTR: {a['ctr']}%"
    for a in top_ads
)

bv = requests.post(f"{MV_BASE}/brand-voices", headers=MV_H, json={
    "name": "LinkedIn Top-Performing Ad Voice",
    "samples": [samples],
}).json()
print(f"Brand Voice: {bv['id']}")

# 4. Generate new ad copy variants
gen = requests.post(f"{MV_BASE}/generations", headers=MV_H, json={
    "brand_voice_id": bv["id"],
    "app_template": "linkedin_ad",
    "prompt": (
        "Generate 5 LinkedIn Sponsored Content ad variants for a B2B SaaS product. "
        "Each variant needs: intro text (under 150 chars for mobile), headline, "
        "description, and CTA button text. Match the winning voice from our top ads. "
        "Vary the hooks: use a stat, a question, a bold claim, social proof, and a trend."
    ),
    "count": 5,
}).json()

for i, g in enumerate(gen.get("results", [gen]), 1):
    print(f"\n--- Variant {i} ---")
    print(g.get("content", g.get("text", ""))[:400])

# 5. Performance benchmark
avg_ctr = sum(a["ctr"] for a in top_ads) / len(top_ads) if top_ads else 0
print(f"\nBenchmark: Top-performer avg CTR = {avg_ctr:.2f}%")
print(f"Generated {len(gen.get('results', [gen]))} variants with brand voice {bv['id']}")

const LI = process.env.LINKEDIN_ACCESS_TOKEN;
const MV = process.env.MAVERA_API_KEY;
const LI_BASE = "https://api.linkedin.com/rest";
const MV_BASE = "https://app.mavera.io/api/v1";
const LI_H = { Authorization: `Bearer ${LI}`, "LinkedIn-Version": "202401", "X-Restli-Protocol-Version": "2.0.0" };
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const AD_ACCOUNT_ID = "508000001";

// 1. Pull creatives
let res = await fetch(
  `${LI_BASE}/adAccounts/${AD_ACCOUNT_ID}/creatives?q=search&count=50`,
  { headers: LI_H }
);
if (res.status === 429) {
  await new Promise(r => setTimeout(r, parseInt(res.headers.get("Retry-After") || "60", 10) * 1000));
  res = await fetch(`${LI_BASE}/adAccounts/${AD_ACCOUNT_ID}/creatives?q=search&count=50`, { headers: LI_H });
}
const creatives = (await res.json()).elements || [];

// 2. Analytics per creative
const creativeData = [];
for (const cr of creatives) {
  const crUrn = `urn:li:sponsoredCreative:${cr.id}`;
  const params = new URLSearchParams({
    q: "analytics", pivot: "CREATIVE", timeGranularity: "ALL",
    "creatives[0]": crUrn,
    "dateRange.start.year": "2025", "dateRange.start.month": "1", "dateRange.start.day": "1",
    "dateRange.end.year": "2025", "dateRange.end.month": "12", "dateRange.end.day": "31",
    fields: "impressions,clicks,likes,comments,shares",
  });
  const r = await fetch(`${LI_BASE}/adAnalytics?${params}`, { headers: LI_H });
  if (r.status === 429) { await new Promise(res => setTimeout(res, 60000)); continue; }
  const metrics = r.ok ? ((await r.json()).elements || [{}])[0] : {};

  const copy = cr.commentary || cr.content?.textAd?.text || "";
  const headline = cr.content?.textAd?.headline || "";
  const impressions = metrics.impressions || 0;
  const clicks = metrics.clicks || 0;

  if (copy && impressions > 0) {
    creativeData.push({
      copy, headline, impressions, clicks,
      ctr: parseFloat(((clicks / impressions) * 100).toFixed(2)),
      engagement: (metrics.likes || 0) + (metrics.comments || 0) + (metrics.shares || 0),
    });
  }
  await new Promise(r => setTimeout(r, 300));
}

// 3. Brand Voice from top ads
const topAds = creativeData.sort((a, b) => b.ctr - a.ctr).slice(0, 10);
const samples = topAds.map(a =>
  `Headline: ${a.headline}\nCopy: ${a.copy.slice(0, 500)}\nCTR: ${a.ctr}%`
).join("\n\n---\n\n");

const bv = await fetch(`${MV_BASE}/brand-voices`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({ name: "LinkedIn Top-Performing Ad Voice", samples: [samples] }),
}).then(r => r.json());
console.log(`Brand Voice: ${bv.id}`);

// 4. Generate variants
const gen = await fetch(`${MV_BASE}/generations`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    brand_voice_id: bv.id,
    app_template: "linkedin_ad",
    prompt: "Generate 5 LinkedIn Sponsored Content ad variants for B2B SaaS. Each needs: intro text (<150 chars), headline, description, CTA. Match winning voice. Vary hooks: stat, question, bold claim, social proof, trend.",
    count: 5,
  }),
}).then(r => r.json());

(gen.results || [gen]).forEach((g, i) => {
  console.log(`\n--- Variant ${i + 1} ---`);
  console.log((g.content || g.text || "").slice(0, 400));
});

const avgCtr = topAds.reduce((s, a) => s + a.ctr, 0) / (topAds.length || 1);
console.log(`\nBenchmark: Top-performer avg CTR = ${avgCtr.toFixed(2)}%`);