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Fetch_Employer_Name
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import streamlit as st
import pandas as pd
from io import BytesIO
from helper import get_res_df

def to_excel(df):
    """
    Convert a Pandas DataFrame to an Excel file in memory.
    
    Parameters:
    df (DataFrame): The DataFrame to be converted to Excel format.

    Returns:
    bytes: The in-memory Excel file data.
    """
    output = BytesIO()
    # Use the Pandas ExcelWriter to write the DataFrame to an in-memory file
    with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
        df.to_excel(writer, index=False)
    processed_data = output.getvalue()
    return processed_data

def process_files(excel_file, text_file):
    """
    Process the uploaded Excel/CSV and text files and return cleaned dataframes.

    Parameters:
    excel_file (UploadedFile): The uploaded Excel or CSV file.
    text_file (UploadedFile): The uploaded text file.

    Returns:
    Tuple[DataFrame, DataFrame]: A tuple containing the cleaned DataFrame from the Excel/CSV file
                                 and a DataFrame created from the text file data.
    """
    print(excel_file, text_file)  # Debugging information

    # Read the Excel/CSV file into a DataFrame
    if excel_file.name.endswith('.csv'):
        df_excel = pd.read_csv(excel_file)
    else:
        df_excel = pd.read_excel(excel_file)
    
    # Ensure the 'cfcf' column values are formatted as zero-padded 6-digit strings
    df_excel['cfcf'] = [str(number).zfill(6) for number in df_excel['cfcf']]

    # Read and process the text file content into a list of lines
    lines = text_file.read().decode('utf-8').splitlines()
    data = [line.strip().split(',') for line in lines]  # Split each line by commas

    # Create a DataFrame from the parsed text file data
    df = pd.DataFrame(data)

    return df_excel, df


# Streamlit UI section
st.title("Fetch Employer")  # Application title

# File uploader widgets to allow users to upload an Excel/CSV file and a text file
uploaded_excel = st.file_uploader("Upload the Master file(.xls or .csv)", type=["csv", "xls", "xlsx"])
uploaded_text = st.file_uploader("Upload your Text file(.txt)", type=["txt"])

# Check if both files are uploaded
if uploaded_excel and uploaded_text:
    st.write("Processing the files...")  # Inform the user that the files are being processed
    master_data, df = process_files(uploaded_excel, uploaded_text)  # Process the files

    st.write("Final Output")  # Display the result of file processing
    res = get_res_df(master_data, df)  # Generate the result DataFrame using the helper function
    st.dataframe(res)  # Show the result in a table format on the web app

    # Convert the result DataFrame to an Excel file for download
    excel_data = to_excel(res)

    # Provide a button for the user to download the result as an Excel file
    st.download_button(label="Download Excel",
                       data=excel_data,
                       file_name='Fetch_Employer_Output.xlsx',
                       mime='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet')


# import streamlit as st
# import pandas as pd
# from io import BytesIO
# from helper import get_res_df

# def to_excel(df):
#     output = BytesIO()
#     with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
#         df.to_excel(writer, index=False)
#     processed_data = output.getvalue()
#     return processed_data

# def process_files(excel_file, text_file):
#     print(excel_file,text_file)
#     if excel_file.name.endswith('.csv'):
#         df_excel = pd.read_csv(excel_file)
#     else:
#         df_excel = pd.read_excel(excel_file)
#     df_excel['cfcf']=[str(number).zfill(6) for number in df_excel['cfcf']]

#     lines = text_file.read().decode('utf-8').splitlines()
#     data = [line.strip().split(',') for line in lines]
#     df = pd.DataFrame(data)

#     return df_excel,df


# st.title("Fetch Employer")

# uploaded_excel = st.file_uploader("Upload the Master file(.xls or .csv)", type=["csv", "xls", "xlsx"])
# uploaded_text = st.file_uploader("Upload your Text file(.txt)", type=["txt"])

# if uploaded_excel and uploaded_text:
#     st.write("Processing the files...")
#     master_data, df = process_files(uploaded_excel, uploaded_text)

#     st.write("Final Output")
#     res = get_res_df(master_data,df)
#     st.dataframe(res)
#     excel_data = to_excel(res)
#     st.download_button(label="Download Excel",
#                     data=excel_data,
#                     file_name='Fetch_Employer_Output.xlsx',
#                     mime='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet')