app.py

import gradio as gr
from transformers import pipeline

ner = pipeline("ner", model="Keyurjotaniya007/xlm-roberta-base-xtreme-multilingual-ner-2.0", aggregation_strategy="simple")

def extract_entities(text):
    results = ner(text)
    return [(ent['word'], ent['entity_group']) for ent in results]

with gr.Blocks() as demo:
    gr.Markdown(instructions)
    with gr.Row():
        inp = gr.Textbox(label="Enter Text", placeholder="Type a sentence in any language...", lines=3)
        out = gr.HighlightedText(label="Named Entities")
    btn = gr.Button("Extract Entities")

    btn.click(fn=extract_entities, inputs=inp, outputs=out)

if __name__ == "__main__":
    demo.launch()