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abuse_type_mapping.py

@@ -0,0 +1,46 @@
+def determine_abuse_type(detected_labels):
+    abuse_type = None
+    abuser_profile = None
+    advice = None
+
+    labels = set(detected_labels)
+
+    if {"extreme_control", "gaslighting", "dismissiveness", "physical_threat"} & labels:
+        abuse_type = "Intimate Terrorism"
+        abuser_profile = "Strategic and controlling"
+        advice = (
+            "This pattern reflects coercive control and potential ongoing abuse. "
+            "Formal support such as domestic violence programs, legal protection, or shelters are recommended. "
+            "Placating or confronting the abuser may increase danger."
+        )
+
+    elif {"mockery", "insults", "deflection", "blame_shifting"} & labels:
+        abuse_type = "Situational Couple Violence"
+        abuser_profile = "Emotionally reactive"
+        advice = (
+            "This may reflect escalated conflict without a long-term control pattern. "
+            "Boundary-setting and conflict de-escalation are key. Professional mediation or trauma-informed therapy may help."
+        )
+
+    elif {"control", "manipulation"}.issubset(labels) and "physical_threat" in labels:
+        abuse_type = "Mutual Violent Control"
+        abuser_profile = "Both partners using control"
+        advice = (
+            "This suggests high mutual danger. Professional separation guidance and safety planning are strongly advised."
+        )
+
+    elif {"physical_threat"} & labels and "extreme_control" not in labels:
+        abuse_type = "Violent Resistance"
+        abuser_profile = "Victim using defensive violence"
+        advice = (
+            "This may reflect a survivor reacting to coercion. Legal and psychological support may help clarify power dynamics."
+        )
+
+    elif {"gaslighting", "obscure_formal", "dismissiveness"} & labels and "physical_threat" not in labels:
+        abuse_type = "Nonviolent Coercive Control"
+        abuser_profile = "Controlling but covert"
+        advice = (
+            "This pattern involves emotional control without physical harm. Emotional validation, documentation, and external support are important."
+        )
+
+    return abuse_type, abuser_profile, advice

app.py

@@ -4,6 +4,7 @@ import numpy as np
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 from transformers import RobertaForSequenceClassification, RobertaTokenizer
 from motif_tagging import detect_motifs
+from abuse_type_mapping import determine_abuse_type
 
 # custom fine-tuned sentiment model
 sentiment_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-sentiment")
@@ -75,6 +76,15 @@ def calculate_abuse_level(scores, thresholds, motif_hits=None):
 
     return base_score
 
+    # Boost score if high-risk motifs were detected
+    motif_hits = motif_hits or []
+    if any(label in motif_hits for label in {"physical_threat", "suicidal_threat", "extreme_control"}):
+        base_score = max(base_score, 75.0)  # Push to "Severe / Harmful Pattern Present"
+    if matched_phrases:
+        pattern_labels_used.extend([label for label, _ in matched_phrases])
+    abuse_level = calculate_abuse_level(scores, adjusted_thresholds, motif_hits=[label for label, _ in matched_phrases])
+    return base_score
+
 def interpret_abuse_level(score):
     if score > 80:
         return "Extreme / High Risk"
@@ -121,17 +131,21 @@ def analyze_messages(input_text, risk_flags):
     if non_abusive_confident and danger_flag_count == 0 and not matched_phrases:
         return "This message is classified as non-abusive."
 
-    abuse_level = calculate_abuse_level(scores, adjusted_thresholds, motif_hits=[label for label, _ in matched_phrases])
+    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)
     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."
-
-    scored_patterns = [
-        (label, score) for label, score in zip(PATTERN_LABELS, scores[:15]) if label != "non_abusive"
-    ]
+    
     # 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]
@@ -151,6 +165,7 @@ def analyze_messages(input_text, risk_flags):
     
     if abuse_level >= 15:
         result += f"Most Likely Patterns:\n{top_pattern_explanations}\n\n"
+        
   
     f"⚠️ Critical Danger Flags Detected: {danger_flag_count} of 3\n"
     "Resources: " + resources + "\n\n"
@@ -169,6 +184,11 @@ def analyze_messages(input_text, risk_flags):
         for label, phrase in matched_phrases:
             phrase_clean = phrase.replace('"', "'").strip()
             result += f"• {label.replace('_', ' ').title()}: “{phrase_clean}”\n"
+
+    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}"
     return result
 
 iface = gr.Interface(

gitattributes

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