Update app.py

app.py

@@ -40,7 +40,7 @@ if uploaded_file is not None:
     # 使用保存的 K-means 模型進行聚類
     kmeans_labels = kmeans.predict(pca_df)
     
-    # 使用保存的階層式聚類結果
+    # 使用保存的Hierarchical Clustering 階層式聚類結果
     hclust_labels = fcluster(linked, 3, criterion='maxclust')
     
     # 使用保存的 DBSCAN 模型進行聚類
@@ -49,12 +49,12 @@ if uploaded_file is not None:
     # ================== 圖表選擇 ==================
     chart_option = st.selectbox(
         "選擇要顯示的聚類結果圖表",
-        ("K-means", "階層式聚類", "DBSCAN")
+        ("K-means", "Hierarchical Clustering", "DBSCAN")
     )
     
     # ================== 根據選擇顯示對應的圖表 ==================
     if chart_option == "K-means":
-        st.subheader("K-means 聚類結果")
+        st.subheader("K-means_聚類結果")
         fig_kmeans, ax_kmeans = plt.subplots()
         ax_kmeans.scatter(pca_df['PC1'], pca_df['PC2'], c=kmeans_labels, cmap='viridis')
         ax_kmeans.set_title('K-means Clustering')
@@ -62,8 +62,8 @@ if uploaded_file is not None:
         ax_kmeans.set_ylabel('PC2')
         st.pyplot(fig_kmeans)
     
-    elif chart_option == "階層式聚類":
-        st.subheader("階層式聚類結果")
+    elif chart_option == "Hierarchical Clustering":
+        st.subheader("Hierarchical Clustering_階層式聚類結果")
         fig_hclust, ax_hclust = plt.subplots()
         ax_hclust.scatter(pca_df['PC1'], pca_df['PC2'], c=hclust_labels, cmap='viridis')
         ax_hclust.set_title('Hierarchical Clustering')
@@ -72,7 +72,7 @@ if uploaded_file is not None:
         st.pyplot(fig_hclust)
     
     elif chart_option == "DBSCAN":
-        st.subheader("DBSCAN 聚類結果")
+        st.subheader("DBSCAN_聚類結果")
         fig_dbscan, ax_dbscan = plt.subplots()
         ax_dbscan.scatter(pca_df['PC1'], pca_df['PC2'], c=dbscan_labels, cmap='viridis')
         ax_dbscan.set_title('DBSCAN Clustering')