import gradio as gr import pandas as pd import tempfile from io import BytesIO def process_woocommerce_data_in_memory(netcom_file): """ Reads the uploaded NetCom CSV file in-memory, processes it to the WooCommerce format, and returns the resulting CSV as bytes, suitable for download. """ # Define the brand-to-logo mapping brand_logo_map = { "Amazon Web Services": "https://devthe.tech/wp-content/uploads/2025/02/aws.png", "Cisco": "https://devthe.tech/wp-content/uploads/2025/02/cisco-e1738593292198-1.webp", "Microsoft": "https://devthe.tech/wp-content/uploads/2025/01/Microsoft-e1737494120985-1.png" } # 1. Read the uploaded CSV into a DataFrame netcom_df = pd.read_csv(netcom_file.name, encoding='latin1') netcom_df.columns = netcom_df.columns.str.strip() # standardize column names # 2. Create aggregated dates and times for each Course ID date_agg = ( netcom_df.groupby('Course ID')['Course Start Date'] .apply(lambda x: ','.join(x.astype(str).unique())) .reset_index(name='Aggregated_Dates') ) time_agg = ( netcom_df.groupby('Course ID') .apply( lambda df: ','.join( f"{st}-{et} {tz}" for st, et, tz in zip(df['Course Start Time'], df['Course End Time'], df['Time Zone']) ) ) .reset_index(name='Aggregated_Times') ) # 3. Extract unique parent products parent_products = netcom_df.drop_duplicates(subset=['Course ID']) # 4. Merge aggregated dates and times parent_products = parent_products.merge(date_agg, on='Course ID', how='left') parent_products = parent_products.merge(time_agg, on='Course ID', how='left') # 5. Create parent (variable) products woo_parent_df = pd.DataFrame({ 'Type': 'variable', 'SKU': parent_products['Course ID'], 'Name': parent_products['Course Name'], 'Published': 1, 'Visibility in catalog': 'visible', 'Short description': parent_products['Decription'], 'Description': parent_products['Decription'], 'Tax status': 'taxable', 'In stock?': 1, 'Stock': 1, 'Sold individually?': 1, 'Regular price': parent_products['SRP Pricing'].replace('[\$,]', '', regex=True), 'Categories': 'courses', 'Images': parent_products['Vendor'].map(brand_logo_map).fillna(''), 'Parent': '', 'Brands': parent_products['Vendor'], 'Attribute 1 name': 'Date', 'Attribute 1 value(s)': parent_products['Aggregated_Dates'], 'Attribute 1 visible': 'visible', 'Attribute 1 global': 1, 'Attribute 2 name': 'Location', 'Attribute 2 value(s)': 'Virtual', 'Attribute 2 visible': 'visible', 'Attribute 2 global': 1, 'Attribute 3 name': 'Time', 'Attribute 3 value(s)': parent_products['Aggregated_Times'], 'Attribute 3 visible': 'visible', 'Attribute 3 global': 1, 'Meta: outline': parent_products['Outline'], 'Meta: days': parent_products['Duration'], 'Meta: location': 'Virtual', 'Meta: overview': parent_products['Target Audience'], 'Meta: objectives': parent_products['Objectives'], 'Meta: prerequisites': parent_products['RequiredPrerequisite'].fillna(''), 'Meta: agenda': parent_products['Outline'] # Agenda now copies the outline }) # 6. Create child (variation) products woo_child_df = pd.DataFrame({ 'Type': 'variation, virtual', 'SKU': netcom_df['Course SID'], 'Name': netcom_df['Course Name'], 'Published': 1, 'Visibility in catalog': 'visible', 'Short description': netcom_df['Decription'], 'Description': netcom_df['Decription'], 'Tax status': 'taxable', 'In stock?': 1, 'Stock': 1, 'Sold individually?': 1, 'Regular price': netcom_df['SRP Pricing'].replace('[\$,]', '', regex=True), 'Categories': 'courses', 'Images': netcom_df['Vendor'].map(brand_logo_map).fillna(''), 'Parent': netcom_df['Course ID'], 'Brands': netcom_df['Vendor'], 'Attribute 1 name': 'Date', 'Attribute 1 value(s)': netcom_df['Course Start Date'], 'Attribute 1 visible': 'visible', 'Attribute 1 global': 1, 'Attribute 2 name': 'Location', 'Attribute 2 value(s)': 'Virtual', 'Attribute 2 visible': 'visible', 'Attribute 2 global': 1, 'Attribute 3 name': 'Time', 'Attribute 3 value(s)': netcom_df.apply( lambda row: f"{row['Course Start Time']}-{row['Course End Time']} {row['Time Zone']}", axis=1 ), 'Attribute 3 visible': 'visible', 'Attribute 3 global': 1, 'Meta: outline': netcom_df['Outline'], 'Meta: days': netcom_df['Duration'], 'Meta: location': 'Virtual', 'Meta: overview': netcom_df['Target Audience'], 'Meta: objectives': netcom_df['Objectives'], 'Meta: prerequisites': netcom_df['RequiredPrerequisite'].fillna(''), 'Meta: agenda': netcom_df['Outline'] # Agenda now copies the outline }) # 7. Combine parent + child woo_final_df = pd.concat([woo_parent_df, woo_child_df], ignore_index=True) # 8. Desired column order column_order = [ 'Type', 'SKU', 'Name', 'Published', 'Visibility in catalog', 'Short description', 'Description', 'Tax status', 'In stock?', 'Stock', 'Sold individually?', 'Regular price', 'Categories', 'Images', 'Parent', 'Brands', 'Attribute 1 name', 'Attribute 1 value(s)', 'Attribute 1 visible', 'Attribute 1 global', 'Attribute 2 name', 'Attribute 2 value(s)', 'Attribute 2 visible', 'Attribute 2 global', 'Attribute 3 name', 'Attribute 3 value(s)', 'Attribute 3 visible', 'Attribute 3 global', 'Meta: outline', 'Meta: days', 'Meta: location', 'Meta: overview', 'Meta: objectives', 'Meta: prerequisites', 'Meta: agenda' ] woo_final_df = woo_final_df[column_order] # 9. Convert to CSV (in memory) output_buffer = BytesIO() woo_final_df.to_csv(output_buffer, index=False, encoding='utf-8-sig') output_buffer.seek(0) return output_buffer def process_file_and_return_csv(uploaded_file): """ - Takes the uploaded file, - Processes it, - Writes the CSV to a temp file, - Returns that path for Gradio to provide as a downloadable file. """ processed_csv_io = process_woocommerce_data_in_memory(uploaded_file) with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp: tmp.write(processed_csv_io.getvalue()) tmp.flush() # ensure data is written to disk temp_path = tmp.name return temp_path app = gr.Interface( fn=process_file_and_return_csv, inputs=gr.File(label="Upload NetCom CSV", file_types=["text", "csv"]), outputs=gr.File(label="Download WooCommerce CSV"), title="NetCom to WooCommerce CSV Processor", description="Upload your NetCom Reseller Schedule CSV to generate the WooCommerce import-ready CSV." ) if __name__ == "__main__": app.launch()