ما هو MLOps؟

from dataclasses import dataclass, field
from typing import List, Tuple

# ─── MLOps Maturity Model ──────────────────────────────────
@dataclass
class MLProject:
    name:               str
    model_type:         str
    version:            str   = "1.0.0"
    accuracy:           float = 0.0
    is_deployed:        bool  = False
    tracking_enabled:   bool  = False
    ci_cd_enabled:      bool  = False
    monitoring_enabled: bool  = False

    def mlops_score(self) -> int:
        return sum([
            25 if self.tracking_enabled   else 0,
            25 if self.is_deployed        else 0,
            25 if self.ci_cd_enabled      else 0,
            25 if self.monitoring_enabled else 0,
        ])

    def maturity_level(self) -> str:
        s = self.mlops_score()
        if s == 0:  return "Level 0 — Manual (لا أتمتة)"
        if s <= 25: return "Level 1 — Tracking"
        if s <= 50: return "Level 2 — Deployed"
        if s <= 75: return "Level 3 — CI/CD"
        return       "Level 4 — Full MLOps ✨"

    def report(self):
        score = self.mlops_score()
        level = self.maturity_level()
        print(f"\n📊 {self.name} (v{self.version})")
        print(f"   النوع    : {self.model_type}")
        print(f"   Accuracy : {self.accuracy:.1%}")
        print(f"   Score    : {score}/100 — {level}")
        checks: List[Tuple[str, bool]] = [
            ("Experiment Tracking", self.tracking_enabled),
            ("Deployed to Prod",    self.is_deployed),
            ("CI/CD Automated",     self.ci_cd_enabled),
            ("Monitoring Active",   self.monitoring_enabled),
        ]
        for label, ok in checks:
            icon = "✅" if ok else "❌"
            print(f"   {icon} {label}")

# ─── قبل MLOps ─────────────────────────────────────────────
print("🔬 الوضع بدون MLOps:")
print("-" * 50)
proto = MLProject(
    name="sentiment-model", model_type="Text Classifier",
    version="1.0.0", accuracy=0.89,
)
proto.report()

# ─── بعد MLOps ─────────────────────────────────────────────
print(f"\n🚀 الوضع بعد تطبيق MLOps:")
print("-" * 50)
prod = MLProject(
    name="sentiment-model", model_type="Text Classifier",
    version="2.3.1", accuracy=0.94,
    is_deployed=True, tracking_enabled=True,
    ci_cd_enabled=True, monitoring_enabled=True,
)
prod.report()

# ─── مكونات MLOps ──────────────────────────────────────────
print(f"\n\n🧱 مكونات MLOps الرئيسية:")
components = [
    ("Data Management",  "DVC, Delta Lake",        "إدارة وإصدار البيانات"),
    ("Experiment Track", "MLflow, W&B",             "تتبع التجارب والمعاملات"),
    ("Model Registry",   "MLflow, HuggingFace Hub", "تخزين وإصدار النماذج"),
    ("CI/CD",            "GitHub Actions, Jenkins", "أتمتة الاختبار والنشر"),
    ("Serving",          "FastAPI, TorchServe",     "خدمة النموذج للإنتاج"),
    ("Monitoring",       "Evidently, Grafana",      "مراقبة الجودة والانجراف"),
]
for comp, tools, desc in components:
    print(f"  📦 {comp:<20} — {desc}")
    print(f"     أدوات: {tools}")
    print()