Marketing Video Library Analysis

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"}

# 1. Fetch entire video library (paginated, 100 per page)
all_videos = []
page = 1
while True:
    resp = requests.get(f"{VM_BASE}/me/videos", headers=VM_H, params={
        "per_page": 100, "page": page,
        "fields": "uri,name,link,duration,stats,created_time,pictures.sizes",
        "sort": "date", "direction": "desc",
    })
    if resp.status_code == 429:
        retry = int(resp.headers.get("Retry-After", 60))
        print(f"Rate limited — waiting {retry}s")
        time.sleep(retry)
        continue
    resp.raise_for_status()
    data = resp.json()

    for v in data.get("data", []):
        vid_id = v["uri"].split("/")[-1]
        all_videos.append({
            "id": vid_id, "name": v["name"], "link": v["link"],
            "duration": v.get("duration", 0),
            "plays": v.get("stats", {}).get("plays", 0),
            "created": v.get("created_time", "")[:10],
        })

    if not data.get("paging", {}).get("next"):
        break
    page += 1
    time.sleep(0.6)

print(f"Library size: {len(all_videos)} videos")

# 2. Analyze each video via Mavera (sample top 20 by plays)
scored = []
for video in sorted(all_videos, key=lambda v: -v["plays"])[:20]:
    upload = requests.post(f"{MV_BASE}/assets", headers=MV_H, json={
        "url": video["link"], "name": video["name"][:80], "type": "video",
    }).json()

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

    r = status.get("results", {})
    scored.append({
        **video,
        "clarity": r.get("message_clarity", {}).get("score", 0),
        "emotion": r.get("emotional_impact", {}).get("score", 0),
        "behavior": r.get("behavioral_effectiveness", {}).get("score", 0),
        "hook": r.get("hook_score", {}).get("score", 0),
    })
    time.sleep(1)

# 3. Catalog ranking via Mave
scores_block = "\n".join(
    f"- \"{s['name'][:50]}\" — plays: {s['plays']:,}, clarity: {s['clarity']}/100, "
    f"emotion: {s['emotion']}/100, behavior: {s['behavior']}/100, hook: {s['hook']}/100"
    for s in scored
)

ranking = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"""Rank this marketing video library by creative quality.

SCORED CATALOG ({len(scored)} videos):
{scores_block}

Produce:
1. **Tier 1 — Promote** (top creative assets): which videos and why
2. **Tier 2 — Optimize** (strong but fixable): what to improve
3. **Tier 3 — Retire** (underperforming creative): replace with what
4. **Patterns**: What do Tier 1 videos share that Tier 3 lacks?
5. **Recommendations**: 3 specific creative briefs for new videos based on the winning patterns""",
}).json()

print("MARKETING VIDEO LIBRARY ANALYSIS")
print("=" * 60)
for s in sorted(scored, key=lambda x: -x["emotion"]):
    print(f"  {s['name'][:40]:<42} Plays:{s['plays']:>8,}  "
          f"Clarity:{s['clarity']:>3}  Emotion:{s['emotion']:>3}  "
          f"Hook:{s['hook']:>3}")
print("\n" + ranking.get("content", "")[:2000])

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" };

// 1. Fetch entire library (paginated)
const allVideos = [];
let page = 1;
while (true) {
  const resp = await fetch(
    `${VM_BASE}/me/videos?per_page=100&page=${page}&fields=uri,name,link,duration,stats,created_time&sort=date&direction=desc`,
    { headers: VM_H }
  );
  if (resp.status === 429) {
    const retry = parseInt(resp.headers.get("Retry-After") || "60", 10);
    console.log(`Rate limited — waiting ${retry}s`);
    await new Promise(r => setTimeout(r, retry * 1000));
    continue;
  }
  const data = await resp.json();

  for (const v of data.data || []) {
    allVideos.push({
      id: v.uri.split("/").pop(), name: v.name, link: v.link,
      duration: v.duration || 0,
      plays: v.stats?.plays || 0,
      created: (v.created_time || "").slice(0, 10),
    });
  }
  if (!data.paging?.next) break;
  page++;
  await new Promise(r => setTimeout(r, 600));
}

console.log(`Library size: ${allVideos.length} videos`);

// 2. Analyze top 20 by plays
const scored = [];
for (const video of allVideos.sort((a, b) => b.plays - a.plays).slice(0, 20)) {
  const upload = await fetch(`${MV_BASE}/assets`, {
    method: "POST", headers: MV_H,
    body: JSON.stringify({ url: video.link, name: video.name.slice(0, 80), 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: [
        "message_clarity", "emotional_impact", "behavioral_effectiveness",
        "hook_score", "pacing", "cognitive_load",
      ],
      metadata: { vimeo_id: video.id, plays: video.plays },
    }),
  }).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 r = status.results || {};
  scored.push({
    ...video,
    clarity: r.message_clarity?.score || 0,
    emotion: r.emotional_impact?.score || 0,
    behavior: r.behavioral_effectiveness?.score || 0,
    hook: r.hook_score?.score || 0,
  });
  await new Promise(r => setTimeout(r, 1000));
}

// 3. Catalog ranking via Mave
const scoresBlock = scored.map(s =>
  `- "${s.name.slice(0, 50)}" — plays: ${s.plays.toLocaleString()}, clarity: ${s.clarity}/100, ` +
  `emotion: ${s.emotion}/100, behavior: ${s.behavior}/100, hook: ${s.hook}/100`
).join("\n");

const ranking = await fetch(`${MV_BASE}/mave/chat`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    message: `Rank this marketing video library by creative quality.\n\nCATALOG (${scored.length} videos):\n${scoresBlock}\n\nProduce:\n1. Tier 1 — Promote (top assets and why)\n2. Tier 2 — Optimize (strong but fixable)\n3. Tier 3 — Retire (replace with what)\n4. Patterns (what Tier 1 shares that Tier 3 lacks)\n5. 3 creative briefs for new videos based on winning patterns`,
  }),
}).then(r => r.json());

console.log("MARKETING VIDEO LIBRARY ANALYSIS");
console.log("=".repeat(60));
scored.sort((a, b) => b.emotion - a.emotion).forEach(s =>
  console.log(`  ${s.name.slice(0, 40).padEnd(42)} Plays:${String(s.plays.toLocaleString()).padStart(8)}  ` +
    `Clarity:${String(s.clarity).padStart(3)}  Emotion:${String(s.emotion).padStart(3)}  ` +
    `Hook:${String(s.hook).padStart(3)}`)
);
console.log("\n" + (ranking.content || "").slice(0, 2000));