Update app.py

app.py

@@ -88,15 +88,18 @@ if data_file is not None:
         fig, ax = plt.subplots(1, 3, figsize=(15, 5))
         ax = ax.flatten()
 
+
         # K-means Clustering
         if kmeans_model is not None:
             kmeans = joblib.load(kmeans_model)
-            kmeans.set_params(n_clusters=kmeans_clusters)
+            kmeans.set_params(n_clusters=kmeans_clusters, n_init='auto')  # Set n_init='auto' for newer versions of sklearn
             kmeans_labels = kmeans.fit_predict(pca_data)
             ax[0].scatter(pca_data[:, 0], pca_data[:, 1], c=kmeans_labels, cmap='viridis')
             ax[0].set_title(f"K-means Clustering (n_clusters={kmeans_clusters})")
         else:
             ax[0].set_title("K-means Model Missing")
+
+    
         
         # Hierarchical Clustering
         if hierarchical_model is not None: