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| import os | |
| import glob | |
| import gradio as gr | |
| from predict_cheque_parser import parse_cheque_with_donut | |
| ##Create list of examples to be loaded | |
| example_list = glob.glob("examples/cheque_parser/*") | |
| faulty_cheques_list = glob.glob("examples/cheque_analyze/*") | |
| example_list = list(map(lambda el:[el], example_list)) | |
| faulty_cheques_list = list(map(lambda el:[el], faulty_cheques_list)) | |
| demo = gr.Blocks(css="#warning {color: red}") | |
| with demo: | |
| gr.Markdown("# **<p align='center'>ChequeEasy: Banking with Transformers </p>**") | |
| gr.Markdown("This space demonstrates the use of Donut proposed in this <a href=\"https://arxiv.org/abs/2111.15664/\">paper </a>") | |
| with gr.Tabs(): | |
| with gr.TabItem("Cheque Parser"): | |
| gr.Markdown("The 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. 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="Courtesy Amount") | |
| amt_in_figures = gr.Textbox(label="Legal Amount") | |
| cheque_date = gr.Textbox(label="Cheque Date") | |
| # micr_code = gr.Textbox(label="MICR code") | |
| # cheque_number = gr.Textbox(label="Cheque Number") | |
| # account_number = gr.Textbox(label="Account Number") | |
| amts_matching = gr.Checkbox(label="Legal & Courtesy Amount Matching", elem_id="warning") | |
| stale_check = gr.Checkbox(label="Stale Cheque") | |
| 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,cheque_date],parse_cheque_with_donut,cache_examples=False) | |
| # micr_code,cheque_number,account_number, | |
| # amts_matching, stale_check]#,cache_examples=True) | |
| with gr.TabItem("Quality Analyzer"): | |
| gr.Markdown("The module is used to detect any mistakes made by bank customers while filling out the cheque or while taking a snapshot of the cheque. At present the model is trained to find mistakes like -'object blocking cheque', 'overwriting in cheque'. ") | |
| with gr.Box(): | |
| gr.Markdown("**Upload Cheque**") | |
| input_image_detect = gr.Image(type='filepath',label="Input Cheque", show_label=True) | |
| with gr.Box(): # with gr.Column(): | |
| gr.Markdown("**Cheque Quality Results:**") | |
| output_detections = gr.Image(label="Analyzed Cheque Image", show_label=True) | |
| output_text = gr.Textbox() | |
| with gr.Box(): | |
| gr.Markdown("**Predict**") | |
| with gr.Row(): | |
| analyze_cheque = gr.Button("Call YOLOS 🤙") | |
| gr.Markdown("**Examples:**") | |
| with gr.Column(): | |
| gr.Examples(faulty_cheques_list, input_image_detect, [output_detections, output_text])#, predict, cache_examples=True) | |
| parse_cheque.click(parse_cheque_with_donut, inputs=input_image_parse, outputs=[payee_name,amt_in_words,amt_in_figures,cheque_date,amts_matching,stale_check]) | |
| # micr_code,cheque_number,account_number, | |
| # amts_matching, stale_check]) | |
| # analyze_cheque.click(predict, inputs=input_image_detect, outputs=[output_detections, output_text]) | |
| gr.Markdown('\n Solution built by: <a href=\"https://www.linkedin.com/in/shivalika-singh/\">Shivalika Singh</a>') | |
| demo.launch(share=True, debug=True) | |