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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()
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