Restore weighted ranking selection in BERT backend while preserving raw cosine similarity scores for frontend UI display

.firebase/hosting..cache

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backend/services/model_bert.py

@@ -128,17 +128,19 @@ def extract_summary(valid, sentences, embeddings, weights, topic_labels, num_sen
             items = clusters[tl]
             idxs, sents_cl, embs_cl, ws_cl = zip(*items)
             centroid = np.mean(embs_cl, axis=0, keepdims=True)
-            # Score = cosine
-            scores   = cosine_similarity(np.array(embs_cl), centroid).flatten()
+            cosine_scores = cosine_similarity(np.array(embs_cl), centroid).flatten()
+            # Ranking uses cosine * source_weight to select correct sentences
+            ranking_scores = cosine_scores * np.array(ws_cl)
             picked   = 0
-            for r in np.argsort(scores)[::-1]:
+            for r in np.argsort(ranking_scores)[::-1]:
                 if picked >= per_cl: break
                 sent     = sents_cl[r]
                 words    = sent.lower().split()
                 trigrams = set(zip(words, words[1:], words[2:])) if len(words) >= 3 else set()
                 if trigrams & used_trigrams: continue
                 used_trigrams |= trigrams
-                selected.append((idxs[r], sent, scores[r], ws_cl[r]))
+                # Store the pure cosine similarity score as the score for display
+                selected.append((idxs[r], sent, cosine_scores[r], ws_cl[r]))
                 picked += 1
 
     selected.sort(key=lambda x: x[0])