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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()
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