Competitor Ad Analysis Showdown

import os, requests, time, tempfile

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"}

COMPETITORS = ["Nike", "Adidas", "Puma"]

all_analyses = []

for brand in COMPETITORS:
    # 1. Search for competitor ads (100 quota units per call)
    search = requests.get(f"{YT_BASE}/search", params={
        "key": YT, "q": f"{brand} official ad 2026",
        "type": "video", "part": "snippet",
        "maxResults": 3, "order": "viewCount",
        "videoDuration": "short",
    }).json()

    if "error" in search:
        print(f"YouTube API error for {brand}: {search['error']['message']}")
        continue

    for item in search.get("items", []):
        video_id = item["id"]["videoId"]
        title = item["snippet"]["title"]

        # 2. Get video details for download URL (1 quota unit)
        details = requests.get(f"{YT_BASE}/videos", params={
            "key": YT, "id": video_id,
            "part": "snippet,contentDetails,statistics",
        }).json()
        video_info = (details.get("items") or [{}])[0]
        view_count = int(video_info.get("statistics", {}).get("viewCount", 0))

        # 3. Upload video URL to Mavera for analysis
        upload = requests.post(f"{MV_BASE}/assets", headers=MV_H, json={
            "url": f"https://www.youtube.com/watch?v={video_id}",
            "name": f"{brand} — {title[:50]}",
            "type": "video",
        }).json()

        # 4. Run Video Analysis
        analysis = requests.post(f"{MV_BASE}/video-analysis", headers=MV_H, json={
            "asset_id": upload["id"],
            "analysis_types": [
                "message_clarity", "emotional_impact", "brand_attribution",
                "hook_score", "cognitive_load", "pacing",
            ],
            "metadata": {"brand": brand, "video_id": video_id, "views": view_count},
        }).json()

        # 5. Poll for completion
        status = {}
        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

        results = status.get("results", {})
        all_analyses.append({
            "brand": brand, "title": title[:50], "video_id": video_id,
            "views": view_count,
            "message_clarity": results.get("message_clarity", {}).get("score", 0),
            "emotional_impact": results.get("emotional_impact", {}).get("score", 0),
            "brand_attribution": results.get("brand_attribution", {}).get("score", 0),
            "hook_score": results.get("hook_score", {}).get("score", 0),
        })
        time.sleep(1)

# 6. Send all scores to Mave for comparative ranking
scores_block = "\n".join(
    f"- {a['brand']}: \"{a['title']}\" — clarity {a['message_clarity']}/100, "
    f"emotion {a['emotional_impact']}/100, attribution {a['brand_attribution']}/100, "
    f"hook {a['hook_score']}/100, views {a['views']:,}"
    for a in all_analyses
)

ranking = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"""Rank these competitor YouTube ads in a competitive showdown.

SCORES:
{scores_block}

For each brand:
1. Overall rank and composite score
2. Biggest creative strength
3. Most exploitable weakness
4. How our brand could beat them (specific creative recommendation)

End with: which competitor is the biggest creative threat and why.""",
}).json()

print("COMPETITOR AD ANALYSIS SHOWDOWN")
print("=" * 60)
for a in sorted(all_analyses, key=lambda x: -x["emotional_impact"]):
    print(f"  {a['brand']:<12} {a['title'][:35]:<38} "
          f"Clarity:{a['message_clarity']:>3}  Emotion:{a['emotional_impact']:>3}  "
          f"Hook:{a['hook_score']:>3}")
print("\n" + ranking.get("content", "")[:1500])

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 COMPETITORS = ["Nike", "Adidas", "Puma"];
const allAnalyses = [];

for (const brand of COMPETITORS) {
  // 1. Search for competitor ads (100 quota units)
  const search = await fetch(
    `${YT_BASE}/search?key=${YT}&q=${encodeURIComponent(`${brand} official ad 2026`)}` +
    `&type=video&part=snippet&maxResults=3&order=viewCount&videoDuration=short`
  ).then(r => r.json());

  if (search.error) {
    console.error(`YouTube error for ${brand}: ${search.error.message}`);
    continue;
  }

  for (const item of search.items || []) {
    const videoId = item.id.videoId;
    const title = item.snippet.title;

    // 2. Video details (1 quota unit)
    const details = await fetch(
      `${YT_BASE}/videos?key=${YT}&id=${videoId}&part=snippet,contentDetails,statistics`
    ).then(r => r.json());
    const viewCount = parseInt(details.items?.[0]?.statistics?.viewCount || "0", 10);

    // 3. Upload to Mavera
    const upload = await fetch(`${MV_BASE}/assets`, {
      method: "POST", headers: MV_H,
      body: JSON.stringify({
        url: `https://www.youtube.com/watch?v=${videoId}`,
        name: `${brand} — ${title.slice(0, 50)}`,
        type: "video",
      }),
    }).then(r => r.json());

    // 4. Video Analysis
    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", "brand_attribution",
          "hook_score", "cognitive_load", "pacing",
        ],
        metadata: { brand, video_id: videoId, views: viewCount },
      }),
    }).then(r => r.json());

    // 5. Poll
    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 results = status.results || {};
    allAnalyses.push({
      brand, title: title.slice(0, 50), videoId, views: viewCount,
      messageClarity: results.message_clarity?.score || 0,
      emotionalImpact: results.emotional_impact?.score || 0,
      brandAttribution: results.brand_attribution?.score || 0,
      hookScore: results.hook_score?.score || 0,
    });
    await new Promise(r => setTimeout(r, 1000));
  }
}

// 6. Comparative ranking via Mave
const scoresBlock = allAnalyses.map(a =>
  `- ${a.brand}: "${a.title}" — clarity ${a.messageClarity}/100, ` +
  `emotion ${a.emotionalImpact}/100, attribution ${a.brandAttribution}/100, ` +
  `hook ${a.hookScore}/100, views ${a.views.toLocaleString()}`
).join("\n");

const ranking = await fetch(`${MV_BASE}/mave/chat`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    message: `Rank these competitor YouTube ads in a showdown.\n\n${scoresBlock}\n\nFor each brand: 1) Overall rank 2) Biggest strength 3) Most exploitable weakness 4) How to beat them. End with: who is the biggest creative threat?`,
  }),
}).then(r => r.json());

console.log("COMPETITOR AD ANALYSIS SHOWDOWN");
console.log("=".repeat(60));
allAnalyses
  .sort((a, b) => b.emotionalImpact - a.emotionalImpact)
  .forEach(a =>
    console.log(`  ${a.brand.padEnd(12)} ${a.title.slice(0, 35).padEnd(38)} ` +
      `Clarity:${String(a.messageClarity).padStart(3)}  Emotion:${String(a.emotionalImpact).padStart(3)}  ` +
      `Hook:${String(a.hookScore).padStart(3)}`)
  );
console.log("\n" + (ranking.content || "").slice(0, 1500));