Webinar Series Intelligence

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"}

ALBUM_ID = "12345678"

# 1. Fetch all videos in the showcase/album (series)
album_resp = requests.get(f"{VM_BASE}/me/albums/{ALBUM_ID}/videos", headers=VM_H, params={
    "per_page": 50, "sort": "manual",
    "fields": "uri,name,link,duration,stats,created_time,description",
}).json()

if "error" in album_resp:
    raise SystemExit(f"Vimeo API error: {album_resp.get('developer_message', '')}")

sessions = []
for v in album_resp.get("data", []):
    vid_id = v["uri"].split("/")[-1]
    stats = v.get("stats", {})
    sessions.append({
        "id": vid_id, "name": v["name"], "link": v["link"],
        "duration": v.get("duration", 0),
        "plays": stats.get("plays", 0),
        "finishes": stats.get("finishes", 0),
        "finish_rate": round(stats.get("finishes", 0) / max(stats.get("plays", 1), 1) * 100, 1),
        "created": v.get("created_time", "")[:10],
        "description": v.get("description", "")[:200],
    })

# 2. Get album metadata
album_meta = requests.get(f"{VM_BASE}/me/albums/{ALBUM_ID}", headers=VM_H, params={
    "fields": "name,description,metadata.connections.videos.total",
}).json()
series_name = album_meta.get("name", f"Album {ALBUM_ID}")

print(f"Series: \"{series_name}\" — {len(sessions)} sessions")

# 3. Analyze each session via Mavera
session_scores = []
for i, session in enumerate(sessions):
    print(f"  Analyzing session {i+1}/{len(sessions)}: \"{session['name'][:40]}\"")

    upload = requests.post(f"{MV_BASE}/assets", headers=MV_H, json={
        "url": session["link"], "name": f"[S{i+1}] {session['name'][:60]}", "type": "video",
    }).json()

    analysis = requests.post(f"{MV_BASE}/video-analysis", headers=MV_H, json={
        "asset_id": upload["id"],
        "analysis_types": [
            "emotional_arc", "hook_score", "pacing", "cognitive_load",
            "message_clarity", "behavioral_effectiveness",
        ],
        "metadata": {"session_number": i + 1, "series": series_name},
    }).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", {})
    session_scores.append({
        **session,
        "session_num": i + 1,
        "hook": r.get("hook_score", {}).get("score", 0),
        "emotion_avg": r.get("emotional_arc", {}).get("intensity_avg", 0),
        "emotion_peak": r.get("emotional_arc", {}).get("peak_intensity", 0),
        "clarity": r.get("message_clarity", {}).get("score", 0),
        "pacing_score": r.get("pacing", {}).get("score", 0),
        "cog_load": r.get("cognitive_load", {}).get("average", 0),
        "arc_summary": r.get("emotional_arc", {}).get("summary", ""),
    })
    time.sleep(1)

# 4. Series arc analysis via Mave
session_block = "\n\n".join(
    f"SESSION {s['session_num']}: \"{s['name']}\"\n"
    f"  Date: {s['created']} | Duration: {s['duration']}s | Plays: {s['plays']:,} | Finish Rate: {s['finish_rate']}%\n"
    f"  Hook: {s['hook']}/100 | Emotion Avg: {s['emotion_avg']:.1f}/10 | Peak: {s['emotion_peak']:.1f}/10\n"
    f"  Clarity: {s['clarity']}/100 | Pacing: {s['pacing_score']:.1f}/10 | Cog Load: {s['cog_load']:.1f}/10\n"
    f"  Arc: {s['arc_summary'][:150]}"
    for s in session_scores
)

series_analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"""Analyze engagement patterns across this webinar series.

SERIES: "{series_name}" ({len(session_scores)} sessions)

{session_block}

Produce:
1. **Engagement Arc**: How does audience engagement shift across sessions? Where does it peak and where does it drop?
2. **Drop-Off Diagnosis**: At which session do we lose the most viewers? What specifically about that session's creative quality explains the drop?
3. **Session Comparison**: Which session is the strongest and weakest? What makes them different?
4. **Pacing Pattern**: Does the series maintain energy or does it fatigue viewers? Is cognitive load increasing across sessions?
5. **Recommendations**: How should we restructure the next series to maintain engagement? Specific changes to session order, content density, and hook strategy.
6. **Recovery Opportunities**: Can we save the weakest session with a re-edit? What specifically should change?""",
}).json()

print(f"\nWEBINAR SERIES INTELLIGENCE — \"{series_name}\"")
print("=" * 65)
print(f"{'Session':<8} {'Title':<30} {'Plays':>7} {'Finish%':>8} {'Hook':>6} {'Emotion':>8} {'Clarity':>8}")
print("-" * 65)
for s in session_scores:
    print(f"  S{s['session_num']:<5} {s['name'][:28]:<30} {s['plays']:>7,} {s['finish_rate']:>7.1f}% "
          f"{s['hook']:>5} {s['emotion_avg']:>7.1f} {s['clarity']:>7}")
print("\n" + series_analysis.get("content", "")[:2500])

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" };

const ALBUM_ID = "12345678";

// 1. Fetch series videos from album
const albumResp = await fetch(
  `${VM_BASE}/me/albums/${ALBUM_ID}/videos?per_page=50&sort=manual` +
  `&fields=uri,name,link,duration,stats,created_time,description`,
  { headers: VM_H }
).then(r => r.json());

if (albumResp.error) throw new Error(albumResp.developer_message || albumResp.error);

const sessions = (albumResp.data || []).map(v => {
  const stats = v.stats || {};
  const plays = stats.plays || 0;
  const finishes = stats.finishes || 0;
  return {
    id: v.uri.split("/").pop(), name: v.name, link: v.link,
    duration: v.duration || 0, plays, finishes,
    finishRate: plays > 0 ? Math.round(finishes / plays * 1000) / 10 : 0,
    created: (v.created_time || "").slice(0, 10),
    description: (v.description || "").slice(0, 200),
  };
});

// 2. Album metadata
const albumMeta = await fetch(
  `${VM_BASE}/me/albums/${ALBUM_ID}?fields=name,description`, { headers: VM_H }
).then(r => r.json());
const seriesName = albumMeta.name || `Album ${ALBUM_ID}`;

console.log(`Series: "${seriesName}" — ${sessions.length} sessions`);

// 3. Analyze each session
const sessionScores = [];
for (let i = 0; i < sessions.length; i++) {
  const session = sessions[i];
  console.log(`  Analyzing S${i + 1}: "${session.name.slice(0, 40)}"`);

  const upload = await fetch(`${MV_BASE}/assets`, {
    method: "POST", headers: MV_H,
    body: JSON.stringify({ url: session.link, name: `[S${i + 1}] ${session.name.slice(0, 60)}`, 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: ["emotional_arc", "hook_score", "pacing", "cognitive_load", "message_clarity", "behavioral_effectiveness"],
      metadata: { session_number: i + 1, series: seriesName },
    }),
  }).then(r => r.json());

  let status;
  for (let j = 0; j < 30; j++) {
    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 || {};
  sessionScores.push({
    ...session, sessionNum: i + 1,
    hook: r.hook_score?.score || 0,
    emotionAvg: r.emotional_arc?.intensity_avg || 0,
    emotionPeak: r.emotional_arc?.peak_intensity || 0,
    clarity: r.message_clarity?.score || 0,
    pacingScore: r.pacing?.score || 0,
    cogLoad: r.cognitive_load?.average || 0,
    arcSummary: r.emotional_arc?.summary || "",
  });
  await new Promise(r => setTimeout(r, 1000));
}

// 4. Series analysis
const sessionBlock = sessionScores.map(s =>
  `SESSION ${s.sessionNum}: "${s.name}"\n  Date: ${s.created} | ${s.duration}s | Plays: ${s.plays.toLocaleString()} | Finish: ${s.finishRate}%\n` +
  `  Hook: ${s.hook}/100 | Emotion: ${s.emotionAvg.toFixed(1)}/10 | Clarity: ${s.clarity}/100 | CogLoad: ${s.cogLoad.toFixed(1)}/10`
).join("\n\n");

const seriesAnalysis = await fetch(`${MV_BASE}/mave/chat`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    message: `Analyze engagement across this webinar series.\n\nSERIES: "${seriesName}" (${sessionScores.length} sessions)\n\n${sessionBlock}\n\nProduce:\n1. Engagement arc across sessions\n2. Drop-off diagnosis — which session loses the most viewers and why\n3. Strongest vs weakest session comparison\n4. Pacing/fatigue pattern\n5. Restructuring recommendations for next series\n6. Recovery edits for weakest session`,
  }),
}).then(r => r.json());

console.log(`\nWEBINAR SERIES INTELLIGENCE — "${seriesName}"`);
console.log("=".repeat(65));
sessionScores.forEach(s =>
  console.log(`  S${String(s.sessionNum).padEnd(5)} ${s.name.slice(0, 28).padEnd(30)} ` +
    `${String(s.plays.toLocaleString()).padStart(7)} ${(s.finishRate + "%").padStart(8)} ` +
    `${String(s.hook).padStart(6)} ${s.emotionAvg.toFixed(1).padStart(8)} ${String(s.clarity).padStart(8)}`)
);
console.log("\n" + (seriesAnalysis.content || "").slice(0, 2500));