app.py

import pandas as pd
from transformers import pipeline
import os

def generate_procurement_data():
    # Simulating data generation from 10 to 1150 with incremental material numbers
    data = {
        "Sl No": list(range(10, 1160, 10)),
        "Material Description": [f"BPS 017507, Material Number: 22073{str(i).zfill(5)}" for i in range(65400, 65400 + 1150, 10)],
        "Unit": ["NO"] * 115,
        "Quantity": [20] * 115,
        "Dely Qty": [20] * 115,
        "Dely Date": ["04.11.2024"] * 115,
        "Unit Rate": [205.76] * 115,
        "Value": [4115.2] * 115
    }
    
    # Creating DataFrame
    df = pd.DataFrame(data)
    
    # Saving to Excel
    output_file = "Procurement_Summary_Full_Range.xlsx"
    df.to_excel(output_file, index=False)
    print(f"Excel file '{output_file}' generated successfully with procurement data.")
    return output_file

def main():
    # Assuming an NLP model for possible text processing or future enhancement (optional)
    # You can use a Hugging Face model pipeline, like a text summarization model, if needed for extraction.
    print("Starting procurement data generation...")
    output_file = generate_procurement_data()
    print(f"Output file generated at: {output_file}")

if __name__ == "__main__":
    main()