Update app.py

app.py

@@ -134,18 +134,21 @@ def analyze_messages(input_text, risk_flags):
     scored_patterns = [
         (label, score) for label, score in zip(PATTERN_LABELS, scores[:15]) if label != "non_abusive"]
     
-    pattern_labels_used = [label for label, score in scored_patterns if score > adjusted_thresholds[label]]
-    
-    abuse_level = calculate_abuse_level(scores, adjusted_thresholds)
+    pattern_labels_used = list(set(
+        [label for label, score in scored_patterns if score > adjusted_thresholds[label]] +
+        [label for label, _ in matched_phrases]
+))
+
+    abuse_level = calculate_abuse_level(scores, adjusted_thresholds, motif_hits=[label for label, _ in matched_phrases])
     abuse_description = interpret_abuse_level(abuse_level)
-    pattern_labels_used = [label for label, _ in matched_phrases]
+
     abuse_type, abuser_profile, advice = determine_abuse_type(pattern_labels_used)
 
     if danger_flag_count >= 2:
         resources = "Immediate assistance recommended. Please seek professional help or contact emergency services."
     else:
         resources = "For more information on abuse patterns, consider reaching out to support groups or professional counselors."
-    
+
     # Override top patterns if a high-risk motif was detected
     override_labels = {"physical_threat", "suicidal_threat", "extreme_control"}
     override_matches = [label for label, _ in matched_phrases if label in override_labels]
@@ -154,12 +157,11 @@ def analyze_messages(input_text, risk_flags):
         top_patterns = [(label, 1.0) for label in override_matches]
     else:
         top_patterns = sorted(scored_patterns, key=lambda x: x[1], reverse=True)[:2]
-        top_patterns = sorted(scored_patterns, key=lambda x: x[1], reverse=True)[:2]
 
     top_pattern_explanations = "\n".join([
         f"• {label.replace('_', ' ').title()}: {EXPLANATIONS.get(label, 'No explanation available.')}"
         for label, _ in top_patterns
-    ])
+])
 
     result = f"Abuse Risk Score: {abuse_level}% – {abuse_description}\n\n"
 
@@ -168,8 +170,26 @@ def analyze_messages(input_text, risk_flags):
 
     result += f"⚠️ Critical Danger Flags Detected: {danger_flag_count} of 3\n"
     result += f"Resources: {resources}\n"
-    result += f"🧭 Sentiment: {sentiment_label.title()} (Confidence: {sentiment_score*100:.2f}%)\n"
-
+    result += f"🧠 Sentiment: {sentiment_label.title()} (Confidence: {sentiment_score*100:.2f}%)\n"
+
+# Always include these if available:
+if contextual_flags:
+    result += "\n\n⚠️ You indicated the following:\n" + "\n".join([f"• {flag.replace('_', ' ').title()}" for flag in contextual_flags])
+
+if high_risk_context:
+    result += "\n\n🚨 These responses suggest a high-risk situation. Consider seeking immediate help or safety planning resources."
+
+if matched_phrases:
+    result += "\n\n🚨 Detected High-Risk Phrases:\n"
+    for label, phrase in matched_phrases:
+        phrase_clean = phrase.replace('"', "'").strip()
+        result += f"• {label.replace('_', ' ').title()}: “{phrase_clean}”\n"
+
+# Always show abuse type if available
+if abuse_type:
+    result += f"\n\n🧠 Likely Abuse Type: {abuse_type}"
+    result += f"\n🧠 Abuser Profile: {abuser_profile}"
+    result += f"\n📘 Safety Tip: {advice}"
 # THEN immediately follow with this:
     if contextual_flags:
         result += "\n\n⚠️ You indicated the following:\n" + "\n".join([f"• {flag.replace('_', ' ').title()}" for flag in contextual_flags])