import pickle import gradio as gr import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from transformers import BertTokenizer, BertForSequenceClassification, pipeline, AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline, AutoModelForSeq2SeqLM, AutoModel, RobertaModel, RobertaTokenizer from sentence_transformers import SentenceTransformer from fin_readability_sustainability import BERTClass, do_predict import pandas as pd #import lightgbm #lr_clf_finbert = pickle.load(open("lr_clf_finread_new.pkl",'rb')) tokenizer_read = BertTokenizer.from_pretrained('ProsusAI/finbert') device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model_read = BERTClass(2, "readability") model_read.to(device) model_read.load_state_dict(torch.load('readability_model.bin', map_location=device)['model_state_dict']) def get_readability(text): df = pd.DataFrame({'sentence':[text]}) actual_predictions_read = do_predict(model_read, tokenizer_read, df) score = round(actual_predictions_read[1][0], 4) return score # Reference : https://huggingface.co/humarin/chatgpt_paraphraser_on_T5_base tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base") model = AutoModelForSeq2SeqLM.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base") def paraphrase( question, num_beams=5, num_beam_groups=5, num_return_sequences=5, repetition_penalty=10.0, diversity_penalty=3.0, no_repeat_ngram_size=2, temperature=0.7, max_length=128 ): input_ids = tokenizer( f'paraphrase: {question}', return_tensors="pt", padding="longest", max_length=max_length, truncation=True, ).input_ids outputs = model.generate( input_ids, temperature=temperature, repetition_penalty=repetition_penalty, num_return_sequences=num_return_sequences, no_repeat_ngram_size=no_repeat_ngram_size, num_beams=num_beams, num_beam_groups=num_beam_groups, max_length=max_length, diversity_penalty=diversity_penalty ) res = tokenizer.batch_decode(outputs, skip_special_tokens=True) return res def get_most_readable_paraphrse(text): li_paraphrases = paraphrase(text) li_paraphrases.append(text) best = li_paraphrases[0] score_max = get_readability(best) for i in range(1,len(li_paraphrases)): curr = li_paraphrases[i] score = get_readability(curr) if score > score_max: best = curr score_max = score if best!=text and score_max>.6: ans = "The most redable version of text that I can think of is:\n" + best else: "Sorry! I am not confident. As per my best knowledge, you already have the most readable version of the text!" return ans def set_example_text(example_text): return gr.Textbox.update(value=example_text[0]) with gr.Blocks() as demo: gr.Markdown( """ # FinLanSer Financial Language Simplifier """) text = gr.Textbox(label="Enter text you want to simply (make more readable)") greet_btn = gr.Button("Simplify/Make Readable") output = gr.Textbox(label="Output Box") greet_btn.click(fn=get_most_readable_paraphrse, inputs=text, outputs=output, api_name="get_most_raedable_paraphrse") example_text = gr.Dataset(components=[text], samples=[['Legally assured line of credit with a bank'], ['A mutual fund is a type of financial vehicle made up of a pool of money collected from many investors to invest in securities like stocks, bonds, money market instruments']]) example_text.click(fn=set_example_text, inputs=example_text,outputs=example_text.components) demo.launch()