app.py

from transformers import pipeline
import gradio as gr

# text summarizer
summarizer = pipeline("summarization", model = "facebook/bart-large-cnn")

def get_summary(text):
    output = summarizer(text)
    return output[0]["summary_text"]

# named entity recognition
ner_model = pipeline("ner", model = "dslim/bert-large-NER")

def get_ner(text):
    output = ner_model(text)
    return {"text":text, "entities":output}

demo = gr.Blocks()
with demo:
    gr.Markdown("Try out multiple NLP tasks!")
    with gr.Tab("Text Summarizer"):
        sum_input = gr.Textbox(placeholder="Enter text to summarize...", lines=4)
        sum_output = gr.Textbox()
        sum_btn = gr.Button("Summarize")
        sum_btn.click(get_summary, sum_input, sum_output)
    with gr.Tab("Named Entity Recognition"):
        ner_input = gr.Textbox(placeholder = "Enter text...", lines = 4)
        ner_output = gr.Textbox()
        ner_btn = gr.Button("Get named entities")
        ner_btn.click(get_ner, ner_input, ner_output)
    

demo.launch()