Update app.py

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 import pandas as pd
+import tempfile
 from io import BytesIO
 
 def process_woocommerce_data_in_memory(netcom_file):
@@ -14,7 +15,7 @@ def process_woocommerce_data_in_memory(netcom_file):
         "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)
+    # 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
 
@@ -38,14 +39,14 @@ def process_woocommerce_data_in_memory(netcom_file):
         .reset_index(name='Aggregated_Times')
     )
 
-    # 3. Extract unique parent products from the NetCom data
+    # 3. Extract unique parent products
     parent_products = netcom_df.drop_duplicates(subset=['Course ID'])
 
-    # 4. Merge aggregated dates and times into the parent product DataFrame
+    # 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 the parent (variable) product DataFrame
+    # 5. Create parent (variable) products
     woo_parent_df = pd.DataFrame({
         'Type': 'variable',
         'SKU': parent_products['Course ID'],
@@ -84,7 +85,7 @@ def process_woocommerce_data_in_memory(netcom_file):
         'Meta: agenda': parent_products['Outline']  # Agenda now copies the outline
     })
 
-    # 6. Create the child (variation) product DataFrame
+    # 6. Create child (variation) products
     woo_child_df = pd.DataFrame({
         'Type': 'variation, virtual',
         'SKU': netcom_df['Course SID'],
@@ -125,10 +126,10 @@ def process_woocommerce_data_in_memory(netcom_file):
         'Meta: agenda': netcom_df['Outline']  # Agenda now copies the outline
     })
 
-    # 7. Combine parent and child data
+    # 7. Combine parent + child
     woo_final_df = pd.concat([woo_parent_df, woo_child_df], ignore_index=True)
 
-    # 8. Define the desired column order (matching WooCommerce import format)
+    # 8. Desired column order
     column_order = [
         'Type', 'SKU', 'Name', 'Published', 'Visibility in catalog',
         'Short description', 'Description', 'Tax status', 'In stock?',
@@ -141,7 +142,7 @@ def process_woocommerce_data_in_memory(netcom_file):
     ]
     woo_final_df = woo_final_df[column_order]
 
-    # 9. Convert the final DataFrame to CSV in memory
+    # 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)
@@ -150,22 +151,19 @@ def process_woocommerce_data_in_memory(netcom_file):
 
 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.
+    - 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)
 
-    # Return a dict with the keys Gradio expects for a File output
-    return {
-        "name": "WooCommerce_Mapped_Data.csv",
-        "data": processed_csv_io.getvalue()
-    }
+    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
 
-#########################
-#       Gradio App      #
-#########################
+    return temp_path
 
 app = gr.Interface(
     fn=process_file_and_return_csv,