Create app.py

app.py

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+import pandas as pd
+import requests
+import matplotlib.font_manager as fm
+import matplotlib as mpl
+import streamlit as st
+
+# Download and save the first CSV file
+url1 = "https://mopsfin.twse.com.tw/opendata/t187ap4"
+response1 = requests.get(url1)
+with open("20240627.csv", "wb") as file1:
+    file1.write(response1.content)
+df1 = pd.read_csv("20240627.csv")
+
+# Download and save the second CSV file
+url2 = "https://mopsfin.twse.com.tw/opendata/t187ap46_O_2.csv"
+response2 = requests.get(url2)
+with open("20240628.csv", "wb") as file2:
+    file2.write(response2.content)
+df2 = pd.read_csv("20240628.csv")
+
+# Download and save the third CSV file
+url3 = "https://mopsfin.twse.com.tw/opendata/t187ap46_L_6.csv"
+response3 = requests.get(url3)
+with open("20240629.csv", "wb") as file3:
+    file3.write(response3.content)
+df3 = pd.read_csv("20240629.csv")
+
+# Fill NaN values with 0
+df1.fillna(0, inplace=True)
+df2.fillna(0, inplace=True)
+df3.fillna(0, inplace=True)
+
+# Download and set custom font
+font_url = "https://drive.google.com/uc?id=1eGAsTN1HBpJAkeVM57_C7ccp7hbgSz3_&export=download"
+font_response = requests.get(font_url)
+with open("TaipeiSansTCBeta-Regular.ttf", "wb") as font_file:
+    font_file.write(font_response.content)
+
+fm.fontManager.addfont("TaipeiSansTCBeta-Regular.ttf")
+mpl.rc('font', family='Taipei Sans TC Beta')
+
+# Streamlit app setup
+st.title("Taiwan Stock Exchange Financial Data Analysis")
+
+# Dropdown to select which DataFrame to display
+df_selection = st.selectbox("Select DataFrame to View", ("20240627", "20240628", "20240629"))
+
+if df_selection == "20240627":
+    df = df1
+elif df_selection == "20240628":
+    df = df2
+else:
+    df = df3
+
+st.write(f"Showing data for {df_selection}")
+st.write(df)
+
+# Dynamic plot options
+chart_type = st.selectbox("Select Chart Type", ("Line Chart", "Bar Chart"))
+
+if chart_type == "Line Chart":
+    st.line_chart(df)
+elif chart_type == "Bar Chart":
+    st.bar_chart(df)
+
+# Optionally, more advanced plots using matplotlib
+import matplotlib.pyplot as plt
+
+st.write("Custom Plot")
+
+fig, ax = plt.subplots()
+ax.plot(df.index, df.iloc[:, 0], label="Column 1")
+ax.plot(df.index, df.iloc[:, 1], label="Column 2")
+ax.set_xlabel("Index")
+ax.set_ylabel("Values")
+ax.legend()
+st.pyplot(fig)
+
+# Save the plot if needed
+save_plot = st.button("Save Plot as Image")
+if save_plot:
+    fig.savefig("custom_plot.png")
+    st.write("Plot saved as custom_plot.png")