Update app.py

app.py

@@ -148,15 +148,22 @@ ESCALATION_QUESTIONS = [
 
 def analyze_single_message(text, thresholds, motif_flags):
     motif_hits, matched_phrases = detect_motifs(text)
+
+    # Sentiment Analysis
     input_ids = sentiment_tokenizer(f"emotion: {text}", return_tensors="pt").input_ids
     with torch.no_grad():
         outputs = sentiment_model.generate(input_ids)
     emotion = sentiment_tokenizer.decode(outputs[0], skip_special_tokens=True).strip().lower()
     sentiment = EMOTION_TO_SENTIMENT.get(emotion, "undermining")
     sentiment_score = 0.5 if sentiment == "undermining" else 0.0
+
+    # Contradiction Check
     contradiction_flag = detect_contradiction(text)
+
+    # Motifs
     motifs = [phrase for _, phrase in matched_phrases]
 
+    # Model Prediction
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
         outputs = model(**inputs)
@@ -164,7 +171,7 @@ def analyze_single_message(text, thresholds, motif_flags):
 
     threshold_labels = [label for label, score in zip(LABELS, scores) if score > thresholds[label]]
     top_patterns = sorted([(label, score) for label, score in zip(LABELS, scores)], key=lambda x: x[1], reverse=True)[:2]
-    pattern_labels = [label for label, _ in top_patterns]
+    pattern_labels = threshold_labels + [label for label, _ in matched_phrases]
 
     darvo_score = calculate_darvo_score(pattern_labels, 0.0, sentiment_score, motifs, contradiction_flag)
 
@@ -175,7 +182,6 @@ def analyze_single_message(text, thresholds, motif_flags):
         darvo_score,
         {"label": sentiment, "emotion": emotion}
     )
-
 def analyze_composite(msg1, msg2, msg3, *answers_and_none):
     responses = answers_and_none[:len(ESCALATION_QUESTIONS)]
     none_selected = answers_and_none[-1]