Update app.py

app.py

@@ -112,8 +112,30 @@ def generate_risk_snippet(abuse_score, top_label, escalation_score):
         risk_level = "moderate"
     else:
         risk_level = "low"
-    title, summary, advice = RISK_SNIPPETS[risk_level]
-    return f"\n\n{title}\n{summary} (Pattern: **{str(top_label)}**)\n💡 {advice}"
+
+    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_single_message(text, thresholds, motif_flags):
     motif_hits, matched_phrases = detect_motifs(text)