Update app.py

app.py

@@ -37,15 +37,16 @@ THRESHOLDS = {
     "insults": 0.34,
     "obscure_formal": 0.25,
     "recovery_phase": 0.25,
+    "non_abusive": 0.40
     "suicidal_threat": 0.45,
     "physical_threat": 0.31,
     "extreme_control": 0.36,
-    "non_abusive": 0.40
+
 }
 
 # Define label groups using slicing (first 14: abuse patterns, last 3: danger cues)
-PATTERN_LABELS = LABELS[:14]
-DANGER_LABELS = LABELS[14:17]
+PATTERN_LABELS = LABELS[:15]
+DANGER_LABELS = LABELS[15:18]
 
 def calculate_abuse_level(scores, thresholds):
     triggered_scores = [score for label, score in zip(LABELS, scores) if score > thresholds[label]]
@@ -106,8 +107,8 @@ def analyze_messages(input_text):
     scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
 
     # Count the number of triggered abuse pattern and danger flags based on thresholds
-    pattern_count = sum(score > adjusted_thresholds[label] for label, score in zip(PATTERN_LABELS, scores[:14]))
-    danger_flag_count = sum(score > adjusted_thresholds[label] for label, score in zip(DANGER_LABELS, scores[14:17]))
+    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]))
 
     # Check if 'non_abusive' label is triggered
     non_abusive_score = scores[LABELS.index('non_abusive')]
@@ -132,7 +133,7 @@ def analyze_messages(input_text):
         resources = "For more information on abuse patterns, consider reaching out to support groups or professional counselors."
 
     # Get top 2 highest scoring abuse patterns (excluding 'non_abusive')
-    scored_patterns = [(label, score) for label, score in zip(PATTERN_LABELS, scores[:14])]
+    scored_patterns = [(label, score) for label, score in zip(PATTERN_LABELS, scores[:15])]
     top_patterns = sorted(scored_patterns, key=lambda x: x[1], reverse=True)[:2]
     top_patterns_str = "\n".join([f"• {label.replace('_', ' ').title()}" for label, _ in top_patterns])