app.py

# -*- coding: utf-8 -*-
"""
Gradio 應用程式：進階數據可視化工具
作者：Gemini
版本：1.0 (分段提供 - Part 1)
描述：此部分包含套件導入、常數定義、輔助函數和 CSS 樣式。
"""

# =========================================
# == 套件導入 (Import Libraries) ==
# =========================================
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 # Matplotlib/Seaborn 在此版本中未使用，暫時註解
# import seaborn as sns           # Matplotlib/Seaborn 在此版本中未使用，暫時註解
from plotly.subplots import make_subplots
import re
import json
import colorsys
import traceback # 用於更詳細的錯誤追蹤

# =========================================
# == 常數定義 (Constants) ==
# =========================================

# 圖表類型選項 (Chart Type Options)
# 擴展並稍微調整順序以符合常見用法
CHART_TYPES = [
    # --- 長條圖系列 ---
    "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",
    # --- 折線圖系列 ---
    "折線圖", "多重折線圖", "階梯折線圖",
    # --- 區域圖系列 ---
    "區域圖", "堆疊區域圖", "百分比堆疊區域圖",
    # --- 圓形圖系列 ---
    "圓餅圖", "環形圖",
    # --- 散佈圖系列 ---
    "散點圖", "氣泡圖",
    # --- 分佈圖系列 ---
    "直方圖", "箱型圖", "小提琴圖",
    # --- 關係圖系列 ---
    "熱力圖", "樹狀圖",
    # --- 其他圖表 ---
    "雷達圖", "漏斗圖", "極座標圖", "甘特圖"
]

# 顏色方案選項 (Color Scheme Options)
# 增加更多 Plotly 內建方案並分類
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,
    "循環 - Twilight": px.colors.cyclical.Twilight,
    "循環 - IceFire": px.colors.cyclical.IceFire,
}

# 圖案填充選項 (Pattern Fill Options)
PATTERN_TYPES = [
    "無", "/", "\\", "x", "-", "|", "+", "."
]

# 聚合函數選項 (Aggregation Function Options)
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"
]

# =========================================
# == 輔助函數 (Helper Functions) ==
# =========================================

# --- 顏色處理相關 ---

# 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} ({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 = """
<script>
function copyToClipboard(text) {
    navigator.clipboard.writeText(text).then(() => {
        // 查找或創建通知容器
        let notificationContainer = document.getElementById('clipboard-notification-container');
        if (!notificationContainer) {
            notificationContainer = document.createElement('div');
            notificationContainer.id = 'clipboard-notification-container';
            notificationContainer.style.position = 'fixed';
            notificationContainer.style.bottom = '20px';
            notificationContainer.style.right = '20px';
            notificationContainer.style.zIndex = '10000'; // 確保在最上層
            notificationContainer.style.display = 'flex';
            notificationContainer.style.flexDirection = 'column';
            notificationContainer.style.alignItems = 'flex-end';
            document.body.appendChild(notificationContainer);
        }

        // 創建通知元素
        const notification = document.createElement('div');
        notification.textContent = '已複製: ' + text;
        notification.style.background = 'rgba(0, 0, 0, 0.7)';
        notification.style.color = 'white';
        notification.style.padding = '8px 15px';
        notification.style.borderRadius = '4px';
        notification.style.marginTop = '5px';
        notification.style.fontSize = '14px';
        notification.style.opacity = '1';
        notification.style.transition = 'opacity 0.5s ease-out';
        notification.style.boxShadow = '0 2px 5px rgba(0,0,0,0.2)';

        // 添加到容器並設置消失
        notificationContainer.appendChild(notification);

        setTimeout(() => {
            notification.style.opacity = '0';
            setTimeout(() => {
                notification.remove();
                // 如果容器空了，可以考慮移除容器
                if (notificationContainer.children.length === 0) {
                   // notificationContainer.remove(); // 或者保留以便後續使用
                }
            }, 500); // 等待淡出動畫完成
        }, 1500); // 顯示 1.5 秒
    }).catch(err => {
        console.error('無法複製顏色代碼: ', err);
        // 可以添加一個錯誤提示
    });
}
</script>
"""

# 常見顏色名稱和十六進制代碼
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",
}

# 生成漸變色系
def generate_gradient_colors(start_color, end_color, steps=10):
    """生成從開始顏色到結束顏色的漸變色列表"""
    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):
        # 確保 RGB 值在 0-255 範圍內
        r, g, b = [max(0, min(255, int(c))) for c in rgb]
        return '#{:02x}{:02x}{:02x}'.format(r, g, b)

    try:
        start_rgb = hex_to_rgb(start_color)
        end_rgb = hex_to_rgb(end_color)

        if steps <= 1:
            return [start_color] if steps == 1 else []

        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
    except Exception as e:
        print(f"生成漸變色時出錯: {e}")
        return [start_color, end_color] # 出錯時返回基礎顏色

# 定義一些常用的漸變色系
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"),
    "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"]
}

# 生成顏色卡片展示 HTML
def generate_color_cards():
    """生成包含常見顏色和漸變色的 HTML 卡片展示"""
    # 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_html = ""
    for name, colors in GRADIENTS.items():
        cards = ""
        for i, color in enumerate(colors):
            cards += COLOR_CARD_TEMPLATE.format(
                color_name=f"{name} {i+1}/{len(colors)}",
                color_hex=color
            )
        gradient_cards_html += f"""
        <div style="font-weight: bold; margin-top: 10px; font-size: 14px; color: #555;">{name}</div>
        {COLOR_CARD_STYLE.format(color_cards=cards)}
        """

    # 合成卡片展示HTML
    color_display = f"""
    <div style="font-weight: bold; margin-top: 10px; font-size: 14px; color: #555;">常用單色</div>
    {COLOR_CARD_STYLE.format(color_cards=common_cards)}
    {gradient_cards_html}
    {COPY_SCRIPT}
    """
    return color_display

# --- 數據處理相關 ---

def agg_function_map(func_name):
    """映射中文聚合函數名稱到 Pandas/Numpy 函數或標識符"""
    mapping = {
        "計數": "count",  # Pandas count non-NA values
        "求和": "sum",
        "平均值": "mean",
        "中位數": "median",
        "最大值": "max",
        "最小值": "min",
        "標準差": "std",
        "變異數": "var",
        "第一筆": "first",
        "最後一筆": "last",
        # 'size' is handled specially in create_plot for counting rows including NA
    }
    return mapping.get(func_name, "count") # 默認返回 count

def parse_custom_colors(color_text):
    """解析自定義顏色文本 (例如 "類別A:#FF0000, 類別B:#00FF00")"""
    custom_colors = {}
    if color_text and isinstance(color_text, str) and color_text.strip():
        try:
            # 移除多餘空格並按逗號分割
            pairs = [p.strip() for p in color_text.split(',') if p.strip()]
            for pair in pairs:
                if ':' in pair:
                    # 按第一個冒號分割
                    key, value = pair.split(':', 1)
                    key = key.strip()
                    value = value.strip()
                    # 簡單驗證顏色代碼格式 (以 # 開頭，後跟 3 或 6 個十六進制字符)
                    if re.match(r"^#(?:[0-9a-fA-F]{3}){1,2}$", value):
                        custom_colors[key] = value
                    else:
                        print(f"警告：忽略無效的顏色代碼格式 '{value}' for key '{key}'")
        except Exception as e:
            print(f"解析自定義顏色時出錯: {e}")
            # 出錯時返回空字典
            return {}
    return custom_colors

def update_patterns(*patterns_input):
    """從 Gradio 輸入更新圖案列表，過濾掉 '無'"""
    # patterns_input 會是一個包含 pattern1, pattern2, pattern3... 的元組
    patterns = [p for p in patterns_input if p and p != "無"]
    return patterns

# =========================================
# == CSS 樣式 (CSS Styling) ==
# =========================================
CUSTOM_CSS = """
/* --- 全局和容器 --- */
.gradio-container {
    font-family: 'Inter', 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; /* 使用更現代的字體 */
    background: #f8f9fa; /* 更柔和的背景色 */
    /* overflow: visible !important; */ /* 移除全局 overflow，可能導致問題 */
}

/* --- 應用程式標頭 --- */
.app-header {
    text-align: center;
    margin-bottom: 25px;
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); /* 漸變色更新 */
    padding: 25px 20px;
    border-radius: 12px; /* 圓角加大 */
    color: white;
    box-shadow: 0 6px 12px rgba(0, 0, 0, 0.15); /* 陰影加深 */
}
.app-title {
    font-size: 2.2em; /* 標題加大 */
    font-weight: 700; /* 字重加粗 */
    margin: 0;
    letter-spacing: 1px; /* 增加字間距 */
    text-shadow: 1px 1px 3px rgba(0,0,0,0.2); /* 文字陰影 */
}
.app-subtitle {
    font-size: 1.1em; /* 副標題加大 */
    color: #e0e0e0; /* 副標題顏色調整 */
    margin-top: 8px;
    font-weight: 300;
}

/* --- 區塊標題 --- */
.section-title {
    font-size: 1.4em; /* 區塊標題加大 */
    font-weight: 600; /* 字重調整 */
    color: #343a40; /* 標題顏色加深 */
    border-bottom: 3px solid #7367f0; /* 邊框顏色和寬度調整 */
    padding-bottom: 8px;
    margin-top: 25px;
    margin-bottom: 20px;
}

/* --- 卡片樣式 --- */
.card {
    background-color: white;
    border-radius: 10px;
    padding: 25px; /* 內邊距加大 */
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.08); /* 陰影調整 */
    margin-bottom: 20px;
    transition: transform 0.25s ease-out, box-shadow 0.25s ease-out;
    border: 1px solid #e0e0e0; /* 添加細邊框 */
}
.card:hover {
    transform: translateY(-4px);
    box-shadow: 0 8px 16px rgba(0, 0, 0, 0.12); /* 懸停陰影加強 */
}

/* --- 按鈕樣式 --- */
.primary-button {
    background: linear-gradient(to right, #667eea, #764ba2) !important;
    border: none !important;
    color: white !important;
    font-weight: 600 !important; /* 字重調整 */
    padding: 12px 24px !important; /* 按鈕加大 */
    border-radius: 8px !important; /* 圓角加大 */
    cursor: pointer !important;
    transition: all 0.3s ease !important;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1) !important;
}
.primary-button:hover {
    background: linear-gradient(to right, #764ba2, #667eea) !important; /* 懸停漸變反轉 */
    transform: translateY(-2px) !important;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15) !important; /* 懸停陰影加強 */
}

.secondary-button {
    background: linear-gradient(to right, #89f7fe, #66a6ff) !important; /* 不同漸變色 */
    border: none !important;
    color: #333 !important; /* 文字顏色調整 */
    font-weight: 600 !important;
    padding: 10px 20px !important;
    border-radius: 8px !important;
    cursor: pointer !important;
    transition: all 0.3s ease !important;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1) !important;
}
.secondary-button:hover {
    background: linear-gradient(to right, #66a6ff, #89f7fe) !important;
    transform: translateY(-2px) !important;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15) !important;
}

/* --- 其他 UI 元素 --- */
.tips-box {
    background-color: #e7f3ff; /* 提示框背景色 */
    border-left: 5px solid #66a6ff; /* 提示框邊框 */
    padding: 15px 20px;
    border-radius: 8px;
    margin: 20px 0;
    font-size: 0.95em;
    color: #333;
}
.tips-box code { /* 提示框中的代碼樣式 */
    background-color: #d1e7fd;
    padding: 2px 5px;
    border-radius: 4px;
    font-family: 'Courier New', Courier, monospace;
}

.chart-previewer {
    border: 2px dashed #ced4da; /* 預覽區邊框 */
    border-radius: 10px;
    padding: 20px;
    min-height: 450px; /* 預覽區最小高度 */
    display: flex;
    justify-content: center;
    align-items: center;
    background-color: #ffffff; /* 白色背景 */
    box-shadow: inset 0 0 10px rgba(0,0,0,0.05); /* 內陰影 */
    margin-top: 15px;
}

/* 數據表格預覽 */
.gradio-dataframe table {
    border-collapse: collapse;
    width: 100%;
    font-size: 0.9em;
}
.gradio-dataframe th, .gradio-dataframe td {
    border: 1px solid #dee2e6;
    padding: 8px 10px;
    text-align: left;
}
.gradio-dataframe th {
    background-color: #f8f9fa;
    font-weight: 600;
}
.gradio-dataframe tr:nth-child(even) {
    background-color: #f8f9fa;
}

/* 顏色自定義輸入框 */
.color-customization-input textarea {
    font-family: 'Courier New', Courier, monospace;
    font-size: 0.9em;
}

/* 確保 Gradio Tabs 樣式一致 */
.gradio-tabs .tab-nav button {
    padding: 10px 20px !important;
    font-weight: 500 !important;
    border-radius: 8px 8px 0 0 !important;
    transition: background-color 0.2s ease, color 0.2s ease !important;
}
.gradio-tabs .tab-nav button.selected {
    background-color: #667eea !important;
    color: white !important;
    border-bottom: none !important;
}

/* 調整 Slider 樣式 */
.gradio-slider label {
    margin-bottom: 5px !important;
}
.gradio-slider input[type="range"] {
    cursor: pointer !important;
}

/* 調整 Checkbox/Radio 樣式 */
.gradio-checkboxgroup label, .gradio-radio label {
    padding: 8px 0 !important;
}

/* 調整 Textbox/Textarea 樣式 */
.gradio-textbox textarea, .gradio-textbox input {
    border-radius: 6px !important;
    border: 1px solid #ced4da !important;
    padding: 10px !important;
}
.gradio-textbox textarea:focus, .gradio-textbox input:focus {
    border-color: #80bdff !important;
    box-shadow: 0 0 0 0.2rem rgba(0,123,255,.25) !important;
}

/* 檔案上傳/下載區域 */
.gradio-file .hidden-upload, .gradio-file .download-button {
    border-radius: 6px !important;
}
.gradio-file .upload-button {
     border-radius: 6px !important;
     background: #6c757d !important; /* 上傳按鈕顏色 */
     color: white !important;
     padding: 8px 15px !important;
}
.gradio-file .upload-button:hover {
    background: #5a6268 !important;
}

"""

# =========================================
# == (第一部分結束) ==
# =========================================
# -*- coding: utf-8 -*-
"""
Gradio 應用程式：進階數據可視化工具
作者：Gemini
版本：1.0 (分段提供 - Part 2)
描述：此部分包含核心邏輯函數，如數據處理、圖表創建、導出等。
"""

# =========================================
# == 數據處理函數 (Data Processing Functions) ==
# =========================================

def process_upload(file):
    """
    處理上傳的文件 (CSV 或 Excel)。
    Args:
        file: Gradio 文件對象。
    Returns:
        tuple: (DataFrame 或 None, 狀態消息)。
    """
    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()

        if file_type == 'csv':
            # 嘗試自動檢測編碼
            try:
                df = pd.read_csv(file_path, encoding='utf-8')
            except UnicodeDecodeError:
                try:
                    df = pd.read_csv(file_path, encoding='big5') # 嘗試台灣常用編碼
                except Exception as e:
                    return None, f"❌ 無法使用 UTF-8 或 Big5 解碼 CSV 文件: {e}"
            except Exception as e:
                 return None, f"❌ 讀取 CSV 文件時出錯: {e}"

        elif file_type in ['xls', 'xlsx']:
            try:
                df = pd.read_excel(file_path)
            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。"

        # 清理列名中的潛在空格
        df.columns = df.columns.str.strip()

        # **移除自動添加計數列**
        # df['計數'] = 1 # 不再自動添加

        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):
    """
    解析文本框中輸入的數據 (CSV 或空格分隔)。
    Args:
        text_data (str): 包含數據的字符串。
    Returns:
        tuple: (DataFrame 或 None, 狀態消息)。
    """
    if not text_data or not text_data.strip():
        return None, "❌ 未輸入任何數據。"
    try:
        # 使用 StringIO 模擬文件讀取
        data_io = io.StringIO(text_data.strip())
        first_line = data_io.readline().strip() # 讀取第一行以判斷分隔符
        data_io.seek(0) # 重置讀取位置

        # 判斷分隔符
        if ',' in first_line:
            separator = ','
        elif '\t' in first_line:
            separator = '\t'
        elif ' ' in first_line:
            # 如果包含空格，使用正則表達式匹配多個空格作為分隔符
            separator = r'\s+'
        else:
            # 如果只有一列或無法判斷，默認為逗號，讓 pandas 嘗試
            separator = ','

        try:
            df = pd.read_csv(data_io, sep=separator)
        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}"

        # 清理列名中的潛在空格
        df.columns = df.columns.str.strip()

        # **移除自動添加計數列**
        # df['計數'] = 1 # 不再自動添加

        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):
    """
    根據 DataFrame 更新 Gradio 下拉菜單的選項。
    Args:
        df (pd.DataFrame): 輸入的 DataFrame。
    Returns:
        tuple: 更新後的 Gradio Dropdown 組件 (x, y, group, size)。
    """
    default_choices = ["-- 無數據 --"]
    if df is None or df.empty:
        # 提供預設或空選項
        return (
            gr.Dropdown(choices=default_choices, value=default_choices[0], label="X軸 / 類別"),
            gr.Dropdown(choices=default_choices, value=default_choices[0], label="Y軸 / 數值"),
            gr.Dropdown(choices=["無"] + default_choices, value="無", label="分組列"),
            gr.Dropdown(choices=["無"] + default_choices, value="無", label="大小列")
        )

    columns = df.columns.tolist()
    # 嘗試猜測合適的預設值
    x_default = columns[0] if columns else None
    # 預設 Y 軸為第二列（如果存在），否則為第一列
    y_default = columns[1] if len(columns) > 1 else (columns[0] if columns else None)

    # 移除 '無' 選項中的 None 或空字符串
    valid_columns = [col for col in columns if col is not None and col != ""]
    group_choices = ["無"] + valid_columns
    size_choices = ["無"] + valid_columns

    # 更新下拉選單
    x_dropdown = gr.Dropdown(choices=valid_columns, value=x_default, label="X軸 / 類別")
    y_dropdown = gr.Dropdown(choices=valid_columns, value=y_default, label="Y軸 / 數值")
    group_dropdown = gr.Dropdown(choices=group_choices, value="無", label="分組列")
    size_dropdown = gr.Dropdown(choices=size_choices, value="無", label="大小列")

    return x_dropdown, y_dropdown, group_dropdown, size_dropdown

# =========================================
# == 圖表創建核心函數 (Core Plotting Function) ==
# =========================================
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={}):
    """
    根據用戶選擇創建 Plotly 圖表 (已加入 Null/空白 過濾)。

    Args:
        df (pd.DataFrame): 輸入數據。
        chart_type (str): 圖表類型。
        x_column (str): X軸或類別列。
        y_column (str): Y軸或數值列 (可能為 None)。
        group_column (str, optional): 分組列 (可能為 "無" 或 None)。 Defaults to None.
        size_column (str, optional): 大小列 (可能為 "無" 或 None)。 Defaults to None.
        color_scheme_name (str, optional): 顏色方案名稱。 Defaults to "預設 (Plotly)".
        patterns (list, optional): 圖案列表。 Defaults to [].
        title (str, optional): 圖表標題。 Defaults to "".
        width (int, optional): 圖表寬度。 Defaults to 800.
        height (int, optional): 圖表高度。 Defaults to 500.
        show_grid_str (str, optional): 是否顯示網格 ("是" 或 "否")。 Defaults to "是".
        show_legend_str (str, optional): 是否顯示圖例 ("是" 或 "否")。 Defaults to "是".
        agg_func_name (str, optional): 聚合函數名稱。 Defaults to "計數".
        custom_colors_dict (dict, optional): 自定義顏色映射。 Defaults to {}.

    Returns:
        go.Figure: Plotly 圖表對象。
    """
    # --- 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"  - 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(f"  - show_grid_str: {show_grid_str}", file=sys.stderr)
    print(f"  - show_legend_str: {show_legend_str}", 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 ---
        show_grid = True if show_grid_str == "是" else False
        show_legend = True if show_legend_str == "是" else False

        # --- 1. 輸入驗證 (更嚴格) ---
        if df is None or not isinstance(df, pd.DataFrame) or df.empty:
            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。")
        # NO_DATA_STR = "-- 無數據 --" # 確保此變數已定義或直接使用字符串
        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。可用列: {', '.join(df.columns)}")

        # 判斷是否需要 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 == 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 (從 Radio 傳來的值可能是 NONE_STR)
        # NONE_STR = "無" # 確保此變數已定義或直接使用字符串
        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。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。")

        df_processed = df.copy()
        print(f"原始數據行數: {len(df_processed)}", file=sys.stderr)

        # --- NEW: 過濾 Null/空白值 ---
        columns_to_filter = [x_column]
        if y_needed and y_column: # Filter Y only if it's needed and selected
            columns_to_filter.append(y_column)
        if group_col:
            columns_to_filter.append(group_col)

        # 移除在關鍵列中有 Null (NaN, None) 值的行
        valid_columns_to_filter = [col for col in columns_to_filter if col in df_processed.columns]
        if valid_columns_to_filter:
             original_rows = len(df_processed)
             df_processed.dropna(subset=valid_columns_to_filter, inplace=True)
             print(f"移除 Null ({', '.join(valid_columns_to_filter)}) 後行數: {len(df_processed)} (減少 {original_rows - len(df_processed)} 行)", file=sys.stderr)
        else:
             print("警告: 沒有有效的列用於 Null 值過濾。", file=sys.stderr)

        # 對於 X 軸和分組列，額外移除空白字符串 (轉換為字符串後判斷)
        if x_column in df_processed.columns:
             try:
                original_rows = len(df_processed)
                # 僅移除完全是空白或空字符串的行
                df_processed = df_processed[df_processed[x_column].astype(str).str.strip() != '']
                print(f"移除 X 軸 '{x_column}' 空白字符串後行數: {len(df_processed)} (減少 {original_rows - len(df_processed)} 行)", file=sys.stderr)
             except Exception as e:
                 print(f"警告: 過濾 X 軸 '{x_column}' 空白字符串時出錯: {e}", file=sys.stderr)

        if group_col and group_col in df_processed.columns:
             try:
                original_rows = len(df_processed)
                df_processed = df_processed[df_processed[group_col].astype(str).str.strip() != '']
                print(f"移除分組列 '{group_col}' 空白字符串後行數: {len(df_processed)} (減少 {original_rows - len(df_processed)} 行)", file=sys.stderr)
             except Exception as e:
                 print(f"警告: 過濾分組列 '{group_col}' 空白字符串時出錯: {e}", file=sys.stderr)

        # 檢查過濾後是否還有數據
        if df_processed.empty:
            raise ValueError("過濾掉 Null 或空白值後，沒有剩餘數據可供繪圖。")
        # --- END NEW ---


        # --- 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
        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 y_column: # 這裡 y_column 可能為 None
            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}")
        if size_col:
            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) ---
        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 (可能為 None)
        if needs_aggregation:
            grouping_cols = [x_column] + ([group_col] if group_col else [])
            # 檢查分組列是否有效 (已在驗證部分完成)

            if agg_func_name == "計數":
                # 使用 size() 計算每個組的行數
                agg_df = df_processed.groupby(grouping_cols, observed=False, dropna=False).size() # dropna=False 包含 NaN 類別 (已被過濾?)
                agg_df = agg_df.reset_index(name='__count__')
                y_col_agg = '__count__' # 使用新生成的計數列
            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)
                    agg_df = agg_df.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}")
        else:
            # 不需要聚合，直接使用處理過的數據
            agg_df = df_processed
            y_col_agg = y_column # 保持原始列名 (可能為 None)

        # 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
        required_cols_for_plot = [x_column]
        # 只有在 y_col_agg 有效時才加入檢查
        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)}")


        # --- 4. 獲取顏色方案 ---
        colors = COLOR_SCHEMES.get(color_scheme_name, px.colors.qualitative.Plotly)

        # --- 5. 創建圖表 (核心邏輯) ---
        fig_params = {"title": title, "color_discrete_sequence": colors, "width": width, "height": height} # 移除 data_frame
        if group_col and custom_colors_dict: fig_params["color_discrete_map"] = custom_colors_dict
        # 確定實際用於繪圖的 Y 軸列名 (可能是 '__count__' 或原始 Y 列名)
        effective_y = y_col_agg # 使用聚合後確定的 Y 軸列名

        # --- (繪圖邏輯開始) ---
        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)
        elif chart_type == "堆疊長條圖":
            if not effective_y: raise ValueError("堆疊長條圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.bar(agg_df, x=x_column, y=effective_y, color=group_col, barmode='stack', **fig_params)
        elif chart_type == "百分比堆疊長條圖":
             if not effective_y: raise ValueError("百分比堆疊長條圖需要 Y 軸數值或 '計數' 聚合。")
             fig = px.bar(agg_df, x=x_column, y=effective_y, color=group_col, barmode='relative', text_auto='.1%', **fig_params)
             fig.update_layout(yaxis_title="百分比 (%)")
        elif chart_type == "群組長條圖":
            if not effective_y: raise ValueError("群組長條圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.bar(agg_df, x=x_column, y=effective_y, color=group_col, barmode='group', **fig_params)
        elif chart_type == "水平長條圖":
            if not effective_y: raise ValueError("水平長條圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.bar(agg_df, y=x_column, x=effective_y, color=group_col, orientation='h', **fig_params)
        elif chart_type == "折線圖":
            if not effective_y: raise ValueError("折線圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.line(agg_df, x=x_column, y=effective_y, color=group_col, markers=True, **fig_params)
        elif chart_type == "多重折線圖":
            if not effective_y: raise ValueError("多重折線圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.line(agg_df, x=x_column, y=effective_y, color=group_col, markers=True, **fig_params)
        elif chart_type == "階梯折線圖":
            if not effective_y: raise ValueError("階梯折線圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.line(agg_df, x=x_column, y=effective_y, color=group_col, line_shape='hv', **fig_params)
        elif chart_type == "區域圖":
            if not effective_y: raise ValueError("區域圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.area(agg_df, x=x_column, y=effective_y, color=group_col, **fig_params)
        elif chart_type == "堆疊區域圖":
            if not effective_y: raise ValueError("堆疊區域圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.area(agg_df, x=x_column, y=effective_y, color=group_col, groupnorm=None, **fig_params)
        elif chart_type == "百分比堆疊區域圖":
             if not effective_y: raise ValueError("百分比堆疊區域圖需要 Y 軸數值或 '計數' 聚合。")
             fig = px.area(agg_df, x=x_column, y=effective_y, color=group_col, groupnorm='percent', **fig_params)
             fig.update_layout(yaxis_title="百分比 (%)")
        elif chart_type == "圓餅圖":
            if not effective_y: raise ValueError("圓餅圖需要 Y 軸數值或 '計數' 聚合。")
            if group_col: print("警告：圓餅圖不支持分組列，已忽略。")
            fig = px.pie(agg_df, names=x_column, values=effective_y, **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 == "環形圖":
            if not effective_y: raise ValueError("環形圖需要 Y 軸數值或 '計數' 聚合。")
            if group_col: print("警告：環形圖不支持分組列，已忽略。")
            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":
            # 散點圖不需要聚合，使用原始 y_column
            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。")
            if not size_col: raise ValueError("氣泡圖需要指定 '大小列'。")
            if not pd.api.types.is_numeric_dtype(agg_df[size_col]): raise ValueError(f"大小列 '{size_col}' 必須是數值類型。")
            fig = px.scatter(agg_df, x=x_column, y=y_column, color=group_col, size=size_col, size_max=60, **fig_params)
        elif chart_type == "直方圖":
            # 直方圖使用原始數據的 x_column
            if not pd.api.types.is_numeric_dtype(agg_df[x_column]): raise ValueError(f"直方圖的 X 軸列 '{x_column}' 必須是數值類型。")
            fig = px.histogram(agg_df, x=x_column, color=group_col, **fig_params); fig.update_layout(yaxis_title="計數")
        elif chart_type == "箱型圖":
            # 箱型圖使用原始 y_column
            if not y_column: raise ValueError("箱型圖需要選擇 Y 軸列。")
            if not pd.api.types.is_numeric_dtype(agg_df[y_column]): raise ValueError(f"箱型圖的 Y 軸列 '{y_column}' 必須是數值類型。")
            fig = px.box(agg_df, x=group_col, y=y_column, color=group_col, **fig_params)
            if not group_col: fig = px.box(agg_df, y=y_column, **fig_params)
        elif chart_type == "小提琴圖":
            if not y_column: raise ValueError("小提琴圖需要選擇 Y 軸列。")
            if not pd.api.types.is_numeric_dtype(agg_df[y_column]): raise ValueError(f"小提琴圖的 Y 軸列 '{y_column}' 必須是數值類型。")
            fig = px.violin(agg_df, x=group_col, y=y_column, color=group_col, box=True, points="all", **fig_params)
            if not group_col: fig = px.violin(agg_df, y=y_column, box=True, points="all", **fig_params)
        elif chart_type == "熱力圖":
            if not effective_y: raise ValueError("熱力圖需要 Y 軸數值或 '計數' 聚合。")
            if not group_col: raise ValueError("熱力圖需要 X 軸、Y 軸 和一個 分組列。")
            try:
                if not pd.api.types.is_numeric_dtype(agg_df[effective_y]): raise ValueError(f"熱力圖的值列 '{effective_y}' 必須是數值類型。")
                pivot_df = pd.pivot_table(agg_df, values=effective_y, index=group_col, columns=x_column, aggfunc=agg_function_map(agg_func_name) if agg_func_name != "計數" else 'size')
                fig = px.imshow(pivot_df, color_continuous_scale=px.colors.sequential.Viridis, aspect="auto", text_auto=True, **fig_params);
                fig.update_layout(coloraxis_showscale=True)
            except Exception as pivot_e: raise ValueError(f"創建熱力圖的數據透視表時出錯: {pivot_e}")
        elif chart_type == "樹狀圖":
             if not effective_y: raise ValueError("樹狀圖需要 Y 軸數值或 '計數' 聚合。")
             path = [group_col, x_column] if group_col else [x_column]
             if not pd.api.types.is_numeric_dtype(agg_df[effective_y]): raise ValueError(f"樹狀圖的值列 '{effective_y}' 必須是數值類型。")
             fig = px.treemap(agg_df, path=path, values=effective_y, color=group_col if group_col else x_column, **fig_params)
        elif chart_type == "雷達圖":
            if not effective_y: raise ValueError("雷達圖需要 Y 軸數值或 '計數' 聚合。")
            fig = go.Figure()
            if not pd.api.types.is_numeric_dtype(agg_df[effective_y]): raise ValueError(f"雷達圖的徑向值列 '{effective_y}' 必須是數值類型。")
            if not group_col:
                 theta = agg_df[x_column].tolist(); r = agg_df[effective_y].tolist(); theta.append(theta[0]); r.append(r[0])
                 fig.add_trace(go.Scatterpolar(r=r, theta=theta, fill='toself', name=effective_y if effective_y != '__count__' else '計數', line_color=colors[0]))
            else:
                 categories = agg_df[group_col].unique()
                 for i, category in enumerate(categories):
                     subset = agg_df[agg_df[group_col] == category]; theta = subset[x_column].tolist(); r = subset[effective_y].tolist(); theta.append(theta[0]); r.append(r[0])
                     color = custom_colors_dict.get(str(category), colors[i % len(colors)])
                     fig.add_trace(go.Scatterpolar(r=r, theta=theta, fill='toself', name=str(category), line_color=color))
            fig.update_layout(polar=dict(radialaxis=dict(visible=True)), showlegend=show_legend, title=title, width=width, height=height)
        elif chart_type == "漏斗圖":
            if not effective_y: raise ValueError("漏斗圖需要 Y 軸數值或 '計數' 聚合。")
            if not pd.api.types.is_numeric_dtype(agg_df[effective_y]): raise ValueError(f"漏斗圖的值列 '{effective_y}' 必須是數值類型。")
            sorted_df = agg_df.sort_values(by=effective_y, ascending=False)
            fig = px.funnel(sorted_df, x=effective_y, y=x_column, color=group_col, **fig_params)
        elif chart_type == "極座標圖":
            if not effective_y: raise ValueError("極座標圖需要 Y 軸數值或 '計數' 聚合。")
            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":
             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
                 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。")
                 df_gantt['_start_'] = pd.to_datetime(df_gantt[start_col_gantt], errors='coerce')
                 df_gantt['_end_'] = pd.to_datetime(df_gantt[end_col_gantt], errors='coerce')
                 if df_gantt['_start_'].isnull().any(): raise ValueError(f"開始列 '{start_col_gantt}' 包含無效或無法解析的日期時間格式。")
                 if df_gantt['_end_'].isnull().any(): raise ValueError(f"結束列 '{end_col_gantt}' 包含無效或無法解析的日期時間格式。")
                 fig = px.timeline(df_gantt, x_start='_start_', x_end='_end_', y=task_col_gantt, color=size_col if size_col else None, title=title, color_discrete_sequence=colors, width=width, height=height)
                 fig.update_layout(xaxis_type="date")
             except Exception as gantt_e: raise ValueError(f"創建甘特圖時出錯: {gantt_e}")
        else:
            print(f"警告：未知的圖表類型 '{chart_type}'，使用長條圖代替。")
            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)
        # --- (繪圖邏輯結束) ---


        # --- 6. 應用圖案 (如果支持) ---
        if patterns:
            try:
                num_traces = len(fig.data)
                if chart_type in ["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", "圓餅圖", "環形圖"]:
                    for i, trace in enumerate(fig.data):
                        pattern_index = i % len(patterns)
                        if patterns[pattern_index] != "無": trace.marker.pattern.shape = patterns[pattern_index]; trace.marker.pattern.solidity = 0.4; trace.marker.pattern.fillmode = "replace"
                elif chart_type in ["散點圖", "氣泡圖"]:
                     symbol_map = {"/": "diamond", "\\": "square", "x": "x", "-": "line-ew", "|": "line-ns", "+": "cross", ".": "circle-dot"}
                     for i, trace in enumerate(fig.data):
                         pattern_index = i % len(patterns); symbol = symbol_map.get(patterns[pattern_index])
                         if symbol: trace.marker.symbol = symbol
                elif chart_type in ["折線圖", "多重折線圖", "階梯折線圖"]:
                     dash_map = {"/": "dash", "\\": "dot", "x": "dashdot", "-": "longdash", "|": "solid", "+": "solid", ".": "solid"}
                     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
                elif chart_type in ["區域圖", "堆疊區域圖", "百分比堆疊區域圖"]:
                     print("提示：區域圖的圖案填充支持有限，將嘗試應用線型。")
                     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'
            except Exception as pattern_e: print(f"應用圖案時出錯: {pattern_e}")

        # --- 7. 更新佈局 ---
        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),
            font=dict(family="Inter, sans-serif", size=12),
            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
        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}", 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, 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)

    print("create_plot 函數執行完畢。", file=sys.stderr) # 調試信息
    return fig

# =========================================
# == 導出與下載函數 (Export & Download Functions) ==
# =========================================

def export_data(df, format_type):
    """
    將 DataFrame 導出為指定格式的文件。
    Args:
        df (pd.DataFrame): 要導出的 DataFrame。
        format_type (str): 導出格式 ("CSV", "Excel", "JSON")。
    Returns:
        tuple: (文件路徑或 None, 狀態消息)。 返回文件路徑供 Gradio 下載。
    """
    if df is None or df.empty:
        # 不能直接返回 None，需要返回一個空的 File output
        return None, "❌ 沒有數據可以導出。"

    try:
        if format_type == "CSV":
            filename = "exported_data.csv"
            df.to_csv(filename, index=False, encoding='utf-8-sig') # utf-8-sig 確保 Excel 正確讀取 BOM
        elif format_type == "Excel":
            filename = "exported_data.xlsx"
            df.to_excel(filename, index=False)
        elif format_type == "JSON":
            filename = "exported_data.json"
            df.to_json(filename, orient="records", indent=4, force_ascii=False) # indent 美化輸出, force_ascii=False 處理中文
        else:
            return None, f"❌ 不支持的導出格式: {format_type}"

        return filename, f"✅ 數據已成功準備為 {format_type} 格式，點擊下方鏈接下載。"

    except Exception as e:
        print(f"導出數據時出錯: {e}")
        traceback.print_exc()
        return None, f"❌ 導出數據時出錯: {e}"

def download_figure(fig, format_type="PNG"):
    """
    將 Plotly 圖表導出為圖像文件。
    Args:
        fig (go.Figure): Plotly 圖表對象。
        format_type (str): 導出格式 ("PNG", "SVG", "PDF", "JPEG")。 Defaults to "PNG".
    Returns:
        tuple: (文件路徑或 None, 狀態消息)。 返回文件路徑供 Gradio 下載。
    """
    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}"

        # 使用 fig.write_image 寫入文件
        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 ValueError as ve:
         # kaleido 可能未安裝或配置錯誤
         if "kaleido" in str(ve):
             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
         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) ==
# =========================================
def recommend_chart_settings(df):
    """
    根據輸入的 DataFrame 智能推薦圖表設置。
    Args:
        df (pd.DataFrame): 輸入數據。
    Returns:
        dict: 包含推薦設置和消息的字典。
               Keys: chart_type, x_column, y_column, group_column, agg_function, message
    """
    recommendation = {
        "chart_type": None, "x_column": None, "y_column": None,
        "group_column": "無", "agg_function": None, "message": "無法提供推薦。"
    }

    if df is None or df.empty:
        recommendation["message"] = "ℹ️ 請先上傳或輸入數據。"
        return recommendation

    columns = df.columns.tolist()
    num_cols = df.select_dtypes(include=np.number).columns.tolist()
    cat_cols = df.select_dtypes(include=['object', 'category', 'string']).columns.tolist()
    # 嘗試檢測日期時間列
    date_cols = [col for col in columns if pd.api.types.is_datetime64_any_dtype(df[col]) or ('日期' in col or '時間' in col)]

    # --- 推薦邏輯 ---
    try:
        # 優先處理時間序列
        if date_cols and num_cols:
            recommendation["chart_type"] = "折線圖"
            recommendation["x_column"] = date_cols[0]
            recommendation["y_column"] = num_cols[0]
            recommendation["agg_function"] = "平均值" # 或求和，取決於數據性質
            recommendation["message"] = f"檢測到時間列 '{date_cols[0]}' 和數值列 '{num_cols[0]}'，推薦使用折線圖顯示趨勢。"
        # 兩個數值列 -> 散點圖
        elif len(num_cols) >= 2:
            recommendation["chart_type"] = "散點圖"
            recommendation["x_column"] = num_cols[0]
            recommendation["y_column"] = num_cols[1]
            recommendation["agg_function"] = None # 散點圖通常不需要聚合
            recommendation["message"] = f"檢測到數值列 '{num_cols[0]}' 和 '{num_cols[1]}'，推薦使用散點圖分析相關性。"
        # 一個類別列和一個數值列 -> 長條圖
        elif cat_cols and num_cols:
            recommendation["chart_type"] = "長條圖"
            recommendation["x_column"] = cat_cols[0]
            recommendation["y_column"] = num_cols[0]
            recommendation["agg_function"] = "平均值" # 或求和
            recommendation["message"] = f"檢測到類別列 '{cat_cols[0]}' 和數值列 '{num_cols[0]}'，推薦使用長條圖比較各類別的數值。"
        # 兩個或更多類別列 -> 堆疊/群組長條圖 (計數)
        elif len(cat_cols) >= 2:
            recommendation["chart_type"] = "堆疊長條圖" # 或群組長條圖
            recommendation["x_column"] = cat_cols[0]
            recommendation["y_column"] = None # Y軸由計數聚合產生
            recommendation["group_column"] = cat_cols[1]
            recommendation["agg_function"] = "計數"
            recommendation["message"] = f"檢測到多個類別列 ('{cat_cols[0]}', '{cat_cols[1]}', ...)，推薦使用堆疊長條圖顯示計數分佈。"
        # 只有一個類別列 -> 長條圖 (計數)
        elif cat_cols:
            recommendation["chart_type"] = "長條圖"
            recommendation["x_column"] = cat_cols[0]
            recommendation["y_column"] = None # Y軸由計數聚合產生
            recommendation["agg_function"] = "計數"
            recommendation["message"] = f"檢測到類別列 '{cat_cols[0]}'，推薦使用長條圖顯示其頻數分佈。"
        # 只有數值列 -> 直方圖
        elif num_cols:
            recommendation["chart_type"] = "直方圖"
            recommendation["x_column"] = num_cols[0]
            recommendation["y_column"] = None
            recommendation["agg_function"] = None # 直方圖自動計數
            recommendation["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()

    # 確保推薦的列名是有效的
    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"

    # 如果推薦了聚合但 Y 軸無效，則清空 Y 軸
    if recommendation["agg_function"] and recommendation["agg_function"] != "計數" and not recommendation["y_column"]:
        recommendation["agg_function"] = None # 無法聚合
        recommendation["message"] += " (無法確定聚合的數值列)"

    # 如果聚合是計數，Y 軸應為空
    if recommendation["agg_function"] == "計數":
        recommendation["y_column"] = None # 強制 Y 軸為空，因為值由計數產生

    return recommendation


# =========================================
# == (第二部分結束) ==
# =========================================
# -*- coding: utf-8 -*-
"""
Gradio 應用程式：進階數據可視化工具
作者：Gemini
版本：5.2 (完整修正版 - 清理格式, 確保一致性)
描述：包含所有功能的完整程式碼，修正導入、CSS、格式問題。
"""

# =========================================
# == 套件導入 (Import Libraries) ==
# =========================================
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
from plotly.subplots import make_subplots
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 - 已加入

# =========================================
# == 常數定義 (Constants) ==
# =========================================
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,
    "循環 - Twilight": px.colors.cyclical.Twilight, "循環 - IceFire": px.colors.cyclical.IceFire,
}
PATTERN_TYPES = ["無", "/", "\\", "x", "-", "|", "+", "."]
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 ---
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 = """
<script>
function copyToClipboard(text) {
    navigator.clipboard.writeText(text).then(() => {
        let notificationContainer = document.getElementById('clipboard-notification-container');
        if (!notificationContainer) {
            notificationContainer = document.createElement('div');
            notificationContainer.id = 'clipboard-notification-container';
            notificationContainer.style.position = 'fixed'; notificationContainer.style.bottom = '20px'; notificationContainer.style.right = '20px'; notificationContainer.style.zIndex = '10000'; notificationContainer.style.display = 'flex'; notificationContainer.style.flexDirection = 'column'; notificationContainer.style.alignItems = 'flex-end';
            document.body.appendChild(notificationContainer);
        }
        const notification = document.createElement('div');
        notification.textContent = '已複製: ' + text;
        notification.style.background = 'rgba(0, 0, 0, 0.7)'; notification.style.color = 'white'; notification.style.padding = '8px 15px'; notification.style.borderRadius = '4px'; notification.style.marginTop = '5px'; notification.style.fontSize = '14px'; notification.style.opacity = '1'; notification.style.transition = 'opacity 0.5s ease-out'; notification.style.boxShadow = '0 2px 5px rgba(0,0,0,0.2)';
        notificationContainer.appendChild(notification);
        setTimeout(() => {
            notification.style.opacity = '0';
            setTimeout(() => { notification.remove(); }, 500);
        }, 1500);
    }).catch(err => { console.error('無法複製顏色代碼: ', err); });
}
</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",
}
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)
    try:
        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"]}
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 = ""
    for name, colors in GRADIENTS.items():
        cards = "".join([COLOR_CARD_TEMPLATE.format(color_name=f"{name} {i+1}/{len(colors)}", color_hex=color) for i, color in enumerate(colors)])
        gradient_cards_html += f"""<div style="font-weight: bold; margin-top: 10px; font-size: 14px; color: #555;">{name}</div>{COLOR_CARD_STYLE.format(color_cards=cards)}"""
    return f"""<div style="font-weight: bold; margin-top: 10px; font-size: 14px; color: #555;">常用單色</div>{COLOR_CARD_STYLE.format(color_cards=common_cards)}{gradient_cards_html}{COPY_SCRIPT}"""

# --- 數據處理相關 ---
def agg_function_map(func_name):
    mapping = {"計數": "count", "求和": "sum", "平均值": "mean", "中位數": "median", "最大值": "max", "最小值": "min", "標準差": "std", "變異數": "var", "第一筆": "first", "最後一筆": "last"}
    return mapping.get(func_name, "count")
def parse_custom_colors(color_text):
    custom_colors = {}
    if color_text and isinstance(color_text, str) and color_text.strip():
        try:
            pairs = [p.strip() for p in color_text.split(',') if p.strip()]
            for pair in pairs:
                if ':' in pair:
                    key, value = pair.split(':', 1); key, value = key.strip(), value.strip()
                    if re.match(r"^#(?:[0-9a-fA-F]{3}){1,2}$", value): custom_colors[key] = value
                    else: print(f"警告：忽略無效的顏色代碼 '{value}' for key '{key}'")
        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):
    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):
    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()
        if file_type == 'csv':
            try: df = pd.read_csv(file_path, encoding='utf-8')
            except UnicodeDecodeError:
                try: df = pd.read_csv(file_path, encoding='big5')
                except Exception as e: return None, f"❌ 無法使用 UTF-8 或 Big5 解碼 CSV 文件: {e}"
            except Exception as e: return None, f"❌ 讀取 CSV 文件時出錯: {e}"
        elif file_type in ['xls', 'xlsx']:
            try: df = pd.read_excel(file_path)
            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()
        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):
    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 = ','
        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()
        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_as_radio(df):
    """更新列選擇為 Radio 選項，並為 Y/Group/Size 軸添加 '無'"""
    no_data_choices = [NO_DATA_STR]
    no_data_choices_with_none = [NONE_STR, NO_DATA_STR]

    if df is None or df.empty:
        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_with_none, no_data_update_with_none, no_data_update_with_none

    try:
        columns = df.columns.tolist()
        valid_columns = [str(col) for col in columns if col is not None and str(col) != ""]
        if not valid_columns:
             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_with_none, no_data_update_with_none, no_data_update_with_none

        x_default = valid_columns[0]
        y_default = NONE_STR if len(valid_columns) <= 1 else valid_columns[1]

        y_choices = [NONE_STR] + valid_columns
        group_choices = [NONE_STR] + valid_columns
        size_choices = [NONE_STR] + valid_columns

        return (gr.Radio(choices=valid_columns, value=x_default, label="X軸 / 類別"),
                gr.Radio(choices=y_choices, 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 (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_with_none, no_data_update_with_none, no_data_update_with_none


# =========================================
# == 圖表創建核心函數 (Core Plotting Function) ==
# =========================================
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={}):
    """
    根據用戶選擇創建 Plotly 圖表 (已加入 Null/空白 過濾)。 V5.2 版
    """
    # --- 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"  - 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(f"  - show_grid_str: {show_grid_str}", file=sys.stderr)
    print(f"  - show_legend_str: {show_legend_str}", 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 ---
        show_grid = True if show_grid_str == "是" else False
        show_legend = True if show_legend_str == "是" else False

        # --- 1. 輸入驗證 (更嚴格) ---
        if df is None or not isinstance(df, pd.DataFrame) or df.empty:
            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 == 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。可用列: {', '.join(df.columns)}")

        # 判斷是否需要 Y 軸 (修正 V5.1 錯誤: y_column 可能來自 Radio 且值為 NONE_STR)
        y_column_selected = None if y_column == NONE_STR or y_column == NO_DATA_STR or not y_column else y_column
        y_needed = agg_func_name != "計數" and chart_type not in ["直方圖"]

        if y_needed:
            if not y_column_selected: 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 (不能選 '無')。")
            if y_column_selected not in df.columns: raise ValueError(f"Y 軸列 '{y_column_selected}' 不在數據中。可用列: {', '.join(df.columns)}")
        # else:
            # y_column_selected 保持為 None

        # 處理可選列 (從 Radio 傳來的值可能是 NONE_STR)
        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。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。")

        df_processed = df.copy()
        print(f"原始數據行數: {len(df_processed)}", file=sys.stderr)

        # --- NEW: 過濾 Null/空白值 ---
        columns_to_filter = [x_column]
        if y_needed and y_column_selected: # Filter Y only if it's needed and selected
            columns_to_filter.append(y_column_selected)
        if group_col:
            columns_to_filter.append(group_col)

        valid_columns_to_filter = [col for col in columns_to_filter if col in df_processed.columns]
        if valid_columns_to_filter:
             original_rows = len(df_processed)
             df_processed.dropna(subset=valid_columns_to_filter, inplace=True)
             print(f"移除 Null ({', '.join(valid_columns_to_filter)}) 後行數: {len(df_processed)} (減少 {original_rows - len(df_processed)} 行)", file=sys.stderr)
        else:
             print("警告: 沒有有效的列用於 Null 值過濾。", file=sys.stderr)

        if x_column in df_processed.columns:
             try:
                original_rows = len(df_processed)
                df_processed = df_processed[df_processed[x_column].astype(str).str.strip() != '']
                print(f"移除 X 軸 '{x_column}' 空白字符串後行數: {len(df_processed)} (減少 {original_rows - len(df_processed)} 行)", file=sys.stderr)
             except Exception as e:
                 print(f"警告: 過濾 X 軸 '{x_column}' 空白字符串時出錯: {e}", file=sys.stderr)

        if group_col and group_col in df_processed.columns:
             try:
                original_rows = len(df_processed)
                df_processed = df_processed[df_processed[group_col].astype(str).str.strip() != '']
                print(f"移除分組列 '{group_col}' 空白字符串後行數: {len(df_processed)} (減少 {original_rows - len(df_processed)} 行)", file=sys.stderr)
             except Exception as e:
                 print(f"警告: 過濾分組列 '{group_col}' 空白字符串時出錯: {e}", file=sys.stderr)

        if df_processed.empty:
            raise ValueError("過濾掉 Null 或空白值後，沒有剩餘數據可供繪圖。")
        # --- END NEW ---


        # --- 2. 數據類型轉換與準備 ---
        df_processed[x_column] = df_processed[x_column].astype(str)
        if group_col:
            df_processed[group_col] = df_processed[group_col].astype(str)

        # 僅在需要時轉換 Y 軸和大小列
        if y_column_selected:
            try: df_processed[y_column_selected] = pd.to_numeric(df_processed[y_column_selected], errors='coerce')
            except Exception as e: print(f"警告：轉換 Y 軸列 '{y_column_selected}' 為數值時出錯: {e}")
        if size_col:
            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) ---
        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_selected # 使用處理過的 Y 軸列名 (可能為 None)
        if needs_aggregation:
            grouping_cols = [x_column] + ([group_col] if group_col else [])

            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_selected: raise ValueError(f"聚合函數 '{agg_func_name}' 需要一個有效的 Y 軸數值列 (不能選 '無')。")
                # 確保 Y 軸是數值類型 (除非 first/last)
                if agg_func_pd not in ['first', 'last']:
                    if not pd.api.types.is_numeric_dtype(df_processed[y_column_selected]):
                        try: df_processed[y_column_selected] = pd.to_numeric(df_processed[y_column_selected], errors='raise')
                        except (ValueError, TypeError): raise ValueError(f"Y 軸列 '{y_column_selected}' 必須是數值類型才能執行聚合 '{agg_func_name}'。")
                    # 檢查轉換後是否有非 NaN 值
                    if df_processed[y_column_selected].isnull().all():
                         raise ValueError(f"Y 軸列 '{y_column_selected}' 在轉換為數值後全為無效值 (NaN)，無法執行聚合 '{agg_func_name}'。")

                try:
                    # 執行聚合
                    agg_df = df_processed.groupby(grouping_cols, observed=False, dropna=False)[y_column_selected].agg(agg_func_pd)
                    agg_df = agg_df.reset_index()
                    y_col_agg = y_column_selected # 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}")
        else:
            agg_df = df_processed
            y_col_agg = y_column_selected # 保持處理過的 Y 軸列名 (可能為 None)

        if agg_df is None or agg_df.empty:
             raise ValueError("數據聚合或處理後沒有產生有效結果。")

        # 確保繪圖所需的列存在於 agg_df 中
        required_cols_for_plot = [x_column]
        # 修正：只有在 y_col_agg 實際有值（不是 None）時才加入檢查
        if y_col_agg is not None: 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"處理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)

        # --- 5. 創建圖表 (核心邏輯) ---
        fig_params = {"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
        effective_y = y_col_agg # 使用聚合後或處理過的 Y 軸列名

        # --- (繪圖邏輯開始) ---
        # 修正：繪圖時使用 y_column_selected (處理過的 Y 軸) 而不是原始 y_column
        # 修正：甘特圖需要原始 df，而不是 df_processed 或 agg_df
        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)
        elif chart_type == "堆疊長條圖":
            if not effective_y: raise ValueError("堆疊長條圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.bar(agg_df, x=x_column, y=effective_y, color=group_col, barmode='stack', **fig_params)
        elif chart_type == "百分比堆疊長條圖":
             if not effective_y: raise ValueError("百分比堆疊長條圖需要 Y 軸數值或 '計數' 聚合。")
             fig = px.bar(agg_df, x=x_column, y=effective_y, color=group_col, barmode='relative', text_auto='.1%', **fig_params)
             fig.update_layout(yaxis_title="百分比 (%)")
        elif chart_type == "群組長條圖":
            if not effective_y: raise ValueError("群組長條圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.bar(agg_df, x=x_column, y=effective_y, color=group_col, barmode='group', **fig_params)
        elif chart_type == "水平長條圖":
            if not effective_y: raise ValueError("水平長條圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.bar(agg_df, y=x_column, x=effective_y, color=group_col, orientation='h', **fig_params)
        elif chart_type == "折線圖":
            if not effective_y: raise ValueError("折線圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.line(agg_df, x=x_column, y=effective_y, color=group_col, markers=True, **fig_params)
        elif chart_type == "多重折線圖":
            if not effective_y: raise ValueError("多重折線圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.line(agg_df, x=x_column, y=effective_y, color=group_col, markers=True, **fig_params)
        elif chart_type == "階梯折線圖":
            if not effective_y: raise ValueError("階梯折線圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.line(agg_df, x=x_column, y=effective_y, color=group_col, line_shape='hv', **fig_params)
        elif chart_type == "區域圖":
            if not effective_y: raise ValueError("區域圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.area(agg_df, x=x_column, y=effective_y, color=group_col, **fig_params)
        elif chart_type == "堆疊區域圖":
            if not effective_y: raise ValueError("堆疊區域圖需要 Y 軸數值或 '計數' 聚合。")
            fig = px.area(agg_df, x=x_column, y=effective_y, color=group_col, groupnorm=None, **fig_params)
        elif chart_type == "百分比堆疊區域圖":
             if not effective_y: raise ValueError("百分比堆疊區域圖需要 Y 軸數值或 '計數' 聚合。")
             fig = px.area(agg_df, x=x_column, y=effective_y, color=group_col, groupnorm='percent', **fig_params)
             fig.update_layout(yaxis_title="百分比 (%)")
        elif chart_type == "圓餅圖":
            if not effective_y: raise ValueError("圓餅圖需要 Y 軸數值或 '計數' 聚合。")
            if group_col: print("警告：圓餅圖不支持分組列，已忽略。")
            fig = px.pie(agg_df, names=x_column, values=effective_y, **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 == "環形圖":
            if not effective_y: raise ValueError("環形圖需要 Y 軸數值或 '計數' 聚合。")
            if group_col: print("警告：環形圖不支持分組列，已忽略。")
            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_selected: raise ValueError("散點圖需要選擇 Y 軸列。")
            fig = px.scatter(agg_df, x=x_column, y=y_column_selected, color=group_col, size=size_col, **fig_params)
        elif chart_type == "氣泡圖":
            if not y_column_selected: raise ValueError("氣泡圖需要選擇 Y 軸列。")
            if not size_col: raise ValueError("氣泡圖需要指定 '大小列'。")
            if not pd.api.types.is_numeric_dtype(agg_df[size_col]): raise ValueError(f"大小列 '{size_col}' 必須是數值類型。")
            fig = px.scatter(agg_df, x=x_column, y=y_column_selected, color=group_col, size=size_col, size_max=60, **fig_params)
        elif chart_type == "直方圖":
            if not pd.api.types.is_numeric_dtype(agg_df[x_column]): raise ValueError(f"直方圖的 X 軸列 '{x_column}' 必須是數值類型。")
            fig = px.histogram(agg_df, x=x_column, color=group_col, **fig_params); fig.update_layout(yaxis_title="計數")
        elif chart_type == "箱型圖":
            if not y_column_selected: raise ValueError("箱型圖需要選擇 Y 軸列。")
            if not pd.api.types.is_numeric_dtype(agg_df[y_column_selected]): raise ValueError(f"箱型圖的 Y 軸列 '{y_column_selected}' 必須是數值類型。")
            fig = px.box(agg_df, x=group_col, y=y_column_selected, color=group_col, **fig_params)
            if not group_col: fig = px.box(agg_df, y=y_column_selected, **fig_params)
        elif chart_type == "小提琴圖":
            if not y_column_selected: raise ValueError("小提琴圖需要選擇 Y 軸列。")
            if not pd.api.types.is_numeric_dtype(agg_df[y_column_selected]): raise ValueError(f"小提琴圖的 Y 軸列 '{y_column_selected}' 必須是數值類型。")
            fig = px.violin(agg_df, x=group_col, y=y_column_selected, color=group_col, box=True, points="all", **fig_params)
            if not group_col: fig = px.violin(agg_df, y=y_column_selected, box=True, points="all", **fig_params)
        elif chart_type == "熱力圖":
            if not effective_y: raise ValueError("熱力圖需要 Y 軸數值或 '計數' 聚合。")
            if not group_col: raise ValueError("熱力圖需要 X 軸、Y 軸 和一個 分組列。")
            try:
                if not pd.api.types.is_numeric_dtype(agg_df[effective_y]): raise ValueError(f"熱力圖的值列 '{effective_y}' 必須是數值類型。")
                pivot_df = pd.pivot_table(agg_df, values=effective_y, index=group_col, columns=x_column, aggfunc=agg_function_map(agg_func_name) if agg_func_name != "計數" else 'size')
                fig = px.imshow(pivot_df, color_continuous_scale=px.colors.sequential.Viridis, aspect="auto", text_auto=True, **fig_params);
                fig.update_layout(coloraxis_showscale=True)
            except Exception as pivot_e: raise ValueError(f"創建熱力圖的數據透視表時出錯: {pivot_e}")
        elif chart_type == "樹狀圖":
             if not effective_y: raise ValueError("樹狀圖需要 Y 軸數值或 '計數' 聚合。")
             path = [group_col, x_column] if group_col else [x_column]
             if not pd.api.types.is_numeric_dtype(agg_df[effective_y]): raise ValueError(f"樹狀圖的值列 '{effective_y}' 必須是數值類型。")
             fig = px.treemap(agg_df, path=path, values=effective_y, color=group_col if group_col else x_column, **fig_params)
        elif chart_type == "雷達圖":
            if not effective_y: raise ValueError("雷達圖需要 Y 軸數值或 '計數' 聚合。")
            fig = go.Figure()
            if not pd.api.types.is_numeric_dtype(agg_df[effective_y]): raise ValueError(f"雷達圖的徑向值列 '{effective_y}' 必須是數值類型。")
            if not group_col:
                 theta = agg_df[x_column].tolist(); r = agg_df[effective_y].tolist(); theta.append(theta[0]); r.append(r[0])
                 fig.add_trace(go.Scatterpolar(r=r, theta=theta, fill='toself', name=effective_y if effective_y != '__count__' else '計數', line_color=colors[0]))
            else:
                 categories = agg_df[group_col].unique()
                 for i, category in enumerate(categories):
                     subset = agg_df[agg_df[group_col] == category]; theta = subset[x_column].tolist(); r = subset[effective_y].tolist(); theta.append(theta[0]); r.append(r[0])
                     color = custom_colors_dict.get(str(category), colors[i % len(colors)])
                     fig.add_trace(go.Scatterpolar(r=r, theta=theta, fill='toself', name=str(category), line_color=color))
            fig.update_layout(polar=dict(radialaxis=dict(visible=True)), showlegend=show_legend, title=title, width=width, height=height)
        elif chart_type == "漏斗圖":
            if not effective_y: raise ValueError("漏斗圖需要 Y 軸數值或 '計數' 聚合。")
            if not pd.api.types.is_numeric_dtype(agg_df[effective_y]): raise ValueError(f"漏斗圖的值列 '{effective_y}' 必須是數值類型。")
            sorted_df = agg_df.sort_values(by=effective_y, ascending=False)
            fig = px.funnel(sorted_df, x=effective_y, y=x_column, color=group_col, **fig_params)
        elif chart_type == "極座標圖":
            if not effective_y: raise ValueError("極座標圖需要 Y 軸數值或 '計數' 聚合。")
            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":
             start_col_gantt = y_column_selected; 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
                 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。")
                 df_gantt['_start_'] = pd.to_datetime(df_gantt[start_col_gantt], errors='coerce')
                 df_gantt['_end_'] = pd.to_datetime(df_gantt[end_col_gantt], errors='coerce')
                 if df_gantt['_start_'].isnull().any(): raise ValueError(f"開始列 '{start_col_gantt}' 包含無效或無法解析的日期時間格式。")
                 if df_gantt['_end_'].isnull().any(): raise ValueError(f"結束列 '{end_col_gantt}' 包含無效或無法解析的日期時間格式。")
                 fig = px.timeline(df_gantt, x_start='_start_', x_end='_end_', y=task_col_gantt, color=size_col if size_col else None, title=title, color_discrete_sequence=colors, width=width, height=height)
                 fig.update_layout(xaxis_type="date")
             except Exception as gantt_e: raise ValueError(f"創建甘特圖時出錯: {gantt_e}")
        else:
            print(f"警告：未知的圖表類型 '{chart_type}'，使用長條圖代替。")
            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)
        # --- (繪圖邏輯結束) ---


        # --- 6. 應用圖案 (如果支持) ---
        if patterns:
            try:
                num_traces = len(fig.data)
                if chart_type in ["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", "圓餅圖", "環形圖"]:
                    for i, trace in enumerate(fig.data):
                        pattern_index = i % len(patterns)
                        if patterns[pattern_index] != "無": trace.marker.pattern.shape = patterns[pattern_index]; trace.marker.pattern.solidity = 0.4; trace.marker.pattern.fillmode = "replace"
                elif chart_type in ["散點圖", "氣泡圖"]:
                     symbol_map = {"/": "diamond", "\\": "square", "x": "x", "-": "line-ew", "|": "line-ns", "+": "cross", ".": "circle-dot"}
                     for i, trace in enumerate(fig.data):
                         pattern_index = i % len(patterns); symbol = symbol_map.get(patterns[pattern_index])
                         if symbol: trace.marker.symbol = symbol
                elif chart_type in ["折線圖", "多重折線圖", "階梯折線圖"]:
                     dash_map = {"/": "dash", "\\": "dot", "x": "dashdot", "-": "longdash", "|": "solid", "+": "solid", ".": "solid"}
                     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
                elif chart_type in ["區域圖", "堆疊區域圖", "百分比堆疊區域圖"]:
                     print("提示：區域圖的圖案填充支持有限，將嘗試應用線型。")
                     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'
            except Exception as pattern_e: print(f"應用圖案時出錯: {pattern_e}")

        # --- 7. 更新佈局 ---
        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),
            font=dict(family="Inter, sans-serif", size=12),
            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) # 使用 x_column 作為任務軸標籤
        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}", 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, 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)

    print("create_plot 函數執行完畢。", file=sys.stderr) # 調試信息
    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:
        if format_type == "CSV": filename = "exported_data.csv"; df.to_csv(filename, index=False, encoding='utf-8-sig')
        elif format_type == "Excel": filename = "exported_data.xlsx"; df.to_excel(filename, index=False)
        elif format_type == "JSON": filename = "exported_data.json"; df.to_json(filename, orient="records", indent=4, force_ascii=False)
        else: return None, f"❌ 不支持的導出格式: {format_type}"
        return filename, f"✅ 數據已成功準備為 {format_type} 格式，點擊下方鏈接下載。"
    except Exception as e: print(f"導出數據時出錯: {e}"); traceback.print_exc(); return None, f"❌ 導出數據時出錯: {e}"

def download_figure(fig, format_type="PNG"):
    if fig is None or not fig.data: return None, "❌ 沒有圖表可以導出。"
    try:
        format_lower = format_type.lower(); filename = f"chart_export.{format_lower}"
        import kaleido # 確保導入
        fig.write_image(filename, format=format_lower)
        return filename, f"✅ 圖表已成功準備為 {format_type} 格式，點擊下方鏈接下載。"
    except ImportError: error_msg = "❌ 導出圖表失敗：需要 Kaleido 套件。請在環境中安裝 `pip install -U kaleido`。"; print(error_msg); return None, error_msg
    except ValueError as ve:
        if "kaleido" in str(ve).lower(): error_msg = "❌ 導出圖表失敗：Kaleido 套件無法運行。請檢查其依賴項或嘗試重新安裝。"; print(f"{error_msg}\n{ve}"); traceback.print_exc(); return None, error_msg
        else: print(f"導出圖表時出錯 (ValueError): {ve}"); traceback.print_exc(); return None, f"❌ 導出圖表時出錯: {ve}"
    except Exception as e: print(f"導出圖表時發生未預期錯誤: {e}"); traceback.print_exc(); return None, f"❌ 導出圖表時發生未預期錯誤: {e}"

# =========================================
# == 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
    columns = df.columns.tolist(); num_cols = df.select_dtypes(include=np.number).columns.tolist(); cat_cols = df.select_dtypes(include=['object', 'category', 'string']).columns.tolist()
    date_cols = [col for col in columns if pd.api.types.is_datetime64_any_dtype(df[col]) or ('日期' in col or '時間' in col)]
    try:
        if date_cols and num_cols: recommendation.update({"chart_type": "折線圖", "x_column": date_cols[0], "y_column": num_cols[0], "agg_function": "平均值", "message": f"檢測到時間列 '{date_cols[0]}' 和數值列 '{num_cols[0]}'，推薦使用折線圖顯示趨勢。"})
        elif len(num_cols) >= 2: recommendation.update({"chart_type": "散點圖", "x_column": num_cols[0], "y_column": num_cols[1], "agg_function": None, "message": f"檢測到數值列 '{num_cols[0]}' 和 '{num_cols[1]}'，推薦使用散點圖分析相關性。"})
        elif cat_cols and num_cols: recommendation.update({"chart_type": "長條圖", "x_column": cat_cols[0], "y_column": num_cols[0], "agg_function": "平均值", "message": f"檢測到類別列 '{cat_cols[0]}' 和數值列 '{num_cols[0]}'，推薦使用長條圖比較各類別的數值。"})
        elif len(cat_cols) >= 2: recommendation.update({"chart_type": "堆疊長條圖", "x_column": cat_cols[0], "y_column": None, "group_column": cat_cols[1], "agg_function": "計數", "message": f"檢測到多個類別列 ('{cat_cols[0]}', '{cat_cols[1]}', ...)，推薦使用堆疊長條圖顯示計數分佈。"})
        elif cat_cols: recommendation.update({"chart_type": "長條圖", "x_column": cat_cols[0], "y_column": None, "agg_function": "計數", "message": f"檢測到類別列 '{cat_cols[0]}'，推薦使用長條圖顯示其頻數分佈。"})
        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()
    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"
    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
    return recommendation

# =========================================
# == CSS 樣式 (CSS Styling) ==
# =========================================
# (移除 Dropdown CSS)
CUSTOM_CSS = """
/* --- 全局和容器 --- */
.gradio-container { font-family: 'Inter', 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; background: #f8f9fa; }
/* --- 應用程式標頭 --- */
.app-header { text-align: center; margin-bottom: 25px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 25px 20px; border-radius: 12px; color: white; box-shadow: 0 6px 12px rgba(0, 0, 0, 0.15); }
.app-title { font-size: 2.2em; font-weight: 700; margin: 0; letter-spacing: 1px; text-shadow: 1px 1px 3px rgba(0,0,0,0.2); }
.app-subtitle { font-size: 1.1em; color: #e0e0e0; margin-top: 8px; font-weight: 300; }
/* --- 區塊標題 --- */
.section-title { font-size: 1.4em; font-weight: 600; color: #343a40; border-bottom: 3px solid #7367f0; padding-bottom: 8px; margin-top: 25px; margin-bottom: 20px; }
/* --- 卡片樣式 --- */
.card { background-color: white; border-radius: 10px; padding: 20px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.08); margin-bottom: 20px; transition: transform 0.25s ease-out, box-shadow 0.25s ease-out; border: 1px solid #e0e0e0; }
.card:hover { transform: translateY(-4px); box-shadow: 0 8px 16px rgba(0, 0, 0, 0.12); }
/* --- 按鈕樣式 --- */
.primary-button { background: linear-gradient(to right, #667eea, #764ba2) !important; border: none !important; color: white !important; font-weight: 600 !important; padding: 10px 20px !important; border-radius: 8px !important; cursor: pointer !important; transition: all 0.3s ease !important; box-shadow: 0 2px 4px rgba(0,0,0,0.1) !important; }
.primary-button:hover { background: linear-gradient(to right, #764ba2, #667eea) !important; transform: translateY(-2px) !important; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15) !important; }
.secondary-button { background: linear-gradient(to right, #89f7fe, #66a6ff) !important; border: none !important; color: #333 !important; font-weight: 600 !important; padding: 8px 16px !important; border-radius: 8px !important; cursor: pointer !important; transition: all 0.3s ease !important; box-shadow: 0 2px 4px rgba(0,0,0,0.1) !important; }
.secondary-button:hover { background: linear-gradient(to right, #66a6ff, #89f7fe) !important; transform: translateY(-2px) !important; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15) !important; }
/* --- 下拉選單修正 (Dropdown Fix) --- */
/* 移除自定義下拉選單樣式，使用 Gradio 預設 */
/* --- 其他 UI 元素 --- */
.tips-box { background-color: #e7f3ff; border-left: 5px solid #66a6ff; padding: 15px 20px; border-radius: 8px; margin: 20px 0; font-size: 0.95em; color: #333; }
.tips-box code { background-color: #d1e7fd; padding: 2px 5px; border-radius: 4px; font-family: 'Courier New', Courier, monospace; }
.chart-previewer { border: 2px dashed #ced4da; border-radius: 10px; padding: 15px; min-height: 400px; display: flex; justify-content: center; align-items: center; background-color: #ffffff; box-shadow: inset 0 0 10px rgba(0,0,0,0.05); margin-top: 10px; }
.gradio-dataframe table { border-collapse: collapse; width: 100%; font-size: 0.9em; }
.gradio-dataframe th, .gradio-dataframe td { border: 1px solid #dee2e6; padding: 8px 10px; text-align: left; }
.gradio-dataframe th { background-color: #f8f9fa; font-weight: 600; }
.gradio-dataframe tr:nth-child(even) { background-color: #f8f9fa; }
.color-customization-input textarea { font-family: 'Courier New', Courier, monospace; font-size: 0.9em; }
.gradio-tabs .tab-nav button { padding: 10px 20px !important; font-weight: 500 !important; border-radius: 8px 8px 0 0 !important; transition: background-color 0.2s ease, color 0.2s ease !important; }
.gradio-tabs .tab-nav button.selected { background-color: #667eea !important; color: white !important; border-bottom: none !important; }
.gradio-slider label { margin-bottom: 5px !important; }
.gradio-slider input[type="range"] { cursor: pointer !important; }
/* Radio Button 樣式調整 */
.gradio-radio fieldset { display: flex; flex-wrap: wrap; gap: 5px 15px; } /* 嘗試讓選項水平排列並換行 */
.gradio-radio label { margin-bottom: 0 !important; padding: 5px 0 !important; } /* 調整標籤間距 */
.gradio-radio input[type="radio"] { margin-right: 5px !important; }

.gradio-textbox textarea, .gradio-textbox input { border-radius: 6px !important; border: 1px solid #ced4da !important; padding: 10px !important; }
.gradio-textbox textarea:focus, .gradio-textbox input:focus { border-color: #80bdff !important; box-shadow: 0 0 0 0.2rem rgba(0,123,255,.25) !important; }
.gradio-file .hidden-upload, .gradio-file .download-button { border-radius: 6px !important; }
.gradio-file .upload-button { border-radius: 6px !important; background: #6c757d !important; color: white !important; padding: 8px 15px !important; }
.gradio-file .upload-button:hover { background: #5a6268 !important; }
/* Accordion 樣式微調 (如果重新啟用) */
/* .gradio-accordion > .label { font-weight: 600 !important; font-size: 1.1em !important; padding: 10px 0 !important; } */
"""

# =========================================
# == Gradio UI 介面定義 (Gradio UI Definition) ==
# =========================================
with gr.Blocks(css=CUSTOM_CSS, title="數據視覺化工具_Eddie", theme=gr.themes.Soft()) as demo:

    # --- 應用程式標頭 ---
    gr.HTML("""
    <div class="app-header">
        <h1 class="app-title">📊 數據視覺化工具_Eddie</h1>
        <p class="app-subtitle">上傳或貼上數據，創建專業圖表 (極簡化測試版 - 修正)</p>
    </div>
    """)

    # --- 狀態變量 ---
    data_state = gr.State(None)
    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:

        # --- 數據輸入頁籤 ---
        with gr.TabItem("📁 數據輸入與管理", id=0):
            with gr.Row():
                with gr.Column(scale=2): # 左側：數據輸入
                    gr.HTML('<div class="section-title">1. 上傳或輸入數據</div>')
                    with gr.Group(elem_classes=["card"]):
                        gr.Markdown("您可以上傳本地的 CSV 或 Excel 文件，或直接在下方的文本框中貼上數據。")
                        file_upload = gr.File(label="上傳 CSV / Excel 文件", type="filepath")
                        upload_button = gr.Button("⬆️ 載入文件數據", elem_classes=["primary-button"])
                        upload_status = gr.Textbox(label="載入狀態", lines=1, interactive=False)
                    with gr.Group(elem_classes=["card"]):
                        csv_input = gr.Textbox(label="或者，在此貼上數據 (逗號、Tab 或空格分隔)", placeholder="例如:\n類別,數值\nA,10\nB,20\nC,15...", lines=8, elem_classes=["data-input-textbox"])
                        parse_button = gr.Button("📝 解析貼上數據", elem_classes=["primary-button"])
                        parse_status = gr.Textbox(label="解析狀態", lines=1, interactive=False)
                with gr.Column(scale=3): # 右側：數據預覽與導出
                    gr.HTML('<div class="section-title">2. 數據預覽與導出</div>')
                    with gr.Group(elem_classes=["card"]):
                        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") # 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 (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。")

            with gr.Row(): # 主 Row
                # --- 設定欄 (左側 Column) ---
                with gr.Column(scale=1):
                    gr.Markdown("### 📊 圖表設置")
                    with gr.Group(elem_classes=["card"]):
                        gr.Markdown("**基本設置**")
                        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 = 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 = 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():
                        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)

                    # 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 = 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 (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</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>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>
                """)

    # =========================================
    # == 事件處理 (Event Handling) ==
    # =========================================

    # --- 數據載入與更新 ---
    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_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)]

        # 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 - 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_v5,
        inputs=[data_state, upload_status],
        outputs=[
            data_state, upload_status, data_preview,
            x_column, y_column, group_column, size_column, # 更新 Radio
            chart_output # 更新 Plot 為空
        ]
    )
    parse_button.click(
        parse_data,
        inputs=[csv_input],
        outputs=[data_state, parse_status]
    ).then(
        load_data_and_update_ui_v5,
        inputs=[data_state, parse_status],
        outputs=[
            data_state, parse_status, data_preview,
            x_column, y_column, group_column, size_column,
            chart_output
        ]
    )


    # --- 數據導出 ---
    export_button.click(export_data, inputs=[data_state, export_format], outputs=[export_result, export_status])

    # --- 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)
        # 檢查 y_column 是否為 "無"，如果是，傳遞 None 給 create_plot
        processed_inputs = list(inputs)
        if processed_inputs[3] == NONE_STR: # y_column 在列表索引 3 的位置
             processed_inputs[3] = None
        # 檢查 group_column 是否為 "無"
        if processed_inputs[4] == NONE_STR: # group_column 在列表索引 4 的位置
             processed_inputs[4] = None
        # 檢查 size_column 是否為 "無"
        if processed_inputs[5] == NONE_STR: # size_column 在列表索引 5 的位置
             processed_inputs[5] = None

        return create_plot(*processed_inputs) # 傳遞處理過的輸入

    update_button.click(update_chart_action, inputs=chart_inputs, outputs=[chart_output])

    # --- 導出圖表 ---
    download_button.click(download_figure, inputs=[chart_output, export_img_format], outputs=[export_chart, export_chart_status])

    # --- 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 軸推薦值：如果是計數，則推薦 '無'；否則推薦 Y 列名，如果 Y 列名為 None 也設為 '無'
        y_col_val = NONE_STR if agg_func_val == "計數" else rec_dict.get("y_column", NONE_STR)
        group_col_val = rec_dict.get("group_column", NONE_STR) # 默認為 "無"
        # 返回 Radio 的更新對象
        return [
            gr.Radio(value=chart_type_val),      # 更新 Chart Type 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.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 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"
            y_label, y_needed = "Y軸 / 數值", True
            if is_histogram: y_label, y_needed = "Y軸 (自動計數)", False
            elif is_pie_like: y_label = "數值列 (用於大小/值)"
            elif is_box_violin: y_label = "數值列"
            elif is_gantt: y_label = "開始時間列"
            elif is_radar: y_label = "徑向值 (R)"
            group_label, group_needed = "分組列", chart_type in ["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", "散點圖", "氣泡圖", "箱型圖", "小提琴圖", "熱力圖", "雷達圖", "極座標圖"]
            if is_gantt: group_label, group_needed = "結束時間列", True
            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 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.change(update_element_visibility_v5, inputs=[chart_type], outputs=[y_column, group_column, size_column])

# =========================================
# == 應用程式啟動 (Launch Application) ==
# =========================================
if __name__ == "__main__":
    demo.launch(debug=True)