app.py

import os
import glob
import gradio as gr
from scripts.predict import parse_cheque_with_donut

##Create list of examples to be loaded
example_list = glob.glob("data/*")
example_list = list(map(lambda el: [el], example_list))

demo = gr.Blocks()

with demo:

    gr.Markdown("# **<p align='center'>ChequeEasy: Banking made easy </p>**")
    gr.Markdown(
        'ChequeEasy is a project that aims to simplify the process of approval of cheques and making it easier for both bank officials and customers. \
    This project leverages Donut model proposed in the paper <a href="https://arxiv.org/abs/2111.15664/"> OCR-free Document Understanding Transformer </a> for the parsing of the required data from cheques.'
        'Donut is based on a very simple transformer encoder and decoder architecture. It\'s main USP is that it is an OCR-free approach to Visual Document Understanding (VDU) and can perform tasks like document classification, information extraction as well as VQA. \
    OCR based techniques come with several limitations such as requiring use of additional downstream models, lack of understanding about document structure, requiring use of hand crafted rules for information extraction,etc. \
    Donut helps you get rid of all of these OCR specific limitations. The model for the project has been trained using a subset of this  <a href="https://www.kaggle.com/datasets/medali1992/cheque-images/"> kaggle dataset </a>. The original dataset contains images of cheques of 10 different banks.'
    )

    with gr.Tabs():

        with gr.TabItem("Cheque Parser"):
            gr.Markdown(
                "This module is used to extract details filled by a bank customer from cheques. At present the model is trained to extract details like - Payee Name, Amount in words, Amount in Figures, Bank Name.  \
            This model can be further trained to parse additional details like MICR Code, Cheque Number, Account Number, etc."
            )
            with gr.Box():
                gr.Markdown("**Upload Cheque**")
                input_image_parse = gr.Image(type="filepath", label="Input Cheque")
            with gr.Box():
                gr.Markdown("**Parsed Cheque Data**")

                payee_name = gr.Textbox(label="Payee Name")
                amt_in_words = gr.Textbox(label="Legal Amount")
                amt_in_figures = gr.Textbox(label="Courtesy Amount")
                bank_name = gr.Textbox(label="Bank Name")

            with gr.Box():
                gr.Markdown("**Predict**")
                with gr.Row():
                    parse_cheque = gr.Button("Call Donut 🍩")

            with gr.Column():
                gr.Examples(
                    example_list,
                    [input_image_parse],
                    [payee_name, amt_in_words, amt_in_figures, bank_name],
                    parse_cheque_with_donut,
                    cache_examples=False,
                )

    parse_cheque.click(
        parse_cheque_with_donut,
        inputs=input_image_parse,
        outputs=[payee_name, amt_in_words, amt_in_figures, bank_name],
    )

    gr.Markdown(
        '\n Solution built by: <a href="https://github.com/Nandhagopalan">Nandhagopalan Elangovan</a>'
    )

demo.launch()