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>
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
"""Recommend target accounts from sector/city/goal — deterministic demo accounts."""
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from __future__ import annotations
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from typing import Any
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_SIGNALS = (
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"hiring_sales",
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"website_updated",
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"google_reviews_active",
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"booking_link",
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"growing_team",
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)
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def recommend_accounts(
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sector: str,
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city: str,
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offer: str,
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goal: str,
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*,
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limit: int = 10,
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) -> dict[str, Any]:
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sector_ar = sector or "خدمات B2B"
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city_ar = city or "الرياض"
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base = [
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{
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"company": f"شركة ألفا — {sector_ar}",
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"city": city_ar,
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"fit_score": 88,
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"why_now_ar": "إعلان وظائف مبيعات + صفحة خدمات محدثة.",
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"best_channel": "email_first",
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"risk_level": "low",
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"signals": ["hiring_sales", "website_updated"],
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},
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{
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"company": f"مؤسسة بيتا — {sector_ar}",
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"city": city_ar,
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"fit_score": 82,
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"why_now_ar": "تقييمات Google نشطة — فرصة سمعة محلية.",
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"best_channel": "google_business_draft",
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"risk_level": "low",
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"signals": ["google_reviews_active", "booking_link"],
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},
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{
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"company": f"مجموعة جاما — {sector_ar}",
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"city": "جدة",
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"fit_score": 76,
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"why_now_ar": "توسع فريق — احتمال شراء أدوات نمو.",
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"best_channel": "linkedin_lead_form",
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"risk_level": "medium",
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"signals": ["growing_team"],
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},
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]
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accounts = []
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for i in range(max(1, min(limit, 20))):
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a = dict(base[i % len(base)])
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a["id"] = f"acct_demo_{i+1}"
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a["company"] = f"{a['company']} ({i+1})"
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a["offer_fit_ar"] = f"العرض «{offer or 'Growth OS'}» مناسب لهدف «{goal or 'نمو'}»."
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accounts.append(a)
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return {"accounts": accounts[:limit], "count": len(accounts[:limit]), "demo": True}
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def score_account_fit(account: dict[str, Any]) -> int:
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return int(account.get("fit_score") or 70)
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def explain_why_now(account: dict[str, Any]) -> str:
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return str(account.get("why_now_ar") or "إشارات سوق عامة — راجع التفاصيل قبل التواصل.")
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def recommend_account_source_strategy(account: dict[str, Any]) -> dict[str, Any]:
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ch = str(account.get("best_channel") or "email_first")
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return {
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"account_id": account.get("id"),
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"recommended_first_touch": ch,
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"steps_ar": [
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"تحقق من المصدر والـ opt-in.",
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"جهّز مسودة بريد عبر المنصة.",
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"لا واتساب بارد بدون علاقة.",
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],
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"demo": True,
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}
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def rank_accounts(accounts: list[dict[str, Any]]) -> list[dict[str, Any]]:
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return sorted(accounts, key=lambda x: -score_account_fit(x))
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