Update app.py

app.py

@@ -4,6 +4,7 @@ from time import strftime, gmtime
 import streamlit as st
 from datetime import datetime, timedelta
 import pandas as pd
+import csv
 from io import StringIO
 
 # Function to fetch data from ShareASale
@@ -37,12 +38,6 @@ def parse_csv_to_df(csv_data):
     csv_stream = StringIO(csv_data)
     return pd.read_csv(csv_stream, delimiter='|')
 
-# Create a DataFrame for merchantid to organisation name mapping
-merchant_mapping = pd.DataFrame({
-    'merchantid': [123, 456, 789],  # Replace with your actual merchant IDs
-    'organisation name': ['Company A', 'Company B', 'Company C']  # Replace with your actual organisation names
-})
-
 # Streamlit UI
 st.title("Affiliate Earnings Dashboard")
 st.sidebar.title("Settings")
@@ -65,29 +60,26 @@ if st.sidebar.button("Fetch Data"):
     # Parse the CSV data into a DataFrame
     df = parse_csv_to_df(ledger_data)
     
-    # Merge with merchant mapping
-    df = pd.merge(df, merchant_mapping, on='merchantid', how='left')
-    
     # Filter rows where action is "Transaction Created"
     df_filtered = df.loc[df['action'] == 'Transaction Created']
     
-    # Remove 'ledgerid', 'transid', and 'merchantid' columns
-    df_filtered = df_filtered.drop(columns=['ledgerid', 'transid', 'action', 'merchantid'])
+    # Remove 'ledgerid' and 'transid' columns
+    df_filtered = df_filtered.drop(columns=['ledgerid', 'transid', 'action'])
     
     # Display the DataFrame as a table
     st.write("Transaction Data")
     st.write(df_filtered)
     
-    # Create a second table summing the impact for each unique organisation name
-    df_sumif = df_filtered.groupby('organisation name')['impact'].sum().reset_index()
+    # Create a second table summing the impact for each unique merchantid
+    df_sumif = df_filtered.groupby('merchantid')['impact'].sum().reset_index()
     
     # Calculate the total impact
     total_impact = df_sumif['impact'].sum()
     
     # Add a total row to the DataFrame
-    total_row = pd.DataFrame({'organisation name': ['Total'], 'impact': [total_impact]})
+    total_row = pd.DataFrame({'merchantid': ['Total'], 'impact': [total_impact]})
     df_sumif = pd.concat([df_sumif, total_row], ignore_index=True)
     
     # Display the second DataFrame as a table
-    st.write("Impact Summary by Organisation")
+    st.write("Impact Summary by Merchant")
     st.write(df_sumif)