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| import streamlit as st | |
| from annotated_text import annotated_text | |
| from transformers import pipeline | |
| def process_output(output, text): | |
| lst = [] | |
| i = 0 | |
| for ent in output: | |
| if text[i: ent["start"]] != "": | |
| lst.append(text[i: ent["start"]]) | |
| lst.append((text[ent["start"]: ent["end"]], ent["entity_group"], colors_dict[ent["entity_group"]])) | |
| i = ent["end"] | |
| if text[i:] != "": | |
| lst.append(text[i:]) | |
| return lst | |
| if __name__ == "__main__": | |
| st.set_page_config(page_title="Named Entity Recognizer", page_icon="🐒") | |
| st.markdown(""" <style> | |
| #MainMenu {visibility: hidden;} | |
| footer {visibility: hidden;} | |
| </style> """, unsafe_allow_html=True) | |
| st.header("Named Entity Recognizer") | |
| st.write("Developed with ❤️ by [Balamurugan P](https://www.linkedin.com/in/bala-murugan-62073b212/)") | |
| st.text("") | |
| text = st.text_area('Enter text to find Named Entities :', height=170) | |
| st.text("") | |
| # Loading the pipeline from hub | |
| # Pipeline handles the preprocessing and post processing steps | |
| model_checkpoint = "balamurugan1603/bert-finetuned-ner" | |
| namedEntityRecogniser = pipeline( | |
| "token-classification", model=model_checkpoint, aggregation_strategy="simple" | |
| ) | |
| if st.button("Check Entities"): | |
| st.text("") | |
| output = namedEntityRecogniser(text) | |
| annotated_text(*process_output(output, text)) | |