Create app,py

app,py

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+import gradio as gr
+import pandas as pd
+from datetime import datetime
+import pytz
+
+# Updated Gradio app for YunExpress with new header mappings
+def process_file(file):
+    file_name = file.name.lower()
+    try:
+        if file_name.endswith(('.xls', '.xlsx', '.xlsm')):
+            # Read data from the "DHL" sheet
+            df = pd.read_excel(file.name, sheet_name="DHL")
+        else:
+            return f"Unsupported file format: {file_name}", None
+    except Exception as e:
+        return f"Error reading file: {e}", None
+
+    # New output headers as requested
+    output_headers = [
+        "CustomerOrderNo.", "RoutingCode", "Trackingnumber", "AdditionalServices",
+        "ShipmentProtectionPlusService", "SignatureService", "VatNumber", "EoriNumber",
+        "IossCode", "CountryCode", "Name", "CertificateCode", "Company", "Street", "City",
+        "Province/State", "ZipCode", "phone", "HouseNumber", "Email", "PackageNumber",
+        "PackageWeight", "SenderFiastName", "SenderCompany", "SenderStreet", "SenderCity",
+        "SenderProvince", "SenderPostalCode", "SenderCountry", "SenderTelephone", "SenderEmail",
+        "SenderUSCI", "PlatformName", "PlatformProvince", "PlatformAddress", "PlatformPostalCode",
+        "PlatformPhoneNumber", "PlatformEmail", "SalesPlatformLink", "CurrencyCode", "SKU1",
+        "ItemDescription1", "ForeignItemDescription1", "DeclaredQuantity1", "UnitPrice1",
+        "UnitWeight1", "HsCode1", "Remarks1", "SalesLink1", "Materials1", "Use1", "Brand1",
+        "ModelType1", "Specs1", "FabricCreationMethod1", "ManufacturerID1", "ManufacturerName1",
+        "ManufacturerCountry1", "ManufacturerState1", "ManufacturerCity1", "ManufacturerPostalCode1",
+        "ManufacturerAddress1", "CargoCategory", "PaymentPlatform", "PaymentAccount",
+        "PaymentTransactionNumber"
+    ]
+
+    # Initialize empty output DataFrame
+    output_df = pd.DataFrame("", index=range(len(df)), columns=output_headers)
+
+    # 1. Order Number → CustomerOrderNo. (prefix "LB")
+    if "Order Number" in df.columns:
+        output_df["CustomerOrderNo."] = "LB" + df["Order Number"].astype(str)
+
+    # 2. Name mapping: First + Last + Company → Name
+    if {"Shipping First Name", "Shipping Last Name", "Shipping Company"}.issubset(df.columns):
+        first = df["Shipping First Name"].fillna("").astype(str).str.strip()
+        last  = df["Shipping Last Name"].fillna("").astype(str).str.strip()
+        comp  = df["Shipping Company"].fillna("").astype(str).str.strip()
+        output_df["Name"] = (first + " " + last + " " + comp).str.strip()
+
+    # 3. Street: Address 1 + Address 2 → Street
+    addr1 = df.get("Shipping Address 1", pd.Series([""] * len(df))).fillna("").astype(str).str.strip()
+    addr2 = df.get("Shipping Address 2", pd.Series([""] * len(df))).fillna("").astype(str).str.strip()
+    output_df["Street"] = (addr1 + " " + addr2).str.strip()
+
+    # 4. City
+    if "Shipping City" in df.columns:
+        output_df["City"] = df["Shipping City"]
+
+    # 5. Province/State
+    if "Shipping Province" in df.columns:
+        output_df["Province/State"] = df["Shipping Province"]
+
+    # 6. ZipCode
+    if "Shipping ZIP" in df.columns:
+        output_df["ZipCode"] = df["Shipping ZIP"]
+
+    # 7. CountryCode
+    if "Shipping Country Code" in df.columns:
+        output_df["CountryCode"] = df["Shipping Country Code"]
+
+    # 8. phone
+    if "Shipping Address Phone" in df.columns:
+        output_df["phone"] = df["Shipping Address Phone"]
+
+    # 9. PackageWeight: Total Weight (g) → kg
+    if "Total Weight" in df.columns:
+        output_df["PackageWeight"] = df["Total Weight"] / 1000
+
+    # 10. DeclaredQuantity1: sum of Quantity per Order Number
+    if "Order Number" in df.columns and "Quantity" in df.columns:
+        output_df["DeclaredQuantity1"] = df.groupby("Order Number")["Quantity"].transform("sum")
+
+    # 11. Fixed defaults for every populated CustomerOrderNo.
+    mask = output_df["CustomerOrderNo."].notna() & (output_df["CustomerOrderNo."] != "")
+    output_df.loc[mask, "RoutingCode"] = "HKTHZXR"
+    output_df.loc[mask, "UnitPrice1"]    = 2
+    output_df.loc[mask, "CurrencyCode"] = "USD"
+    output_df.loc[mask, "ItemDescription1"] = "Eye Cosmetic Accessories"
+    output_df.loc[mask, "UnitWeight1"]  = 0.02
+
+    # 12. Remove duplicate rows based on CustomerOrderNo.
+    output_df = output_df.drop_duplicates(subset=["CustomerOrderNo."], keep="first")
+
+    # 13. Generate output filename using HK date
+    hk_tz = pytz.timezone("Asia/Hong_Kong")
+    today_hk = datetime.now(hk_tz).strftime("%y%m%d")
+    output_file_name = f"yunexpress {today_hk}.xlsx"
+
+    # Save to Excel
+    output_df.to_excel(output_file_name, index=False)
+
+    return output_df, output_file_name
+
+# Gradio interface
+with gr.Blocks(title="Shipping - DHL-E-Commerce") as demo:
+    gr.Markdown("# Shipping - YunExpress")
+    with gr.Row():
+        file_input = gr.File(label="Upload Excel File with a DHL sheet")
+        process_button = gr.Button("Process Data")
+    with gr.Row():
+        output_data = gr.DataFrame()
+        output_file_component = gr.File(label="Download Processed File")
+    process_button.click(fn=process_file, inputs=[file_input], outputs=[output_data, output_file_component])
+
+demo.launch()