Update model/anomaly_detector.py

model/anomaly_detector.py

@@ -2,39 +2,38 @@ import numpy as np
 from sklearn.ensemble import IsolationForest
 from utils.preprocessing import extract_features
 
-# Dummy model with placeholder training
-model = IsolationForest(contamination=0.2, random_state=42)
-example_training_data = np.array([
+# Dummy training data (should be replaced with real labeled data)
+train_data = np.array([
     [5, 0, 30, 0.1],
     [10, 1, 50, 0.2],
     [2, 2, 10, 0.3],
     [8, 0, 45, 0.05],
     [3, 1, 12, 0.4]
 ])
-model.fit(example_training_data)
+model = IsolationForest(contamination=0.2, random_state=42)
+model.fit(train_data)
 
-def detect_anomaly(agent_data):
+def detect_anomaly_plain_text(agent_data):
     features = extract_features(agent_data)
-    prediction = model.predict([features])[0]  # -1 = anomaly
+    prediction = model.predict([features])[0]  # -1 is anomaly
     flagged = prediction == -1
 
-    # Override logic: manually flagging on rules
     reasons = []
+    if flagged:
+        if agent_data["missed_visits"] >= 5:
+            reasons.append("a spike in missed visits")
+        if agent_data["weekly_calls"] <= 3:
+            reasons.append("low call frequency")
+        if agent_data["lead_drop_rate"] > 0.6:
+            reasons.append("a high lead drop rate")
+        if agent_data["travel_distance"] < 10:
+            reasons.append("unusual travel distance")
 
-    if agent_data["missed_visits"] >= 5:
-        reasons.append("Missed appointments spike")
-    if agent_data["weekly_calls"] < 3:
-        reasons.append("Low call frequency")
-    if agent_data["lead_drop_rate"] > 0.6:
-        reasons.append("High lead drop rate")
-
-    if reasons:
-        flagged = True
+    reason_text = (
+        f"Agent {agent_data['agent_id']} has been flagged for anomalous behavior due to "
+        + ", ".join(reasons) + "."
+        if flagged else
+        f"Agent {agent_data['agent_id']} shows normal behavior based on current data."
+    )
 
-    return {
-        "agent_id": str(agent_data.get("agent_id", "unknown")),
-        "weekly_calls": int(agent_data["weekly_calls"]),
-        "missed_visits": int(agent_data["missed_visits"]),
-        "flagged": bool(flagged),
-        "reason": ", ".join(reasons) if flagged else "Normal behavior"
-    }
+    return reason_text