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import pdfplumber
import pandas as pd
import re
import gradio as gr
def extract_data(pdf_file):
"""
Extract data from the uploaded PDF for dynamic ranges (e.g., 10 to n).
"""
data = []
columns = ["SI No", "Material Description", "Unit", "Quantity", "Dely Qty", "Dely Date", "Unit Rate", "Value"]
start_si = 10 # Start from SI No 10
end_si = None # Dynamically detect the end SI No
with pdfplumber.open(pdf_file) as pdf:
for page in pdf.pages:
full_text = page.extract_text() # Get the text content for the page
lines = full_text.splitlines() if full_text else []
for line in lines:
try:
# Parse the first column for SI No
si_no_match = re.match(r"^\s*(\d+)\s", line)
if not si_no_match:
continue
si_no = int(si_no_match.group(1))
# Dynamically set the end SI No if higher SI Nos are found
if end_si is None or si_no > end_si:
end_si = si_no
if si_no < start_si:
continue # Skip rows below the start SI No
# Extract Material Description and details dynamically
material_desc = extract_material_description(full_text, si_no)
# Extract remaining fields
parts = line.split()
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])
# Append row data
data.append([si_no, material_desc, unit, quantity, dely_qty, dely_date, unit_rate, value])
except (ValueError, IndexError):
# Skip invalid rows or rows with missing data
continue
# Convert data to DataFrame and save as Excel
df = pd.DataFrame(data, columns=columns)
excel_path = "/tmp/Extracted_PO_Data_Dynamic.xlsx"
df.to_excel(excel_path, index=False)
return excel_path
def extract_material_description(full_text, si_no):
"""
Extract Material Description, including Material Number, HSN Code, and IGST, using unique patterns.
"""
material_desc = ""
# Match the specific SI No row to extract details
si_no_pattern = rf"{si_no}\s+(BPS\s+\d+).*?Material\s+Number:\s+(\d+)"
match = re.search(si_no_pattern, full_text, re.DOTALL)
if match:
bps_code = match.group(1)
material_number = match.group(2)
material_desc += f"{bps_code}\nMaterial Number: {material_number}\n"
# Extract HSN Code
hsn_code_match = re.search(r"HSN\s+Code:\s*(\d+)", full_text)
if hsn_code_match:
hsn_code = hsn_code_match.group(1)
material_desc += f"HSN Code: {hsn_code}\n"
else:
material_desc += "HSN Code: Not Found\n"
# Extract IGST
igst_match = re.search(r"IGST\s*:\s*(\d+)\s*%", full_text)
if igst_match:
igst = igst_match.group(1)
material_desc += f"IGST: {igst} %"
else:
material_desc += "IGST: Not Found"
return material_desc.strip()
def process_pdf(file):
"""
Process the uploaded PDF and return the extracted data.
"""
try:
# Extract text from the PDF
output_path = extract_data(file.name)
return output_path, "Data extraction successful!"
except Exception as e:
return None, f"Error during processing: {str(e)}"
# Gradio Interface
def gradio_interface(pdf_file):
"""
Interface function for Gradio to process the PDF and return the Excel file.
"""
return extract_data(pdf_file.name)
# Define Gradio interface
interface = gr.Interface(
fn=gradio_interface,
inputs=gr.File(label="Upload PDF"),
outputs=gr.File(label="Download Extracted Excel"),
title="Dynamic BHEL PO Data Extractor",
description="Upload a PDF to extract accurate Material Numbers and related data dynamically into an Excel file."
)
if __name__ == "__main__":
interface.launch()
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