mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
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b13cb389cc
- API routers, ACA modules, integrations (draft operators) - Docs, landing pages, scripts (launch readiness, scorecard) - Tests and CI workflow updates for Dealix Co-authored-by: Cursor <cursoragent@cursor.com>
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
"""Channel reputation — pause suggestions when metrics look bad."""
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from __future__ import annotations
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from typing import Any
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def risk_thresholds() -> dict[str, float]:
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return {
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"bounce_rate_max": 0.08,
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"opt_out_rate_max": 0.02,
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"complaint_rate_max": 0.001,
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"min_reply_rate": 0.05,
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}
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def calculate_channel_reputation(metrics: dict[str, Any]) -> dict[str, Any]:
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bounce = float(metrics.get("bounce_rate") or 0)
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opt_out = float(metrics.get("opt_out_rate") or 0)
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complaint = float(metrics.get("complaint_rate") or 0)
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reply = float(metrics.get("reply_rate") or 0)
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th = risk_thresholds()
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score = 100.0
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if bounce > th["bounce_rate_max"]:
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score -= 30
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if opt_out > th["opt_out_rate_max"]:
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score -= 25
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if complaint > th["complaint_rate_max"]:
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score -= 40
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if reply < th["min_reply_rate"]:
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score -= 15
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return {"reputation_score": max(0, min(100, int(score))), "raw": metrics, "demo": True}
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def should_pause_channel(metrics: dict[str, Any]) -> bool:
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rep = calculate_channel_reputation(metrics)
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return rep["reputation_score"] < 40
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def recommend_recovery_action(metrics: dict[str, Any]) -> dict[str, Any]:
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if should_pause_channel(metrics):
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return {
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"action_ar": "أوقف الإرسال، راجع القوائم والمصادر، قلّل الحجم، أعد تفعيل القناة بعد تحسين المحتوى.",
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"demo": True,
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}
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return {"action_ar": "استمر مع مراقبة يومية للردود وopt-out.", "demo": True}
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def summarize_reputation_ar(metrics: dict[str, Any]) -> str:
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rep = calculate_channel_reputation(metrics)
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return f"درجة السمعة للقناة: {rep['reputation_score']}/100."
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