Update app.py

app.py

@@ -1772,34 +1772,34 @@ def create_plot(df, chart_type, x_column, y_column, group_column=None, size_colu
 # =========================================
 # (與 V4 相同)
 def export_data(df, format_type):
-    if df is None or df.empty: return None, "❌ 沒有數據可以導出。"
-    try:
-        if format_type == "CSV": filename = "exported_data.csv"; df.to_csv(filename, index=False, encoding='utf-8-sig')
-        elif format_type == "Excel": filename = "exported_data.xlsx"; df.to_excel(filename, index=False)
-        elif format_type == "JSON": filename = "exported_data.json"; df.to_json(filename, orient="records", indent=4, force_ascii=False)
-        else: return None, f"❌ 不支持的導出格式: {format_type}"
-        return filename, f"✅ 數據已成功準備為 {format_type} 格式，點擊下方鏈接下載。"
-    except Exception as e: print(f"導出數據時出錯: {e}"); traceback.print_exc(); return None, f"❌ 導出數據時出錯: {e}"
+    if df is None or df.empty: return None, "❌ 沒有數據可以導出。"
+    try:
+        if format_type == "CSV": filename = "exported_data.csv"; df.to_csv(filename, index=False, encoding='utf-8-sig')
+        elif format_type == "Excel": filename = "exported_data.xlsx"; df.to_excel(filename, index=False)
+        elif format_type == "JSON": filename = "exported_data.json"; df.to_json(filename, orient="records", indent=4, force_ascii=False)
+        else: return None, f"❌ 不支持的導出格式: {format_type}"
+        return filename, f"✅ 數據已成功準備為 {format_type} 格式，點擊下方鏈接下載。"
+    except Exception as e: print(f"導出數據時出錯: {e}"); traceback.print_exc(); return None, f"❌ 導出數據時出錯: {e}"
 
 def download_figure(fig, format_type="PNG"):
-    if fig is None or not fig.data: return None, "❌ 沒有圖表可以導出。"
-    try:
-        format_lower = format_type.lower(); filename = f"chart_export.{format_lower}"
-        import kaleido # 確保導入
-        fig.write_image(filename, format=format_lower)
-        return filename, f"✅ 圖表已成功準備為 {format_type} 格式，點擊下方鏈接下載。"
-    except ImportError: error_msg = "❌ 導出圖表失敗：需要 Kaleido 套件。請在環境中安裝 `pip install -U kaleido`。"; print(error_msg); return None, error_msg
-    except ValueError as ve:
-         if "kaleido" in str(ve).lower(): error_msg = "❌ 導出圖表失敗：Kaleido 套件無法運行。請檢查其依賴項或嘗試重新安裝。"; print(f"{error_msg}\n{ve}"); traceback.print_exc(); return None, error_msg
-         else: print(f"導出圖表時出錯 (ValueError): {ve}"); traceback.print_exc(); return None, f"❌ 導出圖表時出錯: {ve}"
-    except Exception as e: print(f"導出圖表時發生未預期錯誤: {e}"); traceback.print_exc(); return None, f"❌ 導出圖表時發生未預期錯誤: {e}"
+    if fig is None or not fig.data: return None, "❌ 沒有圖表可以導出。"
+    try:
+        format_lower = format_type.lower(); filename = f"chart_export.{format_lower}"
+        import kaleido # 確保導入
+        fig.write_image(filename, format=format_lower)
+        return filename, f"✅ 圖表已成功準備為 {format_type} 格式，點擊下方鏈接下載。"
+    except ImportError: error_msg = "❌ 導出圖表失敗：需要 Kaleido 套件。請在環境中安裝 `pip install -U kaleido`。"; print(error_msg); return None, error_msg
+    except ValueError as ve:
+        if "kaleido" in str(ve).lower(): error_msg = "❌ 導出圖表失敗：Kaleido 套件無法運行。請檢查其依賴項或嘗試重新安裝。"; print(f"{error_msg}\n{ve}"); traceback.print_exc(); return None, error_msg
+        else: print(f"導出圖表時出錯 (ValueError): {ve}"); traceback.print_exc(); return None, f"❌ 導出圖表時出錯: {ve}"
+    except Exception as e: print(f"導出圖表時發生未預期錯誤: {e}"); traceback.print_exc(); return None, f"❌ 導出圖表時發生未預期錯誤: {e}"
 
 # =========================================
 # == 智能推薦函數 (Recommendation Function) ==
 # =========================================
 # (與 V4 相同)
 def recommend_chart_settings(df):
-    recommendation = {"chart_type": None, "x_column": None, "y_column": None, "group_column": "無", "agg_function": None, "message": "無法提供推薦。"}
+    recommendation = {"chart_type": None, "x_column": None, "y_column": None, "group_column": "無", "agg_function": None, "message": "無法提供推薦。"}
     if df is None or df.empty: recommendation["message"] = "ℹ️ 請先上傳或輸入數據。"; return recommendation
     columns = df.columns.tolist(); num_cols = df.select_dtypes(include=np.number).columns.tolist(); cat_cols = df.select_dtypes(include=['object', 'category', 'string']).columns.tolist()
     date_cols = [col for col in columns if pd.api.types.is_datetime64_any_dtype(df[col]) or ('日期' in col or '時間' in col)]