app.py

import os
import requests
import pandas as pd
import plotly.express as px
import streamlit as st
import matplotlib as mpl
import matplotlib.font_manager as fm
from io import StringIO

# 設定 Streamlit 頁面
st.set_page_config(page_title="ESG & 董事席次數據分析", layout="wide")

# 安裝自訂字型
def install_custom_font():
    font_path = "TaipeiSansTCBeta-Regular.ttf"
    if not os.path.exists(font_path):
        font_url = "https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_&export=download"
        font_response = requests.get(font_url)
        with open(font_path, "wb") as font_file:
            font_file.write(font_response.content)
    fm.fontManager.addfont(font_path)
    mpl.rc('font', family='Taipei Sans TC Beta')

# 執行字型安裝
install_custom_font()

# 下載 ESG 與 董事席次 CSV 資料
esg_url = "https://mopsfin.twse.com.tw/opendata/t187ap46_L_1.csv"
board_url = "https://mopsfin.twse.com.tw/opendata/t187ap46_L_6.csv"

@st.cache_data
def load_data(url):
    response = requests.get(url)
    if response.status_code == 200:
        data = response.content.decode("utf-8-sig")
        df = pd.read_csv(StringIO(data))
        df.fillna(0, inplace=True)  # 填補缺失值
        return df
    else:
        st.error("無法獲取資料，錯誤代碼: " + str(response.status_code))
        return None

# 加載數據
df_esg = load_data(esg_url)
df_board = load_data(board_url)

# Streamlit 標題
st.title("📊 ESG & 董事席次數據分析儀表板")

# 選擇數據類型
data_option = st.sidebar.radio("📂 選擇數據類型", ["ESG 數據", "董事席次數據"])

if data_option == "ESG 數據" and df_esg is not None:
    st.subheader("📌 ESG 數據總覽")
    st.write(df_esg.describe())
    
    st.subheader("📌 ESG 數據預覽")
    st.write(df_esg.head(10))

elif data_option == "董事席次數據" and df_board is not None:
    st.subheader("📌 董事席次數據總覽")
    st.write(df_board.describe())
    
    st.subheader("📌 董事席次數據預覽")
    st.write(df_board.head(10))

# 添加數據篩選
df = df_esg if data_option == "ESG 數據" else df_board
filter_col = st.sidebar.selectbox("選擇要篩選的欄位", df.columns)
unique_values = df[filter_col].unique()
selected_value = st.sidebar.selectbox("選擇篩選值", unique_values)
filtered_df = df[df[filter_col] == selected_value]
st.write(filtered_df.head())

# 確保數據包含數值型欄位
if 'filtered_df' in locals() and filtered_df is not None:
    numeric_cols = filtered_df.select_dtypes(include=['number']).columns
    if len(numeric_cols) > 0:
        col_choice = st.selectbox("選擇要視覺化的數值欄位", numeric_cols)

        if st.button("生成圖表"):
            # 繪製長條圖
            st.subheader("📊 長條圖")
            fig_bar = px.bar(filtered_df.head(10), x=filtered_df.index[:10], y=col_choice, 
                             title=f"{col_choice} 長條圖", color_discrete_sequence=["#1f77b4"])
            st.plotly_chart(fig_bar, use_container_width=True)

            # 繪製圓餅圖
            st.subheader("🥧 圓餅圖")
            fig_pie = px.pie(filtered_df.head(10), names=filtered_df.index[:10], values=col_choice, 
                             title=f"{col_choice} 圓餅圖")
            st.plotly_chart(fig_pie, use_container_width=True)