app.py

import pdfplumber
import pandas as pd
from io import BytesIO
import tempfile
import re
import gradio as gr

def extract_data_from_pdf(pdf_file):
    data = []
    po_number = None
    
    # Save the uploaded file temporarily so pdfplumber can open it
    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
        temp_pdf.write(pdf_file.read())  # Write the uploaded file content to a temporary file
        temp_pdf_path = temp_pdf.name  # Get the file path

    # Now open the temporary file with pdfplumber
    with pdfplumber.open(temp_pdf_path) as pdf:
        for page in pdf.pages:
            text = page.extract_text()

            # Extract PO number once (if not already extracted)
            if po_number is None:
                po_match = re.search(r"Purchase Order : (\w+)", text)
                if po_match:
                    po_number = po_match.group(1)

            # Regex pattern to match the row data
            row_pattern = re.compile(
                r"(\d+)\s+(\d{10,})\s+(\w+)\s+(\d{4}-\d{2}-\d{2})\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)"
            )

            # Find all rows matching the pattern
            for match in row_pattern.finditer(text):
                pos, item_code, unit, delivery_date, quantity, basic_price, amount = match.groups()
                sub_total_match = re.search(r"SUB TOTAL : ([\d.]+)", text)
                sub_total = sub_total_match.group(1) if sub_total_match else ""

                data.append({
                    "Purchase Order": po_number,
                    "Pos.": pos,
                    "Item Code": item_code,
                    "Unit": unit,
                    "Delivery Date": delivery_date,
                    "Quantity": quantity,
                    "Basic Price": basic_price,
                    "Amount": amount,
                    "SUB TOTAL": sub_total
                })

    # Convert the data to a DataFrame
    df = pd.DataFrame(data)
    output = BytesIO()
    with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
        df.to_excel(writer, index=False, sheet_name="Extracted Data")
    output.seek(0)

    return output

# Gradio Interface
iface = gr.Interface(
    fn=extract_data_from_pdf,
    inputs=gr.File(label="Upload PDF"),
    outputs=gr.File(label="Download Excel"),
    title="PDF Data Extractor",
    description="Extract structured data from a PDF and output it as an Excel file."
)

iface.launch()