app.py

# Import necessary libraries
import gradio as gr
from transformers import pipeline

# Load a pre-trained summarization model from Hugging Face
# 'sshleifer/distilbart-cnn-12-6' is a good general-purpose summarization model
# This model is suitable for summarizing various types of text.
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")

def summarize_text(input_text):
    """
    Summarizes the input text using a pre-trained model.

    Args:
        input_text (str): The text to be summarized.

    Returns:
        str: The summarized text or an error message.
    """
    if not input_text or len(input_text.strip()) == 0:
        return "Please provide some text to summarize."

    try:
        # Summarize the input text
        # The summarizer pipeline can handle texts up to a certain length (model dependent).
        # For very long texts, you might need to implement chunking and summarize each chunk.
        # max_length and min_length control the summary length. Adjust these as needed.
        summary = summarizer(input_text, max_length=200, min_length=50, do_sample=False)

        # The pipeline returns a list of dictionaries, we need the 'summary_text' from the first item.
        return summary[0]['summary_text']

    except Exception as e:
        # Catch potential errors during summarization (e.g., text too long for the model)
        return f"Error summarizing text: {e}"

# Create the Gradio interface
# The interface takes a text input (for the text to be summarized)
# and provides a text output (for the summarized text)
iface = gr.Interface(
    fn=summarize_text,
    inputs=gr.Textbox(label="Enter Text to Summarize", lines=10), # Use multiple lines for text input
    outputs=gr.Textbox(label="Summarized Text"),
    title="General Text Summarizer",
    description="Enter any text to get a summary."
)

# To deploy on Hugging Face Spaces, you need this file (e.g., app.py)
# and a requirements.txt file. Hugging Face Spaces will automatically run
# the Gradio app if it finds an interface defined.
# Remove the iface.launch() call when deploying to Hugging Face Spaces.
# The last line should be the interface object itself.
# iface.launch(share=True, debug=True) # Use this line for local testing
iface # Use this line for Hugging Face Spaces deployment