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app (2).py

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+import pandas as pd
+import gradio as gr
+import msoffcrypto
+import io
+import re
+import tempfile
+
+# Define transformation function
+
+def convert_ci(by_line_item_file, combined_file, password):
+    # Decrypt the protected "by line item" file
+    decrypted = io.BytesIO()
+    with open(by_line_item_file.name, "rb") as f:
+        office_file = msoffcrypto.OfficeFile(f)
+        office_file.load_key(password=password)
+        office_file.decrypt(decrypted)
+
+    # Load "by line item" data and skip last 3 rows
+    by_line_item_df_raw = pd.read_excel(decrypted, skiprows=10, header=None)
+    by_line_item_df_raw = by_line_item_df_raw.iloc[:-3]  # drop last 3 rows
+    by_line_item_df = by_line_item_df_raw.iloc[:, [1, 4, 5, 6, 7, 8, 9, 10, 11, 23]].copy()
+
+    # Drop fully empty rows
+    by_line_item_df = by_line_item_df.dropna(how='all')
+
+    # Clean up description for matching
+    def clean_desc(row):
+        desc_parts = [str(val).strip() for val in row.iloc[2:9] if pd.notna(val)]
+        description = " ".join(desc_parts)
+        description = re.sub(r"%", "", description)
+        description = re.sub(r"\d+", "", description)
+        return re.sub(r"[^\w\s]", "", description)
+
+    # by_line_item_df["Part"] = by_line_item_df.iloc[:, 0].astype(str)
+    by_line_item_df["Part"] = ""
+    by_line_item_df["Tariff_Number"] = by_line_item_df.iloc[:, 1].apply(lambda x: str(x).replace('.0', '') if isinstance(x, float) and str(x).endswith('.0') else str(x))
+    by_line_item_df["Commercial_Description"] = by_line_item_df.apply(clean_desc, axis=1)
+    by_line_item_df["MID_Code"] = by_line_item_df.iloc[:, 9].astype(str)
+    by_line_item_df = by_line_item_df[["Part", "Tariff_Number", "Commercial_Description", "MID_Code"]]
+
+    # Load the combined file and extract required fields
+    invoice = pd.read_excel(combined_file.name, header=None)
+    merged_value = invoice.iloc[8, 11]  # L9 = 8th row, 11th col (0-indexed)
+    mawb = invoice.iloc[8, 20]  # U9 = 8th row, 20th col (0-indexed)
+    invoice_no = str(merged_value) if pd.notna(merged_value) else ""
+
+    combined_df_raw = pd.read_excel(combined_file.name, skiprows=10, header=None)
+    combined_df_raw = combined_df_raw.iloc[:-3]  # drop last 3 rows
+    combined_df = combined_df_raw.iloc[:, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12]].copy()
+    combined_df.columns = [
+        "Tariff_Number", "Country_of_Origin", "Quantity", "Gross_Weight_KG",
+        "Total_Line_Value", "Manufacturer_Name", "manufacturer_address",
+        "Manufacturer_City", "Manufacturer_Country", "Manufacturer_Zip", "MID_Code"
+    ]
+
+    # Clean Tariff_Number and MID_Code for consistent merging
+    combined_df["Tariff_Number"] = combined_df["Tariff_Number"].apply(lambda x: str(x).replace('.0', '') if isinstance(x, float) and str(x).endswith('.0') else str(x))
+    combined_df["MID_Code"] = combined_df["MID_Code"].astype(str)
+
+    # Format manufacturer address components
+    def extract_address_parts(row):
+        address = str(row["manufacturer_address"])
+        city = str(row["Manufacturer_City"])
+        address_1 = address
+        state = ""
+
+        if address.endswith("China"):
+            parts = address.split(",")
+            if len(parts) >= 2:
+                state = parts[-2].replace(" Province", "").strip()
+        elif "Singapore" in address:
+            state = ""
+
+        if city and city in address:
+            last_occurrence_index = address.rfind(city)
+            if last_occurrence_index != -1:
+                address_1 = address[:last_occurrence_index].strip().rstrip(",")
+
+        return pd.Series([state, address_1])
+
+    combined_df["Manufacturer_Country"] = combined_df["MID_Code"].str[:2]
+    combined_df[["Manufacturer_State", "Manufacturer_Address_1"]] = combined_df.apply(extract_address_parts, axis=1)
+
+    # Add calculated fields
+    combined_df["Invoice_No"] = invoice_no
+    combined_df["Country_of_Export"] = "CN"
+    combined_df["Quantity_UOM"] = "pcs"
+    combined_df["Buyer_Name"] = "SHEIN DISTRIBUTION CORPORATION"
+    combined_df["Buyer_Address_1"] = "777 S. Alameda St"
+    combined_df["Buyer_Address_2"] = "Suite 400"
+    combined_df["Buyer_City"] = "Log Angeles"
+    combined_df["Buyer_State"] = "CA"
+    combined_df["Buyer_Zip"] = "90021"
+    combined_df["Buyer_Country"] = "US"
+    combined_df["Buyer_ID_Number"] = ""
+    combined_df["Consignee_Name"] = "SHEIN DISTRIBUTION CORPORATION"
+    combined_df["Consignee_Address_1"] = "777 S. Alameda St Suite"
+    combined_df["Consignee_Address_2"] = "Suite 400"
+    combined_df["Consignee_City"] = "Log Angeles"
+    combined_df["Consignee_State"] = "CA"
+    combined_df["Consignee_Zip"] = "90021"
+    combined_df["Consignee_Country"] = "US"
+    combined_df["Consignee_ID_Number"] = ""
+    combined_df["Unit_Price"] = (combined_df["Total_Line_Value"] / combined_df["Quantity"]).round(2)
+    combined_df["Net_Weight_KG"] = (combined_df["Gross_Weight_KG"] / combined_df["Quantity"]).round(2)
+    combined_df["Gross_Weight_KG"] = combined_df["Gross_Weight_KG"].round(3)
+    combined_df["Total_Line_Value"] = combined_df["Total_Line_Value"].round(2)
+
+    # Drop duplicates in by_line_item_df for merge
+    by_line_item_unique = by_line_item_df.drop_duplicates(subset=["Tariff_Number", "MID_Code"])
+
+    # Add empty columns
+    empty_cols = [
+        'SICountry', 'SP1', 'SP2', 'Zone_Status', 
+        'Privileged_Filing_Date', 'Line_Piece_Count', 'ADD_Case_Number',
+        'CVD_Case_Number', 'AD_Non_Reimbursement_Statement',
+        'AD-CVD_Certification_Designation'
+    ]
+    for col in empty_cols:
+        combined_df[col] = ""
+
+    # Merge combined with by_line_item (first match by key)
+    merged_df = combined_df.merge(by_line_item_unique, on=["Tariff_Number", "MID_Code"], how="left")
+
+    column_order = [
+        'Invoice_No', 'Part', 'Commercial_Description', 'Country_of_Origin', 
+        'Country_of_Export', 'Tariff_Number', 'Quantity', 'Quantity_UOM',
+        'Unit_Price', 'Total_Line_Value', 'Net_Weight_KG', 'Gross_Weight_KG',
+        'Manufacturer_Name', 'Manufacturer_Address_1', 'Manufacturer_Address_2',
+        'Manufacturer_City', 'Manufacturer_State', 'Manufacturer_Zip',
+        'Manufacturer_Country', 'MID_Code', 'Buyer_Name', 'Buyer_Address_1',
+        'Buyer_Address_2', 'Buyer_City', 'Buyer_State', 'Buyer_Zip',
+        'Buyer_Country', 'Buyer_ID_Number', 'Consignee_Name',
+        'Consignee_Address_1', 'Consignee_Address_2', 'Consignee_City',
+        'Consignee_State', 'Consignee_Zip', 'Consignee_Country',
+        'Consignee_ID_Number', 'SICountry', 'SP1', 'SP2', 'Zone_Status',
+        'Privileged_Filing_Date', 'Line_Piece_Count', 'ADD_Case_Number',
+        'CVD_Case_Number', 'AD_Non_Reimbursement_Statement',
+        'AD-CVD_Certification_Designation'
+    ]
+    for col in column_order:
+        if col not in merged_df.columns:
+            merged_df[col] = ""
+    merged_df = merged_df[column_order]
+
+    # Save merged output partitioned into 998-row chunks 
+    max_rows = 998
+    outputs = []
+
+    for i, start in enumerate(range(0, len(merged_df), max_rows)):
+        chunk = merged_df.iloc[start : start + max_rows]
+        suffix = chr(65 + i) if i < 26 else f"part{i + 1}"  # A, B, C... then part27+
+        filename = f"{mawb} T01 Manifest - {suffix}.xlsx"
+        with pd.ExcelWriter(filename, engine='openpyxl') as writer:
+            chunk.to_excel(writer, sheet_name="Converted", index=False)
+            for column_cells in writer.sheets['Converted'].columns:
+                max_length = max(len(str(cell.value)) if cell.value is not None else 0 for cell in column_cells)
+                writer.sheets['Converted'].column_dimensions[column_cells[0].column_letter].width = max_length + 2
+        outputs.append(filename)
+
+    return outputs
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=convert_ci,
+    inputs=[
+        gr.File(label="Upload 'By Line Item' File (.xlsx)", type="filepath"),
+        gr.File(label="Upload 'Combined' File (.xlsx)", type="filepath"),
+        gr.Textbox(label="Password for 'By Line Item' File", type="password")
+    ],
+    outputs=gr.File(label="Download Partitioned Files", file_types=[".xlsx"], file_count="multiple"),
+    title="SHEIN CI to Magaya Format Conversion Tool",
+    description="Upload both 'By Line Item' (with password) and 'Combined' Excel files to merge and generate the Manifest in Magaya T01 Format."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio
+pandas
+msoffcrypto-tool
+openpyxl