import pdfplumber import pandas as pd from io import BytesIO import re import gradio as gr def extract_data_from_pdf(pdf_file): data = [] po_number = None # Use pdfplumber with BytesIO for Gradio compatibility with pdfplumber.open(BytesIO(pdf_file.read())) as pdf: for page in pdf.pages: text = page.extract_text() # Extract PO number if available if po_number is None: po_match = re.search(r"Purchase Order : (\w+)", text) po_number = po_match.group(1) if po_match else "N/A" # Regex pattern to match the row data row_pattern = re.compile( r"(\d+)\s+(\d+)\s+(\w+)\s+(\d{4}-\d{2}-\d{2})\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)" ) # Extract matching rows 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 "0.0" 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 and save to Excel 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 # Updated 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()