Update app.py

app.py

@@ -590,6 +590,49 @@ def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
 
     # Run model on messages
     results = [(analyze_single_message(m, THRESHOLDS.copy()), d) for m, d in active]
+    # --- Combined Abuse Escalation Scoring ---
+
+# Extract predicted abuse labels from all messages
+    predicted_labels = [label for r in results for label, _ in r[0][2]]
+
+# Categorize by severity
+    high = {'control'}
+    moderate = {
+        'gaslighting', 'dismissiveness', 'obscure language',
+        'insults', 'contradictory statements', 'guilt tripping'
+    }
+    low = {'blame shifting', 'projection', 'recovery phase'}
+
+# Count severity types
+    counts = {'high': 0, 'moderate': 0, 'low': 0}
+    for label in predicted_labels:
+        if label in high:
+            counts['high'] += 1
+        elif label in moderate:
+            counts['moderate'] += 1
+        elif label in low:
+            counts['low'] += 1
+
+# Derive abuse_risk from combinations
+    if counts['high'] >= 2 and counts['moderate'] >= 2:
+        abuse_risk = 'Critical'
+    elif (counts['high'] >= 2 and counts['moderate'] >= 1) or (counts['moderate'] >= 3) or (counts['high'] >= 1 and counts['moderate'] >= 2):
+        abuse_risk = 'High'
+    elif (counts['moderate'] == 2) or (counts['high'] == 1 and counts['moderate'] == 1) or (counts['moderate'] == 1 and counts['low'] >= 2) or (counts['high'] == 1 and sum(counts.values()) == 1):
+        abuse_risk = 'Moderate'
+    else:
+        abuse_risk = 'Low'
+
+# Combine abuse_risk and checklist score into final risk_level
+    if escalation_score is not None:
+        if escalation_score >= 8 or abuse_risk == 'Critical':
+            risk_level = 'Critical'
+        elif escalation_score >= 5 or abuse_risk == 'High':
+            risk_level = 'High'
+        elif escalation_score >= 2 or abuse_risk == 'Moderate':
+            risk_level = 'Moderate'
+        else:
+        risk_level = 'Low'
     abuse_scores = [r[0][0] for r in results]
     top_labels = [r[0][1][0] if r[0][1] else r[0][2][0][0] for r in results]
     top_scores = [r[0][2][0][1] for r in results]