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| import gradio as gr | |
| from transformers import pipeline | |
| import torch | |
| # Load the BART model for summarization | |
| summarizer = pipeline("summarization", | |
| model="facebook/bart-large-cnn") | |
| # Function to summarize the input text | |
| def summarize_text(input_text): | |
| # Ensure the text is not too short for summarization | |
| if len(input_text.split()) < 10: | |
| return "Please provide a longer text for summarization." | |
| # Summarize the text using BART | |
| summarized_output = summarizer(input_text, | |
| min_length=10, | |
| max_length=100, | |
| do_sample=False) | |
| return summarized_output[0]['summary_text'] | |
| # Create the Gradio interface | |
| interface = gr.Interface( | |
| fn=summarize_text, # Function that summarizes the input | |
| inputs=gr.Textbox(label="Input Text", | |
| lines=10, | |
| placeholder="Enter a long piece of text here..."), # Text input field | |
| outputs=gr.Textbox(label="Summarized Text"), # Output field for the summarized text | |
| title="Text Summarizer", # Title of the app | |
| description="Enter a text, and this app will summarize it using the BART model. The summary will be between 10 and 100 tokens.", # Description | |
| live=False, # Disable live updates while typing | |
| ) | |
| # Launch the interface | |
| interface.launch() | |