import pdfplumber

import pandas as pd

import gradio as gr

# Define function to extract data

def extract_data(pdf_file):

    data = []

    columns = ["SI No", "Material Description", "Unit", "Quantity", "Dely Qty", "Dely Date", "Unit Rate", "Value"] 

    start_si, end_si = 10, 1150

    with pdfplumber.open(pdf_file) as pdf:

        for page in pdf.pages:

            text = page.extract_text().splitlines()

            for line in text:

                parts = line.split()

                try:

                    si_no = int(parts[0])

                    if start_si <= si_no <= end_si:

                        material_desc = " ".join(parts[1:3])

                        unit = parts[3]

                        quantity = int(parts[4])

                        dely_qty = int(parts[5])

                        dely_date = parts[6]

                        unit_rate = float(parts[7])

                        value = float(parts[8])

                        data.append([si_no, material_desc, unit, quantity, dely_qty, dely_date, unit_rate, value])

                except (ValueError, IndexError):

                    continue

    df = pd.DataFrame(data, columns=columns)

    excel_path = "/tmp/Extracted_Purchase_Order_Data.xlsx"

    df.to_excel(excel_path, index=False)

    return excel_path
    
# Set up Gradio interface

iface = gr.Interface(

    fn=extract_data,

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

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

    title="PDF Data Extractor"

)
# Launch the app

iface.launch()