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import pandas as pd
import streamlit as st
from data_processing import process_dataframe, process_journal, remove_na_accounts
from utils import get_table_download_link, to_excel
from io import BytesIO
from llm_agent import set_openai_key, init_agent, get_agent_response
st.title('Accounting Fast Close')
# Ask user for OpenAI API key
openai_api_key = st.sidebar.text_input('Enter your OpenAI API Key', type='password')
if openai_api_key:
set_openai_key(openai_api_key)
# Creating a button to toggle between uploaded documents and findings
view_option = st.selectbox('Choose View', ['Uploaded Documents', 'Findings'])
@st.cache_data
def load_excel_data(uploaded_file):
df = pd.read_excel(uploaded_file)
return df
uploaded_file1 = st.sidebar.file_uploader('Upload your trial balance Excel file', type=['xlsx'])
uploaded_file2 = st.sidebar.file_uploader('Upload your journal entry Excel file', type=['xlsx'])
if uploaded_file1 is not None:
df1 = load_excel_data(uploaded_file1)
# Process the DataFrame
df1 = process_dataframe(df1)
# AI Agent Section
if openai_api_key:
agent = init_agent(openai_api_key)
if uploaded_file2 is not None:
df2 = load_excel_data(uploaded_file2)
df2 = process_journal(df2)
if uploaded_file1 is not None and uploaded_file2 is not None:
# Merge df1 (trial balance) with df2 (journal entries)
df1 = pd.merge(df1, df2, on='Account', how='outer')
# Remove rows with 'Account' as NA
df1 = remove_na_accounts(df1)
# Define the columns we want to fill NaN values with 0
fillna_columns = ['Opening Balance Debit', 'Opening Balance Credit',
'Current Transactions Debit', 'Current Transactions Credit',
'Closing Balance Debit', 'Closing Balance Credit',
'Debit Amount', 'Credit Amount' ]
# Replace NaN values with 0 in the defined columns
df1[fillna_columns] = df1[fillna_columns].fillna(0)
# Compute the differences
df1['Diff Dr.'] = df1['Current Transactions Debit'] - df1['Debit Amount']
df1['Diff Cr.'] = df1['Current Transactions Credit'] - df1['Credit Amount']
excel_data = to_excel(df1) # Move this line to here
# Uploaded Documents Section
if view_option == 'Uploaded Documents':
if uploaded_file1 is not None:
st.write(df1)
# st.markdown(get_table_download_link(excel_data, 'processed_data.xlsx'), unsafe_allow_html=True)
if uploaded_file2 is not None:
# Save the dataframes to an Excel file
excel_data_combined = BytesIO()
with pd.ExcelWriter(excel_data_combined, engine='xlsxwriter') as writer:
df1.to_excel(writer, sheet_name='Trial Balance', index=False)
df2.to_excel(writer, sheet_name='Journal Entry', index=False)
st.markdown(get_table_download_link(excel_data_combined.getvalue(), filename='combined.xlsx'), unsafe_allow_html=True)
elif view_option == 'Findings':
# Logic for findings should be implemented here
st.write(df1)
# AI Agent Section
if openai_api_key and uploaded_file1 is not None:
# Create a chat box for user questions
user_input = st.text_input('Ask a question:')
if user_input:
response = get_agent_response(agent, df1, user_input) # pass df1 as an argument
st.write(response)
if uploaded_file2 is not None:
# Save the dataframes to an Excel file
excel_data_combined = BytesIO()
with pd.ExcelWriter(excel_data_combined, engine='xlsxwriter') as writer:
df1.to_excel(writer, sheet_name='Trial Balance', index=False)
df2.to_excel(writer, sheet_name='Journal Entry', index=False)
st.markdown(get_table_download_link(excel_data_combined.getvalue(), filename='combined.xlsx'), unsafe_allow_html=True)
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