Update app.py

app.py

@@ -446,22 +446,6 @@ def analyze_single_message(text, thresholds):
         for label, score in zip(LABELS, raw_scores):
             logger.debug(f"{label}: {score:.3f}")
 
-            # Initialize lists before checking control
-        threshold_labels = []
-        matched_scores = []
-
-            # Add control check
-        control_score = raw_scores[LABELS.index("control")]
-        if control_score > 0.3:  # Lower threshold for control
-            if "control" not in threshold_labels:
-                threshold_labels.append("control")
-                matched_scores.append(("control", control_score, PATTERN_WEIGHTS["control"]))
-
-            # Get predictions and sort them (continue with existing code)
-        predictions = list(zip(LABELS, raw_scores))
-        sorted_predictions = sorted(predictions, key=lambda x: x[1], reverse=True)
-
-
         # Get predictions and sort them
         predictions = list(zip(LABELS, raw_scores))
         sorted_predictions = sorted(predictions, key=lambda x: x[1], reverse=True)
@@ -474,31 +458,28 @@ def analyze_single_message(text, thresholds):
         if explicit_abuse:
             threshold_labels.append("insults")
             logger.debug("\nForced inclusion of 'insults' due to explicit abuse")
-    
-            for label, score in sorted_predictions:
-                if label == "nonabusive":
-                    continue  # Skip nonabusive label
-                base_threshold = thresholds.get(label, 0.25)
-                if explicit_abuse:
-                    base_threshold *= 0.5
-                if score > base_threshold:
-                    if label not in threshold_labels:
-            threshold_labels.append(label)
-
-# Calculate matched scores (exclude nonabusive)
-            matched_scores = []
-            for label in threshold_labels:
-                if label == "nonabusive":
-                    continue
-                score = raw_scores[LABELS.index(label)]
-                weight = PATTERN_WEIGHTS.get(label, 1.0)
-                if explicit_abuse and label == "insults":
-                    weight *= 1.5
-                matched_scores.append((label, score, weight))
-
-        # Calculate matched scores
+
+        for label, score in sorted_predictions:
+            if label == "nonabusive":
+                continue  # Skip nonabusive label
+            base_threshold = thresholds.get(label, 0.25)
+            if explicit_abuse:
+                base_threshold *= 0.5
+            if score > base_threshold:
+                if label not in threshold_labels:
+                    threshold_labels.append(label)
+
+        # Check for control pattern
+        control_score = raw_scores[LABELS.index("control")]
+        if control_score > 0.3 and "control" not in threshold_labels:  # Lower threshold for control
+            threshold_labels.append("control")
+            logger.debug("\nAdded control pattern due to high control score")
+
+        # Calculate matched scores (exclude nonabusive)
         matched_scores = []
         for label in threshold_labels:
+            if label == "nonabusive":
+                continue
             score = raw_scores[LABELS.index(label)]
             weight = PATTERN_WEIGHTS.get(label, 1.0)
             if explicit_abuse and label == "insults":
@@ -530,18 +511,19 @@ def analyze_single_message(text, thresholds):
             abuse_score = max(abuse_score, 85.0)  # force high score if compound risk detected
         if "control" in [label for label, _, _ in matched_scores]:
             abuse_score = max(abuse_score, 70.0)  # Minimum score for control patterns
+            
         # Get DARVO score
         darvo_score = predict_darvo_score(text)
 
         # Get tone using emotion-based approach
         tone_tag = get_emotional_tone_tag(text, sentiment, threshold_labels, abuse_score)
-         # Check for the specific combination
-        highest_pattern = max(matched_scores, key=lambda x: x[1])[0] if matched_scores else None  # Get highest pattern
+        
+        # Check for the specific combination
+        highest_pattern = max(matched_scores, key=lambda x: x[1])[0] if matched_scores else None
         if sentiment == "supportive" and tone_tag == "neutral" and highest_pattern == "obscure language":
             logger.debug("Message classified as likely non-abusive (supportive, neutral, and obscure language). Returning low risk.")
             return 0.0, [], [], {"label": "supportive"}, 1, 0.0, "neutral"  # Return non-abusive values
 
-        
         # Set stage
         stage = 2 if explicit_abuse or abuse_score > 70 or compound_threat_boost else 1
         
@@ -552,6 +534,7 @@ def analyze_single_message(text, thresholds):
     except Exception as e:
         logger.error(f"Error in analyze_single_message: {e}")
         return 0.0, [], [], {"label": "error"}, 1, 0.0, None
+
 def generate_abuse_score_chart(dates, scores, patterns):
     """Generate a timeline chart of abuse scores"""
     try: