Update app.py

app.py

@@ -1,109 +1,60 @@
 import streamlit as st
-import pandas as pd
 import requests
+import pandas as pd
 import plotly.express as px
-import matplotlib.pyplot as plt
-import io
-
-# 設置顏色主題
-theme = px.colors.qualitative.Bold
-
-# 下載並載入 CSV 數據
-def download_and_load_csv(url):
-    response = requests.get(url)
-    response.encoding = 'utf-8'
-    df = pd.read_csv(io.StringIO(response.text), encoding='utf-8')
-    df = df.fillna(0)  # 避免 NaN 錯誤
-    return df
-
-# 美化圖表
-def beautify_chart(fig):
-    fig.update_layout(
-        font_family="Arial",
-        font_color="#444",
-        title_font_family="Arial",
-        title_font_color="#000",
-        legend_title_font_color="#000",
-        plot_bgcolor='rgba(0,0,0,0)',
-        paper_bgcolor='rgba(0,0,0,0)',
-    )
-    fig.update_xaxes(showline=True, linewidth=2, linecolor='lightgray', gridcolor='lightgray')
-    fig.update_yaxes(showline=True, linewidth=2, linecolor='lightgray', gridcolor='lightgray')
-    return fig
-
-# **生成 Matplotlib 圖表**
-def generate_matplotlib_plot(df, selected_columns):
-    fig, ax = plt.subplots()
-    for col in selected_columns:
-        ax.plot(df["公司名稱"], df[col], label=col)
-    ax.set_xlabel("公司名稱")
-    ax.set_ylabel("排放量")
-    ax.set_title("Matplotlib 折線圖")
-    ax.legend()
-    st.pyplot(fig)
-
-# **生成 Plotly 圖表**
-def generate_plots(df, df_name, selected_columns, chart_type):
-    selected_columns = list(selected_columns)  # 確保是列表
-
-    # 🛠️ 確保選擇的欄位存在
-    existing_columns = set(df.columns)
-    valid_selected_columns = [col for col in selected_columns if col in existing_columns]
-
-    if not valid_selected_columns:
-        st.error(f"❌ 選擇的欄位無法在數據中找到，請檢查欄位名稱！\n\n📋 可用欄位: {list(existing_columns)}")
-        return
-
-    df = df.dropna(subset=["公司名稱"] + valid_selected_columns)  # **移除 NaN 確保長度相同**
-    df[valid_selected_columns] = df[valid_selected_columns].apply(pd.to_numeric, errors='coerce')
+import matplotlib as mpl
+import matplotlib.font_manager as fm
+import os
+from io import StringIO
+
+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')
+
+install_custom_font()
+
+st.title("📊 台股財報數據可視化")
+
+# 直接讀取固定 URL 的 CSV 檔案
+url = "https://mopsfin.twse.com.tw/opendata/t187ap46_L_1.csv"
+response = requests.get(url)
+response.encoding = "utf-8"
+
+df = pd.read_csv(StringIO(response.text))
+df_cleaned = df.fillna(0)  # 填補缺失值
+
+st.subheader("📌 數據預覽")
+st.write(df_cleaned.head())
+
+# 讓用戶篩選數據
+st.subheader("🔍 數據篩選")
+filter_col = st.selectbox("選擇要篩選的欄位", df_cleaned.columns)
+unique_values = df_cleaned[filter_col].unique()
+selected_value = st.selectbox("選擇篩選值", unique_values)
+filtered_df = df_cleaned[df_cleaned[filter_col] == selected_value]
+
+st.write(filtered_df.head())
+
+# 讓用戶選擇數值欄位
+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)
     
-    with st.expander(f"📊 顯示 {df_name} 圖表"):
-        st.subheader(f"{df_name} - {chart_type} 圖")
-        fig = None  # 預設圖表為 None
-
-        if chart_type == "折線圖":
-            fig = px.line(df, x="公司名稱", y=valid_selected_columns, title=f"{df_name} - 折線圖", color_discrete_sequence=theme)
-        elif chart_type == "長條圖":
-            fig = px.bar(df, x="公司名稱", y=valid_selected_columns, title=f"{df_name} - 長條圖", color_discrete_sequence=theme)
-        elif chart_type == "餅圖":
-            total_emissions = df.groupby("公司名稱")[valid_selected_columns].sum().reset_index()
-            total_emissions = total_emissions.melt(id_vars=["公司名稱"], value_vars=valid_selected_columns, var_name="排放類型", value_name="總排放量")
-            fig = px.pie(total_emissions, values='總排放量', names='公司名稱', title=f"{df_name} - 餅圖", color_discrete_sequence=theme, hole=0.3)
-            fig.update_traces(textposition='inside', textinfo='percent+label')
-        
-        if fig:
-            fig = beautify_chart(fig)
-            st.plotly_chart(fig, use_container_width=True)
+    if st.button("生成圖表"):
+        # 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")
+        st.plotly_chart(fig_bar)
         
-        # 額外顯示 Matplotlib 圖表
-        st.subheader("📉 Matplotlib 折線圖")
-        generate_matplotlib_plot(df, valid_selected_columns)
-
-# **下載數據**
-urls = [
-    "https://mopsfin.twse.com.tw/opendata/t187ap46_L_1.csv",
-    "https://mopsfin.twse.com.tw/opendata/t187ap46_O_2.csv",
-    "https://mopsfin.twse.com.tw/opendata/t187ap46_L_6.csv"
-]
-
-dfs = [download_and_load_csv(url) for url in urls]
-combined_df = pd.concat(dfs, ignore_index=True).fillna(0)  # 合併數據並填充 NaN 值
-
-# **Streamlit UI**
-st.title("📊 台灣企業 ESG 數據分析")
-
-st.subheader("📂 數據預覽")
-st.dataframe(combined_df)
-
-# **選擇數據欄位**
-emission_columns = [col for col in ["範疇一排放量(噸CO2e)", "範疇二排放量(噸CO2e)", "範疇三排放量(噸CO2e)"] if col in combined_df.columns]
-selected_columns = st.multiselect("選擇要顯示的排放類別", emission_columns, default=emission_columns[:1] if emission_columns else [])
-
-# **選擇圖表類型**
-chart_type = st.selectbox("選擇圖表類型", ["折線圖", "長條圖", "餅圖"])
-
-# **生成圖表**
-if selected_columns:
-    generate_plots(combined_df, "綜合數據", selected_columns, chart_type)
+        # 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)
 else:
-    st.write("⚠️ 請選擇至少一個排放類別來顯示圖表。")
+    st.warning("⚠ 沒有數值型欄位可供選擇")