Update app.py

app.py

@@ -133,7 +133,7 @@ def get_risk_stage(patterns, sentiment):
         return 4
     return 1
 
-def generate_risk_snippet(abuse_score, top_label, escalation_score, stage=None):
+def generate_risk_snippet(abuse_score, top_label, escalation_score, stage):
     if abuse_score >= 85 or escalation_score >= 16:
         risk_level = "high"
     elif abuse_score >= 60 or escalation_score >= 8:
@@ -246,7 +246,7 @@ def analyze_single_message(text, thresholds):
 def analyze_composite(msg1, msg2, msg3, *answers_and_none):
     responses = answers_and_none[:len(ESCALATION_QUESTIONS)]
     none_selected = answers_and_none[-1]
-    escalation_score = None if none_selected else sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, responses) if a)
+
     messages = [msg1, msg2, msg3]
     active = [m for m in messages if m.strip()]
     if not active:
@@ -260,27 +260,36 @@ def analyze_composite(msg1, msg2, msg3, *answers_and_none):
     stages = [r[4] for r in results]
     darvo_scores = [r[5] for r in results]
 
+    composite_abuse = int(round(sum(abuse_scores) / len(abuse_scores)))
+    top_label = f"{top_labels[0]} – {int(round(top_scores[0] * 100))}%"
+
     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)
-    top_label = f"{top_labels[0]} – {int(round(top_scores[0] * 100))}%"
-    composite_abuse = int(round(sum(abuse_scores) / len(abuse_scores)))
+    darvo_blurb = ""
+    if avg_darvo > 0.25:
+        level = "moderate" if avg_darvo < 0.65 else "high"
+        darvo_blurb = 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."
+
+    if none_selected:
+        escalation_score = 0
+        escalation_potential = "Unknown (Checklist not completed)"
+        escalation_note = "🔍 *This section was not completed. Escalation potential is unknown.*"
+    else:
+        escalation_score = sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, responses) if a)
+        escalation_total = sum(w for _, w in ESCALATION_QUESTIONS)
+        level = "High" if escalation_score >= 16 else "Moderate" if escalation_score >= 8 else "Low"
+        escalation_potential = f"{level} ({escalation_score}/{escalation_total})"
+        escalation_note = "🚨 This indicates how many serious risk factors are present based on your answers to the safety checklist."
 
     out = f"Abuse Intensity: {composite_abuse}%\n"
     out += "📊 This reflects the strength and severity of detected abuse patterns in the message(s).\n\n"
-    if escalation_score is None:
-        out += "Escalation Potential: Unknown (Checklist not completed)\n"
-        out += "🔍 *This section was not completed. Escalation potential is unknown.*\n\n"
-    else:
-        out += f"Escalation Potential: {('High' if escalation_score >= 16 else 'Moderate' if escalation_score >= 8 else 'Low')} ({escalation_score}/{sum(w for _, w in ESCALATION_QUESTIONS)})\n"
-        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, most_common_stage)
+    out += f"Escalation Potential: {escalation_potential}\n"
+    out += f"{escalation_note}\n\n"
+    out += generate_risk_snippet(composite_abuse, top_label, escalation_score)
     out += f"\n\n{stage_text}"
-
-    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."
+    out += darvo_blurb
 
     return out