fix: Removing numpy coercion for keyword selection

app.py

@@ -155,11 +155,8 @@ def generate_plot(
             cluster_embeddings_avg = np.mean(cluster_embeddings, axis=0)
             cluster_words_embeddings = embed_text(cluster_words, model)
             cluster_to_words_similarities = util.dot_score(cluster_embeddings_avg, cluster_words_embeddings)
-            cluster_to_words_similarities = cluster_to_words_similarities.numpy()
-            st.write(cluster_to_words_similarities)
             cluster_to_words_similarities = [(word_ix, similarity) for word_ix, similarity in enumerate(cluster_to_words_similarities)]
             cluster_to_words_similarities = sorted(cluster_to_words_similarities, key=lambda x: x[1], reverse=True)
-            st.write(cluster_to_words_similarities)
             while len(cluster_keyword[label]) < 3:
                 most_descriptive = cluster_to_words_similarities.pop(0)
                 cluster_keyword[label].append(cluster_words[most_descriptive[0]])