Sound/Music Impact Analysis

import os, requests, time

MV = os.environ["MAVERA_API_KEY"]
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

VIDEO_VARIANTS = [
    {"path": "ads/product_demo_trending_sound.mp4", "label": "Trending Sound", "audio": "Espresso remix — high energy, beat drops at cuts"},
    {"path": "ads/product_demo_original_music.mp4", "label": "Original Music", "audio": "Custom lo-fi track — chill, ambient, brand-composed"},
    {"path": "ads/product_demo_voiceover.mp4", "label": "Voiceover Only", "audio": "Founder narration — direct, educational, no background music"},
    {"path": "ads/product_demo_asmr.mp4", "label": "ASMR / Product Sounds", "audio": "Product interaction sounds — tapping, pouring, unboxing"},
]

# 1. Upload all variants and run analysis
analyses = []
for variant in VIDEO_VARIANTS:
    upload = requests.post(f"{MV_BASE}/assets",
        headers={"Authorization": f"Bearer {MV}"},
        files={"file": (variant["label"] + ".mp4", open(variant["path"], "rb"), "video/mp4")},
    ).json()

    analysis = requests.post(f"{MV_BASE}/video-analysis", headers=MV_H, json={
        "asset_id": upload["id"],
        "analysis_types": ["emotional_arc", "mood_congruence", "pacing", "hook_score", "audio_impact"],
        "metadata": {"label": variant["label"], "audio_description": variant["audio"]},
    }).json()

    analyses.append({"id": analysis["id"], "label": variant["label"], "audio": variant["audio"]})
    time.sleep(0.5)

# 2. Poll all analyses
results = []
for a in analyses:
    for _ in range(30):
        time.sleep(3)
        status = requests.get(f"{MV_BASE}/video-analysis/{a['id']}", headers=MV_H).json()
        if status.get("status") == "completed":
            break
    r = status.get("results", {})
    results.append({
        "label": a["label"],
        "audio": a["audio"],
        "emotional_intensity": r.get("emotional_arc", {}).get("intensity_avg", 0),
        "peak_emotion": r.get("emotional_arc", {}).get("peak_emotion", "N/A"),
        "peak_timestamp": r.get("emotional_arc", {}).get("peak_timestamp", 0),
        "mood_congruence": r.get("mood_congruence", {}).get("score", 0),
        "pacing_score": r.get("pacing", {}).get("score", 0),
        "hook_score": r.get("hook_score", {}).get("score", 0),
        "audio_energy": r.get("audio_impact", {}).get("energy", 0),
    })

# 3. Comparative report
results.sort(key=lambda x: -x["emotional_intensity"])

print("SOUND/MUSIC IMPACT ANALYSIS — Same Visual, Different Audio")
print("=" * 70)
print(f"{'Variant':<22} {'Emotion':<10} {'Mood Fit':<10} {'Pacing':<10} {'Hook':<8} {'Audio E'}")
print("-" * 70)
for r in results:
    print(f"{r['label']:<22} {r['emotional_intensity']:.1f}/10   {r['mood_congruence']:.1f}/10   {r['pacing_score']:.1f}/10   {r['hook_score']}/100  {r['audio_energy']:.1f}")

# 4. Recommendation
best = results[0]
worst = results[-1]
delta = best["emotional_intensity"] - worst["emotional_intensity"]
print(f"\nWINNER: {best['label']}")
print(f"  Emotional intensity: {best['emotional_intensity']:.1f}/10 (peak: {best['peak_emotion']} at {best['peak_timestamp']}s)")
print(f"  Mood congruence: {best['mood_congruence']:.1f}/10")
print(f"  vs worst ({worst['label']}): +{delta:.1f} emotional intensity")
print(f"\nRECOMMENDATION: Use '{best['label']}' audio treatment for this visual.")
if best["mood_congruence"] < 6:
    print(f"  ⚠ Mood congruence is low ({best['mood_congruence']:.1f}). Audio energy may not match visual tone — test with audience.")

const MV = process.env.MAVERA_API_KEY;
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const fs = await import("fs");

const VIDEO_VARIANTS = [
  { path: "ads/product_demo_trending_sound.mp4", label: "Trending Sound", audio: "Espresso remix — high energy" },
  { path: "ads/product_demo_original_music.mp4", label: "Original Music", audio: "Custom lo-fi — chill, ambient" },
  { path: "ads/product_demo_voiceover.mp4", label: "Voiceover Only", audio: "Founder narration — educational" },
  { path: "ads/product_demo_asmr.mp4", label: "ASMR / Product Sounds", audio: "Product sounds — tapping, unboxing" },
];

// 1. Upload and analyze
const analyses = [];
for (const variant of VIDEO_VARIANTS) {
  const formData = new FormData();
  const fileBuffer = fs.readFileSync(variant.path);
  formData.append("file", new Blob([fileBuffer], { type: "video/mp4" }), `${variant.label}.mp4`);

  const upload = await fetch(`${MV_BASE}/assets`, {
    method: "POST", headers: { Authorization: `Bearer ${MV}` }, body: formData,
  }).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", "mood_congruence", "pacing", "hook_score", "audio_impact"],
      metadata: { label: variant.label, audio_description: variant.audio },
    }),
  }).then(r => r.json());

  analyses.push({ id: analysis.id, label: variant.label, audio: variant.audio });
  await new Promise(r => setTimeout(r, 500));
}

// 2. Poll
const results = [];
for (const a of analyses) {
  let status;
  for (let i = 0; i < 30; i++) {
    await new Promise(r => setTimeout(r, 3000));
    status = await fetch(`${MV_BASE}/video-analysis/${a.id}`, { headers: MV_H }).then(r => r.json());
    if (status.status === "completed") break;
  }
  const r = status.results || {};
  results.push({
    label: a.label, audio: a.audio,
    emotional_intensity: r.emotional_arc?.intensity_avg || 0,
    peak_emotion: r.emotional_arc?.peak_emotion || "N/A",
    peak_timestamp: r.emotional_arc?.peak_timestamp || 0,
    mood_congruence: r.mood_congruence?.score || 0,
    pacing_score: r.pacing?.score || 0,
    hook_score: r.hook_score?.score || 0,
    audio_energy: r.audio_impact?.energy || 0,
  });
}

// 3. Report
results.sort((a, b) => b.emotional_intensity - a.emotional_intensity);
console.log("SOUND/MUSIC IMPACT ANALYSIS — Same Visual, Different Audio");
console.log("=".repeat(70));
console.log("Variant               Emotion   Mood Fit  Pacing    Hook    Audio E");
console.log("-".repeat(70));
for (const r of results) {
  console.log(`${r.label.padEnd(22)} ${r.emotional_intensity.toFixed(1)}/10   ${r.mood_congruence.toFixed(1)}/10   ${r.pacing_score.toFixed(1)}/10   ${r.hook_score}/100  ${r.audio_energy.toFixed(1)}`);
}

const best = results[0];
const worst = results[results.length - 1];
console.log(`\nWINNER: ${best.label}`);
console.log(`  Emotional intensity: ${best.emotional_intensity.toFixed(1)}/10 (peak: ${best.peak_emotion} at ${best.peak_timestamp}s)`);
console.log(`  vs worst (${worst.label}): +${(best.emotional_intensity - worst.emotional_intensity).toFixed(1)} emotional intensity`);