Update app.py

app.py

@@ -26,10 +26,13 @@ THRESHOLDS = {
 }
 
 PATTERN_WEIGHTS = {
-    "gaslighting": 1.3, "control": 1.2, "dismissiveness": 0.8,
-    "blame shifting": 0.8, "contradictory statements": 0.75
+    "gaslighting": 1.3,
+    "control": 1.2,
+    "dismissiveness": 0.8,
+    "blame shifting": 0.8,
+    "contradictory statements": 0.75,
+    "threat": 1.5  # 🔧 New: raise weight for threat
 }
-
 RISK_STAGE_LABELS = {
     1: "🌀 Risk Stage: Tension-Building\nThis message reflects rising emotional pressure or subtle control attempts.",
     2: "🔥 Risk Stage: Escalation\nThis message includes direct or aggressive patterns, suggesting active harm.",
@@ -130,14 +133,15 @@ def get_risk_stage(patterns, sentiment):
         return 4
     return 1
 
-def generate_risk_snippet(abuse_score, top_label, escalation_score):
+def generate_risk_snippet(abuse_score, top_label, escalation_score, stage=None):
     if abuse_score >= 85 or escalation_score >= 16:
         risk_level = "high"
     elif abuse_score >= 60 or escalation_score >= 8:
         risk_level = "moderate"
+    elif stage == 2 and abuse_score >= 40:
+        risk_level = "moderate"  # 🔧 New rule for escalation stage
     else:
         risk_level = "low"
-
     pattern_label = top_label.split(" – ")[0]
     pattern_score = top_label.split(" – ")[1] if " – " in top_label else ""
 
@@ -266,7 +270,7 @@ def analyze_composite(msg1, msg2, msg3, *answers_and_none):
     out += "📊 This reflects the strength and severity of detected abuse patterns in the message(s).\n\n"
     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)
+    out += generate_risk_snippet(composite_abuse, top_label, escalation_score, most_common_stage)
     out += f"\n\n{stage_text}"
 
     avg_darvo = round(sum(darvo_scores) / len(darvo_scores), 3)