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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() |