Upload backend/scripts/report_metrics.py with huggingface_hub

backend/scripts/report_metrics.py

@@ -0,0 +1,107 @@
+import argparse
+import os
+import sys
+from datetime import datetime, date
+from pathlib import Path
+
+import django
+
+
+ROOT_DIR = Path(__file__).resolve().parents[2]
+BACKEND_DIR = ROOT_DIR / "backend"
+
+HUE_PORTAL_DIR = BACKEND_DIR / "hue_portal"
+
+for path in (HUE_PORTAL_DIR, BACKEND_DIR, ROOT_DIR):
+    if str(path) not in sys.path:
+        sys.path.insert(0, str(path))
+
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "hue_portal.hue_portal.settings")
+django.setup()
+
+from django.db.models import Avg, Count, Q
+from hue_portal.core.models import AuditLog, MLMetrics
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Tổng hợp metrics ML hàng ngày")
+    parser.add_argument("--date", help="Ngày cần tổng hợp (YYYY-MM-DD), mặc định hôm nay")
+    return parser.parse_args()
+
+
+def target_date(arg: str) -> date:
+    if not arg:
+        return date.today()
+    return datetime.strptime(arg, "%Y-%m-%d").date()
+
+
+def compute_metrics(day: date) -> dict:
+    logs = AuditLog.objects.filter(created_at__date=day)
+    total = logs.count()
+    if total == 0:
+        return {
+            "date": day.isoformat(),
+            "total_requests": 0,
+            "intent_accuracy": None,
+            "average_latency_ms": None,
+            "error_rate": None,
+            "intent_breakdown": {},
+        }
+
+    latency_avg = logs.exclude(latency_ms__isnull=True).aggregate(avg=Avg("latency_ms"))["avg"]
+    errors = logs.filter(status__gte=400).count()
+    intents_with_conf = logs.filter(~Q(intent=""), status__lt=400)
+    intent_accuracy = None
+    if intents_with_conf.exists():
+        confident = intents_with_conf.filter(Q(confidence__gte=0.6) | Q(confidence__isnull=True)).count()
+        intent_accuracy = confident / intents_with_conf.count()
+
+    breakdown = (
+        logs.exclude(intent="")
+        .values("intent")
+        .annotate(count=Count("id"))
+        .order_by("intent")
+    )
+    breakdown_dict = {row["intent"]: row["count"] for row in breakdown}
+
+    return {
+        "date": day.isoformat(),
+        "total_requests": total,
+        "intent_accuracy": intent_accuracy,
+        "average_latency_ms": latency_avg,
+        "error_rate": errors / total,
+        "intent_breakdown": breakdown_dict,
+    }
+
+
+def save_metrics(day: date, metrics: dict) -> MLMetrics:
+    obj, _ = MLMetrics.objects.update_or_create(
+        date=day,
+        defaults={
+            "total_requests": metrics["total_requests"],
+            "intent_accuracy": metrics["intent_accuracy"],
+            "average_latency_ms": metrics["average_latency_ms"],
+            "error_rate": metrics["error_rate"],
+            "intent_breakdown": metrics["intent_breakdown"],
+        },
+    )
+    return obj
+
+
+def main():
+    args = parse_args()
+    day = target_date(args.date)
+    metrics = compute_metrics(day)
+    save_metrics(day, metrics)
+
+    print("=== ML Metrics ===")
+    print(f"Ngày: {metrics['date']}")
+    print(f"Tổng request: {metrics['total_requests']}")
+    print(f"Độ chính xác (ước tính): {metrics['intent_accuracy']}")
+    print(f"Latency trung bình (ms): {metrics['average_latency_ms']}")
+    print(f"Tỉ lệ lỗi: {metrics['error_rate']}")
+    print(f"Phân bổ intent: {metrics['intent_breakdown']}")
+
+
+if __name__ == "__main__":
+    main()