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| import gradio as gr | |
| from transformers import pipeline | |
| def summarize_text(input_text): | |
| """ | |
| Function to summarize the input text using a Hugging Face transformers pipeline. | |
| Args: | |
| input_text (str): Text to be summarized. | |
| Returns: | |
| str: Summarized text. | |
| """ | |
| # Load the summarization pipeline using a specific model from Hugging Face | |
| summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6") | |
| # Summarize the text | |
| summary = summarizer(input_text, max_length=130, min_length=30, do_sample=False) | |
| # Extract and return the summarized text | |
| return summary[0]['summary_text'] | |
| def main(): | |
| """ | |
| Main function to launch the Gradio interface. | |
| """ | |
| # Define the Gradio interface | |
| interface = gr.Interface( | |
| fn=summarize_text, | |
| inputs=gr.Textbox(lines=10, placeholder="Enter text here to summarize..."), | |
| outputs="text", | |
| title="Text Summarizer", | |
| description="A simple text summarization app using Hugging Face's transformers. Enter your text and get a summarized version instantly!" | |
| ) | |
| # Launch the app | |
| interface.launch(share=True) | |
| if __name__ == "__main__": | |
| main() | |