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import gradio as gr | |
from transformers import pipeline | |
# Model options available for summarization | |
model_names = ["Falconsai/text_summarization"] | |
def summarize_article(article, model_name, max_length, temperature, top_k, top_p): | |
""" Summarize the provided article text using the specified model and hyperparameters. """ | |
summarizer = pipeline("summarization", model=model_name) | |
summary = summarizer( | |
article, | |
max_length=int(round(max_length)), | |
min_length=30, | |
do_sample=True, | |
temperature=temperature, | |
top_k=int(round(top_k)), | |
top_p=top_p | |
) | |
return summary[0]['summary_text'] | |
# Gradio interface setup | |
iface = gr.Interface( | |
fn=summarize_article, | |
inputs=[ | |
gr.Textbox(lines=10, placeholder="Enter the article text here..."), | |
gr.Dropdown(choices=model_names, label="Select Model"), | |
gr.Slider(minimum=10, maximum=200, step=10, label="Max Length of Summary"), | |
gr.Slider(minimum=0.1, maximum=2, step=0.1, label="Temperature for Sampling"), | |
gr.Slider(minimum=1, maximum=100, step=1, label="Top-k"), | |
gr.Slider(minimum=0.1, maximum=1, step=0.1, label="Top-p") | |
], | |
outputs="text", | |
title="Text Summarization", | |
description="Adjust the parameters below to summarize the article." | |
) | |
iface.launch(debug=True, share=True) | |