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"}
DRAFT_VIDEO_ID = "123456789"
THRESHOLDS = {
"message_clarity": 70,
"emotional_impact": 65,
"hook_score": 60,
"behavioral_effectiveness": 60,
}
# 1. Fetch the draft video from Vimeo
draft = requests.get(f"{VM_BASE}/videos/{DRAFT_VIDEO_ID}", headers=VM_H, params={
"fields": "uri,name,link,duration,privacy,status,pictures.sizes",
}).json()
if "error" in draft:
raise SystemExit(f"Vimeo API error: {draft.get('developer_message', draft.get('error', ''))}")
print(f"Draft: \"{draft['name']}\" | Duration: {draft.get('duration', 0)}s | Privacy: {draft.get('privacy', {}).get('view', 'unknown')}")
# 2. Upload to Mavera and run Video Analysis
upload = requests.post(f"{MV_BASE}/assets", headers=MV_H, json={
"url": draft["link"], "name": f"[DRAFT] {draft['name'][:70]}", "type": "video",
}).json()
analysis = requests.post(f"{MV_BASE}/video-analysis", headers=MV_H, json={
"asset_id": upload["id"],
"analysis_types": list(THRESHOLDS.keys()) + ["pacing", "cognitive_load", "emotional_arc"],
"metadata": {"vimeo_id": DRAFT_VIDEO_ID, "stage": "pre-publish"},
}).json()
# 3. Poll for results
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", {})
scores = {
metric: results.get(metric, {}).get("score", 0)
for metric in THRESHOLDS
}
# 4. Check thresholds
failures = {
metric: {"score": scores[metric], "threshold": threshold}
for metric, threshold in THRESHOLDS.items()
if scores[metric] < threshold
}
print(f"\nSCORECARD:")
for metric, threshold in THRESHOLDS.items():
score = scores[metric]
verdict = "PASS" if score >= threshold else "FAIL"
print(f" {metric:<30} {score:>3}/100 (threshold: {threshold}) [{verdict}]")
if not failures:
print("\n✓ ALL THRESHOLDS MET — Ready to publish")
else:
print(f"\n✗ {len(failures)} THRESHOLD(S) FAILED — Triggering Focus Group review")
# 5. Create Focus Group for improvement feedback
persona_ids = []
for name, desc in [
("Target Customer", "35-year-old marketing director evaluating SaaS tools. Watches product videos to assess vendor quality. Judges professionalism, clarity, and whether the video respects their time."),
("Creative Director", "Senior creative with 15 years in video production. Evaluates pacing, visual storytelling, emotional arc, and hook effectiveness. Gives specific, actionable feedback."),
("Skeptical Buyer", "CFO who dislikes marketing fluff. Wants data, proof points, and clear ROI messaging. Will disengage immediately if the video feels like a hard sell."),
]:
p = requests.post(f"{MV_BASE}/personas", headers=MV_H, json={
"name": name, "description": desc,
}).json()
persona_ids.append(p["id"])
time.sleep(0.3)
failure_summary = "\n".join(
f"- {m}: scored {f['score']}/100 (needs {f['threshold']}+)"
for m, f in failures.items()
)
arc_summary = results.get("emotional_arc", {}).get("summary", "No arc data available")
fg = requests.post(f"{MV_BASE}/focus-groups", headers=MV_H, json={
"name": f"Pre-Publish Review — {draft['name'][:40]}",
"persona_ids": persona_ids,
"questions": [
f"This video failed quality thresholds:\n{failure_summary}\n\nEmotional arc: {arc_summary}\n\nWhat specifically could make this video more compelling?",
"If you could change only ONE thing about the first 5 seconds, what would it be?",
"What is this video trying to make you feel? Is it working? What emotion is missing?",
"Would you share this video with a colleague? Why or why not? What would change your answer?",
],
"responses_per_persona": 2,
}).json()
for _ in range(20):
time.sleep(5)
fg_data = requests.get(f"{MV_BASE}/focus-groups/{fg['id']}", headers=MV_H).json()
if fg_data.get("status") == "completed":
break
print("\nFOCUS GROUP FEEDBACK:")
for resp in fg_data.get("responses", []):
print(f"\n[{resp.get('persona_name', '?')}] {resp.get('question', '')[:60]}...")
print(f" → {resp.get('answer', '')[:400]}")
