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| import gradio as gr | |
| import pandas as pd | |
| 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 (Gradio provides a tempfile-like object) | |
| 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 from the NetCom data | |
| parent_products = netcom_df.drop_duplicates(subset=['Course ID']) | |
| # 4. Merge aggregated dates and times into the parent product DataFrame | |
| 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 the parent (variable) product DataFrame | |
| 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 the child (variation) product DataFrame | |
| 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 and child data | |
| woo_final_df = pd.concat([woo_parent_df, woo_child_df], ignore_index=True) | |
| # 8. Define the desired column order (matching WooCommerce import format) | |
| 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 the final DataFrame 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): | |
| """ | |
| Gradio wrapper function that: | |
| - Takes the uploaded file, | |
| - Processes it, | |
| - Returns a dictionary that Gradio recognizes as a downloadable file. | |
| """ | |
| processed_csv_io = process_woocommerce_data_in_memory(uploaded_file) | |
| # Return a dict with the keys Gradio expects for a File output | |
| return { | |
| "name": "WooCommerce_Mapped_Data.csv", | |
| "data": processed_csv_io.getvalue() | |
| } | |
| ######################### | |
| # Gradio App # | |
| ######################### | |
| 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() | |