import gradio as gr

import pdfplumber

import pandas as pd

import re

def extract_data(pdf_file):

    data = []

    purchase_order, order_date = None, None

    with pdfplumber.open(pdf_file) as pdf:

        for page in pdf.pages:

            text = page.extract_text().splitlines()




            if not purchase_order or not order_date:

                for line in text:

                    po_match = re.search(r'Purchase Order\s*:\s*(P\d+)', line)

                    date_match = re.search(r'Order Date\s*:\s*([\d-]+)', line)

                    if po_match:

                        purchase_order = po_match.group(1)

                    if date_match:

                        order_date = date_match.group(1)




            for line in text:

                parts = line.split()

                try:

                    pos = int(parts[0])

                    if 10 <= pos <= 450:

                        item_code = parts[1]

                        quantity = float(parts[4])

                        basic_price = float(parts[5])

                        sub_total = float(parts[-1])

                        data.append([purchase_order, order_date, pos, item_code, quantity, basic_price, sub_total])

                except (ValueError, IndexError):

                    continue

    df = pd.DataFrame(data, columns=["Purchase Order", "Order Date", "Pos", "Item Code", "Quantity", "Basic Price", "Sub Total"])

    excel_path = "/tmp/Extracted_Purchase_Order_Data.xlsx"

    df.to_excel(excel_path, index=False)

    return excel_path

iface = gr.Interface(

    fn=extract_data,

    inputs=gr.File(label="Upload PDF"),

    outputs=gr.File(label="Download Excel"),

    title="PDF Data Extractor"

)

iface.launch()