Update app.py

app.py

@@ -1,28 +1,38 @@
 import streamlit as st
 import pandas as pd
 
-# Sample DataFrame for demonstration
-data = {
-    'product_code': [1, 2, 3, 4, 5],
-    'label': ['Label1', 'Label2', 'Label3', 'Label4', 'Label1'],
-    'amount': [250, 450, 300, 200, 500]
-}
-df = pd.DataFrame(data)
-
 # Streamlit App
-st.title('DataFrame Column Selector')
+st.title('CSV Column Selector')
+
+# File uploader for CSV files
+uploaded_file = st.file_uploader("Upload CSV file", type=["csv"])
 
-# Display the original DataFrame
-st.subheader('Original DataFrame')
-st.write(df)
+if uploaded_file is not None:
+    # Read the CSV file into a DataFrame
+    df = pd.read_csv(uploaded_file)
+    
+    # Display the original DataFrame
+    st.subheader('Original DataFrame')
+    st.write(df)
 
-# Column selection for inclusion
-st.subheader('Select Columns to Include')
-include_columns = st.multiselect('Select columns to include', options=df.columns, default=df.columns.tolist())
+    # Column selection for inclusion
+    st.subheader('Select Columns to Include')
+    include_columns = st.multiselect('Select columns to include', options=df.columns, default=df.columns.tolist())
 
-# Filter DataFrame to include only selected columns
-filtered_df = df[include_columns]
+    # Filter DataFrame to include only selected columns
+    if include_columns:
+        filtered_df = df[include_columns]
+    else:
+        filtered_df = df
 
-# Display the filtered DataFrame
-st.subheader('Filtered DataFrame')
-st.write(filtered_df)
+    # Display the filtered DataFrame
+    st.subheader('Filtered DataFrame')
+    st.write(filtered_df)
+    
+    # Option to download the filtered DataFrame
+    st.download_button(
+        label="Download Filtered DataFrame",
+        data=filtered_df.to_csv(index=False),
+        file_name='filtered_data.csv',
+        mime='text/csv'
+    )