Update app.py

app.py

@@ -144,20 +144,28 @@ def analyze_single_message(text, thresholds, motif_flags):
     motif_hits, matched_phrases = detect_motifs(text)
     sentiment = custom_sentiment(text)
     sentiment_score = sentiment["score"] if sentiment["label"] == "undermining" else 0.0
+
+    # TEMP: print sentiment to console for debugging
+    print(f"Sentiment label: {sentiment['label']}, score: {sentiment['score']}")
+
     adjusted_thresholds = {k: v * 0.8 for k, v in thresholds.items()} if sentiment['label'] == "undermining" else thresholds.copy()
+
     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]]
     phrase_labels = [label for label, _ in matched_phrases]
     pattern_labels_used = list(set(threshold_labels + phrase_labels))
+
     abuse_level = calculate_abuse_level(scores, adjusted_thresholds, motif_hits)
     top_patterns = sorted([(label, score) for label, score in zip(LABELS, scores)], key=lambda x: x[1], reverse=True)[:2]
     motif_phrases = [text for _, text in matched_phrases]
     contradiction_flag = detect_contradiction(text)
     darvo_score = calculate_darvo_score(pattern_labels_used, 0.0, sentiment_score, motif_phrases, contradiction_flag)
-    return abuse_level, pattern_labels_used, top_patterns, darvo_score
+
+    return abuse_level, pattern_labels_used, top_patterns, darvo_score, sentiment
 
 def analyze_composite(msg1, msg2, msg3, flags):
     thresholds = THRESHOLDS
@@ -165,7 +173,13 @@ def analyze_composite(msg1, msg2, msg3, flags):
     active_messages = [m for m in messages if m.strip()]
     if not active_messages:
         return "Please enter at least one message."
-    results = [analyze_single_message(m, thresholds, flags) for m in active_messages]
+
+    results = []
+    for m in active_messages:
+        result = analyze_single_message(m, thresholds, flags)
+        print(f"Message: {m}")
+        print(f"Sentiment result: {result[4]}")
+        results.append(result)
     abuse_scores = [r[0] for r in results]
     darvo_scores = [r[3] for r in results]
     average_darvo = round(sum(darvo_scores) / len(darvo_scores), 3)