| from transformers import pipeline | |
| import gradio as gr | |
| # Initialize the summarization model using a PyTorch-based model | |
| model = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6") | |
| def predict(prompt): | |
| summary = model(prompt)[0]['summary_text'] | |
| return summary | |
| # Build the Gradio interface | |
| with gr.Blocks() as demo: | |
| textbox = gr.Textbox(placeholder="Enter text block to summarize", lines=4) | |
| gr.Interface(fn=predict, inputs=textbox, outputs="text") | |
| # Launch the demo | |
| demo.launch() | |