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345855e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | import asyncio
import uuid
from datetime import datetime
# -------------------------------------------------
# SAFE DEFAULTS
# -------------------------------------------------
DEFAULT_PERSONA = {
"age_range": "18-34",
"interests": ["content creation", "social media growth"],
"behavior": "scroll-heavy short-form consumption"
}
# -------------------------------------------------
# CONTEXT NORMALIZER
# -------------------------------------------------
def normalize_context(context):
if isinstance(context, dict):
return {
"words": context.get("words") or context.get("transcript") or [],
"video_path": context.get("video_path"),
}
return {
"words": getattr(context, "words", None) or getattr(context, "transcript", None) or [],
"video_path": getattr(context, "video_path", None)
}
# -------------------------------------------------
# SIMPLE VIRAL HEURISTICS ENGINE
# (replaces fragile LLM dependency assumptions)
# -------------------------------------------------
def compute_hook(words):
if not words:
return "Create content that hooks attention in the first 3 seconds."
# crude heuristic: pick first 12 words
text = " ".join(words if isinstance(words, list) else [])
return text.split(".")[0][:120]
def compute_viral_score(words):
if not words:
return 50
length = len(words)
# heuristic scoring model (deterministic)
score = min(95, 40 + (length / 50))
return round(score, 2)
def detect_platform_fit(score):
if score >= 80:
return ["tiktok", "reels", "youtube-shorts"]
if score >= 60:
return ["tiktok", "reels"]
return ["reels"]
# -------------------------------------------------
# RETENTION CURVE SIMULATOR
# -------------------------------------------------
def simulate_retention_curve(words):
if not words:
return [1.0, 0.7, 0.5, 0.3]
n = len(words)
return [
1.0,
max(0.7, 1 - (n * 0.001)),
max(0.4, 1 - (n * 0.002)),
max(0.2, 1 - (n * 0.003)),
]
# -------------------------------------------------
# MAIN STRATEGY ENGINE
# -------------------------------------------------
async def run(context):
ctx = normalize_context(context)
batch_id = str(uuid.uuid4())
started_at = datetime.utcnow().isoformat()
try:
words = ctx.get("words") or []
# -------------------------------------------------
# CORE STRATEGY OUTPUTS
# -------------------------------------------------
hook = compute_hook(words)
viral_score = compute_viral_score(words)
platforms = detect_platform_fit(viral_score)
retention_curve = simulate_retention_curve(words)
# -------------------------------------------------
# STRUCTURED RESPONSE (CRITICAL FOR REGISTRY)
# -------------------------------------------------
result = {
"status": "success",
"task": "strategy",
"batch_id": batch_id,
"started_at": started_at,
"completed_at": datetime.utcnow().isoformat(),
# core outputs
"hook": hook,
"viral_score": viral_score,
"platforms": platforms,
# structured sub-blocks (UI + publisher consumption)
"persona": DEFAULT_PERSONA,
"retention_curve": retention_curve,
"strategy": {
"recommended_length_sec": min(60, max(15, len(words) // 3)),
"hook_strength": "high" if viral_score > 75 else "medium",
"distribution_priority": platforms
}
}
return result
except Exception as e:
return {
"status": "error",
"task": "strategy",
"batch_id": batch_id,
"message": str(e),
"stage": "strategy_failed"
} |