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Added gradio app set up for cheque easy

app.py

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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)

predict_cheque_parser.py

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+from transformers import DonutProcessor, VisionEncoderDecoderModel
+from word2number import w2n
+from dateutil import relativedelta
+from datetime import datetime
+from word2number import w2n
+from textblob import Word
+from PIL import Image
+import torch
+import re
+
+CHEQUE_PARSER_MODEL = "shivi/donut-base-cheque"
+TASK_PROMPT = "<s_cord-v2>"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+def load_donut_model_and_processor():
+    donut_processor = DonutProcessor.from_pretrained(CHEQUE_PARSER_MODEL)
+    model = VisionEncoderDecoderModel.from_pretrained(CHEQUE_PARSER_MODEL)
+    model.to(device)
+    return donut_processor, model
+
+def prepare_data_using_processor(donut_processor,image_path):
+    ## Pass image through donut processor's feature extractor and retrieve image tensor
+    image = load_image(image_path)
+    print("type image:", type(image))
+    pixel_values = donut_processor(image, return_tensors="pt").pixel_values
+    pixel_values = pixel_values.to(device)
+
+    ## Pass task prompt for document (cheque) parsing task to donut processor's tokenizer and retrieve the input_ids
+    decoder_input_ids = donut_processor.tokenizer(TASK_PROMPT, add_special_tokens=False, return_tensors="pt")["input_ids"]
+    decoder_input_ids = decoder_input_ids.to(device)
+
+    return pixel_values, decoder_input_ids
+
+def load_image(image_path):
+    image = Image.open(image_path).convert("RGB")
+    return image
+
+def parse_cheque_with_donut(input_image_path):
+
+    donut_processor, model = load_donut_model_and_processor()
+
+    cheque_image_tensor, input_for_decoder = prepare_data_using_processor(donut_processor,input_image_path)
+    
+    outputs = model.generate(cheque_image_tensor,
+                                decoder_input_ids=input_for_decoder,
+                                max_length=model.decoder.config.max_position_embeddings,
+                                early_stopping=True,
+                                pad_token_id=donut_processor.tokenizer.pad_token_id,
+                                eos_token_id=donut_processor.tokenizer.eos_token_id,
+                                use_cache=True,
+                                num_beams=1,
+                                bad_words_ids=[[donut_processor.tokenizer.unk_token_id]],
+                                return_dict_in_generate=True,
+                                output_scores=True,)
+
+    decoded_output_sequence = donut_processor.batch_decode(outputs.sequences)[0]
+    
+    extracted_cheque_details = decoded_output_sequence.replace(donut_processor.tokenizer.eos_token, "").replace(donut_processor.tokenizer.pad_token, "")
+    ## remove task prompt from token sequence
+    cleaned_cheque_details = re.sub(r"<.*?>", "", extracted_cheque_details, count=1).strip()  
+    ## generate ordered json sequence from output token sequence
+    cheque_details_json = donut_processor.token2json(cleaned_cheque_details)
+    print("cheque_details_json:",cheque_details_json['cheque_details'])
+    
+    ## extract required fields from predicted json
+
+    amt_in_words  = cheque_details_json['cheque_details'][0]['amt_in_words']
+    amt_in_figures = cheque_details_json['cheque_details'][1]['amt_in_figures']
+    macthing_amts = match_legal_and_courstesy_amount(amt_in_words,amt_in_figures)
+    
+    payee_name = cheque_details_json['cheque_details'][2]['payee_name']
+    cheque_date = '06/05/2022'
+    stale_cheque = check_if_cheque_is_stale(cheque_date)
+
+    return payee_name,amt_in_words,amt_in_figures,cheque_date,macthing_amts,stale_cheque
+
+def spell_correction(amt_in_words):
+    corrected_amt_in_words =''
+    words = amt_in_words.split()
+    words = [word.lower() for word in words]
+    for word in words:
+        word = Word(word)
+        corrected_word = word.correct()+' '
+        corrected_amt_in_words += corrected_word
+    return corrected_amt_in_words
+
+def match_legal_and_courstesy_amount(legal_amount,courtesy_amount):
+    macthing_amts = False
+    corrected_amt_in_words = spell_correction(legal_amount)
+    print("corrected_amt_in_words:",corrected_amt_in_words)
+    numeric_legal_amt = w2n.word_to_num(corrected_amt_in_words)
+    print("numeric_legal_amt:",numeric_legal_amt)
+    if int(numeric_legal_amt) == int(courtesy_amount):
+        macthing_amts = True
+    return macthing_amts
+
+def check_if_cheque_is_stale(cheque_issue_date):
+    stale_check = False
+    current_date = datetime.now().strftime('%d/%m/%Y')
+    current_date_ = datetime.strptime(current_date, "%d/%m/%Y")
+    cheque_issue_date_ = datetime.strptime(cheque_issue_date, "%d/%m/%Y")
+    relative_diff = relativedelta.relativedelta(current_date_, cheque_issue_date_)
+    months_difference = (relative_diff.years * 12) + relative_diff.months
+    print("months_difference:",months_difference)
+    if months_difference > 3:
+        stale_check = True
+    return stale_check
+