import os, requests, time
WS = os.environ["WISTIA_API_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
WS_BASE = "https://api.wistia.com"
MV_BASE = "https://app.mavera.io/api/v1"
WS_H = {"Authorization": f"Bearer {WS}", "Accept": "application/json"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}
MEDIA_HASHED_ID = "abc123def4"
# 1. Fetch media stats with engagement data
media_stats = requests.get(
f"{WS_BASE}/v1/stats/medias/{MEDIA_HASHED_ID}.json", headers=WS_H
).json()
media_name = media_stats.get("name", MEDIA_HASHED_ID)
duration = media_stats.get("duration", 0)
play_count = media_stats.get("play_count", 0)
engagement = media_stats.get("engagement", 0)
print(f"Video: \"{media_name}\" | Duration: {duration:.0f}s | Plays: {play_count} | Engagement: {engagement:.1%}")
# 2. Get engagement graph (per-second retention)
engagement_data = media_stats.get("engagement_data", [])
if not engagement_data:
medias_resp = requests.get(
f"{WS_BASE}/v1/stats/medias/{MEDIA_HASHED_ID}/engagement.json", headers=WS_H
).json()
engagement_data = medias_resp if isinstance(medias_resp, list) else []
# 3. Identify drop-off points (where retention drops >10% within 5 seconds)
drop_offs = []
for i in range(5, len(engagement_data)):
current = engagement_data[i]
previous = engagement_data[i - 5]
drop = previous - current
if drop > 0.10:
timestamp_sec = i
mins = timestamp_sec // 60
secs = timestamp_sec % 60
drop_offs.append({
"timestamp": f"{mins}:{secs:02d}",
"seconds": timestamp_sec,
"retention_before": round(previous * 100, 1),
"retention_after": round(current * 100, 1),
"drop_pct": round(drop * 100, 1),
})
# Deduplicate nearby drops (keep the steepest within 10-second windows)
filtered_drops = []
for d in sorted(drop_offs, key=lambda x: -x["drop_pct"]):
if not any(abs(d["seconds"] - f["seconds"]) < 10 for f in filtered_drops):
filtered_drops.append(d)
filtered_drops.sort(key=lambda x: x["seconds"])
print(f"\nIdentified {len(filtered_drops)} significant drop-off points:")
for d in filtered_drops:
print(f" {d['timestamp']} — Retention: {d['retention_before']}% → {d['retention_after']}% (−{d['drop_pct']}%)")
# 4. Send to Mave for creative optimization recommendations
drop_block = "\n".join(
f"- At {d['timestamp']}: retention drops from {d['retention_before']}% to "
f"{d['retention_after']}% (−{d['drop_pct']}% in 5 seconds)"
for d in filtered_drops
)
# 5. Build retention curve summary
curve_points = []
for pct in [0, 10, 25, 50, 75, 90, 100]:
idx = int(len(engagement_data) * pct / 100)
if idx < len(engagement_data):
curve_points.append(f" {pct}% through: {engagement_data[idx]*100:.1f}% still watching")
optimization = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
"message": f"""Analyze these video drop-off points and suggest specific creative edits.
VIDEO: "{media_name}" ({duration:.0f}s, {play_count:,} plays)
OVERALL ENGAGEMENT: {engagement:.1%}
RETENTION CURVE:
{chr(10).join(curve_points)}
DROP-OFF POINTS:
{drop_block}
For each drop-off point:
1. **Likely Cause**: What typically causes viewers to leave at this point in a video? Research best practices.
2. **Specific Edit**: Describe exactly what to change at this timestamp (cut, restructure, add element, change pacing)
3. **Before/After**: Write the transition from what's probably happening now to what should happen instead
4. **Expected Impact**: Estimate retention improvement if this edit is made
Also provide:
5. **Opening Assessment**: Is the first 5 seconds strong enough? What would improve it?
6. **Pacing Diagnosis**: Is the video front-loaded, back-loaded, or evenly paced?
7. **Optimal Length**: Based on the retention curve, what should this video's runtime be?""",
}).json()
print("\nHEATMAP-INFORMED CREATIVE OPTIMIZATION")
print("=" * 60)
print(f"Video: {media_name}")
print(f"Plays: {play_count:,} | Engagement: {engagement:.1%} | Drop-offs: {len(filtered_drops)}")
print("\n" + optimization.get("content", "")[:2500])
