Update app.py

app.py

@@ -430,16 +430,16 @@ def analyze_single_message(text, thresholds):
     emotion_profile = get_emotion_profile(text)
     sentiment_score = emotion_profile.get("anger", 0) + emotion_profile.get("disgust", 0)
 
-    # Get model scores first so they can be used in the neutral override
+    # Get model scores
     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
+    # Sentiment override if neutral is high while critical thresholds are passed
     if emotion_profile.get("neutral", 0) > 0.85 and any(
-        scores[label_idx] > thresholds[LABELS[label_idx]]
-        for label_idx in [LABELS.index(l) for l in ["control", "threat", "blame shifting"]]
+        scores[LABELS.index(l)] > thresholds[l]
+        for l in ["control", "threat", "blame shifting"]
     ):
         sentiment = "undermining"
     else:
@@ -458,16 +458,7 @@ def analyze_single_message(text, thresholds):
         label for label, score in zip(LABELS, scores)
         if score > adjusted_thresholds[label]
     ]
-        # Get tone tag
-    tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, abuse_score)
-    print(f"Emotional Tone Tag: {tone_tag}")
-    # Post-threshold validation: strip recovery if it occurs with undermining sentiment
-    # Override top label if recovery was removed
-    if "recovery" in threshold_labels and tone_tag == "forced accountability flip":
-        threshold_labels.remove("recovery")
-        top_patterns = [p for p in top_patterns if p[0] != "recovery"]
-        print("⚠️ Removing 'recovery' due to undermining sentiment (not genuine repair)")
-        threshold_labels.remove("recovery")
+
     motifs = [phrase for _, phrase in matched_phrases]
 
     darvo_score = calculate_darvo_score(
@@ -479,7 +470,7 @@ def analyze_single_message(text, thresholds):
     )
 
     top_patterns = sorted(
-        [(label, score) for label, score in zip(LABELS, scores) if score > adjusted_thresholds[label]],
+        [(label, score) for label, score in zip(LABELS, scores)],
         key=lambda x: x[1],
         reverse=True
     )[:2]
@@ -493,17 +484,23 @@ def analyze_single_message(text, thresholds):
     abuse_score_raw = compute_abuse_score(matched_scores, sentiment)
     abuse_score = abuse_score_raw
 
+    # Risk stage logic
     stage = get_risk_stage(threshold_labels, sentiment) if threshold_labels else 1
     if weapon_flag and stage < 2:
         stage = 2
-
     if weapon_flag:
         abuse_score_raw = min(abuse_score_raw + 25, 100)
 
-    abuse_score = min(abuse_score_raw, 100 if "threat" in threshold_labels or "control" in threshold_labels else 95)
+    abuse_score = min(
+        abuse_score_raw,
+        100 if "threat" in threshold_labels or "control" in threshold_labels else 95
+    )
 
+    # Tone tag must happen after abuse_score is finalized
+    tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, abuse_score)
 
-    # Debug logs
+    # Debug
+    print(f"Emotional Tone Tag: {tone_tag}")
     print("Emotion Profile:")
     for emotion, score in emotion_profile.items():
         print(f"  {emotion.capitalize():10}: {score}")