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Browse files- app.py +73 -0
- requirements.txt +4 -0
app.py
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# Created by Hansi at 30/08/2023
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import nltk
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nltk.download('punkt')
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nltk.download('averaged_perceptron_tagger')
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import streamlit as st
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from accord_nlp.information_extraction.convertor import entity_pairing, graph_building
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from accord_nlp.information_extraction.ie_pipeline import InformationExtractor
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@st.cache_resource
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def init():
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return InformationExtractor()
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st.set_page_config(
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page_title='ACCORD NLP Demo',
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initial_sidebar_state='expanded',
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layout='wide',
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)
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with st.spinner(text="Initialising..."):
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ie = init()
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def main():
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st.sidebar.title("ACCORD-NLP")
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st.sidebar.markdown("Extract information from text")
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st.sidebar.markdown(
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"[code](https://github.com/Accord-Project/NLP-Framework)"
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)
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st.header("Input a sentence")
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txt = st.text_area('Sentence')
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# st.write(txt)
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# with st.spinner(text="Processing..."):
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# graph = ie.sentence_to_graph(txt)
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if txt:
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# preprocess
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sentence = ie.preprocess(txt)
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st.write(sentence)
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# NER
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with st.spinner(text="Recognising entities..."):
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ner_predictions, ner_raw_outputs = ie.ner_model.predict([sentence])
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st.write(ner_predictions)
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with st.spinner(text="Extracting relations..."):
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# pair entities to predict their relations
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entity_pair_df = entity_pairing(sentence, ner_predictions[0])
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st.write('entity paired')
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# relation extraction
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re_predictions, re_raw_outputs = ie.re_model.predict(entity_pair_df['output'].tolist())
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entity_pair_df['prediction'] = re_predictions
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st.write(re_predictions)
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with st.spinner(text="Building graph..."):
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# build graph
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graph = graph_building(entity_pair_df, view=False)
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# st.success()
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st.header('Entity-Relation Representation')
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st.graphviz_chart(graph)
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if __name__ == '__main__':
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main()
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requirements.txt
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# PyTorch - https://pytorch.org/get-started/locally/
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torch==1.7.0+cpu
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streamlit==1.26.0
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accord-nlp==0.1.8
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