app.py

import gradio as gr
import pandas as pd
from datetime import datetime
import pytz
import os

def process_file(uploaded_file):
    # 1. Read Excel file (first sheet automatically)
    fname = uploaded_file.name.lower()
    if not fname.endswith(('.xls', '.xlsx', '.xlsm')):
        return "❌ Unsupported format. Please upload .xls/.xlsx/.xlsm", None

    try:
        df = pd.read_excel(uploaded_file.name)
    except Exception as e:
        return f"❌ Error reading file: {e}", None

    # 2. Define the output headers in order
    output_headers = [
        "Usage", "District", "Address", "Longitude", "Latitude",
        "Floor", "Unit", "Area", "PriceInMillion", "PricePerSquareFeet",
        "InstrumentDate", "Year", "WeekNumber", "DeliveryDate", "MemoNo."
    ]

    # 3. Prepare an empty DataFrame
    output_df = pd.DataFrame(index=df.index, columns=output_headers)

    # 4. Map the first 7 columns from the input to the relevant fields:
    #    Col 1 → Address
    #    Col 2 → Floor
    #    Col 3 → Unit
    #    Col 4 → Area
    #    Col 5 → PriceInMillion
    #    Col 6 → PricePerSquareFeet
    #    Col 7 → InstrumentDate
    output_df["Address"]            = df.iloc[:, 0]
    output_df["Floor"]              = df.iloc[:, 1]
    output_df["Unit"]               = df.iloc[:, 2]
    output_df["Area"]               = df.iloc[:, 3]
    output_df["PriceInMillion"]     = df.iloc[:, 4]
    output_df["PricePerSquareFeet"] = df.iloc[:, 5]
    output_df["InstrumentDate"]     = pd.to_datetime(df.iloc[:, 6], errors="coerce")

    # 5. Derive Year and ISO Week Number from InstrumentDate
    #    Any invalid dates become NaT and yield NaN year/week
    output_df["Year"] = output_df["InstrumentDate"].dt.year
    output_df["WeekNumber"] = output_df["InstrumentDate"].dt.isocalendar().week

    # 6. Leave Usage, District, Longitude, Latitude, DeliveryDate, MemoNo. empty
    #    (or populate them here if you have logic to do so)

    # 7. Generate filename based on Hong Kong date
    hk_tz = pytz.timezone("Asia/Hong_Kong")
    today_hk = datetime.now(hk_tz).strftime("%y%m%d")
    out_fname = f"data-clean-{today_hk}.xlsx"

    # 8. Save to Excel (in the current working directory)
    output_df.to_excel(out_fname, index=False)

    # Return the DataFrame for preview and the path to download
    return output_df, out_fname

with gr.Blocks(title="Data Cleaner") as demo:
    gr.Markdown("## 🗂️ Excel → Cleaned Data Mapping")
    with gr.Row():
        file_input = gr.File(label="Upload .xls/.xlsx/.xlsm")
        run_btn    = gr.Button("Process")
    with gr.Row():
        df_out       = gr.DataFrame(label="Mapped Data Preview")
        download_btn = gr.File(label="Download Cleaned File")
    run_btn.click(
        fn=process_file,
        inputs=[file_input],
        outputs=[df_out, download_btn]
    )

if __name__ == "__main__":
    demo.launch()