Update app.py

app.py

@@ -898,47 +898,51 @@ def analyze_single_message(text, thresholds):
             logger.debug("Empty text, returning zeros")
             return 0.0, [], [], {"label": "none"}, 1, 0.0, None, {'assessment': 'neutral', 'confidence': 0.5}
             
-        # BOUNDARY HEALTH CHECK - Add this new section
-        logger.debug("\n🛡️ BOUNDARY HEALTH ANALYSIS")
-        logger.debug("-" * 40)
-        healthy_prob = predict_boundary_health(text)
-        boundary_assessment = get_boundary_assessment(text, healthy_prob)
-        logger.debug(f"Boundary Health Score: {healthy_prob:.3f}")
-        logger.debug(f"Boundary Assessment: {boundary_assessment['label']}")
-        
-        # Get sentiment EARLY - BEFORE any early returns
-        sent_inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-        sent_inputs = {k: v.to(device) for k, v in sent_inputs.items()}
-        with torch.no_grad():
-            sent_logits = sentiment_model(**sent_inputs).logits[0]
-            sent_probs = torch.softmax(sent_logits, dim=-1).cpu().numpy()
-    
-        # Add detailed logging
-        logger.debug("\n🎭 SENTIMENT ANALYSIS DETAILS")
-        logger.debug(f"Raw logits: {sent_logits}")
-        logger.debug(f"Probabilities: supportive={sent_probs[0]:.3f}, undermining={sent_probs[1]:.3f}")
-
-        # Make sure we're using the correct index mapping
-        sentiment = SENTIMENT_LABELS[int(np.argmax(sent_probs))]
-        logger.debug(f"Selected sentiment: {sentiment}")
-        
-        # ADD THE OVERRIDE HERE - RIGHT AFTER SENTIMENT IS DEFINED:
-        if (healthy_prob < 0.7 and 
-            sentiment == "supportive" and 
-            len(text.split()) > 50 and
-            any(phrase in text.lower() for phrase in [
-                "i need you to", "i want to understand", "this isn't about", 
-                "about accuracy", "willing to do something different"
-            ])):
-
-            logger.debug("🔄 Boundary assessment override: Sophisticated healthy boundary detected")
-            boundary_assessment = {
-                'assessment': 'healthy',
-                'label': 'Healthy Boundary (Sophisticated)',
-                'confidence': 0.85,
-                'description': 'Complex but healthy boundary-setting communication',
-                'recommendations': ['Continue this thoughtful, direct approach']
-            }
+    # BOUNDARY HEALTH CHECK - Add this new section
+    logger.debug("\n🛡️ BOUNDARY HEALTH ANALYSIS")
+    logger.debug("-" * 40)
+    boundary_class, boundary_confidence = predict_boundary_health(text)  # Unpack the tuple
+    boundary_assessment = get_boundary_assessment(text, boundary_class, boundary_confidence)
+    logger.debug(f"Boundary Class: {boundary_class} ({['Respected', 'Violated', 'Dismissed', 'Manipulative'][boundary_class]})")
+    logger.debug(f"Boundary Confidence: {boundary_confidence:.3f}")
+    logger.debug(f"Boundary Assessment: {boundary_assessment['label']}")
+    
+    # Get sentiment EARLY - BEFORE any early returns
+    sent_inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    sent_inputs = {k: v.to(device) for k, v in sent_inputs.items()}
+    with torch.no_grad():
+        sent_logits = sentiment_model(**sent_inputs).logits[0]
+        sent_probs = torch.softmax(sent_logits, dim=-1).cpu().numpy()
+    
+    # Add detailed logging
+    logger.debug("\n🎭 SENTIMENT ANALYSIS DETAILS")
+    logger.debug(f"Raw logits: {sent_logits}")
+    logger.debug(f"Probabilities: supportive={sent_probs[0]:.3f}, undermining={sent_probs[1]:.3f}")
+    
+    # Make sure we're using the correct index mapping
+    sentiment = SENTIMENT_LABELS[int(np.argmax(sent_probs))]
+    logger.debug(f"Selected sentiment: {sentiment}")
+    
+    # UPDATE THE OVERRIDE CONDITION:
+    # Now we need to check if boundary_class == 0 (Respected) instead of checking a probability
+    if (boundary_class != 0 and  # Not "Respected"
+        boundary_confidence < 0.7 and 
+        sentiment == "supportive" and 
+        len(text.split()) > 50 and
+        any(phrase in text.lower() for phrase in [
+            "i need you to", "i want to understand", "this isn't about", 
+            "about accuracy", "willing to do something different"
+        ])):
+    
+        logger.debug("🔄 Boundary assessment override: Sophisticated healthy boundary detected")
+        boundary_assessment = {
+            'assessment': 'healthy',
+            'label': 'Healthy Boundary (Sophisticated)',
+            'confidence': 0.85,
+            'description': 'Complex but healthy boundary-setting communication',
+            'recommendations': ['Continue this thoughtful, direct approach']
+        }
+
         
         # EARLY SUPPORTIVE MESSAGE CHECK
         innocent_indicators = [