Update app.py

app.py

@@ -525,12 +525,11 @@ def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
         escalation_score = None
         risk_level = "unknown"
     else:
-    escalation_score = sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, answers_and_none[:-1]) if a)
-    escalation_bump = 0  # Will be computed later
-    hybrid_score = escalation_score  # Start with raw
-
+        escalation_score = sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, answers_and_none[:-1]) if a)
+        escalation_bump = 0  # Will be computed later
+        hybrid_score = escalation_score  # Start with raw
     # We'll finalize `risk_level` after computing bump below
-    risk_level = "Low"
+        risk_level = "Low"
     messages = [msg1, msg2, msg3]
     dates = [date1, date2, date3]
     active = [(m, d) for m, d in zip(messages, dates) if m.strip()]
@@ -551,6 +550,12 @@ def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
             escalation_bump += 2
         if stage == 2:
             escalation_bump += 3
+    hybrid_score = escalation_score + escalation_bump
+    risk_level = (
+        "High" if hybrid_score >= 16 else
+        "Moderate" if hybrid_score >= 8 else
+        "Low"
+    )
     for result, date in results:
         assert len(result) == 7, "Unexpected output from analyze_single_message"
     abuse_scores = [r[0][0] for r in results]
@@ -618,7 +623,7 @@ def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
             top_label = f"{sorted_patterns[0][0]} – {int(round(sorted_patterns[0][1] * 100))}%"
         if top_label is None:
             top_label = "Unknown – 0%"
-    out += generate_risk_snippet(composite_abuse, top_label, escalation_score if escalation_score is not None else 0, most_common_stage)
+    out += generate_risk_snippet(composite_abuse, top_label, hybrid_score if escalation_score is not None else 0, most_common_stage)
     out += f"\n\n{stage_text}"
     out += darvo_blurb
     out += "\n\n🎭 **Emotional Tones Detected:**\n"