Update app.py

app.py

@@ -249,10 +249,14 @@ def analyze_composite(msg1, msg2, msg3, *answers_and_none):
     top_scores = [r[2][0][1] for r in results]
     sentiments = [r[3]['label'] for r in results]
     stages = [r[4] for r in results]
+    darvo_scores = [r[5] for r in results]
 
     most_common_stage = max(set(stages), key=stages.count)
     stage_text = RISK_STAGE_LABELS[most_common_stage]
-
+    avg_darvo = round(sum(darvo_scores) / len(darvo_scores), 3)
+    if avg_darvo > 0.25:
+        level = "moderate" if avg_darvo < 0.65 else "high"
+        out += f"\n\n🎭 **DARVO Score: {avg_darvo}** → This indicates a **{level} likelihood** of narrative reversal (DARVO), where the speaker may be denying, attacking, or reversing blame."
     top_label = f"{top_labels[0]} – {int(round(top_scores[0] * 100))}%"
     composite_abuse = int(round(sum(abuse_scores) / len(abuse_scores)))
 
@@ -262,7 +266,13 @@ def analyze_composite(msg1, msg2, msg3, *answers_and_none):
     out += "🚨 This indicates how many serious risk factors are present based on your answers to the safety checklist.\n\n"
     out += generate_risk_snippet(composite_abuse, top_label, escalation_score)
     out += f"\n\n{stage_text}"
-    return out
+
+    avg_darvo = round(sum(darvo_scores) / len(darvo_scores), 3)
+        if avg_darvo > 0.25:
+        level = "moderate" if avg_darvo < 0.65 else "high"
+        out += f"\n\n🎭 **DARVO Score: {avg_darvo}** → This indicates a **{level} likelihood** of narrative reversal (DARVO), where the speaker may be denying, attacking, or reversing blame."
+
+return out
     
 textbox_inputs = [gr.Textbox(label=f"Message {i+1}") for i in range(3)]
 quiz_boxes = [gr.Checkbox(label=q) for q, _ in ESCALATION_QUESTIONS]