app.py

import gradio as gr
from transformers import pipeline

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

# Define the summarization function
def summarize(text):
    # Generate summary with min_length=10 and max_length=100 tokens
    summary = summarizer(text, min_length=10, max_length=100)
    # Extract and return the summarized text
    return summary[0]['summary_text']

# Define input and output components with clear labels
inputs = gr.Textbox(label="Input Text", lines=10)
outputs = gr.Textbox(label="Summary")

# Create the Gradio interface
iface = gr.Interface(
    fn=summarize,
    inputs=inputs,
    outputs=outputs,
    title="Text Summarizer",
    description="Enter a long piece of text and get a summary using the BART model."
)

# Launch the interface
iface.launch()