Update app.py

app.py

@@ -342,14 +342,20 @@ def compute_abuse_score(matched_scores, sentiment):
 def analyze_single_message(text, thresholds):
     motif_hits, matched_phrases = detect_motifs(text)
 
-    # Derive sentiment from emotion intensity
+    # Get emotion profile
     emotion_profile = get_emotion_profile(text)
     sentiment_score = emotion_profile.get("anger", 0) + emotion_profile.get("disgust", 0)
 
-    # Override if neutral tone masks abusive content
+    # Get model scores first so they can be used in the neutral override
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
+
+    # Sentiment override if neutral masks abuse
     if emotion_profile.get("neutral", 0) > 0.85 and any(
-        scores[label] > THRESHOLDS[label]
-        for label in ["control", "threat", "blame shifting"]
+        scores[label_idx] > thresholds[LABELS[label_idx]]
+        for label_idx in [LABELS.index(l) for l in ["control", "threat", "blame shifting"]]
     ):
         sentiment = "undermining"
     else:
@@ -364,11 +370,6 @@ def analyze_single_message(text, thresholds):
 
     contradiction_flag = detect_contradiction(text)
 
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
-
     threshold_labels = [
         label for label, score in zip(LABELS, scores)
         if score > adjusted_thresholds[label]
@@ -408,11 +409,11 @@ def analyze_single_message(text, thresholds):
 
     abuse_score = min(abuse_score_raw, 100 if "threat" in threshold_labels or "control" in threshold_labels else 95)
 
-    # Now that abuse_score is ready → get tone tag
+    # Get tone tag
     tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, abuse_score)
     print(f"Emotional Tone Tag: {tone_tag}")
 
-    # Debug logging
+    # Debug logs
     print("Emotion Profile:")
     for emotion, score in emotion_profile.items():
         print(f"  {emotion.capitalize():10}: {score}")