Update app.py

app.py

@@ -1273,8 +1273,8 @@ def recommend_chart_settings(df):
 """
 Gradio 應用程式：進階數據可視化工具
 作者：Gemini
-版本：4.0 (改用 Radio + 簡化佈局)
-描述：包含所有功能的完整程式碼，大量使用 Radio Button 替代 Dropdown，期望解決 UI 問題。
+版本：5.0 (極簡化 - 單圖表 + 全 Radio)
+描述：包含所有功能的完整程式碼，極度簡化 UI，使用 Radio 替代 Dropdown，移除自動更新。
 """
 
 # =========================================
@@ -1295,67 +1295,30 @@ import re
 import json
 import colorsys
 import traceback # 用於更詳細的錯誤追蹤
+import sys # 用於打印調試信息
 
 # =========================================
 # == 常數定義 (Constants) ==
 # =========================================
-
-# 圖表類型選項 (Chart Type Options) - 保持列表
-CHART_TYPES = [
-    "長條圖", "堆疊長條圖", "百分比堆疊長條圖", "群組長條圖", "水平長條圖",
-    "折線圖", "多重折線圖", "階梯折線圖",
-    "區域圖", "堆疊區域圖", "百分比堆疊區域圖",
-    "圓餅圖", "環形圖",
-    "散點圖", "氣泡圖",
-    "直方圖", "箱型圖", "小提琴圖",
-    "熱力圖", "樹狀圖",
-    "雷達圖", "漏斗圖", "極座標圖", "甘特圖"
-]
-
-# 顏色方案選項 (Color Scheme Options) - 保持字典，用於 Dropdown
+CHART_TYPES = ["長條圖", "堆疊長條圖", "百分比堆疊長條圖", "群組長條圖", "水平長條圖", "折線圖", "多重折線圖", "階梯折線圖", "區域圖", "堆疊區域圖", "百分比堆疊區域圖", "圓餅圖", "環形圖", "散點圖", "氣泡圖", "直方圖", "箱型圖", "小提琴圖", "熱力圖", "樹狀圖", "雷達圖", "漏斗圖", "極座標圖", "甘特圖"]
 COLOR_SCHEMES = {
-    "預設 (Plotly)": px.colors.qualitative.Plotly,
-    "分類 - D3": px.colors.qualitative.D3, "分類 - G10": px.colors.qualitative.G10,
-    "分類 - T10": px.colors.qualitative.T10, "分類 - Alphabet": px.colors.qualitative.Alphabet,
-    "分類 - Dark24": px.colors.qualitative.Dark24, "分類 - Light24": px.colors.qualitative.Light24,
-    "分類 - Set1": px.colors.qualitative.Set1, "分類 - Set2": px.colors.qualitative.Set2,
-    "分類 - Set3": px.colors.qualitative.Set3, "分類 - Pastel": px.colors.qualitative.Pastel,
-    "分類 - Pastel1": px.colors.qualitative.Pastel1, "分類 - Pastel2": px.colors.qualitative.Pastel2,
-    "分類 - Antique": px.colors.qualitative.Antique, "分類 - Bold": px.colors.qualitative.Bold,
-    "分類 - Prism": px.colors.qualitative.Prism, "分類 - Safe": px.colors.qualitative.Safe,
-    "分類 - Vivid": px.colors.qualitative.Vivid,
-    "連續 - Viridis": px.colors.sequential.Viridis, "連續 - Plasma": px.colors.sequential.Plasma,
-    "連續 - Inferno": px.colors.sequential.Inferno, "連續 - Magma": px.colors.sequential.Magma,
-    "連續 - Cividis": px.colors.sequential.Cividis, "連續 - Blues": px.colors.sequential.Blues,
-    "連續 - Reds": px.colors.sequential.Reds, "連續 - Greens": px.colors.sequential.Greens,
-    "連續 - Purples": px.colors.sequential.Purples, "連續 - Oranges": px.colors.sequential.Oranges,
-    "連續 - Greys": px.colors.sequential.Greys, "連續 - Rainbow": px.colors.sequential.Rainbow,
-    "連續 - Turbo": px.colors.sequential.Turbo, "連續 - Electric": px.colors.sequential.Electric,
-    "連續 - Hot": px.colors.sequential.Hot, "連續 - Teal": px.colors.sequential.Teal,
-    "發散 - Spectral": px.colors.diverging.Spectral, "發散 - RdBu": px.colors.diverging.RdBu,
-    "發散 - PRGn": px.colors.diverging.PRGn, "發散 - PiYG": px.colors.diverging.PiYG,
-    "發散 - BrBG": px.colors.diverging.BrBG, "發散 - Geyser": px.colors.diverging.Geyser,
+    "預設 (Plotly)": px.colors.qualitative.Plotly, "分類 - D3": px.colors.qualitative.D3, "分類 - G10": px.colors.qualitative.G10, "分類 - T10": px.colors.qualitative.T10, "分類 - Alphabet": px.colors.qualitative.Alphabet, "分類 - Dark24": px.colors.qualitative.Dark24, "分類 - Light24": px.colors.qualitative.Light24, "分類 - Set1": px.colors.qualitative.Set1, "分類 - Set2": px.colors.qualitative.Set2, "分類 - Set3": px.colors.qualitative.Set3, "分類 - Pastel": px.colors.qualitative.Pastel, "分類 - Pastel1": px.colors.qualitative.Pastel1, "分類 - Pastel2": px.colors.qualitative.Pastel2, "分類 - Antique": px.colors.qualitative.Antique, "分類 - Bold": px.colors.qualitative.Bold, "分類 - Prism": px.colors.qualitative.Prism, "分類 - Safe": px.colors.qualitative.Safe, "分類 - Vivid": px.colors.qualitative.Vivid,
+    "連續 - Viridis": px.colors.sequential.Viridis, "連續 - Plasma": px.colors.sequential.Plasma, "連續 - Inferno": px.colors.sequential.Inferno, "連續 - Magma": px.colors.sequential.Magma, "連續 - Cividis": px.colors.sequential.Cividis, "連續 - Blues": px.colors.sequential.Blues, "連續 - Reds": px.colors.sequential.Reds, "連續 - Greens": px.colors.sequential.Greens, "連續 - Purples": px.colors.sequential.Purples, "連續 - Oranges": px.colors.sequential.Oranges, "連續 - Greys": px.colors.sequential.Greys, "連續 - Rainbow": px.colors.sequential.Rainbow, "連續 - Turbo": px.colors.sequential.Turbo, "連續 - Electric": px.colors.sequential.Electric, "連續 - Hot": px.colors.sequential.Hot, "連續 - Teal": px.colors.sequential.Teal,
+    "發散 - Spectral": px.colors.diverging.Spectral, "發散 - RdBu": px.colors.diverging.RdBu, "發散 - PRGn": px.colors.diverging.PRGn, "發散 - PiYG": px.colors.diverging.PiYG, "發散 - BrBG": px.colors.diverging.BrBG, "發散 - Geyser": px.colors.diverging.Geyser,
     "循環 - Twilight": px.colors.cyclical.Twilight, "循環 - IceFire": px.colors.cyclical.IceFire,
 }
-
-# 圖案填充選項 (Pattern Fill Options) - 保持列表，用於 Radio
 PATTERN_TYPES = ["無", "/", "\\", "x", "-", "|", "+", "."]
-
-# 聚合函數選項 (Aggregation Function Options) - 保持列表，用於 Radio
-AGGREGATION_FUNCTIONS = [
-    "計數", "求和", "平均值", "中位數", "最大值", "最小值", "標準差", "變異數", "第一筆", "最後一筆"
-]
-
-# 導出格式選項
+AGGREGATION_FUNCTIONS = ["計數", "求和", "平均值", "中位數", "最大值", "最小值", "標準差", "變異數", "第一筆", "最後一筆"]
 EXPORT_FORMATS_DATA = ["CSV", "Excel", "JSON"]
 EXPORT_FORMATS_IMG = ["PNG", "SVG", "PDF", "JPEG"]
 YES_NO_CHOICES = ["是", "否"]
+NO_DATA_STR = "-- 無數據 --"
+NONE_STR = "無" # 代表 '無' 選項的值
 
 # =========================================
 # == 輔助函數 (Helper Functions) ==
 # =========================================
-
-# --- 顏色處理相關 ---
+# --- 顏色處理相關 (與 V4 相同) ---
 COLOR_CARD_STYLE = """<div style="display: flex; flex-wrap: wrap; gap: 5px; margin-top: 5px;">{color_cards}</div>"""
 COLOR_CARD_TEMPLATE = """<div title="{color_name} ({color_hex})" style="width: 20px; height: 20px; background-color: {color_hex}; border-radius: 3px; cursor: pointer; border: 1px solid #ddd; transition: transform 0.2s; box-shadow: 0 1px 2px rgba(0,0,0,0.1);" onclick="copyToClipboard('{color_hex}')" onmouseover="this.style.transform='scale(1.15)'; this.style.boxShadow='0 2px 4px rgba(0,0,0,0.2)';" onmouseout="this.style.transform='scale(1)'; this.style.boxShadow='0 1px 2px rgba(0,0,0,0.1)';"></div>"""
 COPY_SCRIPT = """
@@ -1382,35 +1345,18 @@ function copyToClipboard(text) {
 </script>
 """
 COMMON_COLORS = {
-    "紅色": "#FF0000", "亮紅": "#FF5733", "深紅": "#C70039", "橙色": "#FFA500", "亮橙": "#FFC300", "深橙": "#D35400",
-    "黃色": "#FFFF00", "亮黃": "#F1C40F", "金色": "#FFD700", "綠色": "#008000", "亮綠": "#2ECC71", "深綠": "#1E8449",
-    "橄欖綠": "#808000", "藍色": "#0000FF", "亮藍": "#3498DB", "深藍": "#2874A6", "天藍": "#87CEEB", "紫色": "#800080",
-    "亮紫": "#9B59B6", "深紫": "#6C3483", "薰衣草紫": "#E6E6FA", "粉紅色": "#FFC0CB", "亮粉": "#FF69B4", "深粉": "#C71585",
-    "棕色": "#A52A2A", "亮棕": "#E59866", "深棕": "#6E2C00", "青色": "#00FFFF", "藍綠色": "#008080", "綠松石色": "#40E0D0",
-    "洋紅": "#FF00FF", "紫紅色": "#DC143C", "灰色": "#808080", "淺灰": "#D3D3D3", "深灰": "#696969", "石板灰": "#708090",
-    "黑色": "#000000", "白色": "#FFFFFF", "米色": "#F5F5DC",
+    "紅色": "#FF0000", "亮紅": "#FF5733", "深紅": "#C70039", "橙色": "#FFA500", "亮橙": "#FFC300", "深橙": "#D35400", "黃色": "#FFFF00", "亮黃": "#F1C40F", "金色": "#FFD700", "綠色": "#008000", "亮綠": "#2ECC71", "深綠": "#1E8449", "橄欖綠": "#808000", "藍色": "#0000FF", "亮藍": "#3498DB", "深藍": "#2874A6", "天藍": "#87CEEB", "紫色": "#800080", "亮紫": "#9B59B6", "深紫": "#6C3483", "薰衣草紫": "#E6E6FA", "粉紅色": "#FFC0CB", "亮粉": "#FF69B4", "深粉": "#C71585", "棕色": "#A52A2A", "亮棕": "#E59866", "深棕": "#6E2C00", "青色": "#00FFFF", "藍綠色": "#008080", "綠松石色": "#40E0D0", "洋紅": "#FF00FF", "紫紅色": "#DC143C", "灰色": "#808080", "淺灰": "#D3D3D3", "深灰": "#696969", "石板灰": "#708090", "黑色": "#000000", "白色": "#FFFFFF", "米色": "#F5F5DC",
 }
 def generate_gradient_colors(start_color, end_color, steps=10):
-    def hex_to_rgb(hex_color):
-        hex_color = hex_color.lstrip('#')
-        return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
-    def rgb_to_hex(rgb):
-        r, g, b = [max(0, min(255, int(c))) for c in rgb]
-        return '#{:02x}{:02x}{:02x}'.format(r, g, b)
+    def hex_to_rgb(hex_color): hex_color = hex_color.lstrip('#'); return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))
+    def rgb_to_hex(rgb): r, g, b = [max(0, min(255, int(c))) for c in rgb]; return '#{:02x}{:02x}{:02x}'.format(r, g, b)
     try:
-        start_rgb, end_rgb = hex_to_rgb(start_color), hex_to_rgb(end_color)
+        start_rgb, end_rgb = hex_to_rgb(start_color), hex_to_rgb(end_color);
         if steps <= 1: return [start_color] if steps == 1 else []
         r_step, g_step, b_step = [(end_rgb[i] - start_rgb[i]) / (steps - 1) for i in range(3)]
         return [rgb_to_hex((start_rgb[0] + r_step * i, start_rgb[1] + g_step * i, start_rgb[2] + b_step * i)) for i in range(steps)]
-    except Exception as e:
-        print(f"生成漸變色時出錯: {e}"); return [start_color, end_color]
-GRADIENTS = {
-    "紅→黃": generate_gradient_colors("#FF0000", "#FFFF00"), "藍→綠": generate_gradient_colors("#0000FF", "#00FF00"),
-    "紫→粉": generate_gradient_colors("#800080", "#FFC0CB"), "紅→藍": generate_gradient_colors("#FF0000", "#0000FF"),
-    "黑→白": generate_gradient_colors("#000000", "#FFFFFF"), "藍→紅 (發散)": generate_gradient_colors("#0000FF", "#FF0000"),
-    "綠→紫 (發散)": generate_gradient_colors("#00FF00", "#800080"),
-    "彩虹 (簡易)": ["#FF0000", "#FF7F00", "#FFFF00", "#00FF00", "#0000FF", "#4B0082", "#9400D3"]
-}
+    except Exception as e: print(f"生成漸變色時出錯: {e}"); return [start_color, end_color]
+GRADIENTS = {"紅→黃": generate_gradient_colors("#FF0000", "#FFFF00"), "藍→綠": generate_gradient_colors("#0000FF", "#00FF00"), "紫→粉": generate_gradient_colors("#800080", "#FFC0CB"), "紅→藍": generate_gradient_colors("#FF0000", "#0000FF"), "黑→白": generate_gradient_colors("#000000", "#FFFFFF"), "藍→紅 (發散)": generate_gradient_colors("#0000FF", "#FF0000"), "綠→紫 (發散)": generate_gradient_colors("#00FF00", "#800080"), "彩虹 (簡易)": ["#FF0000", "#FF7F00", "#FFFF00", "#00FF00", "#0000FF", "#4B0082", "#9400D3"]}
 def generate_color_cards():
     common_cards = "".join([COLOR_CARD_TEMPLATE.format(color_name=name, color_hex=hex_code) for name, hex_code in COMMON_COLORS.items()])
     gradient_cards_html = ""
@@ -1436,14 +1382,13 @@ def parse_custom_colors(color_text):
         except Exception as e: print(f"解析自定義顏色時出錯: {e}"); return {}
     return custom_colors
 def update_patterns(*patterns_input):
-    # 確保只返回有效圖案
     return [p for p in patterns_input if p in PATTERN_TYPES and p != "無"]
 
-
 # =========================================
 # == 數據處理函數 (Data Processing Functions) ==
 # =========================================
 def process_upload(file):
+    # (與 V4 相同)
     if file is None: return None, "❌ 未上傳任何文件。"
     try:
         file_path = file.name; file_type = file_path.split('.')[-1].lower()
@@ -1458,53 +1403,62 @@ def process_upload(file):
             except Exception as e: return None, f"❌ 讀取 Excel 文件時出錯: {e}"
         else: return None, f"❌ 不支持的文件類型: '{file_type}'。請上傳 CSV 或 Excel 文件。"
         df.columns = df.columns.str.strip()
-        # 清理數據中的潛在空格（可選，但有助於分組）
-        # for col in df.select_dtypes(include=['object', 'string']).columns:
-        #     df[col] = df[col].str.strip()
         return df, f"✅ 成功載入 '{file_path.split('/')[-1]}'，共 {len(df)} 行，{len(df.columns)} 列。"
     except Exception as e: print(f"處理上傳文件時發生未預期錯誤: {e}"); traceback.print_exc(); return None, f"❌ 處理文件時發生未預期錯誤: {e}"
 
 def parse_data(text_data):
+    # (與 V4 相同)
     if not text_data or not text_data.strip(): return None, "❌ 未輸入任何數據。"
     try:
         data_io = io.StringIO(text_data.strip()); first_line = data_io.readline().strip(); data_io.seek(0)
         if ',' in first_line: separator = ','
         elif '\t' in first_line: separator = '\t'
         elif ' ' in first_line: separator = r'\s+'
-        else: separator = ',' # 默認
-        try:
-            df = pd.read_csv(data_io, sep=separator, skipinitialspace=True) # skipinitialspace 處理分隔符後的空格
+        else: separator = ','
+        try: df = pd.read_csv(data_io, sep=separator, skipinitialspace=True)
         except pd.errors.ParserError as pe: return None, f"❌ 解析數據時出錯：可能是分隔符錯誤或數據格式問題。檢測到的分隔符: '{separator}'. 錯誤: {pe}"
         except Exception as e: return None, f"❌ 解析數據時出錯: {e}"
         df.columns = df.columns.str.strip()
-        # 清理數據中的潛在空格
-        # for col in df.select_dtypes(include=['object', 'string']).columns:
-        #     df[col] = df[col].str.strip()
         return df, f"✅ 成功解析數據，共 {len(df)} 行，{len(df.columns)} 列。"
     except Exception as e: print(f"解析文本數據時發生未預期錯誤: {e}"); traceback.print_exc(); return None, f"❌ 解析數據時發生未預期錯誤: {e}"
 
-def update_columns(df):
-    default_choices = ["-- 無數據 --"]
+def update_columns_as_radio(df):
+    """修改：更新列選擇為 Radio 選項"""
+    no_data_choices = [NO_DATA_STR]
+    no_data_choices_with_none = [NONE_STR, NO_DATA_STR]
+
     if df is None or df.empty:
-        # 返回空的 Dropdown 更新，避免錯誤
-        no_data_update = gr.Dropdown(choices=default_choices, value=default_choices[0])
-        no_data_update_with_none = gr.Dropdown(choices=["無"] + default_choices, value="無")
+        # 返回空的 Radio 更新
+        no_data_update = gr.Radio(choices=no_data_choices, value=no_data_choices[0])
+        no_data_update_with_none = gr.Radio(choices=no_data_choices_with_none, value=NONE_STR)
         return no_data_update, no_data_update, no_data_update_with_none, no_data_update_with_none
 
     try:
         columns = df.columns.tolist()
-        x_default = columns[0] if columns else None
-        y_default = columns[1] if len(columns) > 1 else (columns[0] if columns else None)
         valid_columns = [str(col) for col in columns if col is not None and str(col) != ""] # 確保列名是字符串
-        group_choices, size_choices = ["無"] + valid_columns, ["無"] + valid_columns
-        return (gr.Dropdown(choices=valid_columns, value=x_default, label="X軸 / 類別"),
-                gr.Dropdown(choices=valid_columns, value=y_default, label="Y軸 / 數值"),
-                gr.Dropdown(choices=group_choices, value="無", label="分組列"),
-                gr.Dropdown(choices=size_choices, value="無", label="大小列"))
+        if not valid_columns: # 如果沒有有效列
+             no_data_update = gr.Radio(choices=no_data_choices, value=no_data_choices[0])
+             no_data_update_with_none = gr.Radio(choices=no_data_choices_with_none, value=NONE_STR)
+             return no_data_update, no_data_update, no_data_update_with_none, no_data_update_with_none
+
+        x_default = valid_columns[0]
+        # Y 軸預設選第二個（如果存在），否則選第一個
+        y_default = valid_columns[1] if len(valid_columns) > 1 else valid_columns[0]
+
+        # 為 group 和 size 添加 "無" 選項
+        group_choices = [NONE_STR] + valid_columns
+        size_choices = [NONE_STR] + valid_columns
+
+        # 返回 Radio 更新對象
+        # 注意：如果列過多，Radio 會很長，但這是為了測試功能性
+        return (gr.Radio(choices=valid_columns, value=x_default, label="X軸 / 類別"),
+                gr.Radio(choices=valid_columns, value=y_default, label="Y軸 / 數值"),
+                gr.Radio(choices=group_choices, value=NONE_STR, label="分組列"),
+                gr.Radio(choices=size_choices, value=NONE_STR, label="大小列"))
     except Exception as e:
-        print(f"更新列選項時出錯: {e}")
-        no_data_update = gr.Dropdown(choices=default_choices, value=default_choices[0])
-        no_data_update_with_none = gr.Dropdown(choices=["無"] + default_choices, value="無")
+        print(f"更新列選項 (Radio) 時出錯: {e}")
+        no_data_update = gr.Radio(choices=no_data_choices, value=no_data_choices[0])
+        no_data_update_with_none = gr.Radio(choices=no_data_choices_with_none, value=NONE_STR)
         return no_data_update, no_data_update, no_data_update_with_none, no_data_update_with_none
 
 
@@ -1514,6 +1468,23 @@ def update_columns(df):
 def create_plot(df, chart_type, x_column, y_column, group_column=None, size_column=None,
                 color_scheme_name="預設 (Plotly)", patterns=[], title="", width=800, height=500,
                 show_grid_str="是", show_legend_str="是", agg_func_name="計數", custom_colors_dict={}):
+    # --- 添加調試信息 ---
+    print("-" * 20, file=sys.stderr)
+    print(f"調用 create_plot:", file=sys.stderr)
+    print(f"  - df type: {type(df)}", file=sys.stderr)
+    if isinstance(df, pd.DataFrame):
+        print(f"  - df empty: {df.empty}", file=sys.stderr)
+        print(f"  - df shape: {df.shape}", file=sys.stderr)
+        # print(f"  - df head:\n{df.head()}", file=sys.stderr) # 打印頭部數據幫助調試
+    print(f"  - chart_type: {chart_type}", file=sys.stderr)
+    print(f"  - x_column: {x_column}", file=sys.stderr)
+    print(f"  - y_column: {y_column}", file=sys.stderr)
+    print(f"  - group_column: {group_column}", file=sys.stderr)
+    print(f"  - size_column: {size_column}", file=sys.stderr)
+    print(f"  - agg_func_name: {agg_func_name}", file=sys.stderr)
+    print("-" * 20, file=sys.stderr)
+    # --- 結束調試信息 ---
+
     fig = go.Figure()
     try:
         # --- 0. 將 "是"/"否" 轉換為布林值 ---
@@ -1521,40 +1492,37 @@ def create_plot(df, chart_type, x_column, y_column, group_column=None, size_colu
         show_legend = True if show_legend_str == "是" else False
 
         # --- 1. 輸入驗證 (更嚴格) ---
-        if df is None or df.empty: raise ValueError("沒有有效的數據可供繪圖。")
+        if df is None or not isinstance(df, pd.DataFrame) or df.empty: # 嚴格檢查 df
+            raise ValueError("沒有有效的 DataFrame 數據可供繪圖。請先載入數據。")
         if not chart_type: raise ValueError("請選擇圖表類型。")
         if not agg_func_name: raise ValueError("請選擇聚合函數。")
-        if not x_column or x_column == "-- 無數據 --": raise ValueError("請選擇有效的 X 軸或類別列。")
+        if not x_column or x_column == NO_DATA_STR: raise ValueError("請選擇有效的 X 軸或類別列。")
 
         # 檢查列是否存在
-        if x_column not in df.columns: raise ValueError(f"X 軸列 '{x_column}' 不在數據中。")
+        if x_column not in df.columns: raise ValueError(f"X 軸列 '{x_column}' 不在數據中。可用列: {', '.join(df.columns)}")
 
         # 只有在非計數且非直方圖時才嚴格要求 Y 軸
         y_needed = agg_func_name != "計數" and chart_type not in ["直方圖"]
         if y_needed:
-            if not y_column or y_column == "-- 無數據 --": raise ValueError("此圖表類型和聚合函數需要選擇有效的 Y 軸或數值列。")
-            if y_column not in df.columns: raise ValueError(f"Y 軸列 '{y_column}' 不在數據中。")
+            if not y_column or y_column == NO_DATA_STR: raise ValueError("此圖表類型和聚合函數需要選擇有效的 Y 軸或數值列。")
+            if y_column not in df.columns: raise ValueError(f"Y 軸列 '{y_column}' 不在數據中。可用列: {', '.join(df.columns)}")
         else:
             y_column = None # 如果不需要 Y 軸，明確設為 None
 
         # 處理可選列
-        group_col = None if group_column == "無" or not group_column else group_column
-        size_col = None if size_column == "無" or not size_column else size_column
+        group_col = None if group_column == NONE_STR or not group_column else group_column
+        size_col = None if size_column == NONE_STR or not size_column else size_column
 
-        if group_col and group_col not in df.columns: raise ValueError(f"分組列 '{group_col}' 不在數據中。")
-        if size_col and size_col not in df.columns: raise ValueError(f"大小列 '{size_col}' 不在數據中。")
+        if group_col and group_col not in df.columns: raise ValueError(f"分組列 '{group_col}' 不在數據中。可用列: {', '.join(df.columns)}")
+        if size_col and size_col not in df.columns: raise ValueError(f"大小列 '{size_col}' 不在數據中。可用列: {', '.join(df.columns)}")
         if group_col == x_column: raise ValueError("分組列不能與 X 軸列相同。")
-        # if y_needed and y_column == x_column: raise ValueError("Y 軸列不能與 X 軸列相同（除非特殊情況）。") # 允許某些情況相同
 
         df_processed = df.copy()
 
         # --- 2. 數據類型轉換與準備 ---
-        # 將 X 軸和分組列強制轉為字符串，以便正確分組
+        # (與 V4 相同)
         df_processed[x_column] = df_processed[x_column].astype(str)
-        if group_col:
-            df_processed[group_col] = df_processed[group_col].astype(str)
-
-        # 嘗試將 Y 軸和大小列轉為數值
+        if group_col: df_processed[group_col] = df_processed[group_col].astype(str)
         if y_column:
             try: df_processed[y_column] = pd.to_numeric(df_processed[y_column], errors='coerce')
             except Exception as e: print(f"警告：轉換 Y 軸列 '{y_column}' 為數值時出錯: {e}")
@@ -1562,48 +1530,39 @@ def create_plot(df, chart_type, x_column, y_column, group_column=None, size_colu
             try: df_processed[size_col] = pd.to_numeric(df_processed[size_col], errors='coerce')
             except Exception as e: print(f"警告：轉換大小列 '{size_col}' 為數值時出錯: {e}")
 
-
         # --- 3. 數據聚合 (如果需要) ---
+        # (與 V4 相同)
         needs_aggregation = chart_type not in ["散點圖", "氣泡圖", "直方圖", "箱型圖", "小提琴圖", "甘特圖"]
         agg_df = None
-        y_col_agg = y_column # 預設 Y 軸列名
+        y_col_agg = y_column
         if needs_aggregation:
             grouping_cols = [x_column] + ([group_col] if group_col else [])
-
+            invalid_grouping_cols = [col for col in grouping_cols if col not in df_processed.columns]
+            if invalid_grouping_cols: raise ValueError(f"以下分組/X軸列不在數據中: {', '.join(invalid_grouping_cols)}")
             if agg_func_name == "計數":
                 agg_df = df_processed.groupby(grouping_cols, observed=False, dropna=False).size().reset_index(name='__count__')
                 y_col_agg = '__count__'
             else:
                 agg_func_pd = agg_function_map(agg_func_name)
                 if not y_column: raise ValueError(f"聚合函數 '{agg_func_name}' 需要一個有效的 Y 軸數值列。")
-                # 確保 Y 軸是數值類型 (除非 first/last)
                 if agg_func_pd not in ['first', 'last'] and not pd.api.types.is_numeric_dtype(df_processed[y_column]):
-                     # 嘗試再次轉換，如果失敗則報錯
                      try: df_processed[y_column] = pd.to_numeric(df_processed[y_column], errors='raise')
                      except (ValueError, TypeError): raise ValueError(f"Y 軸列 '{y_column}' 必須是數值類型才能執行聚合 '{agg_func_name}'。")
-
                 try:
-                    # 執行聚合
                     agg_df = df_processed.groupby(grouping_cols, observed=False, dropna=False)[y_column].agg(agg_func_pd).reset_index()
-                    y_col_agg = y_column # 保持原始列名
-                except Exception as agg_e:
-                    raise ValueError(f"執行聚合 '{agg_func_name}' 時出錯: {agg_e}")
+                    y_col_agg = y_column
+                except Exception as agg_e: raise ValueError(f"執行聚合 '{agg_func_name}' 時出錯: {agg_e}")
         else:
             agg_df = df_processed
-            y_col_agg = y_column # 保持原始列名 (可能為 None)
+            y_col_agg = y_column
 
-        # 再次檢查聚合後的 DataFrame
-        if agg_df is None or agg_df.empty:
-             raise ValueError("數據聚合後沒有產生有效結果。")
-        # 確保繪圖所需的列存在於 agg_df 中
+        if agg_df is None or agg_df.empty: raise ValueError("數據聚合後沒有產生有效結果。")
         required_cols_for_plot = [x_column]
         if y_col_agg: required_cols_for_plot.append(y_col_agg)
         if group_col: required_cols_for_plot.append(group_col)
         if size_col: required_cols_for_plot.append(size_col)
         missing_cols = [col for col in required_cols_for_plot if col not in agg_df.columns]
-        if missing_cols:
-             raise ValueError(f"聚合後的數據缺少繪圖所需的列: {', '.join(missing_cols)}")
-
+        if missing_cols: raise ValueError(f"聚合後的數據缺少繪圖所需的列: {', '.join(missing_cols)}")
 
         # --- 4. 獲取顏色方案 ---
         colors = COLOR_SCHEMES.get(color_scheme_name, px.colors.qualitative.Plotly)
@@ -1611,12 +1570,9 @@ def create_plot(df, chart_type, x_column, y_column, group_column=None, size_colu
         # --- 5. 創建圖表 (核心邏輯) ---
         fig_params = {"data_frame": agg_df, "title": title, "color_discrete_sequence": colors, "width": width, "height": height}
         if group_col and custom_colors_dict: fig_params["color_discrete_map"] = custom_colors_dict
-
-        # --- (繪圖邏輯開始) ---
-        # (與 V3 版本相同，此處省略以保持簡潔，實際代碼應包含完整的 if/elif 結構)
-        # 確保 y_col_agg 在不需要時為 None
         effective_y = y_col_agg if y_needed or agg_func_name == "計數" else None
 
+        # --- (繪圖邏輯 - 與 V4 相同，省略) ---
         if chart_type == "長條圖":
             if not effective_y: raise ValueError("長條圖需要 Y 軸數值或 '計數' 聚合。")
             fig = px.bar(agg_df, x=x_column, y=effective_y, color=group_col, **fig_params)
@@ -1663,7 +1619,7 @@ def create_plot(df, chart_type, x_column, y_column, group_column=None, size_colu
             fig = px.pie(agg_df, names=x_column, values=effective_y, hole=0.4, **fig_params)
             if not group_col and custom_colors_dict: fig.update_traces(marker=dict(colors=[custom_colors_dict.get(str(cat), colors[i % len(colors)]) for i, cat in enumerate(agg_df[x_column])]))
         elif chart_type == "散點圖":
-            if not y_column: raise ValueError("散點圖需要選擇 Y 軸列。") # 散點圖必須有 Y
+            if not y_column: raise ValueError("散點圖需要選擇 Y 軸列。")
             fig = px.scatter(agg_df, x=x_column, y=y_column, color=group_col, size=size_col, **fig_params)
         elif chart_type == "氣泡圖":
             if not y_column: raise ValueError("氣泡圖需要選擇 Y 軸列。")
@@ -1721,13 +1677,10 @@ def create_plot(df, chart_type, x_column, y_column, group_column=None, size_colu
             if not pd.api.types.is_numeric_dtype(agg_df[effective_y]): raise ValueError(f"極座標圖的徑向值列 '{effective_y}' 必須是數值類型。")
             fig = px.bar_polar(agg_df, r=effective_y, theta=x_column, color=group_col if group_col else x_column, **fig_params)
         elif chart_type == "甘特圖":
-             # 甘特圖使用原始數據，需要開始和結束列
-             start_col_gantt = y_column # Y 軸被用作開始列
-             end_col_gantt = group_col # 分組列被用作結束列
-             task_col_gantt = x_column # X 軸被用作任務列
+             start_col_gantt = y_column; end_col_gantt = group_col; task_col_gantt = x_column
              if not start_col_gantt or not end_col_gantt: raise ValueError("甘特圖需要指定 開始列 (Y軸) 和 結束列 (分組列)。")
              try:
-                 df_gantt = df.copy() # 使用原始 df
+                 df_gantt = df.copy()
                  if start_col_gantt not in df_gantt.columns: raise ValueError(f"開始列 '{start_col_gantt}' 不在數據中。")
                  if end_col_gantt not in df_gantt.columns: raise ValueError(f"結束列 '{end_col_gantt}' 不在數據中。")
                  if task_col_gantt not in df_gantt.columns: raise ValueError(f"任務列 '{task_col_gantt}' 不在數據中。")
@@ -1746,6 +1699,7 @@ def create_plot(df, chart_type, x_column, y_column, group_column=None, size_colu
 
 
         # --- 6. 應用圖案 (如果支持) ---
+        # (與 V4 相同，省略)
         if patterns:
             try:
                 num_traces = len(fig.data)
@@ -1768,10 +1722,11 @@ def create_plot(df, chart_type, x_column, y_column, group_column=None, size_colu
                      dash_map = {"/": "dash", "\\": "dot", "x": "dashdot", "-": "longdash"}
                      for i, trace in enumerate(fig.data):
                          pattern_index = i % len(patterns); dash = dash_map.get(patterns[pattern_index])
-                         if dash: trace.line.dash = dash; trace.fill = 'tonexty' if hasattr(trace, 'stackgroup') and trace.stackgroup else 'tozeroy' # 修正 stackgroup 判斷
+                         if dash: trace.line.dash = dash; trace.fill = 'tonexty' if hasattr(trace, 'stackgroup') and trace.stackgroup else 'tozeroy'
             except Exception as pattern_e: print(f"應用圖案時出錯: {pattern_e}")
 
         # --- 7. 更新佈局 ---
+        # (與 V4 相同)
         fig.update_layout(
             showlegend=show_legend, xaxis=dict(showgrid=show_grid), yaxis=dict(showgrid=show_grid),
             template="plotly_white", margin=dict(l=60, r=40, t=80 if title else 40, b=60),
@@ -1779,25 +1734,25 @@ def create_plot(df, chart_type, x_column, y_column, group_column=None, size_colu
             hoverlabel=dict(bgcolor="white", font_size=12, font_family="Inter, sans-serif"),
             legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1) if show_legend else None,
         )
-        # 根據圖表類型更新軸標籤
         final_y_label = y_col_agg if y_col_agg != '__count__' else '計數'
         if chart_type in ["圓餅圖", "環形圖", "漏斗圖", "樹狀圖"]: fig.update_layout(xaxis_title=None, yaxis_title=None)
         elif chart_type == "水平長條圖": fig.update_layout(xaxis_title=final_y_label, yaxis_title=x_column)
         elif chart_type == "直方圖": fig.update_layout(xaxis_title=x_column, yaxis_title='計數')
-        elif chart_type == "甘特圖": fig.update_layout(xaxis_title="時間", yaxis_title=x_column) # 甘特圖軸標籤
+        elif chart_type == "甘特圖": fig.update_layout(xaxis_title="時間", yaxis_title=x_column)
         else: fig.update_layout(xaxis_title=x_column, yaxis_title=final_y_label)
 
     except ValueError as ve:
-        print(f"圖表創建錯誤 (ValueError): {ve}"); traceback.print_exc()
+        print(f"圖表創建錯誤 (ValueError): {ve}", file=sys.stderr); traceback.print_exc(file=sys.stderr)
         fig = go.Figure(); fig.add_annotation(text=f"⚠️ 創建圖表時出錯:<br>{ve}", align='left', showarrow=False, font=dict(size=14, color="red")); fig.update_layout(xaxis_visible=False, yaxis_visible=False)
     except Exception as e:
-        error_message = f"❌ 創建圖表時發生未預期錯誤:\n{traceback.format_exc()}"; print(error_message)
+        error_message = f"❌ 創建圖表時發生未預期錯誤:\n{traceback.format_exc()}"; print(error_message, file=sys.stderr)
         fig = go.Figure(); user_error_msg = f"⚠️ 創建圖表時發生內部錯誤。<br>請檢查數據和設置。<br>詳細錯誤: {str(e)[:100]}..."; fig.add_annotation(text=user_error_msg, align='left', showarrow=False, font=dict(size=14, color="red")); fig.update_layout(xaxis_visible=False, yaxis_visible=False)
     return fig
 
 # =========================================
 # == 導出與下載函數 (Export & Download Functions) ==
 # =========================================
+# (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:
@@ -1812,26 +1767,19 @@ 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}"
-        # 確保有 kaleido
-        import kaleido
+        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 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:
-         # kaleido 可能安裝但無法運行
-         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 "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。"}
     if df is None or df.empty: recommendation["message"] = "ℹ️ 請先上傳或輸入數據。"; return recommendation
@@ -1846,18 +1794,17 @@ def recommend_chart_settings(df):
         elif num_cols: recommendation.update({"chart_type": "直方圖", "x_column": num_cols[0], "y_column": None, "agg_function": None, "message": f"檢測到數值列 '{num_cols[0]}'，推薦使用直方圖查看其分佈。"})
         else: recommendation["message"] = "無法根據當前數據結構提供明確的圖表推薦。"
     except Exception as e: recommendation["message"] = f"❌ 推薦時出錯: {e}"; print(f"智能推薦時出錯: {e}"); traceback.print_exc()
-    # 驗證推薦的列名
     if recommendation["x_column"] and recommendation["x_column"] not in columns: recommendation["x_column"] = None
     if recommendation["y_column"] and recommendation["y_column"] not in columns: recommendation["y_column"] = None
     if recommendation["group_column"] != "無" and recommendation["group_column"] not in columns: recommendation["group_column"] = "無"
-    # 清理無效聚合
     if recommendation["agg_function"] and recommendation["agg_function"] != "計數" and not recommendation["y_column"]: recommendation["agg_function"] = None; recommendation["message"] += " (無法確定聚合的數值列)"
-    if recommendation["agg_function"] == "計數": recommendation["y_column"] = None # 計數時 Y 軸應為空
+    if recommendation["agg_function"] == "計數": recommendation["y_column"] = None
     return recommendation
 
 # =========================================
 # == CSS 樣式 (CSS Styling) ==
 # =========================================
+# (與 V4 相同)
 CUSTOM_CSS = """
 /* --- 全局和容器 --- */
 .gradio-container { font-family: 'Inter', 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; background: #f8f9fa; }
@@ -1907,23 +1854,21 @@ CUSTOM_CSS = """
 # =========================================
 # == Gradio UI 介面定義 (Gradio UI Definition) ==
 # =========================================
-with gr.Blocks(css=CUSTOM_CSS, title="進階數據可視化工具 V4", theme=gr.themes.Soft()) as demo:
+with gr.Blocks(css=CUSTOM_CSS, title="進階數據可視化工具 V5", theme=gr.themes.Soft()) as demo:
 
     # --- 應用程式標頭 ---
     gr.HTML("""
     <div class="app-header">
-        <h1 class="app-title">📊 進階數據可視化工具 V4</h1>
-        <p class="app-subtitle">上傳或貼上數據，輕鬆創建和比較多種專業圖表 (改用 Radio 選項)</p>
+        <h1 class="app-title">📊 進階數據可視化工具 V5</h1>
+        <p class="app-subtitle">上傳或貼上數據，創建專業圖表 (極簡化測試版)</p>
     </div>
     """)
 
     # --- 狀態變量 ---
     data_state = gr.State(None)
-    custom_colors_state_1 = gr.State({})
-    patterns_state_1 = gr.State([])
-    custom_colors_state_2 = gr.State({})
-    patterns_state_2 = gr.State([])
-    recommendation_state = gr.State({}) # 用於存儲推薦結果
+    custom_colors_state = gr.State({}) # 只保留一組狀態
+    patterns_state = gr.State([])
+    recommendation_state = gr.State({})
 
     # --- 主頁籤佈局 ---
     with gr.Tabs() as tabs:
@@ -1948,116 +1893,79 @@ with gr.Blocks(css=CUSTOM_CSS, title="進階數據可視化工具 V4", theme=gr.
                         gr.Markdown("下方將顯示載入或解析後的數據預覽。")
                         data_preview = gr.Dataframe(label="數據表格預覽", interactive=False)
                         with gr.Row():
-                            export_format = gr.Radio(EXPORT_FORMATS_DATA, label="選擇導出格式", value="CSV") # 改用 Radio
+                            export_format = gr.Radio(EXPORT_FORMATS_DATA, label="選擇導出格式", value="CSV") # Radio
                             export_button = gr.Button("⬇️ 導出預覽數據", elem_classes=["secondary-button"])
                         export_result = gr.File(label="導出文件下載", interactive=False)
                         export_status = gr.Textbox(label="導出狀態", lines=1, interactive=False)
 
-        # --- 圖表創建頁籤 (左右佈局, 移除 Accordion, 使用 Radio) ---
-        with gr.TabItem("📈 圖表創建與比較", id=1):
-            gr.HTML('<div class="section-title">創建與比較圖表 (左右並列)</div>')
-            gr.Markdown("在這裡，您可以分別設置並生成兩張���表，方便進行對比分析。")
-
-            with gr.Row(): # 主 Row，包含左右兩個圖表區塊
-                # --- 圖表一 (左側 Column) ---
-                with gr.Column(scale=1):
-                    gr.Markdown("### 📊 圖表一")
-                    with gr.Group(elem_classes=["card"]): # 將所有設定放在一個卡片中
-                        gr.Markdown("**基本設置**")
-                        chart_type_1 = gr.Radio(CHART_TYPES, label="圖表類型", value="長條圖", interactive=True) # 改用 Radio
-                        recommend_button_1 = gr.Button("🧠 智能推薦", elem_classes=["secondary-button"], size="sm")
-                        chart_title_1 = gr.Textbox(label="圖表標題", placeholder="圖表一標題")
-                        agg_function_1 = gr.Radio(AGGREGATION_FUNCTIONS, label="聚合函數", value="計數") # 改用 Radio
-
-                        gr.Markdown("**數據映射**")
-                        x_column_1 = gr.Dropdown(["-- 無數據 --"], label="X軸 / 類別", info="選擇圖表主要分類或 X 軸") # 保留 Dropdown
-                        y_column_1 = gr.Dropdown(["-- 無數據 --"], label="Y軸 / 數值", info="選擇圖表數值或 Y 軸 (計數時可忽略)") # 保留 Dropdown
-                        group_column_1 = gr.Dropdown(["無"], label="分組列", info="用於生成多系列或堆疊") # 保留 Dropdown
-                        size_column_1 = gr.Dropdown(["無"], label="大小列", info="用於氣泡圖等控制點的大小") # 保留 Dropdown
-
-                        gr.Markdown("**顯示選項**")
-                        chart_width_1 = gr.Slider(300, 1600, 700, step=50, label="寬度 (px)")
-                        chart_height_1 = gr.Slider(300, 1000, 450, step=50, label="高度 (px)")
-                        show_grid_1 = gr.Radio(YES_NO_CHOICES, label="顯示網格", value="是") # 改用 Radio
-                        show_legend_1 = gr.Radio(YES_NO_CHOICES, label="顯示圖例", value="是") # 改用 Radio
-                        color_scheme_1 = gr.Dropdown(list(COLOR_SCHEMES.keys()), label="顏色方案", value="預設 (Plotly)") # 保留 Dropdown
-                        gr.HTML('<div style="margin-top: 10px;"><b>顏色參考</b> (點擊複製)</div>')
-                        gr.HTML(generate_color_cards(), elem_id="color_display_1")
-
-                        gr.Markdown("**圖案與自定義顏色**")
-                        pattern1_1 = gr.Radio(PATTERN_TYPES, label="圖案1", value="無") # 改用 Radio
-                        pattern2_1 = gr.Radio(PATTERN_TYPES, label="圖案2", value="無") # 改用 Radio
-                        pattern3_1 = gr.Radio(PATTERN_TYPES, label="圖案3", value="無") # 改用 Radio
-                        color_customization_1 = gr.Textbox(label="自定義顏色", placeholder="類別A:#FF5733, 類別B:#33CFFF", info="格式: 類別名:十六進制顏色代碼, ...", elem_classes=["color-customization-input"])
-
-                    # 圖表一：操作按鈕
-                    with gr.Row():
-                        update_button_1 = gr.Button("🔄 更新圖表一", variant="primary", elem_classes=["primary-button"])
-                        export_img_format_1 = gr.Radio(EXPORT_FORMATS_IMG, label="導出格式", value="PNG", scale=1) # 改用 Radio
-                        download_button_1 = gr.Button("💾 導出圖表一", elem_classes=["secondary-button"], scale=1)
-                    export_chart_1 = gr.File(label="圖表一文件下載", interactive=False)
-                    export_chart_status_1 = gr.Textbox(label="導出狀態", lines=1, interactive=False)
-
-                    # 圖表一：預覽區域
-                    with gr.Group(elem_classes=["chart-previewer"]):
-                        chart_output_1 = gr.Plot(label="", elem_id="chart_preview_1")
-
+        # --- 圖表創建頁籤 (單圖表, 移除 Accordion, 使用 Radio) ---
+        with gr.TabItem("📈 圖表創建", id=1):
+            gr.HTML('<div class="section-title">創建圖表</div>')
+            gr.Markdown("在此設置並生成圖表。")
 
-                # --- 圖表二 (右側 Column) ---
+            with gr.Row(): # 主 Row
+                # --- 設定欄 (左側 Column) ---
                 with gr.Column(scale=1):
-                    gr.Markdown("### 📊 圖表二")
+                    gr.Markdown("### 📊 圖表設置")
                     with gr.Group(elem_classes=["card"]):
                         gr.Markdown("**基本設置**")
-                        chart_type_2 = gr.Radio(CHART_TYPES, label="圖表類型", value="折線圖", interactive=True) # 改用 Radio
-                        chart_title_2 = gr.Textbox(label="圖表標題", placeholder="圖表二標題")
-                        agg_function_2 = gr.Radio(AGGREGATION_FUNCTIONS, label="聚合函數", value="平均值") # 改用 Radio
-
-                        gr.Markdown("**數據映射**")
-                        x_column_2 = gr.Dropdown(["-- 無數據 --"], label="X軸 / 類別", info="選��圖表主要分類或 X 軸") # 保留 Dropdown
-                        y_column_2 = gr.Dropdown(["-- 無數據 --"], label="Y軸 / 數值", info="選擇圖表數值或 Y 軸 (計數時可忽略)") # 保留 Dropdown
-                        group_column_2 = gr.Dropdown(["無"], label="分組列", info="用於生成多系列或堆疊") # 保留 Dropdown
-                        size_column_2 = gr.Dropdown(["無"], label="大小列", info="用於氣泡圖等控制點的大小") # 保留 Dropdown
+                        chart_type = gr.Radio(CHART_TYPES, label="圖表類型", value="長條圖", interactive=True) # Radio
+                        recommend_button = gr.Button("🧠 智能推薦", elem_classes=["secondary-button"], size="sm")
+                        chart_title = gr.Textbox(label="圖表標題", placeholder="我的圖表")
+                        agg_function = gr.Radio(AGGREGATION_FUNCTIONS, label="聚合函數", value="計數") # Radio
+
+                        gr.Markdown("**數據映射 (請選擇)**")
+                        # 使用 Radio 進行欄位選擇 - 如果欄位過多會很長！
+                        x_column = gr.Radio([NO_DATA_STR], label="X軸 / 類別", info="選擇圖表主要分類或 X 軸")
+                        y_column = gr.Radio([NO_DATA_STR], label="Y軸 / 數值", info="選擇圖表數值或 Y 軸 (計數時可忽略)")
+                        group_column = gr.Radio([NONE_STR, NO_DATA_STR], label="分組列", info="用於生成多系列或堆疊", value=NONE_STR)
+                        size_column = gr.Radio([NONE_STR, NO_DATA_STR], label="大小列", info="用於氣泡圖等控制點的大小", value=NONE_STR)
 
                         gr.Markdown("**顯示選項**")
-                        chart_width_2 = gr.Slider(300, 1600, 700, step=50, label="寬度 (px)")
-                        chart_height_2 = gr.Slider(300, 1000, 450, step=50, label="高度 (px)")
-                        show_grid_2 = gr.Radio(YES_NO_CHOICES, label="顯示網格", value="是") # 改用 Radio
-                        show_legend_2 = gr.Radio(YES_NO_CHOICES, label="顯示圖例", value="是") # 改用 Radio
-                        color_scheme_2 = gr.Dropdown(list(COLOR_SCHEMES.keys()), label="顏色方案", value="分類 - Set2") # 保留 Dropdown
-                        # 顏色參考共用
+                        chart_width = gr.Slider(300, 1600, 700, step=50, label="寬度 (px)")
+                        chart_height = gr.Slider(300, 1000, 450, step=50, label="高度 (px)")
+                        show_grid = gr.Radio(YES_NO_CHOICES, label="顯示網格", value="是") # Radio
+                        show_legend = gr.Radio(YES_NO_CHOICES, label="顯示圖例", value="是") # Radio
+                        color_scheme = gr.Dropdown(list(COLOR_SCHEMES.keys()), label="顏色方案", value="預設 (Plotly)") # 保留 Dropdown
+                        gr.HTML('<div style="margin-top: 10px;"><b>顏色參考</b> (點擊複製)</div>')
+                        gr.HTML(generate_color_cards(), elem_id="color_display")
 
                         gr.Markdown("**圖案與自定義顏色**")
-                        pattern1_2 = gr.Radio(PATTERN_TYPES, label="圖案1", value="無") # 改用 Radio
-                        pattern2_2 = gr.Radio(PATTERN_TYPES, label="圖案2", value="無") # 改用 Radio
-                        pattern3_2 = gr.Radio(PATTERN_TYPES, label="圖案3", value="無") # 改用 Radio
-                        color_customization_2 = gr.Textbox(label="自定義顏色", placeholder="類別C:#FFC300, 類別D:#C70039", info="格式: 類別名:十六進制顏色代碼, ...", elem_classes=["color-customization-input"])
-
-                    # 圖表二：操作按鈕
+                        pattern1 = gr.Radio(PATTERN_TYPES, label="圖案1", value="無") # Radio
+                        pattern2 = gr.Radio(PATTERN_TYPES, label="圖案2", value="無") # Radio
+                        pattern3 = gr.Radio(PATTERN_TYPES, label="圖案3", value="無") # Radio
+                        color_customization = gr.Textbox(label="自定義顏色", placeholder="類別A:#FF5733, 類別B:#33CFFF", info="格式: 類別名:十六進制顏色代碼, ...", elem_classes=["color-customization-input"])
+
+                # --- 預覽與操作欄 (右側 Column) ---
+                with gr.Column(scale=2):
+                    # 操作按鈕 (預覽上方)
+                    gr.HTML('<div class="section-title" style="margin-top:0; margin-bottom:10px;">操作</div>')
+                    update_button = gr.Button("🔄 更新圖表", variant="primary", elem_classes=["primary-button"])
                     with gr.Row():
-                        update_button_2 = gr.Button("🔄 更新圖表二", variant="primary", elem_classes=["primary-button"])
-                        export_img_format_2 = gr.Radio(EXPORT_FORMATS_IMG, label="導出格式", value="PNG", scale=1) # 改用 Radio
-                        download_button_2 = gr.Button("💾 導出圖表二", elem_classes=["secondary-button"], scale=1)
-                    export_chart_2 = gr.File(label="圖表二文件下載", interactive=False)
-                    export_chart_status_2 = gr.Textbox(label="導出狀態", lines=1, interactive=False)
+                        export_img_format = gr.Radio(EXPORT_FORMATS_IMG, label="導出格式", value="PNG", scale=1) # Radio
+                        download_button = gr.Button("💾 導出圖表", elem_classes=["secondary-button"], scale=1)
+                    export_chart = gr.File(label="圖表文件下載", interactive=False)
+                    export_chart_status = gr.Textbox(label="導出狀態", lines=1, interactive=False)
 
-                    # 圖表二：預覽區域
+                    # 預覽區域
+                    gr.HTML('<div class="section-title" style="margin-top:20px; margin-bottom:10px;">圖表預覽</div>')
                     with gr.Group(elem_classes=["chart-previewer"]):
-                        chart_output_2 = gr.Plot(label="", elem_id="chart_preview_2")
+                        chart_output = gr.Plot(label="", elem_id="chart_preview")
 
 
         # --- 使用說明頁籤 ---
         with gr.TabItem("❓ 使用說明", id=2):
              with gr.Group(elem_classes=["card"]):
                 gr.HTML("""
-                <div class="section-title">使用說明</div>
+                <div class="section-title">使用說明 (V5 - 極簡測試版)</div>
                 <h3>數據輸入</h3>
                 <ul><li>點擊 "上傳 CSV / Excel 文件" 按鈕選擇本地文件，或在文本框中直接貼上數據。</li><li>支持逗號 (<code>,</code>)、製表符 (<code>Tab</code>) 或空格 (<code> </code>) 分隔的數據。</li><li>第一行通常被視為欄位名稱（表頭）。</li><li>數據載入或解析成功後，會在右側顯示預覽。</li><li>您可以使用 "導出預覽數據" 功能將處理後的數據保存為 CSV、Excel 或 JSON 格式。</li></ul>
-                <h3>圖表創建與比較</h3>
-                <ul><li>此頁面提供左右兩個獨立的圖表設置和預覽區域（圖表一、圖表二）。</li><li><strong>智能推薦：</strong>點擊圖表一的 "智能推薦" 按鈕，系統會根據數據結構嘗試為圖表一推薦合適的設置。</li><li><strong>圖表類型：</strong>選擇您想創建的圖表樣式 (使用點選按鈕)。</li><li><strong>聚合函數：</strong>決定如何匯總 Y 軸數據 (使用點選按鈕)。
-                    <ul><li><strong style="color: #7367f0;">【重要】單欄計數：</strong>若要統計某一欄位中各個項目出現的次數（例如，統計不同產品的銷售筆數），請在 <strong>X軸/類別</strong> 選擇該欄位，並將 <strong>聚合函數</strong> 設為 <strong>計數</strong>，此時 <strong>無需選擇 Y軸/數值</strong>。然後選擇「長條圖」或「圓餅圖」。</li></ul>
-                </li><li><strong>數據映射 (下拉選單)：</strong><ul><li><strong>X軸/類別：</strong>圖表的主要分類軸。</li><li><strong>Y軸/數值：</strong>圖表的數值軸。若聚合函數為 "計數"，此項可忽略。</li><li><strong>分組列：</strong>用於創建堆疊、分組或多系列圖表。</li><li><strong>大小列：</strong>主要用於氣泡圖，控制點的大小。</li></ul></li><li><strong>顯示選項：</strong>調整圖表的外觀，如寬度、高度、顏色方案、是否顯示網格和圖例。</li><li><strong>圖案與自定義顏色：</strong><ul><li>為圖表系列添加不同的填充圖案（適用於部分圖表類型，如條形圖）。</li><li>通過 "類別名:顏色代碼" 的格式為特定類別指定顏色 (例如 <code>正面:#2ca02c, 負面:#d62728</code>)。</li></ul></li><li>點擊 "更新圖表" 按鈕生成或刷新對應的圖表預覽。</li><li>使用 "導出圖表" 功能將生成的圖表保存為圖片文件。</li></ul>
+                <h3>圖表創建</h3>
+                <ul><li>此頁面僅提供一個圖表進行測試。</li><li><strong>智能推薦：</strong>點擊 "智能推薦" 按鈕，系統會根據數據結構嘗試推薦合適的設置。</li><li><strong>圖表類型/聚合函數/圖案等：</strong>使用點選按鈕 (Radio) 進行選擇。</li>
+                    <ul><li><strong style="color: #7367f0;">【重要】單欄計數：</strong>若要統計某一欄位中各個項目出現的次數，請在 <strong>X軸/類別</strong> 選擇該欄位，並將 <strong>聚合函數</strong> 設為 <strong>計數</strong>，此時 <strong>無需選擇 Y軸/數值</strong>。然後選擇「長條圖」或「圓餅圖」。</li></ul>
+                </li><li><strong>數據映射 (欄位選擇)：</strong>使用點選按鈕 (Radio) 選擇 X軸、Y軸、分組列、大小列。<strong>注意：如果數據欄位過多，這裡會顯示得很長。</strong></li><li><strong>顯示選項/顏色/自定義：</strong>調整圖表外觀。顏色方案仍為下拉選單。</li><li>點擊 <strong>"更新圖表"</strong> 按鈕生成或刷新圖表預覽 (已移除自動更新)。</li><li>使用 "導出圖表" 功能將生成的圖表保存為圖片文件。</li></ul>
                 <h3>提示</h3>
-                <ul><li>如果圖表無法顯示或出現錯誤，請檢查數據格式、列選擇以及聚合函數是否合理。</li><li>確保數值列確實包含數字，日期列包含有效的日期格式。</li><li>部分圖表類型對數據結構有特定要求（例如，熱力圖、甘特圖）。</li><li>如果欄位選擇的下拉選單仍然有問題，可能是由於數據欄位過多或 Gradio 的限制，嘗試簡化數據或在本地環境運行。</li></ul>
+                <ul><li>如果圖表無法顯示或出現錯誤，請檢查數據格式、列選擇以及聚合函數是否合理。</li><li>如果欄位選擇的 Radio 按鈕區域過長或無法使用，表示此方法不適用於您的數據，且 Gradio 可能存在根本的元件衝突問題。</li></ul>
                 """)
 
     # =========================================
@@ -2065,80 +1973,37 @@ with gr.Blocks(css=CUSTOM_CSS, title="進階數據可視化工具 V4", theme=gr.
     # =========================================
 
     # --- 數據載入與更新 ---
-    def load_data_and_update_ui(df, status_msg):
-        """輔助函數：更新數據狀態、預覽和所有下拉列表 (暫時不繪製初始圖表)"""
+    def load_data_and_update_ui_v5(df, status_msg):
+        """輔助函數：更新數據狀態、預覽和所有列選擇 Radio"""
+        print("調用 load_data_and_update_ui_v5...", file=sys.stderr)
         preview_df = df if df is not None else pd.DataFrame()
-        # 更新列下拉選單
-        col_updates = update_columns(df)
+        # 更新列選擇 Radio
+        col_updates = update_columns_as_radio(df)
         if col_updates is None or len(col_updates) != 4:
-             print("警告: update_columns 未返回預期的 4 個組件更新。")
-             # 返回空更新，避免錯誤
-             updates = [df, status_msg, preview_df] + [gr.update()] * 8 # 8 個下拉選單更新
-             # 額外返回兩個空的 Plot 更新以匹配輸出數量
-             updates += [gr.Plot(value=None), gr.Plot(value=None)]
-             return updates
-
-        # 準備所有更新 (狀態、狀態消息、預覽表格、8 個下拉選單)
-        updates = [df, status_msg, preview_df] + list(col_updates) * 2
-        # 添加兩個空的 Plot 更新以匹配輸出數量
-        updates += [gr.Plot(value=None), gr.Plot(value=None)] # 讓圖表區域保持空白
-
-        # 檢查返回的列表長度是否為 13
-        if len(updates) != 13:
-             print(f"錯誤：load_data_and_update_ui 返回了 {len(updates)} 個元素，預期 13 個。")
-             # 嘗試返回一個包含 13 個 None 的列表以避免崩潰
-             return [None] * 13
+             print("警告: update_columns_as_radio 未返回預期的 4 個組件更新。", file=sys.stderr)
+             # 返回空更新，避免錯誤 (狀態, 消息, 預覽, 4個Radio, 1個Plot)
+             return [df, status_msg, preview_df] + [gr.update()] * 4 + [gr.Plot(value=None)]
 
-        return updates
-
-        # 準備所有更新
-        updates = [df, status_msg, preview_df] + list(col_updates) * 2
-
-        # 準備觸發圖表一初始繪圖的輸入
-        # 使用更新後的下拉列表的值（如果有效）或預設值
-        x_col_val = col_updates[0].value if col_updates[0].value else None
-        y_col_val = col_updates[1].value if col_updates[1].value else None
-        # 獲取圖表一的其他預設值
-        chart_type_val = "長條圖" # 來自 chart_type_1 的預設值
-        agg_func_val = "計數"   # 來自 agg_function_1 的預設值
-
-        # 只有在數據有效且 X 軸有效時才嘗試繪圖
-        initial_plot = go.Figure() # 默認空圖
-        if df is not None and not df.empty and x_col_val and x_col_val != "-- 無數據 --":
-             try:
-                 # 使用預設值嘗試繪製圖表一
-                 initial_plot = create_plot(df, chart_type_val, x_col_val, y_col_val,
-                                            None, None, # group, size
-                                            "預設 (Plotly)", [], "", 700, 450, # color, patterns, title, w, h
-                                            "是", "是", agg_func_val, {}) # grid, legend, agg, custom_colors
-             except Exception as initial_plot_e:
-                 print(f"載入數據後嘗試初始繪圖時出錯: {initial_plot_e}")
-                 initial_plot = go.Figure()
-                 initial_plot.add_annotation(text=f"⚠️ 無法生成初始圖表:<br>{initial_plot_e}", align='left', showarrow=False, font=dict(size=14, color="orange"))
-                 initial_plot.update_layout(xaxis_visible=False, yaxis_visible=False)
-
-        # 將初始圖表添加到更新列表中
-        updates.append(initial_plot)
-        # 圖表二保持空白
-        updates.append(gr.Plot(value=None)) # 更新圖表二為空
+        # 準備所有更新 (狀態, 消息, 預覽表格, 4 個 Radio)
+        updates = [df, status_msg, preview_df] + list(col_updates)
+        # 添加一個空的 Plot 更新
+        updates.append(gr.Plot(value=None)) # 初始不繪圖
 
+        print(f"load_data_and_update_ui_v5 返回 {len(updates)} 個更新。", file=sys.stderr)
         return updates
 
-
-    # 綁定數據載入事件
+    # 綁定數據載入事件 - 移除初始繪圖
     upload_button.click(
         process_upload,
         inputs=[file_upload],
         outputs=[data_state, upload_status]
     ).then(
-        load_data_and_update_ui,
+        load_data_and_update_ui_v5,
         inputs=[data_state, upload_status],
         outputs=[
             data_state, upload_status, data_preview,
-            x_column_1, y_column_1, group_column_1, size_column_1, # 圖表一 Dropdowns
-            x_column_2, y_column_2, group_column_2, size_column_2, # 圖表二 Dropdowns
-            chart_output_1, # 圖表一初始繪圖
-            chart_output_2  # 圖表二初始空白
+            x_column, y_column, group_column, size_column, # 更新 Radio
+            chart_output # 更新 Plot 為空
         ]
     )
     parse_button.click(
@@ -2146,13 +2011,12 @@ with gr.Blocks(css=CUSTOM_CSS, title="進階數據可視化工具 V4", theme=gr.
         inputs=[csv_input],
         outputs=[data_state, parse_status]
     ).then(
-        load_data_and_update_ui,
+        load_data_and_update_ui_v5,
         inputs=[data_state, parse_status],
         outputs=[
             data_state, parse_status, data_preview,
-            x_column_1, y_column_1, group_column_1, size_column_1,
-            x_column_2, y_column_2, group_column_2, size_column_2,
-            chart_output_1, chart_output_2
+            x_column, y_column, group_column, size_column,
+            chart_output
         ]
     )
 
@@ -2160,63 +2024,56 @@ with gr.Blocks(css=CUSTOM_CSS, title="進階數據可視化工具 V4", theme=gr.
     # --- 數據導出 ---
     export_button.click(export_data, inputs=[data_state, export_format], outputs=[export_result, export_status])
 
-    # --- 圖表一：顏色和圖案狀態 ---
-    color_customization_1.change(parse_custom_colors, inputs=[color_customization_1], outputs=[custom_colors_state_1])
-    patterns_inputs_1 = [pattern1_1, pattern2_1, pattern3_1]
-    for pattern_dd in patterns_inputs_1: pattern_dd.change(update_patterns, inputs=patterns_inputs_1, outputs=[patterns_state_1])
-
-    # --- 圖表一：更新圖表 ---
-    chart_inputs_1 = [data_state, chart_type_1, x_column_1, y_column_1, group_column_1, size_column_1, color_scheme_1, patterns_state_1, chart_title_1, chart_width_1, chart_height_1, show_grid_1, show_legend_1, agg_function_1, custom_colors_state_1]
-    update_button_1.click(create_plot, inputs=chart_inputs_1, outputs=[chart_output_1])
-    def auto_update_chart_1(*inputs): return create_plot(*inputs)
-    # 綁定 change 事件到會影響圖表的 UI 組件
-    for input_component in [chart_type_1, x_column_1, y_column_1, group_column_1, size_column_1, color_scheme_1, chart_title_1, chart_width_1, chart_height_1, show_grid_1, show_legend_1, agg_function_1, color_customization_1, pattern1_1, pattern2_1, pattern3_1]:
-        if input_component is not None and hasattr(input_component, 'change'):
-             input_component.change(auto_update_chart_1, inputs=chart_inputs_1, outputs=[chart_output_1])
+    # --- 顏色和圖案狀態 ---
+    color_customization.change(parse_custom_colors, inputs=[color_customization], outputs=[custom_colors_state])
+    patterns_inputs = [pattern1, pattern2, pattern3]
+    for pattern_radio in patterns_inputs: # 改為 Radio
+        pattern_radio.change(update_patterns, inputs=patterns_inputs, outputs=[patterns_state])
+
+    # --- 更新圖表 (僅通過按鈕) ---
+    chart_inputs = [data_state, chart_type, x_column, y_column, group_column, size_column, color_scheme, patterns_state, chart_title, chart_width, chart_height, show_grid, show_legend, agg_function, custom_colors_state]
+
+    def update_chart_action(*inputs):
+        """按鈕點擊時的處理函數，包含調試信息"""
+        print("="*30, file=sys.stderr)
+        print("更新圖表按鈕點擊!", file=sys.stderr)
+        # 打印傳入 create_plot 的數據狀態
+        df_input = inputs[0] # data_state 在列表第一個
+        print(f"  - data_state type in handler: {type(df_input)}", file=sys.stderr)
+        if isinstance(df_input, pd.DataFrame):
+            print(f"  - data_state empty in handler: {df_input.empty}", file=sys.stderr)
+        else:
+             print(f"  - data_state is not a DataFrame!", file=sys.stderr)
+        print("="*30, file=sys.stderr)
+        return create_plot(*inputs)
 
+    update_button.click(update_chart_action, inputs=chart_inputs, outputs=[chart_output])
 
-    # --- 圖表一：導出圖表 ---
-    download_button_1.click(download_figure, inputs=[chart_output_1, export_img_format_1], outputs=[export_chart_1, export_chart_status_1])
+    # --- 導出圖表 ---
+    download_button.click(download_figure, inputs=[chart_output, export_img_format], outputs=[export_chart, export_chart_status])
 
-    # --- 圖表一：智能推薦 ---
-    def apply_recommendation(rec_dict):
+    # --- 智能推薦 ---
+    def apply_recommendation_v5(rec_dict):
         if not isinstance(rec_dict, dict): print("警告：apply_recommendation 收到非字典輸入。"); return [gr.update()] * 5
         chart_type_val = rec_dict.get("chart_type"); x_col_val = rec_dict.get("x_column"); agg_func_val = rec_dict.get("agg_function")
-        y_col_val = None if agg_func_val == "計數" else rec_dict.get("y_column"); group_col_val = rec_dict.get("group_column", "無")
+        y_col_val = None if agg_func_val == "計數" else rec_dict.get("y_column"); group_col_val = rec_dict.get("group_column", NONE_STR) # 默認為 "無"
         # 返回 Radio 和 Dropdown 的更新
         return [
             gr.Radio(value=chart_type_val),      # 更新 Chart Type Radio
-            gr.Dropdown(value=x_col_val),    # 更新 X Column Dropdown
-            gr.Dropdown(value=y_col_val),    # 更新 Y Column Dropdown
-            gr.Dropdown(value=group_col_val),# 更新 Group Column Dropdown
-            gr.Radio(value=agg_func_val)       # 更新 Agg Function Radio
+            gr.Radio(value=x_col_val),           # 更新 X Column Radio
+            gr.Radio(value=y_col_val),           # 更新 Y Column Radio
+            gr.Radio(value=group_col_val),       # 更新 Group Column Radio
+            gr.Radio(value=agg_func_val)         # 更新 Agg Function Radio
         ]
 
-    recommend_button_1.click(recommend_chart_settings, inputs=[data_state], outputs=[recommendation_state]).then(
-        apply_recommendation, inputs=[recommendation_state], outputs=[chart_type_1, x_column_1, y_column_1, group_column_1, agg_function_1]
-    ).then(create_plot, inputs=chart_inputs_1, outputs=[chart_output_1]) # 應用推薦後觸發更新
-
-    # --- 圖表二：顏色和圖案狀態 ---
-    color_customization_2.change(parse_custom_colors, inputs=[color_customization_2], outputs=[custom_colors_state_2])
-    patterns_inputs_2 = [pattern1_2, pattern2_2, pattern3_2]
-    for pattern_dd in patterns_inputs_2: pattern_dd.change(update_patterns, inputs=patterns_inputs_2, outputs=[patterns_state_2])
-
-    # --- 圖表二：更新圖表 ---
-    chart_inputs_2 = [data_state, chart_type_2, x_column_2, y_column_2, group_column_2, size_column_2, color_scheme_2, patterns_state_2, chart_title_2, chart_width_2, chart_height_2, show_grid_2, show_legend_2, agg_function_2, custom_colors_state_2]
-    update_button_2.click(create_plot, inputs=chart_inputs_2, outputs=[chart_output_2])
-    def auto_update_chart_2(*inputs): return create_plot(*inputs)
-    # 綁定 change 事件到會影響圖表的 UI 組件
-    for input_component in [chart_type_2, x_column_2, y_column_2, group_column_2, size_column_2, color_scheme_2, chart_title_2, chart_width_2, chart_height_2, show_grid_2, show_legend_2, agg_function_2, color_customization_2, pattern1_2, pattern2_2, pattern3_2]:
-        if input_component is not None and hasattr(input_component, 'change'):
-             input_component.change(auto_update_chart_2, inputs=chart_inputs_2, outputs=[chart_output_2])
-
-    # --- 圖表二：導出圖表 ---
-    download_button_2.click(download_figure, inputs=[chart_output_2, export_img_format_2], outputs=[export_chart_2, export_chart_status_2])
-
-    # --- 圖表類型改變時更新 UI 元素可見性 (保持不變) ---
-    def update_element_visibility(chart_type):
+    recommend_button.click(recommend_chart_settings, inputs=[data_state], outputs=[recommendation_state]).then(
+        apply_recommendation_v5, inputs=[recommendation_state], outputs=[chart_type, x_column, y_column, group_column, agg_function]
+    ) # 應用推薦後不自動更新圖表，讓用戶點擊按鈕
+
+    # --- 圖表類型改變時更新 UI 元素可見性 ---
+    def update_element_visibility_v5(chart_type):
         try:
-            # (與 V3 相同的邏輯)
+            # (與 V4 相同的邏輯)
             is_pie_like = chart_type in ["圓餅圖", "環形圖", "漏斗圖", "樹狀圖"]; is_histogram = chart_type == "直方圖"
             is_box_violin = chart_type in ["箱型圖", "小提琴圖"]; is_gantt = chart_type == "甘特圖"
             is_heatmap = chart_type == "熱力圖"; is_radar = chart_type == "雷達圖"
@@ -2231,13 +2088,12 @@ with gr.Blocks(css=CUSTOM_CSS, title="進階數據可視化工具 V4", theme=gr.
             elif is_heatmap: group_label, group_needed = "行/列 分組", True
             size_label, size_needed = "大小列", chart_type in ["氣泡圖", "散點圖"]
             if is_gantt: size_label, size_needed = "顏色列 (可選)", True
-            # 返回 Dropdown 的更新對象
+            # 返回 Radio 的更新對象
             return (gr.update(label=y_label, visible=y_needed), gr.update(label=group_label, visible=group_needed), gr.update(label=size_label, visible=size_needed))
         except Exception as e: print(f"Error in update_element_visibility: {e}"); return (gr.update(), gr.update(), gr.update())
 
     # 綁定到 Radio chart_type 的 change 事件
-    chart_type_1.change(update_element_visibility, inputs=[chart_type_1], outputs=[y_column_1, group_column_1, size_column_1])
-    chart_type_2.change(update_element_visibility, inputs=[chart_type_2], outputs=[y_column_2, group_column_2, size_column_2])
+    chart_type.change(update_element_visibility_v5, inputs=[chart_type], outputs=[y_column, group_column, size_column])
 
 # =========================================
 # == 應用程式啟動 (Launch Application) ==