Update app.py

app.py

@@ -183,8 +183,7 @@ 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)
@@ -193,15 +192,17 @@ def analyze_single_message(text, thresholds):
     threshold_labels = [
         label for label, score in zip(LABELS, scores)
         if score > adjusted_thresholds[label]
-    ]
+]
+
     motifs = [phrase for _, phrase in matched_phrases]
+
     darvo_score = calculate_darvo_score(
-        pattern_labels,
+        threshold_labels,
         sentiment_before=0.0,
         sentiment_after=sentiment_score,
         motifs_found=motifs,
         contradiction_flag=contradiction_flag
-
+)
     top_patterns = sorted(
         [(label, score) for label, score in zip(LABELS, scores)],
         key=lambda x: x[1],
@@ -273,7 +274,7 @@ def analyze_composite(msg1, msg2, msg3, *answers_and_none):
         level = "moderate" if avg_darvo < 0.65 else "high"
         out += f"\n\n🎭 **DARVO Score: {avg_darvo}** → This indicates a **{level} likelihood** of narrative reversal (DARVO), where the speaker may be denying, attacking, or reversing blame."
 
-return out
+    return out
     
 textbox_inputs = [gr.Textbox(label=f"Message {i+1}") for i in range(3)]
 quiz_boxes = [gr.Checkbox(label=q) for q, _ in ESCALATION_QUESTIONS]