add embeddings explanation and dimensionality reduction explanation

app.py

@@ -745,6 +745,7 @@ elif dimensionality_name == 'PCA':
 
     st.code(f"""\
        from sklearn.decomposition import PCA
+       import numpy as np
 
         pca = PCA(n_components=2)
         embedding_matrix = np.array(list(embeddings.values()))
@@ -803,6 +804,7 @@ elif dimensionality_name == 't-SNE':
 
     st.code(f"""\
         from sklearn.manifold import TSNE
+        import numpy as np
 
         tsne = TSNE(n_components=2, perplexity=2, random_state=0)
         embedding_matrix = np.array(list(embeddings.values()))