ayushsinha's picture
Upload 2 files
d89a765 verified
raw
history blame
691 Bytes
import gradio as gr
from transformers import pipeline
# Load the summarization model
summarizer = pipeline("summarization", model="AventIQ-AI/t5-text-summarizer")
# Define the summarization function
def summarize_text(input_text):
summary = summarizer(input_text, max_length=150, min_length=30, do_sample=False)
return summary[0]['summary_text']
# Create the Gradio UI
iface = gr.Interface(
fn=summarize_text,
inputs=gr.Textbox(lines=5, placeholder="Enter text to summarize..."),
outputs="text",
title="T5 Text Summarizer",
description="Enter a passage, and the T5 model will generate a concise summary."
)
# Launch the app
iface.launch()