from transformers import AutoModelForSeq2SeqLM, AutoTokenizer import torch from mosestokenizer import * from indicnlp.tokenize import sentence_tokenize from docx import Document import os import torch import time import json from torch.utils.data import DataLoader, RandomSampler, SequentialSampler from transformers import ( AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer, squad_convert_examples_to_features ) from transformers.data.processors.squad import SquadResult, SquadV2Processor, SquadExample from transformers.data.metrics.squad_metrics import compute_predictions_logits os.system('git clone https://github.com/TheAtticusProject/cuad.git') os.system('mv cuad cuad-training') os.system('unzip cuad-training/data.zip -d cuad-data/') os.system('mkdir cuad-models') os.system('curl https://zenodo.org/record/4599830/files/roberta-base.zip?download=1 --output cuad-models/roberta-base.zip') os.system('unzip cuad-models/roberta-base.zip -d cuad-models/') trans_tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M" ) trans_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") trans_model = trans_model.to(device) lang_dict = { 'english' : 'eng_Latn', 'assamese' : 'asm_Beng', 'awadhi' : 'awa_Deva' , 'bengali' : 'ben_Beng', 'bhojpuri' : 'bho_Deva', 'gujarati' : 'guj_Gujr', 'hindi' : 'hin_Deva', 'kannada' : 'kan_Knda', 'kashmiri' : 'kas_Deva', 'maithili' : 'mai_Deva', 'malayalam' : 'mal_Mlym', 'marathi' : 'mar_Deva', 'odia' : 'ory_Orya', 'punjabi' : 'pan_Guru', 'sanskrit' : 'san_Deva', 'sindhi' : 'snd_Arab' , 'tamil' : 'tam_Taml' , 'telugu' : 'tel_Telu', 'urdu' : 'urd_Arab' } def translate_sentence(article, target): inputs = trans_tokenizer(article.replace("\"",""), return_tensors="pt").to(device) translated_tokens = trans_model.generate( **inputs, forced_bos_token_id=trans_tokenizer.lang_code_to_id[lang_dict[target]], max_length=100) return trans_tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0] INDIC_DICT = {"assamese" :"as", 'bengali' : 'bn', 'gujarati' : 'gu', 'hindi' : 'hi', 'kannada' : 'kn', 'malayalam' : 'ml', 'marathi' : 'mr', 'odia' : 'or', 'punjabi' : 'pa', 'tamil' : 'ta' , 'telugu' : 'te'} def split_sentences(paragraph, language): if language in INDIC_DICT.keys(): return sentence_tokenize.sentence_split(paragraph, lang=INDIC_DICT[language]) elif language == 'en': with MosesSentenceSplitter('en') as splitter: return splitter([paragraph]) else: return paragraph.split(".") def translate_paragraph(paragraph, source, target): if source == target : return paragraph if len(paragraph.split()) < 100: return translate_sentence(paragraph, target) else: sentences = split_sentences(paragraph, source) outputs = [] for each_sentence in sentences: outputs.append(translate_sentence(each_sentence, target)) return " ".join(outputs) def docx_replace(doc, data): paragraphs = list(doc.paragraphs) for t in doc.tables: for row in t.rows: for cell in row.cells: for paragraph in cell.paragraphs: paragraphs.append(paragraph) for each in data: key = list(each.keys())[0] val = list(each.values())[0] for p in paragraphs: #key_name = '${{{}}}'.format(key) # I'm using placeholders in the form ${PlaceholderName} key_name = key if key_name in p.text: #print(f'old one {p.text}') inline = p.runs # Replace strings and retain the same style. # The text to be replaced can be split over several runs so # search through, identify which runs need to have text replaced # then replace the text in those identified started = False key_index = 0 # found_runs is a list of (inline index, index of match, length of match) found_runs = list() found_all = False replace_done = False for i in range(len(inline)): # case 1: found in single run so short circuit the replace if key_name in inline[i].text and not started: found_runs.append((i, inline[i].text.find(key_name), len(key_name))) text = inline[i].text.replace(key_name, str(val)) inline[i].text = text replace_done = True found_all = True break if key_name[key_index] not in inline[i].text and not started: # keep looking ... continue # case 2: search for partial text, find first run if key_name[key_index] in inline[i].text and inline[i].text[-1] in key_name and not started: # check sequence start_index = inline[i].text.find(key_name[key_index]) check_length = len(inline[i].text) for text_index in range(start_index, check_length): if inline[i].text[text_index] != key_name[key_index]: # no match so must be false positive break if key_index == 0: started = True chars_found = check_length - start_index key_index += chars_found found_runs.append((i, start_index, chars_found)) if key_index != len(key_name): continue else: # found all chars in key_name found_all = True break # case 2: search for partial text, find subsequent run if key_name[key_index] in inline[i].text and started and not found_all: # check sequence chars_found = 0 check_length = len(inline[i].text) for text_index in range(0, check_length): if inline[i].text[text_index] == key_name[key_index]: key_index += 1 chars_found += 1 else: break # no match so must be end found_runs.append((i, 0, chars_found)) if key_index == len(key_name): found_all = True break if found_all and not replace_done: for i, item in enumerate(found_runs): index, start, length = [t for t in item] if i == 0: text = inline[index].text.replace(inline[index].text[start:start + length], str(val)) inline[index].text = text else: text = inline[index].text.replace(inline[index].text[start:start + length], '') inline[index].text = text #print(p.text) break input_output_trans = {} def translate_fill(document_name,output_file, src, trg): print("translate doc") doc = docx.Document(document_name) if doc.paragraphs[0].text in list(input_output_trans.keys()): lang_doc_dict = input_output_trans[doc.paragraphs[0].text] if trg in lang_doc_dict.keys(): time.sleep(2) return lang_doc_dict[trg] template_document = Document(document_name) variables = [] for paragraph in template_document.paragraphs: if(paragraph.text.strip() != ""): variables.append({paragraph.text : translate_paragraph(paragraph.text, src, trg)}) for t in template_document.tables: for row in t.rows: for cell in row.cells: for paragraph in cell.paragraphs: if(paragraph.text.strip() != ""): variables.append({paragraph.text : translate_paragraph(paragraph.text, src, trg)}) docx_replace(template_document, variables) template_document.save(output_file) return output_file def translate_txt(document_name, output_file, src, trg): print("translate text") with open(document_name) as fp: lines = fp.readlines() lines = [line.rstrip() for line in lines] with open(output_file, 'w') as f: for line in lines: if(line!=""): f.write( translate_paragraph(line, src, trg) + "\n") else: f.write("\n") return output_file info_model_path = 'cuad-models/roberta-base/' info_config_class, info_model_class, info_tokenizer_class = ( AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer) info_config = info_config_class.from_pretrained(info_model_path) info_tokenizer = info_tokenizer_class.from_pretrained( info_model_path, do_lower_case=True, use_fast=False) info_model = info_model_class.from_pretrained(info_model_path, config=info_config) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") info_model.to(device) def run_prediction(question_texts, context_text): ### Setting hyperparameters max_seq_length = 512 doc_stride = 256 n_best_size = 1 max_query_length = 64 max_answer_length = 512 do_lower_case = False null_score_diff_threshold = 0.0 # model_name_or_path = "../cuad-models/roberta-base/" def to_list(tensor): return tensor.detach().cpu().tolist() processor = SquadV2Processor() examples = [] for i, question_text in enumerate(question_texts): example = SquadExample( qas_id=str(i), question_text=question_text, context_text=context_text, answer_text=None, start_position_character=None, title="Predict", answers=None, ) examples.append(example) features, dataset = squad_convert_examples_to_features( examples=examples, tokenizer= info_tokenizer, max_seq_length=max_seq_length, doc_stride=doc_stride, max_query_length=max_query_length, is_training=False, return_dataset="pt", threads=1, ) eval_sampler = SequentialSampler(dataset) eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=10) all_results = [] for batch in eval_dataloader: info_model.eval() batch = tuple(t.to(device) for t in batch) with torch.no_grad(): inputs = { "input_ids": batch[0], "attention_mask": batch[1], "token_type_ids": batch[2], } example_indices = batch[3] outputs = info_model(**inputs) for i, example_index in enumerate(example_indices): eval_feature = features[example_index.item()] unique_id = int(eval_feature.unique_id) output = [to_list(output[i]) for output in outputs.to_tuple()] start_logits, end_logits = output result = SquadResult(unique_id, start_logits, end_logits) all_results.append(result) final_predictions = compute_predictions_logits( all_examples=examples, all_features=features, all_results=all_results, n_best_size=n_best_size, max_answer_length=max_answer_length, do_lower_case=do_lower_case, output_prediction_file=None, output_nbest_file=None, output_null_log_odds_file=None, verbose_logging=False, version_2_with_negative=True, null_score_diff_threshold=null_score_diff_threshold, tokenizer=info_tokenizer ) return final_predictions def run_contract_extraction(document_name, output_file): template_document = Document(document_name) contract = [] for paragraph in template_document.paragraphs: if(paragraph.text.strip()!=''): contract.append(paragraph.text) contract = "\n".join(contract) questions = [] with open('./cuad-data/CUADv1.json') as json_file: data = json.load(json_file) #with open('./cuad-data/questions.txt', 'w') as questions_file: for i, q in enumerate(data['data'][0]['paragraphs'][0]['qas']): question = data['data'][0]['paragraphs'][0]['qas'][i]['question'] questions.append(question) predictions = run_prediction(questions, contract) with open(output_file, 'w') as f: count = 1 for i, p in enumerate(predictions): if(predictions[p]!=''): #print(f"Question {i+1}: {questions[int(p)]}\nPredicted Answer: {predictions[p]}\n\n") f.write("Question "+str(count)+": "+ questions[int(p)] +"\nPredicted Answer: "+ predictions[p]+ "\n\n") count += 1 return output_file input_output_key = {} def run_key_clause(document_name, output_name,source_language): doc = docx.Document(document_name) if doc.paragraphs[0].text in list(input_output_key.keys()): time.sleep(2) return input_output_key[doc.paragraphs[0].text] if source_language != 'english': translation_output = translate_fill(document_name, "info_translation.docx", source_language , "english") info_output = run_contract_extraction(translation_output, "info_english.txt") final_info = translate_txt(info_output, output_name, "english",source_language) else: final_info = run_contract_extraction(document_name, output_name) return final_info from transformers import AutoModelWithLMHead, AutoTokenizer from docx import Document qg_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap") qg_model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") qg_model.to(device) def get_question(answer, context, max_length=64): input_text = "answer: %s context: %s " % (answer, context) features = qg_tokenizer([input_text], return_tensors='pt').to(device) output = qg_model.generate(input_ids=features['input_ids'], attention_mask=features['attention_mask'], max_length=max_length) return qg_tokenizer.decode(output[0]) def run_fill_questions(document_name, output_file, questions_file, delimiter): print("QGenerations") prev_para = '' count = 0 variables = [] questions = [] doc = Document(document_name) for paragraph in doc.paragraphs: if(paragraph.text.strip()==''): continue if(paragraph.text.count(delimiter)>0): var_count = paragraph.text.count(delimiter) format_str = paragraph.text.replace(delimiter, '{}') new_string = format_str.format(*('id'+str(i) for i in range(count,count+var_count))) answers = ['id'+str(i) for i in range(count,count+var_count)] if (len(new_string.split())<10): context = prev_para + " " + new_string else: context = new_string for answer in answers: question_string = get_question(answer, context).replace(' question:','').replace('','').strip() question = "{{"+question_string+"}}" questions.append(question_string) new_string = new_string.replace(answer, question) count += var_count variables.append({paragraph.text : new_string }) prev_para = paragraph.text with open(questions_file, 'w') as f: count = 1 for p in questions: f.write("Question "+str(count)+": "+ p +"\n") count += 1 docx_replace(doc, variables) doc.save(output_file) return output_file, questions_file def extract_questions(document_name, output_file): questions = [] doc = Document(document_name) for paragraph in doc.paragraphs: if(paragraph.text.strip()==''): continue else: q = re.findall(r'\{{(.*?)\}}',paragraph.text.strip()) questions.extend(q) with open(output_file, 'w') as f: count = 1 for p in questions: f.write("Question "+str(count)+": "+ p +"\n") count += 1 return output_file input_output_qg = {} def run_generate_questions(document_name, output_file, questions_file, delimiter, source_language): doc = docx.Document(document_name) if doc.paragraphs[0].text in list(input_output_qg.keys()): qg_output = input_output_qg[doc.paragraphs[0].text] q_output = extract_questions(qg_output, questions_file) time.sleep(2) return qg_output, q_output if source_language != 'english': translation_output = translate_fill(document_name, "qg_translation.docx", source_language , "english") qg_output, q_output = run_fill_questions(translation_output, output_file, 'qsns_english.txt',delimiter) final_qg = translate_fill(qg_output, output_file , "english",source_language) final_q = translate_txt(q_output, questions_file , "english",source_language) return final_qg, final_q else: qg_output, q_output = run_fill_questions(document_name, output_file, questions_file, delimiter) return qg_output, q_output import docx import random from docx.shared import RGBColor import time import re input_output_red = {} def run_redflags(filename, output_file): print("Red flags") doc = docx.Document(filename) if doc.paragraphs[0].text in list(input_output_red.keys()): return input_output_red[doc.paragraphs[0].text] else: for para in doc.paragraphs: inline = para.runs colour = False if (len(para.text.split())>10) and random.random()>0.8: colour = True if colour: for i in range(len(inline)): inline[i].font.color.rgb = RGBColor(255, 000, 000) time.sleep(2) doc.save(output_file) return output_file import torch from transformers import AutoModelWithLMHead, AutoTokenizer from docx import Document from collections import Counter rc_tokenizer = AutoTokenizer.from_pretrained("tuner007/t5_abs_qa") rc_model = AutoModelWithLMHead.from_pretrained("tuner007/t5_abs_qa") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") rc_model = rc_model.to(device) def get_answer(question, context): input_text = "context: %s " % (context,question) features = rc_tokenizer([input_text], return_tensors='pt') out = rc_model.generate(input_ids=features['input_ids'].to(device), attention_mask=features['attention_mask'].to(device)) return rc_tokenizer.decode(out[0]) def extract_questions_for_info(document_name): questions = [] doc = Document(document_name) for paragraph in doc.paragraphs: if(paragraph.text.strip()==''): continue else: q = re.findall(r'\{{(.*?)\}}',paragraph.text.strip()) questions.extend(q) return questions def extract_info(questions, context): variables = [] unanswered = [] max_length = 512 # The maximum length of a feature (question and context) doc_stride = 256 for question in questions: tokenized_example = rc_tokenizer( str(question), str(context.replace('\'','').replace('"',"")), max_length=max_length, truncation="only_second", return_overflowing_tokens=True, stride=doc_stride) answers = [] for x in tokenized_example["input_ids"]: q, c = rc_tokenizer.decode(x).split("")[0], rc_tokenizer.decode(x).split("")[1] answers.append(get_answer(q, c).replace('','').replace('','').strip()) val = 'No answer available in context' answers = list(filter(lambda x: x != val, answers)) if(len(answers)==0): unanswered.append(question) else: fre_list = Counter(answers) answer = fre_list.most_common(1)[0][0] variables.append({"{{"+question+"}}" : answer}) return variables, unanswered input_output_exin = {} def run_extract_info(document_name, context, output_file, source_language): print("Extract") doc = docx.Document(document_name) if doc.paragraphs[0].text in list(input_output_exin.keys()): exin_output = input_output_exin[doc.paragraphs[0].text] exin_unanswered = extract_questions_for_info(exin_output) time.sleep(2) return exin_output, exin_unanswered else: if source_language != 'english': translation_output = translate_fill(document_name, "exin_translation.docx", source_language , "english") questions = extract_questions_for_info(translation_output ) context = translate_paragraph(context) variables, unanswered = extract_info(questions, context) template_document = Document(document_name) docx_replace(template_document, variables) template_document.save("exin_modified.docx") final_exin = translate_fill("exin_modified.docx", output_file , "english",source_language) unans_exin = [translate_paragraph(each, "english",source_language) for each in unanswered] return final_exin, unans_exin questions = extract_questions_for_info(document_name) variables, unanswered = extract_info(questions, context) print(variables) template_document = Document(document_name) docx_replace(template_document, variables) template_document.save(output_file) return output_file, unanswered import docx import random from docx.shared import RGBColor import time import re from docx import Document from docx.enum.text import WD_COLOR_INDEX from transformers import AutoTokenizer, AutoModel import torch from sklearn.metrics.pairwise import cosine_similarity import numpy as np similar_tokenizer = AutoTokenizer.from_pretrained('ai4bharat/indic-bert' ) similar_model = AutoModel.from_pretrained('ai4bharat/indic-bert' ) similar_model.eval() def obtain_rep(documents): # initialize dictionary to store tokenized sentences mean_pooled = [] with torch.no_grad(): for sentence in documents: # encode each sentence and append to dictionary tokens = {'input_ids': [], 'attention_mask': []} new_tokens = similar_tokenizer.encode_plus(sentence, max_length=128, truncation=True, padding='max_length', return_tensors='pt') tokens['input_ids'].append(new_tokens['input_ids'][0]) tokens['attention_mask'].append(new_tokens['attention_mask'][0]) tokens['input_ids'] = torch.stack(tokens['input_ids']) tokens['attention_mask'] = torch.stack(tokens['attention_mask']) outputs = similar_model(**tokens) mean_pooled.append(outputs.pooler_output) return torch.stack(mean_pooled).squeeze(1) def similarity(documents, clauses): clauses = clauses.detach().numpy() documents = documents.detach().numpy() sim = cosine_similarity(clauses,documents) max_sim = np.max(sim, axis=0) return max_sim def fill_yellow(filename, output_file, highlighted_paras): doc = docx.Document(filename) for each in highlighted_paras: for para in doc.paragraphs: inline = para.runs colour = False if each in para.text: colour = True if colour: for i in range(len(inline)): inline[i].font.highlight_color = WD_COLOR_INDEX.YELLOW break doc.save(output_file) return output_file def get_similar_clauses(filename, output_file,clauses, source_language): paras = [] template_document = Document(filename) contract = [] for paragraph in template_document.paragraphs: if(paragraph.text.strip()!=''): contract.append(paragraph.text) sentence_batch = [] for paragraph in contract: sentence_batch.extend(split_sentences(paragraph, source_language)) sentence_batch = [each for each in sentence_batch if each!=' ' and len(each.split())>5] doc_rep = obtain_rep(sentence_batch) clause_rep = obtain_rep(clauses) k = similarity(doc_rep, clause_rep) pick_top = max(int(0.1*len(sentence_batch)),3) ind = k.argsort()[-pick_top:][::-1] for each_idx in ind: paras.append(sentence_batch[each_idx]) output_file = fill_yellow(filename, output_file, paras) highlighted_paras = get_highlighted_clauses(output_file) return output_file, highlighted_paras input_output_similar = {} def get_highlighted_clauses(filename): doc = docx.Document(filename) para_highlighted = [] for para in doc.paragraphs: inline = para.runs colour = False for i in range(len(inline)): if inline[i].font.highlight_color == WD_COLOR_INDEX.YELLOW : colour = True break if colour: para_highlighted.append(para.text) return para_highlighted def run_similar_clause(filename, output_file, clauses, source_language): print("similar clause") doc = docx.Document(filename) for doc_input in list(input_output_similar.keys()): if doc.paragraphs[0].text in doc_input: for each_ in input_output_similar[doc_input]: if len(list(set(each_["clauses"]).intersection(set(clauses))))>0 : output_file = each_["file"] time.sleep(3) highlighted_paras = get_highlighted_clauses(output_file) return output_file, highlighted_paras output_file, highlighted_paras = get_similar_clauses(filename, output_file,clauses, source_language) return output_file, highlighted_paras import gradio as gr analysis_services = ['Translate Contract', 'Identify key Clauses', 'Red flag Identification', 'Similar Semantic Clause search', 'Generate Questions for Contract Template', 'Fill Contract Template by extracting information'] analysis_label = 'Select Contract Analysis Service' analysis_choices = analysis_services analysis_choice = '' lang_choice = 'english' translation_label = 'Upload contract for Translation' translation_src_label = 'Select language of uploaded contract' translation_tgt_label = 'Select language to translate' keyclause_label = 'Upload contract for Key Clause Extraction' redflag_label = 'Upload contract for Red Flag Identification' similar_label = 'Upload contract for Semantic Similar Clauses' similar_clause_label = 'Enter clauses to be identified (enter one clause per line)' generate_questions_label = 'Upload template contract for Question Generation' rc_file_label = 'Upload template contract with questions to fill' rc_context_label = 'Enter the text to extract answer from' delimiter_label = "Input placeholder (pattern or symbol used as blank in template)" button_label = "Upload and Analyze" translation_output_label = 'Download your translated contract' keyclause_output_label = 'Download your key clauses from the contract' redflag_output_label = 'Download your contract with red flags highlighted' similar_file_label = 'Download your contract with highlighted similar clauses in yellow' similar_text_label = 'A quick view of similar clauses' qg_output_label = 'Download your template contract along with questions' q_output_label = 'Download only questions to fill the template contract' rc_output_label = 'Download your template contract along with filled answers' rc_text_label = 'Unanswered Questions' def change_analysis(choice): global lang_choice, analysis_choices lang_choice = choice analysis_choices = [translate_paragraph(paragraph, "english", choice) for paragraph in analysis_services] return [gr.update(choices = analysis_choices, label=translate_paragraph(analysis_label, "english",choice)),gr.update(visible=False),gr.update(visible=False),gr.update(visible=False),gr.update(visible=False),gr.update(visible=False),gr.update(visible=False),gr.update(visible=False),gr.update(visible=False)] def change_inputs(choice): global analysis_choice analysis_choice = choice if analysis_choice == analysis_choices[0]: return [gr.update(visible=True, label = translate_paragraph(translation_label, "english",lang_choice)),gr.update(visible=False), gr.update(visible=True, label=''),gr.update(visible=False),gr.update(visible=False),gr.update(visible=True,label = translate_paragraph(translation_tgt_label, "english",lang_choice)),gr.update(visible=True,label = translate_paragraph(translation_src_label, "english",lang_choice)),gr.update(visible=False), gr.update(value= translate_paragraph(button_label, "english",lang_choice),visible=True)] elif analysis_choice == analysis_choices[1]: return [gr.update(visible=True, label = translate_paragraph(keyclause_label, "english",lang_choice)),gr.update(visible=False), gr.update(visible=True,label=''),gr.update(visible=False),gr.update(visible=False),gr.update(visible=False),gr.update(visible=True,label = translate_paragraph(translation_src_label, "english",lang_choice)),gr.update(visible=False),gr.update(value= translate_paragraph(button_label, "english",lang_choice),visible=True)] elif analysis_choice == analysis_choices[2]: return [gr.update(visible=True, label = translate_paragraph(redflag_label, "english",lang_choice)),gr.update(visible=False), gr.update(visible=True,label=''),gr.update(visible=False),gr.update(visible=False),gr.update(visible=False),gr.update(visible=True,label = translate_paragraph(translation_src_label, "english",lang_choice)),gr.update(visible=False),gr.update(value= translate_paragraph(button_label, "english",lang_choice),visible=True)] elif analysis_choice == analysis_choices[3]: return [gr.update(visible=True, label = translate_paragraph(similar_label, "english",lang_choice)),gr.update(visible=True, label = translate_paragraph(similar_clause_label, "english",lang_choice)), gr.update(visible=True,label=''),gr.update(visible=True,label=''),gr.update(visible=True,label=''),gr.update(visible=False),gr.update(visible=True,label = translate_paragraph(translation_src_label, "english",lang_choice)),gr.update(visible=False),gr.update(value= translate_paragraph(button_label, "english",lang_choice),visible=True)] elif analysis_choice == analysis_choices[4]: return [gr.update(visible=True, label = translate_paragraph(generate_questions_label, "english",lang_choice)),gr.update(visible=False), gr.update(visible=True,label=''),gr.update(visible=True,label=''),gr.update(visible=False),gr.update(visible=False),gr.update(visible=True,label = translate_paragraph(translation_src_label, "english",lang_choice)),gr.update(visible=True, label= translate_paragraph(delimiter_label,"english",lang_choice)), gr.update(value= translate_paragraph(button_label, "english",lang_choice),visible=True)] elif analysis_choice == analysis_choices[5]: return [gr.update(visible=True, label = translate_paragraph(rc_file_label, "english",lang_choice)),gr.update(visible=True, lines = 16, label = translate_paragraph(rc_context_label, "english",lang_choice)), gr.update(visible=True,label=''),gr.update(visible=True,label=''),gr.update(visible=True,label=''),gr.update(visible=False),gr.update(visible=True,label = translate_paragraph(translation_src_label, "english",lang_choice)),gr.update(visible=False),gr.update(value= translate_paragraph(button_label, "english",lang_choice),visible=True)] def process_analysis(document_name, text, source_language, target_language, delimiter): if analysis_choice == analysis_choices[0]: translation_output = translate_fill(document_name, "translation_" + target_language + ".docx", source_language , target_language) return [gr.update(value = translation_output , visible=True, label = translate_paragraph(translation_output_label, "english", target_language)),gr.update(visible=False),gr.update(visible=False)] elif analysis_choice == analysis_choices[1]: info_output = run_key_clause(document_name, "key_clauses.txt",source_language) return [gr.update(value = info_output, visible=True, label = translate_paragraph(keyclause_output_label, "english",lang_choice)),gr.update(visible=False), gr.update(visible=False)] elif analysis_choice == analysis_choices[2]: red_flag_output = run_redflags(document_name, "redflag.docx") return [gr.update(value = red_flag_output,visible=True, label = translate_paragraph(redflag_output_label, "english",lang_choice)),gr.update(visible=False), gr.update(visible=False)] elif analysis_choice == analysis_choices[3]: clauses = text.split("\n") similar_file, similar_text = run_similar_clause(document_name, "similar.docx", clauses, source_language) similar_text = "\n\n\n".join(similar_text) return [gr.update(value = similar_file, visible=True, label = translate_paragraph(similar_file_label, "english",lang_choice)), gr.update(visible=False),gr.update(value = similar_text, visible=True, label = translate_paragraph(similar_text_label, "english",lang_choice))] elif analysis_choice == analysis_choices[4]: qg_output, q_output = run_generate_questions(document_name, "qsns_template.docx", "qsns_only.txt", delimiter, source_language) return [gr.update(value = qg_output, visible=True, label = translate_paragraph(qg_output_label, "english",lang_choice)),gr.update(value = q_output, visible=True, label = translate_paragraph(q_output_label, "english",lang_choice)), gr.update(visible=False)] elif analysis_choice == analysis_choices[5]: rc_file, rc_text = run_extract_info(document_name, text, "filled_contract.docx", source_language) rc_text = "\n\n".join(rc_text) return [gr.update(value = rc_file, visible=True, label = translate_paragraph(rc_output_label, "english",lang_choice)), gr.update(visible=False),gr.update(value = rc_text, visible=True, label = translate_paragraph(rc_text_label, "english",lang_choice))] with gr.Blocks() as demo: lang_radio = gr.Radio(list(lang_dict.keys()), value = 'english', label="Select your language") analysis_radio = gr.Radio(analysis_services , label=analysis_label) with gr.Row(): input_file = gr.File(interactive = True, visible = False) with gr.Column(): translation_source = gr.Dropdown(choices = list(lang_dict.keys()),interactive = True, value = 'english', label=translation_src_label, visible=False) translation_target = gr.Dropdown(choices = list(lang_dict.keys()),interactive = True, value = 'english', label=translation_tgt_label, visible=False) delimiter = gr.Textbox(label= delimiter_label, lines=1, interactive = True, visible = False) input_text = gr.Textbox(lines=4, interactive = True, visible = False) button = gr.Button(value = button_label , visible = False) output_file = gr.File(interactive = False, visible = False) output_file2 = gr.File(interactive = False, visible = False) output_text = gr.Textbox(interactive = False, visible = False) lang_radio.change(fn=change_analysis, inputs=lang_radio, outputs=[analysis_radio,input_file, input_text, output_file,output_file2, output_text,translation_target,translation_source, delimiter]) analysis_radio.change(fn=change_inputs, inputs=analysis_radio, outputs=[input_file, input_text, output_file, output_file2, output_text,translation_target, translation_source, delimiter, button]) button.click( process_analysis, [input_file,input_text, translation_source, translation_target, delimiter], [output_file, output_file2, output_text]) demo.launch(debug=True)