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https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
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56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
"""Monthly ROI / proof pack — demo structures."""
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from __future__ import annotations
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from typing import Any
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def build_demo_proof_pack() -> dict[str, Any]:
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return {
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"executive_summary_ar": "شهر تجريبي: 12 فرصة مؤهلة، 4 اجتماعات بعد موافقة، 0 إرسال بارد، 3 تسريبات إيرادات مكتشفة.",
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"pipeline_created_sar": 180000,
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"qualified_leads": 12,
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"meetings_booked": 4,
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"response_rates": {"whatsapp_opt_in": 0.41, "email": 0.18},
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"revenue_influenced_sar": 42000,
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"top_signals": ["hiring_sales", "booking_link", "new_branch"],
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"best_messages_ar": ["مقدمة قصيرة + سبب الآن + طلب اجتماع 20 دقيقة"],
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"blocked_risky_outreach": 6,
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"revenue_leaks_found": 3,
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"next_month_plan_ar": "توسيع قطاع واحد + ربط proof pack تلقائياً من Revenue Memory.",
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"roi_calculation": calculate_roi_summary(
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subscription_sar=2999,
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influenced_revenue_sar=42000,
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hours_saved=18,
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),
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"renewal_recommendation_ar": "تجديد مع إضافة أداء مؤهل إذا وُجدت عقود تأهيل.",
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}
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def calculate_roi_summary(
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*,
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subscription_sar: float,
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influenced_revenue_sar: float,
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hours_saved: float,
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hourly_cost_sar: float = 350.0,
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) -> dict[str, Any]:
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saved_sar = hours_saved * hourly_cost_sar
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multiple = round((influenced_revenue_sar + saved_sar) / subscription_sar, 2) if subscription_sar else 0.0
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return {
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"subscription_sar": subscription_sar,
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"influenced_revenue_sar": influenced_revenue_sar,
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"time_value_sar": round(saved_sar, 2),
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"value_to_price_multiple": multiple,
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}
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def grade_account_health(
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*,
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brief_opens_4w: int,
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approvals_4w: int,
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blocks_4w: int,
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) -> dict[str, Any]:
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score = min(100, brief_opens_4w * 3 + approvals_4w * 5 + min(blocks_4w, 10) * 2)
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status = "healthy" if score >= 60 else "at_risk"
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return {"health_score": score, "status": status}
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