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import torch | |
import gradio as gr | |
from transformers import pipeline | |
# Use a pipeline as a high-level helper | |
device = 0 if torch.cuda.is_available() else -1 | |
text_summary = pipeline("summarization", model="Falconsai/text_summarization",device=device,torch_dtype=torch.bfloat16) | |
def summary(input): | |
output = text_summary(input) | |
return output[0]['summary_text'] | |
gr.close_all() | |
# Create the Gradio interface | |
demo = gr.Interface( | |
fn=summary, | |
inputs=[gr.Textbox(label="INPUT THE PASSAGE TO SUMMARIZE ABOVE 10 LINES", lines=10)], | |
outputs=[gr.Textbox(label="SUMMARIZED TEXT", lines=4)], | |
title="PAVISHINI @ GenAI Project 1: Text Summarizer", | |
description="This application is used to summarize the text" | |
) | |
demo.launch() | |