Update app.py

app.py

@@ -1,1587 +1,102 @@
 import gradio as gr
 import pandas as pd
-import numpy as np
 import plotly.express as px
 import plotly.graph_objects as go
-import io
-import base64
-from PIL import Image
-import matplotlib.pyplot as plt
-import seaborn as sns
-from plotly.subplots import make_subplots
-import re
-import json
-import colorsys
 
-# 擴展的圖表類型
-CHART_TYPES = [
-    "長條圖", "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", 
-    "圓餅圖", "環形圖", "散點圖", "氣泡圖", 
-    "區域圖", "堆疊區域圖", "雷達圖", "熱力圖", 
-    "箱型圖", "小提琴圖", "漏斗圖", "樹狀圖", 
-    "直方圖", "極座標圖", "甘特圖"
-]
-
-# 可用的顏色方案 (美觀且有實用價值的顏色選擇)
-COLOR_SCHEMES = {
-    "默認": px.colors.qualitative.Plotly,
-    "Pastel": px.colors.qualitative.Pastel,
-    "Safe": px.colors.qualitative.Safe, 
-    "Vivid": px.colors.qualitative.Vivid,
-    "Prism": px.colors.qualitative.Prism,
-    "Antique": px.colors.qualitative.Antique,
-    "Bold": px.colors.qualitative.Bold,
-    "Pastel1": px.colors.qualitative.Pastel1,
-    "Pastel2": px.colors.qualitative.Pastel2,
-    "Set1": px.colors.qualitative.Set1,
-    "Set2": px.colors.qualitative.Set2,
-    "Set3": px.colors.qualitative.Set3,
-    "藍綠色系": px.colors.sequential.Blues,
-    "紅色系": px.colors.sequential.Reds,
-    "綠色系": px.colors.sequential.Greens,
-    "紫色系": px.colors.sequential.Purples,
-    "灰度": px.colors.sequential.Greys,
-    "彩虹": px.colors.sequential.Rainbow,
-    "漸變藍綠": px.colors.sequential.Turbo,
-    "漸變紫橙": px.colors.diverging.Spectral,
-    "漸變紅藍": px.colors.diverging.RdBu,
-    "漸變棕綠": px.colors.diverging.BrBG
-}
-
-# 圖案填充類型 (黑白印刷用)
-PATTERN_TYPES = [
-    "無", "/", "\\", "x", "-", "|", "+", ".", "*"
-]
-
-# 統計函數選項
-AGGREGATION_FUNCTIONS = [
-    "計數", "求和", "平均值", "最大值", "最小值", "中位數", "標準差", "變異數"
-]
-
-# HTML顏色展示卡片樣式
-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}" style="
-    width: 25px; 
-    height: 25px; 
-    background-color: {color_hex}; 
-    border-radius: 3px; 
-    cursor: pointer;
-    border: 1px solid #ddd;
-    transition: transform 0.2s;
-" onclick="copyToClipboard('{color_hex}')" onmouseover="this.style.transform='scale(1.1)'" onmouseout="this.style.transform='scale(1)'"></div>
-"""
-
-COPY_SCRIPT = """
-<script>
-function copyToClipboard(text) {
-    navigator.clipboard.writeText(text);
-    const notification = document.createElement('div');
-    notification.textContent = '已複製: ' + text;
-    notification.style.position = 'fixed';
-    notification.style.bottom = '20px';
-    notification.style.right = '20px';
-    notification.style.padding = '10px';
-    notification.style.background = '#333';
-    notification.style.color = 'white';
-    notification.style.borderRadius = '4px';
-    notification.style.zIndex = '1000';
-    document.body.appendChild(notification);
-    setTimeout(() => {
-        notification.style.opacity = '0';
-        notification.style.transition = 'opacity 0.5s';
-        setTimeout(() => document.body.removeChild(notification), 500);
-    }, 1500);
-}
-</script>
-"""
-
-# 常見的顏色名稱和十六進制代碼
-COMMON_COLORS = {
-    "紅色": "#FF0000", "橙色": "#FFA500", "黃色": "#FFFF00", "綠色": "#008000", 
-    "藍色": "#0000FF", "紫色": "#800080", "粉紅色": "#FFC0CB", "棕色": "#A52A2A",
-    "灰色": "#808080", "黑色": "#000000", "白色": "#FFFFFF", "青色": "#00FFFF", 
-    "洋紅": "#FF00FF", "淺藍": "#ADD8E6", "淺綠": "#90EE90", "淺黃": "#FFFFE0"
-}
-
-# 生成漸變色系
-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):
-        return '#{:02x}{:02x}{:02x}'.format(int(rgb[0]), int(rgb[1]), int(rgb[2]))
-    
-    start_rgb = hex_to_rgb(start_color)
-    end_rgb = hex_to_rgb(end_color)
-    
-    r_step = (end_rgb[0] - start_rgb[0]) / (steps - 1)
-    g_step = (end_rgb[1] - start_rgb[1]) / (steps - 1)
-    b_step = (end_rgb[2] - start_rgb[2]) / (steps - 1)
-    
-    gradient_colors = []
-    for i in range(steps):
-        r = start_rgb[0] + r_step * i
-        g = start_rgb[1] + g_step * i
-        b = start_rgb[2] + b_step * i
-        gradient_colors.append(rgb_to_hex((r, g, b)))
-    
-    return gradient_colors
-
-# 為顏色選擇添加的各種漸變色系
-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"),
-    "彩虹": ["#FF0000", "#FF7F00", "#FFFF00", "#00FF00", "#0000FF", "#4B0082", "#9400D3"]
-}
-
-# 生成顏色卡片展示
-def generate_color_cards():
-    # 常見顏色卡片
-    common_cards = ""
-    for name, hex_code in COMMON_COLORS.items():
-        common_cards += COLOR_CARD_TEMPLATE.format(color_name=name, color_hex=hex_code)
-    
-    # 漸變色系卡片
-    gradient_cards = {}
-    for name, colors in GRADIENTS.items():
-        gradient_cards[name] = ""
-        for i, color in enumerate(colors):
-            gradient_cards[name] += COLOR_CARD_TEMPLATE.format(
-                color_name=f"{name} {i+1}/{len(colors)}", 
-                color_hex=color
-            )
-    
-    # 合成卡片展示HTML
-    color_display = f"""
-    <div style="font-weight: bold; margin-top: 10px;">常見顏色</div>
-    {COLOR_CARD_STYLE.format(color_cards=common_cards)}
-    """
-    
-    for name, cards in gradient_cards.items():
-        color_display += f"""
-        <div style="font-weight: bold; margin-top: 10px;">{name}</div>
-        {COLOR_CARD_STYLE.format(color_cards=cards)}
-        """
-    
-    color_display += COPY_SCRIPT
-    
-    return color_display
+def create_plot(df, chart_type, x_col, y_col, group_col):
+    if df is None or x_col not in df.columns or y_col not in df.columns:
+        return go.Figure()
 
-def agg_function_map(func_name):
-    """映射中文統計函數名稱到Pandas函數"""
-    mapping = {
-        "求和": "sum",
-        "平均值": "mean",
-        "最大值": "max",
-        "最小值": "min",
-        "計數": "count",
-        "中位數": "median",
-        "標準差": "std",
-        "變異數": "var"
-    }
-    return mapping.get(func_name, "count")
+    group_col = None if group_col == "無" else group_col
 
-def create_plot(df, chart_type, x_column, y_column, group_column=None, size_column=None, 
-               color_scheme="默認", patterns=[], title="", width=700, height=500, 
-               show_grid=True, show_legend=True, agg_function="計數", custom_colors={}):
-    """創建圖表函數"""
-    
-    # 數據預處理
-    if df is None or df.empty:
-        return go.Figure()
-    
-    # 確保列存在
-    if x_column not in df.columns or y_column not in df.columns:
-        return go.Figure()
-    
-    # 獲取選擇的顏色方案
-    colors = COLOR_SCHEMES[color_scheme]
-    
-    # 將非數值列轉換為類別型
-    for col in df.columns:
-        if df[col].dtype == 'object' or df[col].dtype == 'string':
-            df[col] = df[col].astype('category')
-    
-    # 使用pandas的groupby進行數據聚合
-    agg_func = agg_function_map(agg_function)
-    
-    # 設置圖表參數
-    fig_params = {
-        "title": title
-    }
-    
-    # 創建基本圖形
-    fig = go.Figure()
-    
     try:
-        # 基於選擇的圖表類型創建圖表
         if chart_type == "長條圖":
-            # 進行數據分組和聚合
-            if group_column and group_column in df.columns and group_column != x_column:
-                grouped_df = df.groupby([x_column, group_column])[y_column].agg(agg_func).reset_index()
-                fig = px.bar(grouped_df, x=x_column, y=y_column, color=group_column, 
-                             color_discrete_sequence=colors, **fig_params)
+            if group_col:
+                fig = px.bar(df, x=x_col, y=y_col, color=group_col)
             else:
-                grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-                fig = px.bar(grouped_df, x=x_column, y=y_column, color_discrete_sequence=colors, **fig_params)
-                
-            # 應用自定義顏色和圖案
-            if patterns and len(patterns) > 0:
-                for i, bar in enumerate(fig.data):
-                    pattern_index = i % len(patterns)
-                    if patterns[pattern_index] != "無":
-                        bar.marker.pattern = {
-                            'shape': patterns[pattern_index],
-                            'solidity': 0.5
-                        }
-        
-        if chart_type == "堆疊長條圖":
-            if group_column and group_column in df.columns:
-                # 明確將字符串列轉換為類別型
-                df[x_column] = df[x_column].astype('category')
-                df[group_column] = df[group_column].astype('category')
-
-                # 根據使用者選的聚合函數進行分組計算
-                if agg_func == "count" or y_column == "計數":
-                    grouped_df = df.groupby([x_column, group_column]).size().reset_index(name='__y__')
-                else:
-                    grouped_df = df.groupby([x_column, group_column])[y_column].agg(agg_func).reset_index(name='__y__')
-
-                # 建立樞紐表
-                pivot_df = grouped_df.pivot_table(index=x_column, columns=group_column,
-                                                  values='__y__', aggfunc='sum').reset_index().fillna(0)
-
-                # 類別清單
-                categories = pivot_df.columns.tolist()
-                categories.remove(x_column)
-
-                # 加入每一組 Bar
-                for i, category in enumerate(categories):
-                    color = colors[i % len(colors)]
-                    if str(category) in custom_colors:
-                        color = custom_colors[str(category)]
-
-                    pattern_shape = None
-                    if patterns and i < len(patterns) and patterns[i] != "無":
-                        pattern_shape = patterns[i]
-
-                    fig.add_trace(go.Bar(
-                        x=pivot_df[x_column],
-                        y=pivot_df[category],
-                        name=str(category),
-                        marker_color=color,
-                        marker_pattern_shape=pattern_shape
-                    ))
+                fig = px.bar(df, x=x_col, y=y_col)
 
+        elif chart_type == "堆疊長條圖":
+            if group_col:
+                df_agg = df.groupby([x_col, group_col])[y_col].sum().reset_index()
+                fig = px.bar(df_agg, x=x_col, y=y_col, color=group_col)
                 fig.update_layout(barmode='stack')
             else:
-                # 如果沒有分組欄，退回一般長條圖的做法
-                grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-                fig = px.bar(grouped_df, x=x_column, y=y_column, color_discrete_sequence=colors, **fig_params)
-        
-        elif chart_type == "群組長條圖":
-            if group_column and group_column in df.columns:
-                # 明確將字符串列轉換為類別型
-                df[x_column] = df[x_column].astype('category')
-                df[group_column] = df[group_column].astype('category')
-                
-                # 先進行分組統計
-                group_df = df.groupby([x_column, group_column])[y_column].agg(agg_func).reset_index()
-                
-                # 創建樞紐表
-                pivot_df = group_df.pivot_table(index=x_column, columns=group_column, 
-                                              values=y_column).reset_index().fillna(0)
-                
-                # 獲取所有類別
-                categories = pivot_df.columns.tolist()
-                categories.remove(x_column)
-                
-                for i, category in enumerate(categories):
-                    color = colors[i % len(colors)]
-                    if str(category) in custom_colors:
-                        color = custom_colors[str(category)]
-                        
-                    pattern_shape = None
-                    if patterns and i < len(patterns) and patterns[i] != "無":
-                        pattern_shape = patterns[i]
-                        
-                    fig.add_trace(go.Bar(
-                        x=pivot_df[x_column],
-                        y=pivot_df[category],
-                        name=str(category),
-                        marker_color=color,
-                        marker_pattern_shape=pattern_shape
-                    ))
-                
-                fig.update_layout(barmode='group')
-            else:
-                grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-                fig = px.bar(grouped_df, x=x_column, y=y_column, **fig_params)
-                
-        elif chart_type == "水平長條圖":
-            if group_column and group_column in df.columns:
-                grouped_df = df.groupby([x_column, group_column])[y_column].agg(agg_func).reset_index()
-                fig = px.bar(grouped_df, y=x_column, x=y_column, color=group_column, 
-                           color_discrete_sequence=colors, orientation='h', **fig_params)
-            else:
-                grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-                fig = px.bar(grouped_df, y=x_column, x=y_column, orientation='h', 
-                           color_discrete_sequence=colors, **fig_params)
-        
+                df_agg = df.groupby(x_col)[y_col].sum().reset_index()
+                fig = px.bar(df_agg, x=x_col, y=y_col)
+
         elif chart_type == "折線圖":
-            if group_column and group_column in df.columns and group_column != x_column:
-                grouped_df = df.groupby([x_column, group_column])[y_column].agg(agg_func).reset_index()
-                fig = px.line(grouped_df, x=x_column, y=y_column, color=group_column, 
-                            color_discrete_sequence=colors, markers=True, **fig_params)
-                
-                # 根據自定義顏色和線型
-                for i, trace in enumerate(fig.data):
-                    if i < len(patterns) and patterns[i] != "無":
-                        if patterns[i] == '/':
-                            trace.line.dash = 'dash'
-                        elif patterns[i] == '\\':
-                            trace.line.dash = 'dot'
-                        elif patterns[i] == 'x':
-                            trace.line.dash = 'dashdot'
-                        elif patterns[i] == '-':
-                            trace.line.dash = 'longdash'
-                        else:
-                            trace.line.dash = 'solid'
-            else:
-                grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-                fig = px.line(grouped_df, x=x_column, y=y_column, markers=True, 
-                            color_discrete_sequence=colors, **fig_params)
-        
-        elif chart_type == "多重折線圖":
-            if group_column and group_column in df.columns:
-                # 明確將字符串列轉換為類別型
-                df[x_column] = df[x_column].astype('category')
-                df[group_column] = df[group_column].astype('category')
-                
-                # 先進行分組統計
-                group_df = df.groupby([x_column, group_column])[y_column].agg(agg_func).reset_index()
-                
-                # 創建樞紐表
-                pivot_df = group_df.pivot_table(index=x_column, columns=group_column, 
-                                              values=y_column).reset_index().fillna(0)
-                
-                # 獲取所有類別
-                categories = pivot_df.columns.tolist()
-                categories.remove(x_column)
-                
-                for i, category in enumerate(categories):
-                    color = colors[i % len(colors)]
-                    if str(category) in custom_colors:
-                        color = custom_colors[str(category)]
-                    
-                    line_dash = 'solid'
-                    if patterns and i < len(patterns) and patterns[i] != "無":
-                        if patterns[i] == '/':
-                            line_dash = 'dash'
-                        elif patterns[i] == '\\':
-                            line_dash = 'dot'
-                        elif patterns[i] == 'x':
-                            line_dash = 'dashdot'
-                        elif patterns[i] == '-':
-                            line_dash = 'longdash'
-                    
-                    fig.add_trace(go.Scatter(
-                        x=pivot_df[x_column],
-                        y=pivot_df[category],
-                        mode='lines+markers',
-                        name=str(category),
-                        line=dict(color=color, dash=line_dash),
-                        marker=dict(color=color)
-                    ))
+            if group_col:
+                fig = px.line(df, x=x_col, y=y_col, color=group_col, markers=True)
             else:
-                grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-                fig = px.line(grouped_df, x=x_column, y=y_column, markers=True, **fig_params)
-        
-        elif chart_type == "階梯折線圖":
-            if group_column and group_column in df.columns:
-                # 明確將字符串列轉換為類別型
-                df[x_column] = df[x_column].astype('category')
-                df[group_column] = df[group_column].astype('category')
-                
-                # 先進行分組統計
-                group_df = df.groupby([x_column, group_column])[y_column].agg(agg_func).reset_index()
-                
-                # 創建樞紐表
-                pivot_df = group_df.pivot_table(index=x_column, columns=group_column, 
-                                              values=y_column).reset_index().fillna(0)
-                
-                # 獲取所有類別
-                categories = pivot_df.columns.tolist()
-                categories.remove(x_column)
-                
-                for i, category in enumerate(categories):
-                    color = colors[i % len(colors)]
-                    if str(category) in custom_colors:
-                        color = custom_colors[str(category)]
-                    
-                    line_dash = 'solid'
-                    if patterns and i < len(patterns) and patterns[i] != "無":
-                        if patterns[i] == '/':
-                            line_dash = 'dash'
-                        elif patterns[i] == '\\':
-                            line_dash = 'dot'
-                        elif patterns[i] == 'x':
-                            line_dash = 'dashdot'
-                        elif patterns[i] == '-':
-                            line_dash = 'longdash'
-                    
-                    fig.add_trace(go.Scatter(
-                        x=pivot_df[x_column],
-                        y=pivot_df[category],
-                        mode='lines',
-                        name=str(category),
-                        line=dict(shape='hv', color=color, dash=line_dash)
-                    ))
-            else:
-                grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-                fig.add_trace(go.Scatter(
-                    x=grouped_df[x_column],
-                    y=grouped_df[y_column],
-                    mode='lines',
-                    line=dict(shape='hv', color=colors[0])
-                ))
-        
-        elif chart_type == "圓餅圖":
-            # 圓餅圖只需要分類和對應的數值
-            grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-            
-            # 設置自定義顏色
-            pie_colors = colors
-            if custom_colors and len(custom_colors) > 0:
-                pie_colors = [custom_colors.get(str(cat), colors[i % len(colors)]) 
-                             for i, cat in enumerate(grouped_df[x_column])]
-            
-            # 設置自定義圖案
-            pattern_shapes = None
-            if patterns and len(patterns) > 0:
-                pattern_shapes = [p if p != "無" else None for p in patterns]
-                if len(pattern_shapes) < len(grouped_df):
-                    # 重複圖案以匹配數據長度
-                    pattern_shapes = pattern_shapes * (len(grouped_df) // len(pattern_shapes) + 1)
-                    pattern_shapes = pattern_shapes[:len(grouped_df)]
-            
-            fig = px.pie(grouped_df, names=x_column, values=y_column, 
-                       color_discrete_sequence=pie_colors, **fig_params)
-            
-            # 應用圖案填充 (Plotly可能不支持直接在餅圖上應用花紋，此處為嘗試)
-            if pattern_shapes:
-                for i, trace in enumerate(fig.data):
-                    trace.marker.pattern = dict(
-                        shape=pattern_shapes[i % len(pattern_shapes)] if i < len(pattern_shapes) else None,
-                        solidity=0.5
-                    )
-        
-        elif chart_type == "環形圖":
-            grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-            
-            # 設置自定義顏色
-            pie_colors = colors
-            if custom_colors and len(custom_colors) > 0:
-                pie_colors = [custom_colors.get(str(cat), colors[i % len(colors)]) 
-                             for i, cat in enumerate(grouped_df[x_column])]
-            
-            fig = px.pie(grouped_df, names=x_column, values=y_column, hole=0.4,
-                       color_discrete_sequence=pie_colors, **fig_params)
-            
-            # 應用圖案填充
-            if patterns and len(patterns) > 0:
-                for i, trace in enumerate(fig.data):
-                    trace.marker.pattern = dict(
-                        shape=patterns[i % len(patterns)] if patterns[i % len(patterns)] != "無" else None,
-                        solidity=0.5
-                    )
-        
+                fig = px.line(df, x=x_col, y=y_col, markers=True)
+
         elif chart_type == "散點圖":
-            if group_column and group_column in df.columns:
-                fig = px.scatter(df, x=x_column, y=y_column, color=group_column,
-                              color_discrete_sequence=colors, **fig_params)
-                
-                if size_column and size_column in df.columns:
-                    fig = px.scatter(df, x=x_column, y=y_column, color=group_column,
-                                  size=size_column, color_discrete_sequence=colors, **fig_params)
-            else:
-                fig = px.scatter(df, x=x_column, y=y_column, color_discrete_sequence=colors, **fig_params)
-            
-            # 應用散點圖符號
-            if patterns and len(patterns) > 0:
-                for i, trace in enumerate(fig.data):
-                    pattern_idx = i % len(patterns)
-                    if patterns[pattern_idx] != "無":
-                        if patterns[pattern_idx] == '/':
-                            trace.marker.symbol = 'diamond'
-                        elif patterns[pattern_idx] == '\\':
-                            trace.marker.symbol = 'square'
-                        elif patterns[pattern_idx] == 'x':
-                            trace.marker.symbol = 'x'
-                        elif patterns[pattern_idx] == '-':
-                            trace.marker.symbol = 'line-ew'
-                        elif patterns[pattern_idx] == '|':
-                            trace.marker.symbol = 'line-ns'
-                        elif patterns[pattern_idx] == '+':
-                            trace.marker.symbol = 'cross'
-                        elif patterns[pattern_idx] == '.':
-                            trace.marker.symbol = 'circle'
-                        elif patterns[pattern_idx] == '*':
-                            trace.marker.symbol = 'star'
-                        else:
-                            trace.marker.symbol = 'circle'
-        
-        elif chart_type == "氣泡圖":
-            if size_column and size_column in df.columns:
-                if group_column and group_column in df.columns:
-                    fig = px.scatter(df, x=x_column, y=y_column, color=group_column,
-                                  size=size_column, size_max=30, 
-                                  color_discrete_sequence=colors, **fig_params)
-                else:
-                    fig = px.scatter(df, x=x_column, y=y_column, size=size_column,
-                                  size_max=30, color_discrete_sequence=colors, **fig_params)
-            else:
-                # 如果沒有指定大小列，則退回到一般散點圖
-                fig = px.scatter(df, x=x_column, y=y_column, color_discrete_sequence=colors, **fig_params)
-        
-        elif chart_type == "區域圖":
-            grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-            fig = px.area(grouped_df, x=x_column, y=y_column, 
-                        color_discrete_sequence=colors, **fig_params)
-            
-            # 應用填充圖案
-            if patterns and len(patterns) > 0 and patterns[0] != "無":
-                for trace in fig.data:
-                    trace.fill = 'tozeroy'
-                    # Plotly的區域圖不直接支持填充圖案，但可以使用線條樣式來模擬
-                    if patterns[0] == '/':
-                        trace.line.dash = 'dash'
-                    elif patterns[0] == '\\':
-                        trace.line.dash = 'dot'
-                    elif patterns[0] == 'x':
-                        trace.line.dash = 'dashdot'
-        
-        elif chart_type == "堆疊區域圖":
-            if group_column and group_column in df.columns:
-                # 明確將字符串列轉換為類別型
-                df[x_column] = df[x_column].astype('category')
-                df[group_column] = df[group_column].astype('category')
-                
-                # 先進行分組統計
-                group_df = df.groupby([x_column, group_column])[y_column].agg(agg_func).reset_index()
-                
-                # 創建樞紐表
-                pivot_df = group_df.pivot_table(index=x_column, columns=group_column, 
-                                              values=y_column).reset_index().fillna(0)
-                
-                # 獲取所有類別
-                categories = pivot_df.columns.tolist()
-                categories.remove(x_column)
-                
-                # 建立堆疊區域圖
-                for i, category in enumerate(categories):
-                    color = colors[i % len(colors)]
-                    if str(category) in custom_colors:
-                        color = custom_colors[str(category)]
-                    
-                    # 添加區域軌跡
-                    fig.add_trace(go.Scatter(
-                        x=pivot_df[x_column],
-                        y=pivot_df[category],
-                        mode='lines',
-                        line=dict(width=0.5, color=color),
-                        stackgroup='one',
-                        name=str(category)
-                    ))
-            else:
-                grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-                fig = px.area(grouped_df, x=x_column, y=y_column, **fig_params)
-        
-        elif chart_type == "雷達圖":
-            if group_column and group_column in df.columns:
-                # 對於每個組創建一個雷達圖的軌跡
-                groups = df[group_column].unique()
-                
-                for i, group in enumerate(groups):
-                    group_data = df[df[group_column] == group]
-                    grouped_df = group_data.groupby(x_column)[y_column].agg(agg_func).reset_index()
-                    
-                    theta = grouped_df[x_column].tolist()
-                    r = grouped_df[y_column].tolist()
-                    
-                    # 封閉雷達圖
-                    theta.append(theta[0])
-                    r.append(r[0])
-                    
-                    color = colors[i % len(colors)]
-                    if str(group) in custom_colors:
-                        color = custom_colors[str(group)]
-                    
-                    fig.add_trace(go.Scatterpolar(
-                        r=r,
-                        theta=theta,
-                        fill='toself',
-                        name=str(group),
-                        line_color=color
-                    ))
+            if group_col:
+                fig = px.scatter(df, x=x_col, y=y_col, color=group_col)
             else:
-                grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-                
-                theta = grouped_df[x_column].tolist()
-                r = grouped_df[y_column].tolist()
-                
-                # 封閉雷達圖
-                theta.append(theta[0])
-                r.append(r[0])
-                
-                fig.add_trace(go.Scatterpolar(
-                    r=r,
-                    theta=theta,
-                    fill='toself',
-                    line_color=colors[0]
-                ))
-            
-            fig.update_layout(
-                polar=dict(
-                    radialaxis=dict(
-                        visible=True
-                    )
-                )
-            )
-        
-        elif chart_type == "熱力圖":
-            # 熱力圖需要兩個分類變量和一個連續變量
-            if group_column and group_column in df.columns:
-                # 創建樞紐表
-                pivot_df = df.pivot_table(index=x_column, columns=group_column, 
-                                         values=y_column, aggfunc=agg_func)
-                
-                # 創建熱力圖
-                fig = px.imshow(pivot_df, color_continuous_scale=px.colors.sequential.Viridis, **fig_params)
-                fig.update_layout(coloraxis_showscale=True)
-            else:
-                # 如果沒有組列，則沒有足夠的維度來創建熱力圖
-                fig = go.Figure()
-                fig.add_annotation(
-                    text="熱力圖需要選擇一個分組列",
-                    showarrow=False,
-                    font=dict(size=16)
-                )
-        
-        elif chart_type == "箱型圖":
-            if group_column and group_column in df.columns:
-                fig = px.box(df, x=group_column, y=y_column, color=group_column,
-                          color_discrete_sequence=colors, **fig_params)
-            else:
-                fig = px.box(df, y=y_column, color_discrete_sequence=colors, **fig_params)
-        
-        elif chart_type == "小提琴圖":
-            if group_column and group_column in df.columns:
-                fig = px.violin(df, x=group_column, y=y_column, color=group_column,
-                             box=True, points="all", color_discrete_sequence=colors, **fig_params)
-            else:
-                fig = px.violin(df, y=y_column, box=True, points="all",
-                             color_discrete_sequence=colors, **fig_params)
-        
-        elif chart_type == "漏斗圖":
-            grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-            
-            # 按值排序
-            grouped_df = grouped_df.sort_values(by=y_column, ascending=False)
-            
-            # 設置自定義顏色
-            funnel_colors = colors[:len(grouped_df)]
-            if custom_colors and len(custom_colors) > 0:
-                funnel_colors = [custom_colors.get(str(cat), colors[i % len(colors)]) 
-                               for i, cat in enumerate(grouped_df[x_column])]
-            
-            # 創建漏斗圖
-            fig = go.Figure(go.Funnel(
-                y=grouped_df[x_column],
-                x=grouped_df[y_column],
-                textposition="inside",
-                textinfo="value+percent initial",
-                marker={"color": funnel_colors}
-            ))
-            
-            fig.update_layout(title=title)
-        
-        elif chart_type == "樹狀圖":
-            if group_column and group_column in df.columns:
-                # 創建層次結構
-                fig = px.treemap(df, path=[group_column, x_column], values=y_column,
-                              color_discrete_sequence=colors, **fig_params)
-            else:
-                grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-                fig = px.treemap(grouped_df, path=[x_column], values=y_column,
-                              color_discrete_sequence=colors, **fig_params)
-        
-        elif chart_type == "直方圖":
-            # 直方圖顯示單一變量的分佈
-            fig = px.histogram(df, x=x_column, color=group_column if group_column else None,
-                            color_discrete_sequence=colors, **fig_params)
-        
-        elif chart_type == "極座標圖":
-            grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-            
-            # 設置自定義顏色
-            polar_colors = colors[:len(grouped_df)]
-            if custom_colors and len(custom_colors) > 0:
-                polar_colors = [custom_colors.get(str(cat), colors[i % len(colors)]) 
-                              for i, cat in enumerate(grouped_df[x_column])]
-            
-            # 創建極座標條形圖
-            fig = px.bar_polar(grouped_df, r=y_column, theta=x_column,
-                            color=x_column, color_discrete_sequence=polar_colors, **fig_params)
-        
-        elif chart_type == "甘特圖":
-            # 甘特圖需要開始和結束時間
-            # 假設x_column是任務名稱，y_column是開始時間，group_column是結束時間
-            if group_column and group_column in df.columns:
-                # 確保日期時間格式
-                try:
-                    df[y_column] = pd.to_datetime(df[y_column])
-                    df[group_column] = pd.to_datetime(df[group_column])
-                    
-                    # 創建甘特圖
-                    fig = px.timeline(df, x_start=y_column, x_end=group_column, y=x_column,
-                                   color=size_column if size_column else None,
-                                   color_discrete_sequence=colors, **fig_params)
-                    
-                    fig.update_layout(xaxis_type="date")
-                except:
-                    fig = go.Figure()
-                    fig.add_annotation(
-                        text="無法將列轉換為日期格式，甘特圖需要日期時間格式的開始和結束列",
-                        showarrow=False,
-                        font=dict(size=14)
-                    )
-            else:
-                fig = go.Figure()
-                fig.add_annotation(
-                    text="甘特圖需要開始日期和結束日期列",
-                    showarrow=False,
-                    font=dict(size=16)
-                )
-        
-        else:
-            # 默認圖表類型
-            grouped_df = df.groupby(x_column)[y_column].agg(agg_func).reset_index()
-            fig = px.bar(grouped_df, x=x_column, y=y_column, **fig_params)
-    
-    except Exception as e:
-        # 處理創建圖表時的錯誤
-        fig = go.Figure()
-        fig.add_annotation(
-            text=f"創建圖表時出錯: {str(e)}",
-            showarrow=False,
-            font=dict(size=14)
-        )
-    
-    # 設置網格和圖例
-    fig.update_layout(
-        width=width,
-        height=height,
-        showlegend=show_legend,
-        xaxis=dict(showgrid=show_grid),
-        yaxis=dict(showgrid=show_grid),
-        # 現代化樣式設置
-        template="plotly_white",
-        margin=dict(l=50, r=50, t=50, b=50),
-        font=dict(family="Arial, sans-serif"),
-        hoverlabel=dict(
-            bgcolor="white",
-            font_size=12,
-            font_family="Arial, sans-serif"
-        )
-    )
-    
-    return fig
+                fig = px.scatter(df, x=x_col, y=y_col)
 
-def process_upload(file):
-    """處理上傳的文件"""
-    try:
-        if file is None:
-            return None, "未上傳文件"
-        
-        # 檢查文件類型
-        file_type = file.name.split('.')[-1].lower()
-        
-        if file_type == 'csv':
-            df = pd.read_csv(file.name)
-        elif file_type in ['xls', 'xlsx']:
-            df = pd.read_excel(file.name)
         else:
-            return None, f"不支持的文件類型: {file_type}。請上傳CSV或Excel文件。"
-        
-        # 添加計數列
-        df['計數'] = 1
-        
-        return df, f"成功載入數據，共{len(df)}行，{len(df.columns)}列"
-    
-    except Exception as e:
-        return None, f"載入文件時出錯: {str(e)}"
-
-def parse_data(csv_data):
-    """解析CSV文本數據，支持逗號或空格分隔"""
-    try:
-        if not csv_data or csv_data.strip() == "":
-            return None, "未提供數據"
-        
-        # 嘗試檢測分隔符
-        first_line = csv_data.strip().split('\n')[0]
-        if ',' in first_line:
-            # 優先使用逗號作為分隔符
-            df = pd.read_csv(io.StringIO(csv_data), sep=',')
-        elif ' ' in first_line or '\t' in first_line:
-            # 如果沒有逗號但有空格或制表符，使用空格作為分隔符
-            df = pd.read_csv(io.StringIO(csv_data), sep='\\s+')
-        else:
-            # 默認使用逗號
-            df = pd.read_csv(io.StringIO(csv_data))
-        
-        # 添加計數列
-        df['計數'] = 1
-        
-        return df, f"成功解析數據，共{len(df)}行，{len(df.columns)}列"
-    
-    except Exception as e:
-        return None, f"解析數據時出錯: {str(e)}"
-        
-def export_data(df, format_type):
-    """導出數據為各種格式"""
-    if df is None or df.empty:
-        return None, "沒有數據可以導出"
-    
-    try:
-        if format_type == "CSV":
-            buffer = io.StringIO()
-            df.to_csv(buffer, index=False)
-            data = buffer.getvalue()
-            filename = "exported_data.csv"
-            mime_type = "text/csv"
-        
-        elif format_type == "Excel":
-            buffer = io.BytesIO()
-            df.to_excel(buffer, index=False)
-            data = buffer.getvalue()
-            filename = "exported_data.xlsx"
-            mime_type = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
-        
-        elif format_type == "JSON":
-            buffer = io.StringIO()
-            data = df.to_json(orient="records")
-            filename = "exported_data.json"
-            mime_type = "application/json"
-        
-        else:
-            return None, f"不支持的導出格式: {format_type}"
-        
-        return (data, filename, mime_type), f"數據已成功導出為{format_type}格式"
-    
-    except Exception as e:
-        return None, f"導出數據時出錯: {str(e)}"
+            fig = go.Figure()
+            fig.add_annotation(text="目前支援：長條圖、堆疊圖、折線圖、散點圖", showarrow=False)
 
-def update_columns(df):
-    """更新列選擇下拉菜單"""
-    if df is None or df.empty:
-        # 默認列
-        return gr.Dropdown(choices=["類別", "數值", "計數"], value="類別"), gr.Dropdown(choices=["類別", "數值", "計數"], value="計數"), gr.Dropdown(choices=["無", "類別", "數值", "計數"], value="無"), gr.Dropdown(choices=["無", "類別", "數值", "計數"], value="無")
-    
-    columns = df.columns.tolist()
-    x_dropdown = gr.Dropdown(choices=columns, value=columns[0] if columns else None)
-    y_dropdown = gr.Dropdown(choices=columns, value="計數" if "計數" in columns else (columns[1] if len(columns) > 1 else columns[0]))
-    group_dropdown = gr.Dropdown(choices=["無"] + columns, value="無")
-    size_dropdown = gr.Dropdown(choices=["無"] + columns, value="無")
-    
-    return x_dropdown, y_dropdown, group_dropdown, size_dropdown
+        fig.update_layout(template="plotly_white", height=400)
+        return fig
 
-def download_figure(fig, format_type="PNG"):
-    """導出圖表為圖像"""
-    if fig is None:
-        return None, "沒有圖表可以導出"
-    
-    try:
-        # 選擇導出格式
-        if format_type == "PNG":
-            img_bytes = fig.to_image(format="png")
-            mime_type = "image/png"
-            ext = "png"
-        elif format_type == "SVG":
-            img_bytes = fig.to_image(format="svg")
-            mime_type = "image/svg+xml"
-            ext = "svg"
-        elif format_type == "PDF":
-            img_bytes = fig.to_image(format="pdf")
-            mime_type = "application/pdf"
-            ext = "pdf"
-        elif format_type == "JPEG":
-            img_bytes = fig.to_image(format="jpeg")
-            mime_type = "image/jpeg"
-            ext = "jpg"
-        else:
-            img_bytes = fig.to_image(format="png")
-            mime_type = "image/png"
-            ext = "png"
-        
-        # 創建文件對象
-        filename = f"chart_export.{ext}"
-        
-        return (img_bytes, filename, mime_type), f"圖表已成功導出為{format_type}格式"
-    
     except Exception as e:
-        return None, f"導出圖表時出錯: {str(e)}"
+        fig = go.Figure()
+        fig.add_annotation(text=f"圖表錯誤：{str(e)}", showarrow=False)
+        return fig
 
-def recommend_chart_settings(df):
-    """智能分析數據並推薦最佳圖表設置"""
+def update_column_options(df):
     if df is None or df.empty:
-        return {
-            "message": "請先上傳或輸入數據"
-        }
-    
-    # 獲取列名和數據類型
-    columns = df.columns.tolist()
-    num_columns = df.select_dtypes(include=['number']).columns.tolist()
-    cat_columns = df.select_dtypes(include=['object', 'category']).columns.tolist()
-    
-    # 排除"計數"列
-    if "計數" in num_columns:
-        num_columns.remove("計數")
-    
-    # 統計數據特徵
-    num_rows = len(df)
-    num_unique_values = {col: df[col].nunique() for col in columns}
-    
-    # 初始化推薦結果
-    recommendation = {
-        "chart_type": None,
-        "x_column": None,
-        "y_column": None,
-        "group_column": None,
-        "agg_function": None,
-        "message": ""
-    }
-    
-    # 根據數據特徵推薦圖表
-    # 案例1: 有2個類別列，需要分析它們之間的關係
-    if len(cat_columns) >= 2 and '計數' in columns:
-        # 例如機構類型和情緒分析數據
-        recommendation["chart_type"] = "堆疊長條圖"
-        recommendation["x_column"] = cat_columns[0]  # 第一個類別列作為X軸
-        recommendation["y_column"] = "計數"          # 使用計數列作為Y軸
-        recommendation["group_column"] = cat_columns[1]  # 第二個類別列作為分組
-        recommendation["agg_function"] = "求和"
-        recommendation["message"] = f"檢測到有類別列 '{cat_columns[0]}' 和 '{cat_columns[1]}'，推薦使用堆疊長條圖來顯示兩者關係"
-    
-    # 案例2: 只有一個類別列
-    elif len(cat_columns) == 1 and '計數' in columns:
-        recommendation["chart_type"] = "長條圖"
-        recommendation["x_column"] = cat_columns[0]
-        recommendation["y_column"] = "計數"
-        recommendation["agg_function"] = "求和"
-        recommendation["message"] = f"檢測到類別列 '{cat_columns[0]}'，推薦使用長條圖來顯示分佈"
-    
-    # 案例3: 有時間序列數據
-    elif any("日期" in col or "時間" in col for col in columns) and len(num_columns) > 0:
-        date_col = next((col for col in columns if "日期" in col or "時間" in col), None)
-        recommendation["chart_type"] = "折線圖"
-        recommendation["x_column"] = date_col
-        recommendation["y_column"] = num_columns[0] if num_columns else "計數"
-        recommendation["agg_function"] = "平均值"
-        recommendation["message"] = f"檢測到時間列 '{date_col}'，推薦使用折線圖來顯示趨勢"
-    
-    # 案例4: 有多個數值列
-    elif len(num_columns) >= 2:
-        recommendation["chart_type"] = "散點圖"
-        recommendation["x_column"] = num_columns[0]
-        recommendation["y_column"] = num_columns[1]
-        recommendation["message"] = f"檢測到數值列 '{num_columns[0]}' 和 '{num_columns[1]}'，推薦使用散點圖來分析相關性"
-    
-    # 預設情況
-    else:
-        if len(cat_columns) > 0 and "計數" in columns:
-            recommendation["chart_type"] = "長條圖"
-            recommendation["x_column"] = cat_columns[0]
-            recommendation["y_column"] = "計數"
-            recommendation["agg_function"] = "求和"
-            recommendation["message"] = "根據數據結構，推薦使用長條圖"
-        elif len(cat_columns) > 0 and len(num_columns) > 0:
-            recommendation["chart_type"] = "長條圖"
-            recommendation["x_column"] = cat_columns[0]
-            recommendation["y_column"] = num_columns[0]
-            recommendation["agg_function"] = "平均值"
-            recommendation["message"] = "根據數據結構，推薦使用長條圖"
-        else:
-            recommendation["message"] = "無法確定最佳圖表類型，請手動選擇"
-    
-    return recommendation
-
-# CSS樣式
-
-CUSTOM_CSS = """
-.gradio-container {
-    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-    background: linear-gradient(to bottom right, #f7f9fc, #e9f0f8);
-    overflow: visible !important;
-}
-
-.app-header {
-    text-align: center;
-    margin-bottom: 20px;
-    background: linear-gradient(90deg, #4568dc, #3a6073);
-    padding: 20px;
-    border-radius: 10px;
-    color: white;
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-}
-
-.app-title {
-    font-size: 32px;
-    font-weight: bold;
-    margin: 0;
-    display: inline-block;
-    background: linear-gradient(to right, #ffffff, #e0e0e0);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-}
-
-.app-subtitle {
-    font-size: 16px;
-    color: #f0f0f0;
-    margin-top: 5px;
-}
-
-.section-title {
-    font-size: 20px;
-    font-weight: bold;
-    color: #333;
-    border-bottom: 2px solid #4568dc;
-    padding-bottom: 5px;
-    margin-top: 20px;
-    margin-bottom: 15px;
-}
-
-.card {
-    background-color: white;
-    border-radius: 10px;
-    padding: 20px;
-    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-    margin-bottom: 20px;
-    transition: transform 0.3s, box-shadow 0.3s;
-}
-
-.card:hover {
-    transform: translateY(-5px);
-    box-shadow: 0 10px 20px rgba(0, 0, 0, 0.1);
-}
-
-.primary-button {
-    background: linear-gradient(90deg, #4568dc, #3a6073) !important;
-    border: none !important;
-    color: white !important;
-    font-weight: bold !important;
-    padding: 10px 20px !important;
-    border-radius: 5px !important;
-    cursor: pointer !important;
-    transition: all 0.3s ease !important;
-}
-
-.primary-button:hover {
-    background: linear-gradient(90deg, #3a6073, #4568dc) !important;
-    transform: translateY(-2px) !important;
-    box-shadow: 0 5px 10px rgba(0, 0, 0, 0.1) !important;
-}
-
-.secondary-button {
-    background: linear-gradient(90deg, #6a85b6, #bac8e0) !important;
-    border: none !important;
-    color: white !important;
-    font-weight: bold !important;
-    padding: 10px 20px !important;
-    border-radius: 5px !important;
-    cursor: pointer !important;
-    transition: all 0.3s ease !important;
-}
-
-.secondary-button:hover {
-    background: linear-gradient(90deg, #bac8e0, #6a85b6) !important;
-    transform: translateY(-2px) !important;
-    box-shadow: 0 5px 10px rgba(0, 0, 0, 0.1) !important;
-}
-
-.color-panel {
-    background-color: white;
-    border-radius: 5px;
-    padding: 10px;
-    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
-}
-
-.tips-box {
-    background-color: #f1f7fe;
-    border-left: 4px solid #4568dc;
-    padding: 15px;
-    border-radius: 5px;
-    margin: 20px 0;
-}
-
-.chart-previewer {
-    border: 2px dashed #ccc;
-    border-radius: 10px;
-    padding: 20px;
-    min-height: 400px;
-    display: flex;
-    justify-content: center;
-    align-items: center;
-    background-color: rgba(255, 255, 255, 0.7);
-}
-
-/* Loading animation */
-@keyframes pulse {
-    0% { opacity: 0.6; }
-    50% { opacity: 1; }
-    100% { opacity: 0.6; }
-}
-
-.loading {
-    animation: pulse 1.5s infinite;
-}
-
-/* 下拉選單位置修正 */
-.gradio-dropdown {
-    position: relative !important;
-    overflow: visible !important;  /* ✅ 關鍵補充 */
-    z-index: 10 !important;        /* 確保浮在上層 */
-}
-
-.gradio-dropdown .choices__list--dropdown {
-    position: absolute !important;
-    top: 100% !important;
-    left: 0 !important;
-    z-index: 1000 !important;
-    max-height: 300px !important;
-    overflow-y: auto !important;
-    background: white !important;
-    border: 1px solid #ddd !important;
-    border-radius: 4px !important;
-    width: 100% !important;
-    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1) !important;
-}
-
-/* 確保下拉項目可見 */
-.gradio-dropdown .choices__list--dropdown .choices__item {
-    padding: 8px 10px !important;
-    cursor: pointer !important;
-}
-
-"""
-
-# 現代化UI界面
-with gr.Blocks(css=CUSTOM_CSS, title="進階數據可視化工具") as demo:
-    gr.HTML("""
-    <div class="app-header">
-        <h1 class="app-title">🎨 進階數據可視化工具</h1>
-        <p class="app-subtitle">上傳數據，創建各種專業圖表，輕鬆實現數據可視化</p>
-    </div>
-    """)
-    
-    # 狀態變量
-    data_state = gr.State(None)
-    custom_colors_state = gr.State({})
-    patterns_state = gr.State([])
-    
-    with gr.Tabs():
-        # 數據輸入頁籤
-        with gr.TabItem("📊 數據輸入") as tab_data:
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.HTML('<div class="section-title">上傳或輸入數據</div>')
-                    
-                    with gr.Group(elem_classes=["card"]):
-                        file_upload = gr.File(label="上傳CSV或Excel文件")
-                        upload_button = gr.Button("載入文件", elem_classes=["primary-button"])
-                        upload_status = gr.Textbox(label="上傳狀態", lines=2)
-                    
-                    with gr.Group(elem_classes=["card"]):
-                        csv_input = gr.Textbox(
-                            label="直接輸入數據（逗號或空格分隔）", 
-                            placeholder="類別,數值\nA,10\nB,20\nC,15\nD,25\nE,30\n\n或\n\n類別 數值\nA 10\nB 20\nC 15\nD 25\nE 30", 
-                            lines=10
-                        )
-                        parse_button = gr.Button("解析數據", elem_classes=["primary-button"])
-                        parse_status = gr.Textbox(label="解析狀態", lines=2)
-                
-                with gr.Column(scale=1):
-                    gr.HTML('<div class="section-title">數據預覽</div>')
-                    
-                    with gr.Group(elem_classes=["card"]):
-                        data_preview = gr.Dataframe(label="數據表格預覽", interactive=False)
-                        
-                        with gr.Row():
-                            export_format = gr.Dropdown(
-                                ["CSV", "Excel", "JSON"], 
-                                label="導出格式", 
-                                value="CSV"
-                            )
-                            export_button = gr.Button("導出數據", elem_classes=["secondary-button"])
-                        
-                        export_result = gr.File(label="導出結果")
-                        export_status = gr.Textbox(label="導出狀態", lines=2)
-        
-        # 圖表創建頁籤
-        with gr.TabItem("📈 圖表創建") as tab_chart:
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.HTML('<div class="section-title">圖表設置</div>')
-                    
-                    with gr.Group(elem_classes=["card"]):
-                        chart_type = gr.Dropdown(
-                            CHART_TYPES, 
-                            label="📊 圖表類型", 
-                            value="長條圖",
-                            interactive=True
-                        )
-                        
-                        with gr.Row():
-                            recommend_button = gr.Button("🧠 智能推薦圖表", variant="secondary", elem_classes=["secondary-button"])
-                            recommendation_result = gr.Textbox(label="推薦結果", lines=2)
-                        
-                        chart_title = gr.Textbox(label="📝 圖表標題", placeholder="我的數據圖表")
-                        
-                        agg_function = gr.Dropdown(
-                            AGGREGATION_FUNCTIONS,
-                            label="🔄 聚合函數",
-                            value="計數",
-                            info="選擇如何彙總數據"
-                        )
-                        
-                        gr.HTML("<div class='section-title'>數據映射</div>")
-                        
-                        # 軸和分組選擇
-                        x_column = gr.Dropdown(["類別"], label="X軸（或類別）")
-                        y_column = gr.Dropdown(["計數"], label="Y軸（或數值）")
-                        group_column = gr.Dropdown(["無"], label="分組列（用於多系列圖表）")
-                        size_column = gr.Dropdown(["無"], label="大小列（用於氣泡圖等）")
-                        
-                        gr.HTML("<div class='tips-box'>💡 提示: 選擇不同圖表類型時，界面會自動調整顯示相關設置選項</div>")
-                
-                with gr.Column(scale=1):
-                    gr.HTML('<div class="section-title">顯示選項</div>')
-                    
-                    with gr.Group(elem_classes=["card"]):
-                        # 尺寸控制
-                        with gr.Row():
-                            chart_width = gr.Slider(300, 1200, 800, label="圖表寬度")
-                            chart_height = gr.Slider(300, 800, 500, label="圖表高度")
-                        
-                        with gr.Row():
-                            show_grid = gr.Checkbox(label="顯示網格", value=True)
-                            show_legend = gr.Checkbox(label="顯示圖例", value=True)
-                        
-                        color_scheme = gr.Dropdown(
-                            list(COLOR_SCHEMES.keys()),
-                            label="🎨 顏色方案",
-                            value="默認"
-                        )
-                        
-                        gr.HTML('<div style="margin-top: 10px;"><b>顏色參考</b> (點擊顏色可複製顏色代碼)</div>')
-                        gr.HTML(generate_color_cards(), elem_id="color_display")
-                    
-                    # 圖案和顏色自定義區
-                    with gr.Group(elem_classes=["card"]):
-                        gr.HTML('<div class="section-title">自定義圖案和顏色</div>')
-                        
-                        # 動態添加圖案，先默認提供三個
-                        with gr.Row():
-                            pattern1 = gr.Dropdown(PATTERN_TYPES, label="圖案1", value="無")
-                            pattern2 = gr.Dropdown(PATTERN_TYPES, label="圖案2", value="無")
-                            pattern3 = gr.Dropdown(PATTERN_TYPES, label="圖案3", value="無")
-                        
-                        # 自定義顏色區域
-                        color_customization = gr.Textbox(
-                            label="自定義顏色 (格式: 類別1:#FF0000,類別2:#00FF00)", 
-                            placeholder="正面:#2ca02c,負面:#ff7f0e,中性:#1f77b4", 
-                            info="輸入類別名稱和十六進制顏色代碼，用逗號分隔多個項目"
-                        )
-                        
-                        with gr.Row():
-                            update_button = gr.Button("更新圖表", variant="primary", elem_classes=["primary-button"])
-                        
-                        with gr.Row():
-                            export_img_format = gr.Dropdown(
-                                ["PNG", "SVG", "PDF", "JPEG"], 
-                                label="導出格式", 
-                                value="PNG"
-                            )
-                            download_button = gr.Button("導出圖表", elem_classes=["secondary-button"])
-                        
-                        export_chart = gr.File(label="導出的圖表")
-                        export_chart_status = gr.Textbox(label="導出狀態", lines=2)
-            
-            # 圖表預覽區
-            gr.HTML('<div class="section-title">圖表預覽</div>')
-            with gr.Group(elem_classes=["chart-previewer"]):
-                chart_output = gr.Plot(label="", elem_id="chart_preview")
-        
-        # 使用說明頁籤
-        with gr.TabItem("📖 使用說明") as tab_help:
-            gr.HTML("""
-            <div class="card">
-                <div class="section-title">使用說明</div>
-                
-                <h3>數據輸入</h3>
-                <ul>
-                    <li>上傳CSV或Excel文件，或在文本框中直接輸入數據</li>
-                    <li>第一行被視為欄位名稱（表頭），不會納入統計</li>
-                    <li>支持逗號分隔(CSV)或空格分隔的數據格式</li>
-                    <li>系統會自動添加「計數」列，方便進行計數統計</li>
-                </ul>
-                
-                <h3>圖表創建</h3>
-                <ul>
-                    <li><strong>智能推薦：</strong>系統可根據您的數據結構智能推薦最適合的圖表類型和設置</li>
-                    <li><strong>圖表類型：</strong>支持20多種專業圖表，包括長條圖、堆疊長條圖、折線圖、圓餅圖等</li>
-                    <li><strong>聚合函數：</strong>選擇如何彙總數據（計數、求和、平均值、最大值等）</li>
-                    <li><strong>分組列：</strong>用於創建多系列圖表，例如按類別分組的長條圖</li>
-                    <li><strong>大小列：</strong>用於氣泡圖等需要額外數值控制大小的圖表</li>
-                </ul>
-                
-                <h3>自定義選項</h3>
-                <ul>
-                    <li><strong>顏色方案：</strong>選擇預設的顏色系列，包括明亮、柔和、漸變等多種風格</li>
-                    <li><strong>自定義顏色：</strong>為特定類別設置顏色，格式為"類別1:#FF0000,類別2:#00FF00"</li>
-                    <li><strong>圖案填充：</strong>為圖表元素設置填充圖案，特別適用於黑白印刷</li>
-                    <li><strong>導出格式：</strong>支持PNG、SVG、PDF和JPEG格式導出</li>
-                </ul>
-                
-                <h3>常見使用場景</h3>
-                <ul>
-                    <li><strong>分類數據分析：</strong>使用長條圖或圓餅圖展示不同類別的分布</li>
-                    <li><strong>多分類比較：</strong>使用堆疊���條圖或群組長條圖展示多個分類維度的關係</li>
-                    <li><strong>趨勢分析：</strong>使用折線圖或區域圖展示數據隨時間的變化</li>
-                    <li><strong>相關性分析：</strong>使用散點圖或熱力圖分析變量之間的關係</li>
-                </ul>
-            </div>
-            """)
-    
-    # 輔助函數
-    def parse_custom_colors(color_text):
-        """解析自定義顏色文本"""
-        custom_colors = {}
-        if color_text and color_text.strip():
-            try:
-                pairs = color_text.split(',')
-                for pair in pairs:
-                    if ':' in pair:
-                        key, value = pair.split(':', 1)
-                        custom_colors[key.strip()] = value.strip()
-            except:
-                pass
-        return custom_colors
-    
-    def update_patterns(p1, p2, p3):
-        """更新圖案列表"""
-        patterns = []
-        if p1:
-            patterns.append(p1)
-        if p2:
-            patterns.append(p2)
-        if p3:
-            patterns.append(p3)
-        return patterns
-    
-    # 事件處理
-    upload_button.click(
-        process_upload, 
-        inputs=[file_upload], 
-        outputs=[data_state, upload_status]
-    ).then(
-        lambda df: df if df is not None else pd.DataFrame(),
-        inputs=[data_state],
-        outputs=[data_preview]
-    ).then(
-        update_columns,
-        inputs=[data_state],
-        outputs=[x_column, y_column, group_column, size_column]
-    )
-    
-    parse_button.click(
-        parse_data,
-        inputs=[csv_input],
-        outputs=[data_state, parse_status]
-    ).then(
-        lambda df: df if df is not None else pd.DataFrame(),
-        inputs=[data_state],
-        outputs=[data_preview]
-    ).then(
-        update_columns,
-        inputs=[data_state],
-        outputs=[x_column, y_column, group_column, size_column]
-    )
-    
-    export_button.click(
-        export_data,
-        inputs=[data_state, export_format],
-        outputs=[export_result, export_status]
-    )
-    
-    # 處理圖案和顏色設置
-    color_customization.change(
-        parse_custom_colors,
-        inputs=[color_customization],
-        outputs=[custom_colors_state]
-    )
-    
-    pattern1.change(
-        update_patterns,
-        inputs=[pattern1, pattern2, pattern3],
-        outputs=[patterns_state]
-    )
-    
-    pattern2.change(
-        update_patterns,
-        inputs=[pattern1, pattern2, pattern3],
-        outputs=[patterns_state]
-    )
-    
-    pattern3.change(
-        update_patterns,
-        inputs=[pattern1, pattern2, pattern3],
-        outputs=[patterns_state]
-    )
-    
-    # 更新圖表
-    update_button.click(
-        lambda df, chart_type, x_col, y_col, group_col, size_col, color_scheme, patterns, title, width, height, show_grid, show_legend, agg_func, custom_colors: 
-        create_plot(
-            df, chart_type, x_col, y_col, 
-            None if group_col == "無" else group_col, 
-            None if size_col == "無" else size_col,
-            color_scheme, patterns, title, width, height, 
-            show_grid, show_legend, agg_func, custom_colors
-        ),
-        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
-        ],
-        outputs=[chart_output]
-    )
-    
-    # 導出圖表
-    download_button.click(
-        download_figure,
-        inputs=[chart_output, export_img_format],
-        outputs=[export_chart, export_chart_status]
-    )
-    
-    # 圖表類型改變時更新界面元素可見性
-    def update_element_visibility(chart_type):
-        """根據圖表類型更新UI元素的可見性"""
-        # 圓餅圖和環形圖不需要X軸，只需要類別和數值
-        is_pie_chart = chart_type in ["圓餅圖", "環形圖"]
-        
-        # 氣泡圖需要額外的大小控制列
-        needs_size_column = chart_type in ["氣泡圖", "甘特圖", "樹狀圖"]
-        
-        # 需要分組列的圖表類型
-        needs_group_column = chart_type in [
-            "群組長條圖", "堆疊長條圖", "多重折線圖", "堆疊區域圖",
-            "熱力圖", "雷達圖", "散點圖", "氣泡圖"
-        ]
-        
-        return (
-            gr.update(visible=not is_pie_chart, label="類別列" if is_pie_chart else "X軸"),
-            gr.update(visible=True, label="數值列" if is_pie_chart else "Y軸"),
-            gr.update(visible=needs_group_column, label="分組列"),
-            gr.update(visible=needs_size_column, label="大小列")
-        )
-    
-    chart_type.change(
-        update_element_visibility,
-        inputs=[chart_type],
-        outputs=[x_column, y_column, group_column, size_column]
-    ).then(
-        lambda df, chart_type, x_col, y_col, group_col, size_col, color_scheme, patterns, title, width, height, show_grid, show_legend, agg_func, custom_colors: 
-        create_plot(
-            df, chart_type, x_col, y_col, 
-            None if group_col == "無" else group_col, 
-            None if size_col == "無" else size_col,
-            color_scheme, patterns, title, width, height, 
-            show_grid, show_legend, agg_func, custom_colors
-        ),
-        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
-        ],
-        outputs=[chart_output]
-    )
-    
-    # 智能推薦功能
-    recommend_button.click(
-        recommend_chart_settings,
-        inputs=[data_state],
-        outputs=[recommendation_result]
-    ).then(
-        lambda rec: (
-            rec.get("chart_type") if isinstance(rec, dict) and rec.get("chart_type") else None,
-            rec.get("x_column") if isinstance(rec, dict) and rec.get("x_column") else None,
-            rec.get("y_column") if isinstance(rec, dict) and rec.get("y_column") else None,
-            rec.get("group_column") if isinstance(rec, dict) and rec.get("group_column") else "無",
-            rec.get("agg_function") if isinstance(rec, dict) and rec.get("agg_function") else None
-        ),
-        inputs=[recommendation_result],
-        outputs=[chart_type, x_column, y_column, group_column, agg_function]
-    ).then(
-        lambda df, chart_type, x_col, y_col, group_col, size_col, color_scheme, patterns, title, width, height, show_grid, show_legend, agg_func, custom_colors: 
-        create_plot(
-            df, chart_type, x_col, y_col, 
-            None if group_col == "無" else group_col, 
-            None if size_col == "無" else size_col,
-            color_scheme, patterns, title, width, height, 
-            show_grid, show_legend, agg_func, custom_colors
-        ),
-        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
-        ],
-        outputs=[chart_output]
-    )
-    
-    # 其他輸入變化自動更新圖表
-    for input_component in [x_column, y_column, group_column, size_column, agg_function, color_scheme]:
-        input_component.change(
-            lambda df, chart_type, x_col, y_col, group_col, size_col, color_scheme, patterns, title, width, height, show_grid, show_legend, agg_func, custom_colors: 
-            create_plot(
-                df, chart_type, x_col, y_col, 
-                None if group_col == "無" else group_col, 
-                None if size_col == "無" else size_col,
-                color_scheme, patterns, title, width, height, 
-                show_grid, show_legend, agg_func, custom_colors
-            ),
-            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
-            ],
-            outputs=[chart_output]
-        )
-
-# 啟動應用
-demo.launch()     
+        return gr.update(choices=[]), gr.update(choices=[]), gr.update(choices=["無"])
+    cols = df.columns.tolist()
+    return gr.update(choices=cols, value=cols[0]), gr.update(choices=cols, value=cols[-1]), gr.update(choices=["無"] + cols, value="無")
+
+with gr.Blocks(title="雙圖比較") as demo:
+    gr.Markdown("## 📊 雙圖比較視覺化工具")
+
+    with gr.Row():
+        file = gr.File(label="上傳 CSV")
+        status = gr.Textbox(label="狀態", interactive=False)
+
+    df_state = gr.State()
+
+    def load_csv(file_obj):
+        try:
+            df = pd.read_csv(file_obj.name)
+            return df, f"成功載入 {len(df)} 筆資料"
+        except Exception as e:
+            return None, f"讀取錯誤: {e}"
+
+    file.change(fn=load_csv, inputs=[file], outputs=[df_state, status])
+
+    gr.Markdown("### 📌 圖表設定")
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("#### 圖一")
+            chart_type1 = gr.Dropdown(["長條圖", "堆疊長條圖", "折線圖", "散點圖"], label="圖表類型", value="長條圖")
+            x1 = gr.Dropdown(label="X 軸")
+            y1 = gr.Dropdown(label="Y 軸")
+            group1 = gr.Dropdown(label="分組欄位", choices=["無"], value="無")
+            plot1 = gr.Plot()
+
+        with gr.Column():
+            gr.Markdown("#### 圖二")
+            chart_type2 = gr.Dropdown(["長條圖", "堆疊長條圖", "折線圖", "散點圖"], label="圖表類型", value="堆疊長條圖")
+            x2 = gr.Dropdown(label="X 軸")
+            y2 = gr.Dropdown(label="Y 軸")
+            group2 = gr.Dropdown(label="分組欄位", choices=["無"], value="無")
+            plot2 = gr.Plot()
+
+    update_btn = gr.Button("產生圖表")
+
+    update_btn.click(fn=create_plot, inputs=[df_state, chart_type1, x1, y1, group1], outputs=[plot1])
+    update_btn.click(fn=create_plot, inputs=[df_state, chart_type2, x2, y2, group2], outputs=[plot2])
+    file.change(fn=update_column_options, inputs=[df_state], outputs=[x1, y1, group1])
+    file.change(fn=update_column_options, inputs=[df_state], outputs=[x2, y2, group2])
+
+demo.launch()