Plot RES by K-means Cluster Labels

app.py

@@ -709,7 +709,8 @@ def compute_global_regression(df_combined, embedding_cols, tsne_params, df_f1, r
         "silhouette": silhouette,
         "inertias": inertias,
         "silhouette_opt": silhouette_opt,
-        "elbow_k": elbow_k
+        "elbow_k": elbow_k,
+        "classes_k": labels_opt,
     }
     
     if reduction_method == "PCA":
@@ -1112,7 +1113,8 @@ def run_model(model_name):
         fig_all.ygrid.grid_line_color = "gray"
         
         # Plotear las muestras reales, agrupadas por label
-        for label in unique_subsets["real"]:
+        # for label in unique_subsets["real"]:
+        for label in result["labels_opt"]:
             subset = df_all["real"][df_all["real"]['label'] == label]
             source = ColumnDataSource(data={
                 'x': subset['x'],