Influencer Content Analysis

import os, requests, time

YT = os.environ["YOUTUBE_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
YT_BASE = "https://www.googleapis.com/youtube/v3"
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

BRAND_GUIDELINES = """
Brand tone: Approachable, expert, optimistic. Never cynical or sarcastic.
Visual style: Clean, bright, modern. Avoid cluttered frames or dark aesthetics.
Audience: 25-40, professionals, health-conscious.
Deal-breakers: Profanity, controversial opinions, competitor endorsements, clickbait.
"""

INFLUENCERS = [
    {"name": "FitnessCreator1", "channel_id": "UC_CHANNEL_ID_1"},
    {"name": "NutritionGuru", "channel_id": "UC_CHANNEL_ID_2"},
    {"name": "WellnessVlogger", "channel_id": "UC_CHANNEL_ID_3"},
]

scorecards = []

for influencer in INFLUENCERS:
    # 1. Search for recent videos by channel (100 quota units)
    search = requests.get(f"{YT_BASE}/search", params={
        "key": YT, "channelId": influencer["channel_id"],
        "type": "video", "part": "snippet",
        "maxResults": 5, "order": "date",
    }).json()

    if not search.get("items"):
        print(f"No videos found for {influencer['name']}")
        continue

    video_ids = [item["id"]["videoId"] for item in search["items"]]

    # 2. Get video stats (1 quota unit)
    details = requests.get(f"{YT_BASE}/videos", params={
        "key": YT, "id": ",".join(video_ids),
        "part": "snippet,statistics,contentDetails",
    }).json()

    video_scores = []
    for video in (details.get("items") or [])[:3]:
        vid_id = video["id"]
        stats = video.get("statistics", {})

        # 3. Upload to Mavera and analyze
        upload = requests.post(f"{MV_BASE}/assets", headers=MV_H, json={
            "url": f"https://www.youtube.com/watch?v={vid_id}",
            "name": f"{influencer['name']} — {video['snippet']['title'][:40]}",
            "type": "video",
        }).json()

        analysis = requests.post(f"{MV_BASE}/video-analysis", headers=MV_H, json={
            "asset_id": upload["id"],
            "analysis_types": [
                "emotional_arc", "pacing", "visual_complexity",
                "cognitive_load", "hook_score", "brand_safety",
            ],
        }).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", {})
        video_scores.append({
            "title": video["snippet"]["title"][:50],
            "views": int(stats.get("viewCount", 0)),
            "hook": r.get("hook_score", {}).get("score", 0),
            "emotion": r.get("emotional_arc", {}).get("intensity_avg", 0),
            "brand_safe": r.get("brand_safety", {}).get("score", 0),
            "pacing": r.get("pacing", {}).get("score", 0),
        })
        time.sleep(1)

    # 4. Brand alignment evaluation via Mave
    scores_text = "\n".join(
        f"- \"{v['title']}\" — views: {v['views']:,}, hook: {v['hook']}/100, "
        f"emotion: {v['emotion']:.1f}/10, brand-safe: {v['brand_safe']}/100, pacing: {v['pacing']:.1f}/10"
        for v in video_scores
    )

    alignment = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
        "message": f"""Evaluate influencer "{influencer['name']}" for brand partnership.

BRAND GUIDELINES:
{BRAND_GUIDELINES}

VIDEO ANALYSIS SCORES:
{scores_text}

Score 1-100 on:
1. Brand tone alignment
2. Visual style match
3. Audience overlap potential
4. Content quality consistency
5. Risk level (100 = lowest risk)

Overall recommendation: PARTNER / CONSIDER / PASS
Explain each score with evidence from the video analyses.""",
    }).json()

    avg_hook = sum(v["hook"] for v in video_scores) / len(video_scores) if video_scores else 0
    avg_safe = sum(v["brand_safe"] for v in video_scores) / len(video_scores) if video_scores else 0

    scorecards.append({
        "name": influencer["name"],
        "videos_analyzed": len(video_scores),
        "avg_hook": round(avg_hook, 1),
        "avg_brand_safety": round(avg_safe, 1),
        "assessment": alignment.get("content", "")[:600],
    })

# 5. Print scorecards
print("INFLUENCER CONTENT ANALYSIS")
print("=" * 60)
for sc in scorecards:
    print(f"\n{sc['name']} ({sc['videos_analyzed']} videos analyzed)")
    print(f"  Avg Hook: {sc['avg_hook']}/100 | Avg Brand Safety: {sc['avg_brand_safety']}/100")
    print(f"  {sc['assessment'][:500]}")

const YT = process.env.YOUTUBE_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const YT_BASE = "https://www.googleapis.com/youtube/v3";
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const BRAND_GUIDELINES = `Tone: Approachable, expert, optimistic. Never cynical.
Visual: Clean, bright, modern. Avoid clutter/dark aesthetics.
Audience: 25-40 professionals, health-conscious.
Deal-breakers: Profanity, controversial takes, competitor endorsements, clickbait.`;

const INFLUENCERS = [
  { name: "FitnessCreator1", channelId: "UC_CHANNEL_ID_1" },
  { name: "NutritionGuru", channelId: "UC_CHANNEL_ID_2" },
  { name: "WellnessVlogger", channelId: "UC_CHANNEL_ID_3" },
];

const scorecards = [];

for (const inf of INFLUENCERS) {
  // 1. Recent videos (100 quota units)
  const search = await fetch(
    `${YT_BASE}/search?key=${YT}&channelId=${inf.channelId}&type=video&part=snippet&maxResults=5&order=date`
  ).then(r => r.json());

  if (!search.items?.length) { console.log(`No videos: ${inf.name}`); continue; }

  const videoIds = search.items.map(i => i.id.videoId);

  // 2. Stats (1 quota unit)
  const details = await fetch(
    `${YT_BASE}/videos?key=${YT}&id=${videoIds.join(",")}&part=snippet,statistics,contentDetails`
  ).then(r => r.json());

  const videoScores = [];
  for (const video of (details.items || []).slice(0, 3)) {
    const upload = await fetch(`${MV_BASE}/assets`, {
      method: "POST", headers: MV_H,
      body: JSON.stringify({
        url: `https://www.youtube.com/watch?v=${video.id}`,
        name: `${inf.name} — ${video.snippet.title.slice(0, 40)}`, 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", "pacing", "visual_complexity", "cognitive_load", "hook_score", "brand_safety"],
      }),
    }).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 res = status.results || {};
    videoScores.push({
      title: video.snippet.title.slice(0, 50),
      views: parseInt(video.statistics?.viewCount || "0", 10),
      hook: res.hook_score?.score || 0, emotion: res.emotional_arc?.intensity_avg || 0,
      brandSafe: res.brand_safety?.score || 0, pacing: res.pacing?.score || 0,
    });
    await new Promise(r => setTimeout(r, 1000));
  }

  // 4. Brand alignment
  const scoresText = videoScores.map(v =>
    `- "${v.title}" — views: ${v.views.toLocaleString()}, hook: ${v.hook}/100, emotion: ${v.emotion.toFixed(1)}/10, brand-safe: ${v.brandSafe}/100`
  ).join("\n");

  const alignment = await fetch(`${MV_BASE}/mave/chat`, {
    method: "POST", headers: MV_H,
    body: JSON.stringify({
      message: `Evaluate "${inf.name}" for partnership.\n\nBRAND:\n${BRAND_GUIDELINES}\n\nSCORES:\n${scoresText}\n\nScore 1-100: tone alignment, visual match, audience overlap, quality consistency, risk level. Recommendation: PARTNER/CONSIDER/PASS.`,
    }),
  }).then(r => r.json());

  const avgHook = videoScores.reduce((s, v) => s + v.hook, 0) / (videoScores.length || 1);
  const avgSafe = videoScores.reduce((s, v) => s + v.brandSafe, 0) / (videoScores.length || 1);

  scorecards.push({
    name: inf.name, videosAnalyzed: videoScores.length,
    avgHook: avgHook.toFixed(1), avgBrandSafety: avgSafe.toFixed(1),
    assessment: (alignment.content || "").slice(0, 600),
  });
}

console.log("INFLUENCER CONTENT ANALYSIS");
console.log("=".repeat(60));
for (const sc of scorecards) {
  console.log(`\n${sc.name} (${sc.videosAnalyzed} videos)`);
  console.log(`  Avg Hook: ${sc.avgHook}/100 | Brand Safety: ${sc.avgBrandSafety}/100`);
  console.log(`  ${sc.assessment.slice(0, 500)}`);
}