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import gradio as gr
from transformers import pipeline
# Load the summarization model
summarizer = pipeline(task="summarization",
model="sshleifer/distilbart-cnn-12-6")
# Function to summarize input text
def summarize(input,
min_length,
max_length):
output = summarizer(input,
min_length = min_length,
max_length = max_length)
return output[0]['summary_text']
# Create the Gradio interface
SUMMARIZER = gr.Interface(
fn=summarize,
inputs=[gr.Textbox(label='Text to summarize', lines=6),
gr.Slider(label='Min Length', minimum=10, maximum=50, value=10),
gr.Slider(label='Max Length', minimum=50, maximum=200, value=100)],
outputs=[gr.Textbox(label='Result', lines=3)],
allow_flagging='never'
)
# Add Markdown content
markdown_content_summarizer = gr.Markdown(
"""
<div style='text-align: center; font-family: "Times New Roman";'>
<h1 style='color: #FF6347;'>Text Summarization with DistilBART-CNN</h1>
<h3 style='color: #4682B4;'>Model: sshleifer/distilbart-cnn-12-6</h3>
<h3 style='color: #32CD32;'>Made By: Md. Mahmudun Nabi</h3>
</div>
"""
)
# Combine the Markdown content and the demo interface
summarizer_with_markdown = gr.Blocks()
with summarizer_with_markdown:
markdown_content_summarizer.render()
SUMMARIZER.render() |