Update app.py

app.py

@@ -56,6 +56,34 @@ PATTERN_WEIGHTS = {
     "contradictory statements": 0.75
 }
 
+RISK_SNIPPETS = {
+    "low": (
+        "🟢 Risk Level: Low",
+        "The language patterns here do not strongly indicate abuse.",
+        "Continue to check in with yourself and notice how you feel in response to repeated patterns."
+    ),
+    "moderate": (
+        "⚠️ Risk Level: Moderate to High",
+        "This language includes control, guilt, or reversal tactics.",
+        "These patterns often lead to emotional confusion and reduced self-trust. Document these messages or talk with someone safe."
+    ),
+    "high": (
+        "🛑 Risk Level: High",
+        "Language includes threats or coercive control, which are strong indicators of escalation.",
+        "Consider creating a safety plan or contacting a support line. Trust your sense of unease."
+    )
+}
+
+def generate_risk_snippet(abuse_score, top_label):
+    if abuse_score >= 85:
+        risk_level = "high"
+    elif abuse_score >= 60:
+        risk_level = "moderate"
+    else:
+        risk_level = "low"
+    title, summary, advice = RISK_SNIPPETS[risk_level]
+    return f"\n\n{title}\n{summary} (Pattern: **{top_label}**)\n💡 {advice}"
+
 # --- DARVO Detection Tools ---
 DARVO_PATTERNS = {
     "blame shifting", "projection", "dismissiveness", "guilt tripping", "contradictory statements"
@@ -97,7 +125,6 @@ def calculate_darvo_score(patterns, sentiment_before, sentiment_after, motifs_fo
     )
     return round(min(darvo_score, 1.0), 3)
 
-
 def custom_sentiment(text):
     inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
@@ -168,6 +195,7 @@ def analyze_composite(msg1, msg2, msg3, flags):
     if average_darvo > 0.25:
         darvo_descriptor = "moderate" if average_darvo < 0.65 else "high"
         result += f"\n\nDARVO Score: {average_darvo} → This indicates a **{darvo_descriptor} likelihood** of narrative reversal (DARVO), where the speaker may be denying, attacking, or reversing blame."
+    result += generate_risk_snippet(composite_score, top_label[0])
     return result
 
 textbox_inputs = [