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| import streamlit as st | |
| from datasets import load_dataset | |
| from spacy_streamlit import visualize_textcat, visualize_ner | |
| import spacy_streamlit | |
| import pandas as pd | |
| from transformers import pipeline | |
| import spacy | |
| # we write text | |
| st.title('Skills Extraction Project') | |
| # we write markdown | |
| st.markdown('This NLP project helps you extract skills from job description. You just need to paste a job description and directly access the required skills for a specific vacancy. Save time!', unsafe_allow_html=False) | |
| def get_model(): | |
| return spacy.load("en_pipeline") | |
| nlp = get_model() | |
| raw_text = st.text_area(label="Insert your job description") | |
| #if raw_text != "": | |
| docx = nlp(raw_text) | |
| spacy_streamlit.visualize_ner(docx, labels = nlp.get_pipe('ner').labels) | |