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import gradio as gr | |
from transformers import pipeline | |
# Initialize the BART summarization model from Hugging Face | |
summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
# Define the function that will summarize the text | |
def summarize_text(input_text): | |
summary = summarizer(input_text, max_length=150, min_length=50, do_sample=False) | |
return summary[0]['summary_text'] | |
# Create a Gradio interface | |
iface = gr.Interface(fn=summarize_text, | |
inputs="text", | |
outputs="text", | |
title="Text Summarization App", | |
description="This app summarizes long-form text (articles, papers, books) into concise key points or a paragraph.", | |
examples=[["Enter your article text here"]]) | |
# Launch the interface | |
iface.launch() | |