Create app.py

app.py

@@ -0,0 +1,177 @@
+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
+    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:
+    - Takes the uploaded file,
+    - Processes it,
+    - Returns a tuple that Gradio can interpret as a downloadable file.
+    """
+    processed_csv_io = process_woocommerce_data_in_memory(uploaded_file)
+
+    # Gradio expects a tuple (filename, file_obj) when returning a downloadable file
+    return ("WooCommerce_Mapped_Data.csv", 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()