import os, requests, time, re
EL_KEY = os.environ["ELEVENLABS_API_KEY"]
EL_BASE = "https://api.elevenlabs.io/v1"
MV = os.environ["MAVERA_API_KEY"]
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}
os.makedirs("ad_production", exist_ok=True)
VOICE_ID, QUALITY_THRESHOLD = "21m00Tcm4TlvDq8ikWAM", 8
# 1. Generate ad script variations
gen = requests.post(f"{MV_BASE}/generations", headers=MV_H, json={
"prompt": "Write 3 variations of a 30-second radio ad for 'Beacon' AI marketing analytics. "
"Target: marketing directors. Confident tone. Each: hook, proof point, CTA.\n"
"VARIATION A: problem-focused. VARIATION B: outcome-focused. VARIATION C: story-focused.",
}).json()
script_text = gen.get("output") or gen.get("content") or ""
variations = [v.strip() for v in re.split(r"VARIATION\s+[A-C]", script_text) if v.strip()]
print(f"Generated {len(variations)} variations")
results = []
for vi, script in enumerate(variations[:3]):
label = chr(65 + vi)
settings = {"stability": 0.5 + vi * 0.05, "similarity_boost": 0.75 + vi * 0.05,
"style": 0.3 + vi * 0.1, "use_speaker_boost": True}
best_score, best_path = 0, None
for take in range(1, 4):
# 2. Generate audio
tts = requests.post(f"{EL_BASE}/text-to-speech/{VOICE_ID}",
headers={"xi-api-key": EL_KEY, "Content-Type": "application/json"},
json={"text": script, "model_id": "eleven_multilingual_v2", "voice_settings": settings})
if tts.status_code != 200:
print(f" TTS error {tts.status_code}"); time.sleep(3); continue
path = f"ad_production/var_{label}_take_{take}.mp3"
with open(path, "wb") as f: f.write(tts.content)
print(f"Var {label} Take {take}: {path} ({len(tts.content) // 1024} KB)")
time.sleep(2)
# 3. Quality assessment
quality = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
"message": f"Audio ad quality assessor. Variation {label}, Take {take}.\n"
f"SCRIPT:\n{script}\nSETTINGS: {settings}\n\n"
"Score 1-10: PACING, HOOK STRENGTH, CLARITY, EMOTIONAL IMPACT, CTA EFFECTIVENESS.\n"
"OVERALL SCORE (1-10). If below 8, suggest adjusted stability/similarity_boost/style values.",
}).json()
feedback = quality.get("content", "")
score = int(m[1]) if (m := re.search(r"OVERALL[^0-9]*(\d+)", feedback, re.I)) else 0
print(f" Score: {score}/10")
if score > best_score: best_score, best_path = score, path
if score >= QUALITY_THRESHOLD: print(" Production-ready!"); break
# 4. Adjust voice settings from feedback
for key, pat in [("stability", r"stability[:\s]*([\d.]+)"),
("similarity_boost", r"similarity[_ ]?boost[:\s]*([\d.]+)"),
("style", r"style[:\s]*([\d.]+)")]:
if m := re.search(pat, feedback, re.I):
settings[key] = min(1.0, max(0.0, float(m[1])))
time.sleep(2)
results.append({"var": label, "score": best_score, "path": best_path})
print(f"\n{'='*60}\nPRODUCTION SUMMARY\n{'='*60}")
for r in results:
status = "APPROVED" if r["score"] >= QUALITY_THRESHOLD else "NEEDS REVIEW"
print(f" Variation {r['var']}: {r['score']}/10 — {status} — {r['path']}")
winner = max(results, key=lambda r: r["score"])
print(f"\nRecommended: Variation {winner['var']} ({winner['score']}/10)")
