app.py

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

# 初始化默認數據
default_data = pd.DataFrame({
    "類別": ["A", "B", "C", "D", "E"],
    "數值": [10, 20, 15, 25, 30]
})

# 可用的顏色方案
COLOR_SCHEMES = {
    "默認": px.colors.qualitative.Plotly,
    "藍綠色系": px.colors.sequential.Blues,
    "紅色系": px.colors.sequential.Reds,
    "綠色系": px.colors.sequential.Greens,
    "彩虹色": px.colors.sequential.Turbo
}

def create_plot(df, chart_type, x_column, y_column, color_scheme, title, width, height, show_grid, show_legend):
    """創建圖表函數"""
    
    # 數據預處理
    if df is None or df.empty:
        df = default_data
    
    # 確保列存在
    if x_column not in df.columns:
        x_column = df.columns[0] if len(df.columns) > 0 else "類別"
    
    if y_column not in df.columns:
        y_column = df.columns[1] if len(df.columns) > 1 else "數值"
    
    # 獲取選擇的顏色方案
    colors = COLOR_SCHEMES[color_scheme]
    
    # 設置圖表參數
    fig_params = {
        "width": width,
        "height": height,
        "title": title
    }
    
    # 基於選擇的圖表類型創建圖表
    if chart_type == "長條圖":
        fig = px.bar(df, x=x_column, y=y_column, color_discrete_sequence=colors, **fig_params)
    
    elif chart_type == "折線圖":
        fig = px.line(df, x=x_column, y=y_column, markers=True, color_discrete_sequence=colors, **fig_params)
    
    elif chart_type == "圓餅圖":
        fig = px.pie(df, names=x_column, values=y_column, color_discrete_sequence=colors, **fig_params)
    
    elif chart_type == "散點圖":
        fig = px.scatter(df, x=x_column, y=y_column, color_discrete_sequence=colors, **fig_params)
    
    elif chart_type == "區域圖":
        fig = px.area(df, x=x_column, y=y_column, color_discrete_sequence=colors, **fig_params)
    
    else:  # 默認使用長條圖
        fig = px.bar(df, x=x_column, y=y_column, color_discrete_sequence=colors, **fig_params)
    
    # 設置網格
    fig.update_layout(
        showlegend=show_legend,
        xaxis=dict(showgrid=show_grid),
        yaxis=dict(showgrid=show_grid)
    )
    
    return fig

def process_upload(file):
    """處理上傳的文件"""
    try:
        if file is None:
            return None, "未上傳文件"
        
        # 檢查文件類型
        file_type = file.name.split('.')[-1].lower()
        
        if file_type == 'csv':
            df = pd.read_csv(file.name)
        elif file_type in ['xls', 'xlsx']:
            df = pd.read_excel(file.name)
        else:
            return None, f"不支持的文件類型: {file_type}。請上傳CSV或Excel文件。"
        
        return df, f"成功載入數據，共{len(df)}行，{len(df.columns)}列"
    
    except Exception as e:
        return None, f"載入文件時出錯: {str(e)}"

def parse_data(csv_data):
    """解析CSV文本數據"""
    try:
        if not csv_data or csv_data.strip() == "":
            return None, "未提供數據"
        
        # 使用StringIO讀取CSV文本
        df = pd.read_csv(io.StringIO(csv_data))
        return df, f"成功解析數據，共{len(df)}行，{len(df.columns)}列"
    
    except Exception as e:
        return None, f"解析數據時出錯: {str(e)}"

def export_data(df, format_type):
    """導出數據為各種格式"""
    if df is None or df.empty:
        return None, "沒有數據可以導出"
    
    try:
        if format_type == "CSV":
            buffer = io.StringIO()
            df.to_csv(buffer, index=False)
            data = buffer.getvalue()
            filename = "exported_data.csv"
            mime_type = "text/csv"
        
        elif format_type == "Excel":
            buffer = io.BytesIO()
            df.to_excel(buffer, index=False)
            data = buffer.getvalue()
            filename = "exported_data.xlsx"
            mime_type = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
        
        elif format_type == "JSON":
            buffer = io.StringIO()
            data = df.to_json(orient="records")
            filename = "exported_data.json"
            mime_type = "application/json"
        
        else:
            return None, f"不支持的導出格式: {format_type}"
        
        return (data, filename, mime_type), f"數據已成功導出為{format_type}格式"
    
    except Exception as e:
        return None, f"導出數據時出錯: {str(e)}"

def update_columns(df):
    """更新列選擇下拉菜單"""
    if df is None or df.empty:
        # 默認列
        return gr.Dropdown(choices=["類別", "數值"], value="類別"), gr.Dropdown(choices=["類別", "數值"], value="數值")
    
    columns = df.columns.tolist()
    x_dropdown = gr.Dropdown(choices=columns, value=columns[0] if columns else None)
    y_dropdown = gr.Dropdown(choices=columns, value=columns[1] if len(columns) > 1 else columns[0])
    
    return x_dropdown, y_dropdown

def download_figure(fig):
    """導出圖表為圖像"""
    if fig is None:
        return None, "沒有圖表可以導出"
    
    try:
        # 將Plotly圖表轉換為PNG圖像
        img_bytes = fig.to_image(format="png")
        
        # 創建PIL圖像對象
        img = Image.open(io.BytesIO(img_bytes))
        
        return img, "圖表已成功導出為PNG圖像"
    
    except Exception as e:
        return None, f"導出圖表時出錯: {str(e)}"

# 建立Gradio界面
with gr.Blocks(title="數據可視化工具") as demo:
    gr.Markdown("# 數據可視化工具")
    gr.Markdown("上傳CSV或Excel文件，或直接在下方輸入數據來創建各種圖表")
    
    # 狀態變量
    data_state = gr.State(None)
    
    with gr.Tabs():
        # 數據輸入頁籤
        with gr.TabItem("數據輸入"):
            with gr.Row():
                with gr.Column():
                    file_upload = gr.File(label="上傳CSV或Excel文件")
                    upload_button = gr.Button("載入文件")
                    upload_status = gr.Textbox(label="上傳狀態")
                
                with gr.Column():
                    csv_input = gr.Textbox(label="或直接輸入CSV數據（逗號分隔）", placeholder="類別,數值\nA,10\nB,20\nC,15\nD,25\nE,30", lines=10)
                    parse_button = gr.Button("解析數據")
                    parse_status = gr.Textbox(label="解析狀態")
            
            with gr.Row():
                data_preview = gr.Dataframe(label="數據預覽")
                
                with gr.Column():
                    export_format = gr.Dropdown(["CSV", "Excel", "JSON"], label="導出格式", value="CSV")
                    export_button = gr.Button("導出數據")
                    export_result = gr.File(label="導出結果")
                    export_status = gr.Textbox(label="導出狀態")
        
        # 圖表創建頁籤
        with gr.TabItem("圖表創建"):
            with gr.Row():
                with gr.Column():
                    chart_type = gr.Dropdown(
                        ["長條圖", "折線圖", "圓餅圖", "散點圖", "區域圖"], 
                        label="圖表類型", 
                        value="長條圖"
                    )
                    
                    x_column = gr.Dropdown(["類別"], label="X軸（或類別）")
                    y_column = gr.Dropdown(["數值"], label="Y軸（或數值）")
                    
                    chart_title = gr.Textbox(label="圖表標題", placeholder="我的數據圖表")
                    
                    color_scheme = gr.Dropdown(
                        list(COLOR_SCHEMES.keys()),
                        label="顏色方案",
                        value="默認"
                    )
                
                with gr.Column():
                    chart_width = gr.Slider(300, 1200, 700, label="圖表寬度")
                    chart_height = gr.Slider(300, 800, 500, label="圖表高度")
                    
                    show_grid = gr.Checkbox(label="顯示網格", value=True)
                    show_legend = gr.Checkbox(label="顯示圖例", value=True)
                    
                    update_button = gr.Button("更新圖表")
                    download_button = gr.Button("導出為PNG圖像")
            
            with gr.Row():
                chart_output = gr.Plot(label="圖表預覽")
                download_output = gr.Image(label="導出的圖表", visible=False)
    
    # 事件處理
    upload_button.click(
        process_upload, 
        inputs=[file_upload], 
        outputs=[data_state, upload_status]
    ).then(
        lambda df: df if df is not None else pd.DataFrame(),
        inputs=[data_state],
        outputs=[data_preview]
    ).then(
        update_columns,
        inputs=[data_state],
        outputs=[x_column, y_column]
    )
    
    parse_button.click(
        parse_data,
        inputs=[csv_input],
        outputs=[data_state, parse_status]
    ).then(
        lambda df: df if df is not None else pd.DataFrame(),
        inputs=[data_state],
        outputs=[data_preview]
    ).then(
        update_columns,
        inputs=[data_state],
        outputs=[x_column, y_column]
    )
    
    export_button.click(
        export_data,
        inputs=[data_state, export_format],
        outputs=[export_result, export_status]
    )
    
    update_button.click(
        create_plot,
        inputs=[data_state, chart_type, x_column, y_column, color_scheme, 
                chart_title, chart_width, chart_height, show_grid, show_legend],
        outputs=[chart_output]
    )
    
    download_button.click(
        download_figure,
        inputs=[chart_output],
        outputs=[download_output, gr.Textbox(label="下載狀態")]
    ).then(
        lambda: gr.update(visible=True),
        outputs=[download_output]
    )
    
    # 自動顯示圖表預覽
    chart_type.change(
        create_plot,
        inputs=[data_state, chart_type, x_column, y_column, color_scheme, 
                chart_title, chart_width, chart_height, show_grid, show_legend],
        outputs=[chart_output]
    )
    
    x_column.change(
        create_plot,
        inputs=[data_state, chart_type, x_column, y_column, color_scheme, 
                chart_title, chart_width, chart_height, show_grid, show_legend],
        outputs=[chart_output]
    )
    
    y_column.change(
        create_plot,
        inputs=[data_state, chart_type, x_column, y_column, color_scheme, 
                chart_title, chart_width, chart_height, show_grid, show_legend],
        outputs=[chart_output]
    )

# 啟動應用
demo.launch()