Create app.py

app.py

@@ -46,29 +46,36 @@ if uploaded_file is not None:
     # 使用保存的 DBSCAN 模型進行聚類
     dbscan_labels = dbscan.fit_predict(pca_df)
     
-    # ================== K-means 聚類圖表 ==================
-    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')
-    ax_kmeans.set_xlabel('PC1')
-    ax_kmeans.set_ylabel('PC2')
-    st.pyplot(fig_kmeans)
+    # ================== 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")
+    )
     
-    # ================== 階層式聚類圖表 ==================
-    st.subheader("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')
-    ax_hclust.set_xlabel('PC1')
-    ax_hclust.set_ylabel('PC2')
-    st.pyplot(fig_hclust)
+    # ================== 根據選擇顯示對應的圖表 ==================
+    if chart_option == "K-means":
+        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')
+        ax_kmeans.set_xlabel('PC1')
+        ax_kmeans.set_ylabel('PC2')
+        st.pyplot(fig_kmeans)
     
-    # ================== DBSCAN 聚類圖表 ==================
-    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')
-    ax_dbscan.set_xlabel('PC1')
-    ax_dbscan.set_ylabel('PC2')
-    st.pyplot(fig_dbscan)
+    elif chart_option == "階層式聚類":
+        st.subheader("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')
+        ax_hclust.set_xlabel('PC1')
+        ax_hclust.set_ylabel('PC2')
+        st.pyplot(fig_hclust)
+    
+    elif chart_option == "DBSCAN":
+        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')
+        ax_dbscan.set_xlabel('PC1')
+        ax_dbscan.set_ylabel('PC2')
+        st.pyplot(fig_dbscan)