Update app.py

app.py

@@ -2,6 +2,7 @@ import pandas as pd
 import requests
 import plotly.express as px
 import streamlit as st
+import time
 import matplotlib.font_manager as fm
 import matplotlib as mpl
 
@@ -25,45 +26,45 @@ def download_csv(url, filename):
     return df
 
 # URLs to download data from
-urls = [
-    "https://mopsfin.twse.com.tw/opendata/t187ap46_L_1.csv",  # Original file - ESG
-    "https://mopsfin.twse.com.tw/opendata/t187ap46_O_2.csv",  # Renewable Energy
-    "https://mopsfin.twse.com.tw/opendata/t187ap46_L_6.csv"   # Board of Directors
-]
+urls = {
+    "溫室氣體": "https://mopsfin.twse.com.tw/opendata/t187ap46_L_1.csv",  # ESG
+    "再生能源": "https://mopsfin.twse.com.tw/opendata/t187ap46_O_2.csv",  # Renewable Energy
+    "董事會": "https://mopsfin.twse.com.tw/opendata/t187ap46_L_6.csv"   # Board of Directors
+}
 
 # Filenames to save CSV data
-filenames = ["data01.csv", "data02.csv", "data03.csv"]
-
-# Download and process each CSV
-dfs = [download_csv(url, filename) for url, filename in zip(urls, filenames)]
+filenames = {
+    "溫室氣體": "data01.csv",
+    "再生能源": "data02.csv",
+    "董事會": "data03.csv"
+}
 
 # Streamlit app title
 st.title("ESG Data Visualization with Streamlit")
 
-# Pie Chart for the first DataFrame
-st.header("Pie Chart for ESG Data")
-fig_pie = px.pie(
-    dfs[0], 
-    names=dfs[0].columns[0], 
-    values=dfs[0].columns[1], 
-    title="Pie Chart for ESG Data"
-)
-fig_pie.update_layout(font_family="Taipei Sans TC Beta")
-st.plotly_chart(fig_pie)
-
-# Bar Chart for the second DataFrame
-st.header("Bar Chart for Renewable Energy Data")
-fig_bar = px.bar(
-    dfs[1], 
-    x=dfs[1].columns[0], 
-    y=dfs[1].columns[1], 
-    title="Bar Chart for Renewable Energy Data"
-)
-fig_bar.update_layout(font_family="Taipei Sans TC Beta")
-st.plotly_chart(fig_bar)
+# Dropdown menu for data selection
+selected_option = st.selectbox("選擇資料類型", options=list(urls.keys()))
 
-# Optionally, show the raw DataFrames
-st.header("Raw DataFrames")
-for i, df in enumerate(dfs):
-    st.subheader(f"DataFrame {i + 1}")
+# Button to load data
+if st.button("載入資料"):
+    with st.spinner('資料載入中...'):
+        # Simulate a delay for the process bar
+        for _ in range(5):
+            time.sleep(0.2)
+        df = download_csv(urls[selected_option], filenames[selected_option])
+    
+    # Displaying the DataFrame
+    st.header(f"{selected_option} 資料")
     st.dataframe(df)
+
+    # Plotting the data
+    if selected_option == "溫室氣體":
+        fig = px.pie(df, names=df.columns[0], values=df.columns[1], title=f"Pie Chart for {selected_option}")
+    elif selected_option == "再生能源":
+        fig = px.bar(df, x=df.columns[0], y=df.columns[1], title=f"Bar Chart for {selected_option}")
+    elif selected_option == "董事會":
+        fig = px.bar(df, x=df.columns[0], y=df.columns[1], title=f"Bar Chart for {selected_option}")
+
+    # Apply custom font to the plot
+    fig.update_layout(font_family="Taipei Sans TC Beta")
+    st.plotly_chart(fig)