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import pdfplumber
import pandas as pd
import gradio as gr
import re
# Define function to extract data
def extract_data(pdf_file):
data = []
columns = ["Purchase Order No", "Date", "SI No", "Material Number", "Material Description", "HSN Code", "IGST", "Unit", "Quantity", "Dely Qty", "Dely Date", "Unit Rate", "Value"]
# Example Purchase Order Details (Adjust accordingly)
purchase_order_no = "7200018552"
purchase_order_date = "28.09.2024"
with pdfplumber.open(pdf_file) as pdf:
for page in pdf.pages:
text = page.extract_text().splitlines()
for i, line in enumerate(text):
parts = line.split()
try:
si_no = int(parts[0]) # Extract SI No
if si_no % 10 == 0: # Assuming SI numbers are in multiples of 10
# Extracting fields based on pattern and order as per the provided format
material_desc = "BPS 017507" # Based on your example; adjust if dynamic
material_number = parts[3] if "Material" in parts else "220736540000" # Default if not found
hsn_code = "8310" # Fixed HSN Code
igst = "18%" # Fixed IGST
unit = parts[4]
quantity = int(parts[5])
dely_qty = int(parts[6])
dely_date = parts[7]
unit_rate = float(parts[8])
value = float(parts[9])
# Append extracted data in specified order
data.append([
purchase_order_no,
purchase_order_date,
si_no,
material_number,
material_desc,
hsn_code,
igst,
unit,
quantity,
dely_qty,
dely_date,
unit_rate,
value
])
except (ValueError, IndexError):
continue # Skip lines that don't match the format
# Convert to DataFrame with specified columns
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()
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