app.py

# load ner model
from transformers import pipeline

ner_pipe = pipeline(
    'ner',
    model= 'dslim/bert-base-NER',
    aggregation_strategy = 'simple'
)
def ext_entities(sentence):
    doc = ner_pipe(sentence.title())
    outputs = ''
    for t in doc:
        outputs += f"{t['word']} - {t['entity_group']}\n{t['score']}\n"
    return outputs
import gradio as gr

demo = gr.Interface(fn=ext_entities,inputs='text',outputs='text',title='Extract Entities')
demo.launch()