Create anomaly_service.py

services/anomaly_service.py

@@ -0,0 +1,21 @@
+import numpy as np
+
+def track_anomalies(anomaly_counts):
+    """Track anomaly counts and return statistics."""
+    if not anomaly_counts:
+        return {"mean": 0, "std": 0}
+    mean = np.mean(anomaly_counts)
+    std = np.std(anomaly_counts)
+    return {"mean": mean, "std": std}
+
+def predict_anomaly(anomaly_counts, threshold=2.5):
+    """Predict potential issues based on anomaly spikes."""
+    if len(anomaly_counts) < 5:  # Need enough data for prediction
+        return False
+    stats = track_anomalies(anomaly_counts)
+    recent_counts = anomaly_counts[-5:]  # Last 5 frames
+    # Flag a potential issue if the recent anomaly count is significantly higher than the mean
+    for count in recent_counts:
+        if stats["std"] > 0 and (count - stats["mean"]) / stats["std"] > threshold:
+            return True
+    return False