Update app.py

app.py

@@ -69,22 +69,12 @@ def calculate_abuse_level(scores, thresholds, motif_hits=None):
     ]
     base_score = round(np.mean(triggered_scores) * 100, 2) if triggered_scores else 0.0
 
-    # 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"
+        base_score = max(base_score, 75.0)
 
     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"
@@ -115,9 +105,17 @@ def analyze_messages(input_text, risk_flags):
         outputs = model(**inputs)
     scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
 
-    pattern_count = sum(score > adjusted_thresholds[label] for label, score in zip(PATTERN_LABELS, scores[:15]))
-    danger_flag_count = sum(score > adjusted_thresholds[label] for label, score in zip(DANGER_LABELS, scores[15:18]))
+    pattern_labels_used = list(set(
+        [label for label, score in zip(PATTERN_LABELS, scores[:15]) 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)
+
+    abuse_type, abuser_profile, advice = determine_abuse_type(pattern_labels_used)
+
+    danger_flag_count = sum(score > adjusted_thresholds[label] for label, score in zip(DANGER_LABELS, scores[15:18]))
     contextual_flags = risk_flags if risk_flags else []
     if len(contextual_flags) >= 2:
         danger_flag_count += 1
@@ -132,24 +130,9 @@ def analyze_messages(input_text, risk_flags):
         return "This message is classified as non-abusive."
 
     scored_patterns = [
-        (label, score) for label, score in zip(PATTERN_LABELS, scores[:15]) if label != "non_abusive"]
-    
-    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)
-
-    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."
+        (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]
 
@@ -161,33 +144,17 @@ def analyze_messages(input_text, risk_flags):
     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"
+    resources = "Immediate assistance recommended. Please seek professional help or contact emergency services." if danger_flag_count >= 2 else "For more information on abuse patterns, consider reaching out to support groups or professional counselors."
 
+    result = f"Abuse Risk Score: {abuse_level}% – {abuse_description}\n\n"
     if abuse_level >= 15:
         result += f"Most Likely Patterns:\n{top_pattern_explanations}\n\n"
-
     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"
 
-# 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
-
-# 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])
 
@@ -222,4 +189,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.queue().launch()
+    iface.queue().launch()