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from predict import run_prediction |
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from io import StringIO |
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import json |
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import gradio as gr |
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import spacy |
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from spacy import displacy |
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import torch |
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import nltk |
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from score_fincat import score_fincat |
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from sus_fls import get_sustainability,fls |
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from Cuad_others import quad,summarize_text,fin_ner |
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nltk.download('punkt') |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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def load_questions(): |
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questions = [] |
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with open('questions.txt') as f: |
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questions = f.readlines() |
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return questions |
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questions = load_questions() |
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def mainFun(query,file): |
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answer,answer_p=quad(query,file.name) |
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return answer_p,summarize_text(answer),fin_ner(answer),score_fincat(answer),get_sustainability(answer),fls(answer) |
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iface = gr.Interface(fn=mainFun, inputs=[gr.Dropdown(choices=questions,label='SEARCH QUERY'),gr.inputs.File(label='TXT FILE')], title="CONBERT",description="CONTRACT REVIEW TOOL",article='Article', outputs=[gr.outputs.Textbox(label='Answer'),gr.outputs.Textbox(label='Summary'),gr.HighlightedText(label='NER'),gr.HighlightedText(label='CLAIM'),gr.HighlightedText(label='SUSTAINABILITY'),gr.HighlightedText(label='FLS')], allow_flagging="never") |
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iface.launch() |
