Update app.py

app.py

@@ -108,6 +108,38 @@ def analyze_single_message(text, thresholds):
     stage = get_risk_stage(threshold_labels, sentiment)
 
     return abuse_score, threshold_labels, top_patterns, result, stage
+    
+def generate_risk_snippet(abuse_score, top_label, escalation_score):
+    if abuse_score >= 85 or escalation_score >= 16:
+        risk_level = "high"
+    elif abuse_score >= 60 or escalation_score >= 8:
+        risk_level = "moderate"
+    else:
+        risk_level = "low"
+
+    pattern_label = top_label.split(" – ")[0]
+    pattern_score = top_label.split(" – ")[1] if " – " in top_label else ""
+
+    base = f"\n\n🛑 Risk Level: {risk_level.capitalize()}\n"
+    base += f"This message shows strong indicators of **{pattern_label}**. "
+
+    if risk_level == "high":
+        base += "The language may reflect patterns of emotional control, even when expressed in soft or caring terms.\n"
+    elif risk_level == "moderate":
+        base += "There are signs of emotional pressure or indirect control that may escalate if repeated.\n"
+    else:
+        base += "The message does not strongly indicate abuse, but it's important to monitor for patterns.\n"
+
+    base += "\n💡 *Why this might be flagged:*\n"
+    base += (
+        "This message may seem supportive, but language like “Do you need me to come home?” can sometimes carry implied pressure, especially if declining leads to guilt, tension, or emotional withdrawal. "
+        "The model looks for patterns that reflect subtle coercion, obligation, or reversal dynamics—even when not overtly aggressive.\n"
+    )
+
+    base += f"\nDetected Pattern: **{pattern_label} ({pattern_score})**\n"
+    base += "🧠 You can review the pattern in context. This tool highlights possible dynamics—not judgments."
+
+    return base
 
 def analyze_composite(msg1, msg2, msg3, *answers_and_none):
     responses = answers_and_none[:len(ESCALATION_QUESTIONS)]