Create app.py

app.py

@@ -0,0 +1,210 @@
+import gradio as gr
+import pandas as pd
+from datetime import datetime
+import pytz
+import openpyxl
+
+def process_file(file):
+    file_name = file.name.lower()
+    try:
+        if file_name.endswith('.csv'):
+            # CSV files do not have multiple sheets.
+            return "Error: Please upload an Excel file with a UPS sheet.", None
+        elif file_name.endswith(('.xls', '.xlsx', '.xlsm')):
+            # Only read data from the "UPS" sheet.
+            df = pd.read_excel(file.name, sheet_name="UPS")
+        else:
+            return f"Unsupported file format: {file_name}", None
+    except Exception as e:
+        return f"Error reading file: {str(e)}", None
+
+    # Define the new header list as specified.
+    output_headers = [
+        "Contact Name", "Company or Name", "Country", "Address 1", "Address 2", "Address 3",
+        "City", "State/Prov/Other", "Postal Code", "Telephone", "Ext", "Residential Ind",
+        "Consignee Email", "Packaging Type", "Customs Value", "Weight", "Length", "Width",
+        "Height", "Unit of Measure", "Description of Goods", "Documents of No Commercial Value",
+        "GNIFC", "Pkg Decl Value", "Service", "Delivery Confirm", "Shipper Release", "Ret of Documents",
+        "Saturday Deliver", "Carbon Neutral", "Large Package", "Addl handling", "Reference 1",
+        "Reference 2", "Reference 3", "QV Notif 1-Addr", "QV Notif 1-Ship", "QV Notif 1-Excp",
+        "QV Notif 1-Delv", "QV Notif 2-Addr", "QV Notif 2-Ship", "QV Notif 2-Excp", "QV Notif 2-Delv",
+        "QV Notif 3-Addr", "QV Notif 3-Ship", "QV Notif 3-Excp", "QV Notif 3-Delv", "QV Notif 4-Addr",
+        "QV Notif 4-Ship", "QV Notif 4-Excp", "QV Notif 4-Delv", "QV Notif 5-Addr", "QV Notif 5-Ship",
+        "QV Notif 5-Excp", "QV Notif 5-Delv", "QV Notif Msg", "QV Failure Addr", "UPS Premium Care",
+        "ADL Location ID", "ADL Media Type", "ADL Language", "ADL Notification Addr", "ADL Failure Addr",
+        "ADL COD Value", "ADL Deliver to Addressee", "ADL Shipper Media Type", "ADL Shipper Language",
+        "ADL Shipper Notification Addr", "ADL Direct Delivery Only", "Electronic Package Release Authentication",
+        "Lithium Ion Alone", "Lithium Ion In Equipment", "Lithium Ion With_Equipment", "Lithium Metal Alone",
+        "Lithium Metal In Equipment", "Lithium Metal With Equipment", "Weekend Commercial Delivery",
+        "Dry Ice Weight", "Merchandise Description", "UPS SurePost®Limited Quantity/Lithium Battery"
+    ]
+    
+    # Create an output DataFrame with the new header list.
+    output_df = pd.DataFrame("", index=range(len(df)), columns=output_headers)
+    
+    # Mapping from input file to output based on provided rules:
+    # 1. Shipping First Name + Shipping Last Name + Shipping Company -> Contact Name and Company or Name
+    if {"Shipping First Name", "Shipping Last Name", "Shipping Company"}.issubset(df.columns):
+        first_name = df["Shipping First Name"].fillna("").astype(str).str.strip()
+        last_name = df["Shipping Last Name"].fillna("").astype(str).str.strip()
+        company = df["Shipping Company"].fillna("").astype(str).str.strip()
+        combined = (first_name + " " + last_name + " " + company).str.strip()
+        output_df["Contact Name"] = combined
+        output_df["Company or Name"] = combined
+
+    # 2. Shipping Country Code -> Country
+    if "Shipping Country Code" in df.columns:
+        output_df["Country"] = df["Shipping Country Code"].fillna("").astype(str).str.strip()
+    
+    # 3. Shipping Address 1 -> Address 1
+    if "Shipping Address 1" in df.columns:
+        output_df["Address 1"] = df["Shipping Address 1"].fillna("").astype(str).str.strip()
+    
+    # 4. Shipping Address 2 -> Address 2
+    if "Shipping Address 2" in df.columns:
+        output_df["Address 2"] = df["Shipping Address 2"].fillna("").astype(str).str.strip()
+    
+    # 5. Shipping City -> City
+    if "Shipping City" in df.columns:
+        output_df["City"] = df["Shipping City"].fillna("").astype(str).str.strip()
+    
+    # 6. Shipping Province -> State/Prov/Other
+    if "Shipping Province" in df.columns:
+        output_df["State/Prov/Other"] = df["Shipping Province"].fillna("").astype(str).str.strip()
+    
+    # Convert full US state names to their 2-letter abbreviations if applicable.
+    us_states = {
+        'alabama': 'AL',
+        'alaska': 'AK',
+        'arizona': 'AZ',
+        'arkansas': 'AR',
+        'california': 'CA',
+        'colorado': 'CO',
+        'connecticut': 'CT',
+        'delaware': 'DE',
+        'florida': 'FL',
+        'georgia': 'GA',
+        'hawaii': 'HI',
+        'idaho': 'ID',
+        'illinois': 'IL',
+        'indiana': 'IN',
+        'iowa': 'IA',
+        'kansas': 'KS',
+        'kentucky': 'KY',
+        'louisiana': 'LA',
+        'maine': 'ME',
+        'maryland': 'MD',
+        'massachusetts': 'MA',
+        'michigan': 'MI',
+        'minnesota': 'MN',
+        'mississippi': 'MS',
+        'missouri': 'MO',
+        'montana': 'MT',
+        'nebraska': 'NE',
+        'nevada': 'NV',
+        'new hampshire': 'NH',
+        'new jersey': 'NJ',
+        'new mexico': 'NM',
+        'new york': 'NY',
+        'north carolina': 'NC',
+        'north dakota': 'ND',
+        'ohio': 'OH',
+        'oklahoma': 'OK',
+        'oregon': 'OR',
+        'pennsylvania': 'PA',
+        'rhode island': 'RI',
+        'south carolina': 'SC',
+        'south dakota': 'SD',
+        'tennessee': 'TN',
+        'texas': 'TX',
+        'utah': 'UT',
+        'vermont': 'VT',
+        'virginia': 'VA',
+        'washington': 'WA',
+        'west virginia': 'WV',
+        'wisconsin': 'WI',
+        'wyoming': 'WY'
+    }
+    
+    def convert_state(state):
+        if state:
+            state_lower = state.lower()
+            return us_states.get(state_lower, state)
+        return state
+    
+    output_df["State/Prov/Other"] = output_df["State/Prov/Other"].apply(convert_state)
+    
+    # 7. Shipping ZIP -> Postal Code
+    if "Shipping ZIP" in df.columns:
+        output_df["Postal Code"] = df["Shipping ZIP"].fillna("").astype(str).str.strip()
+    
+    # 8. Shipping Address Phone -> Telephone
+    if "Shipping Address Phone" in df.columns:
+        output_df["Telephone"] = df["Shipping Address Phone"].fillna("").astype(str).str.strip()
+    
+    # 9. Email -> Consignee Email
+    if "Email" in df.columns:
+        output_df["Consignee Email"] = df["Email"].fillna("").astype(str).str.strip()
+    
+    # 10. Total Weight -> Weight (divide by 1000)
+    if "Total Weight" in df.columns:
+        def convert_weight(x):
+            try:
+                if pd.isna(x) or x == "":
+                    return ""
+                return float(x) / 1000
+            except:
+                return ""
+        output_df["Weight"] = df["Total Weight"].apply(convert_weight)
+    
+    # Fixed value mapping (applied for rows with data in Contact Name)
+    mask = output_df["Contact Name"].notna() & (output_df["Contact Name"] != "")
+    output_df.loc[mask, "Packaging Type"] = "2"
+    output_df.loc[mask, "Unit of Measure"] = "KG"
+    output_df.loc[mask, "Description of Goods"] = "Eyes Beauty Color Cosmetics"
+    output_df.loc[mask, "Service"] = "7"
+    
+    # Delete duplicate rows based on "Contact Name", keeping the first occurrence.
+    output_df = output_df.drop_duplicates(subset=["Contact Name"], keep="first")
+    
+    # Generate the output file name using the current Hong Kong date.
+    hk_timezone = pytz.timezone("Asia/Hong_Kong")
+    current_date_hk = datetime.now(hk_timezone).strftime("%y%m%d")
+    output_file_name = f"UPS {current_date_hk}.csv"
+    
+    # Save the output DataFrame to a CSV file without the header.
+    output_df.to_csv(output_file_name, index=False, header=False)
+    
+    return output_df, output_file_name
+
+with gr.Blocks(title="Shipping - UPS") as demo:
+    gr.Markdown("# Shipping - UPS")
+    with gr.Row():
+        file_input = gr.File(label="Upload Excel File with a UPS 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])
+    
+    # Updated hyperlink block with separate sections for Shipping Tools and Administration Tools.
+    gr.HTML(
+        """
+        <div style="text-align: center; font-size: 16px; margin-top: 20px;">
+            <h3>Shipping Tools</h3>
+            <a href="https://huggingface.co/spaces/leadingbridge/shipping-dhl-e-commerce">DHL</a> | 
+            <a href="https://huggingface.co/spaces/leadingbridge/shipping-ec-ship">EC-Ship</a> | 
+            <a href="https://huggingface.co/spaces/leadingbridge/shipping-fedex">Fedex</a> | 
+            <a href="https://huggingface.co/spaces/leadingbridge/shipping-UPS">UPS</a>
+        </div>
+        <div style="text-align: center; font-size: 16px; margin-top: 20px;">
+            <h3>Administration Tools</h3>
+            <a href="https://huggingface.co/spaces/leadingbridge/email-template">Email Template</a> | 
+            <a href="https://huggingface.co/spaces/leadingbridge/product-feed">Google Merchant</a> | 
+            <a href="https://huggingface.co/spaces/leadingbridge/tss-order">Order Processing</a>
+        </div>
+        """
+    )
+
+demo.launch()