Update app.py

app.py

@@ -61,28 +61,32 @@ if uploaded_file is not None:
         feature_importance = feature_importance.sort_values('Random Forest', ascending=False)
 
         # 繪製特徵重要性圖表
-        def plot_importance(model):
-            plt.figure(figsize=(10, 6))
-            plt.bar(feature_importance['Feature'], feature_importance[model])
-            plt.title(f'{model} Feature Importance')
+        def plot_importance():
+            plt.figure(figsize=(12, 8))
+            width = 0.25  # 條形圖寬度
+            indices = np.arange(len(feature_importance['Feature']))
+
+            plt.bar(indices - width, feature_importance['Linear Regression'], width=width, label='Linear Regression')
+            plt.bar(indices, feature_importance['CART'], width=width, label='CART')
+            plt.bar(indices + width, feature_importance['Random Forest'], width=width, label='Random Forest')
+
+            plt.title('Feature Importance Comparison Across Models')
             plt.xlabel('Features')
             plt.ylabel('Importance')
-            plt.xticks(rotation=45, ha='right')
+            plt.xticks(indices, feature_importance['Feature'], rotation=45, ha='right')
+            plt.legend()
             st.pyplot(plt)
 
         # Streamlit UI
         st.title("自定義CSV檔案分析 - 特徵重要性分析")
-        st.write("選擇一個模型來查看其特徵重要性：")
-
-        # 下拉選擇模型
-        model = st.selectbox("選擇模型", ["Linear Regression", "CART", "Random Forest"])
+        st.write("以下是 Linear Regression、CART 和 Random Forest 的特徵重要性對比圖表：")
 
         # 顯示圖表
-        plot_importance(model)
+        plot_importance()
 
         # 顯示數據框
-        st.write(f"{model} 特徵重要性數據：")
-        st.dataframe(feature_importance[['Feature', model]])
+        st.write("特徵重要性數據：")
+        st.dataframe(feature_importance)
 
     else:
         st.error("上傳的檔案中找不到 'target' 欄位，請確認檔案格式。")