| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForTokenClassification,RobertaTokenizer | |
| import torch | |
| from fin_readability_sustainability import BERTClass, do_predict | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| tokenizer_sus = RobertaTokenizer.from_pretrained('roberta-base') | |
| model_sustain = BERTClass(2, "sustanability") | |
| model_sustain.to(device) | |
| model_sustain.load_state_dict(torch.load('sustainability_model.bin', map_location=device)['model_state_dict']) | |
| from nltk.tokenize import sent_tokenize | |
| def get_sustainability(text): | |
| df = pd.DataFrame({'sentence':sent_tokenize(text)}) | |
| actual_predictions_sustainability = do_predict(model_sustain, tokenizer_sus, df) | |
| highlight = [] | |
| for sent, prob in zip(df['sentence'].values, actual_predictions_sustainability[1]): | |
| if prob>=4.384316: | |
| highlight.append((sent, 'non-sustainable')) | |
| elif prob<=1.423736: | |
| highlight.append((sent, 'sustainable')) | |
| else: | |
| highlight.append((sent, '-')) | |
| return highlight | |
| b6 = gr.Button("Get Sustainability") | |
| b6.click(get_sustainability, inputs = text, outputs = gr.HighlightedText()) | |
| iface = gr.Interface(fn=get_sustainability, inputs=gr.inputs.Textbox(lines=5, placeholder="Enter Financial Text here..."), title="CONBERT",description="SUSTAINABILITY TOOL", outputs=gr.HighlightedText(), allow_flagging="never") | |
| iface.launch() |