| import gradio as gr |
| from transformers import pipeline |
|
|
| summarizer = pipeline("summarization", model="facebook/bart-large-cnn") |
|
|
| def summarize_text(text, min_len, max_len): |
| word_count = len(text.split()) |
| if word_count < min_len: |
| return f"Error: The text should have at least {min_len} words." |
| elif word_count > max_len: |
| return f"Error: The text should have no more than {max_len} words." |
| |
| summary = summarizer(text, min_length=min_len, max_length=max_len) |
| return summary[0]['summary_text'] |
|
|
| interface = gr.Interface( |
| fn=summarize_text, |
| inputs=[ |
| gr.Textbox(label="Enter Text", lines=10, placeholder="Paste your long text here..."), |
| gr.Slider(label="Min Length", minimum=10, maximum=50, step=1, value=10), |
| gr.Slider(label="Max Length", minimum=50, maximum=150, step=1, value=100) |
| ], |
| outputs=gr.Textbox(label="Summarized Text"), |
| title="Text Summarizer with Sliders", |
| description="This app uses the BART model to summarize your text. The input text must be between the min and max length you set using the sliders." |
| ) |
|
|
| interface.launch() |
|
|
