app.py

import torch
import gradio as gr
from transformers import pipeline

# Load summarization pipeline
text_summary = pipeline(
    "summarization",
    model="sshleifer/distilbart-cnn-12-6",
    torch_dtype=torch.float32   # CPU stable
)

def summary(input_text):
    result = text_summary(input_text)
    return result[0]['summary_text']

# Gradio app
demo = gr.Interface(
    fn=summary,
    inputs=[gr.Textbox(label="Input text to summarize", lines=6)],
    outputs=[gr.Textbox(label="Summarized text", lines=4)],
    title="@GenAILearniverse Project 1: Text Summarizer",
    description="This application summarizes long text into short and meaningful summaries."
)

demo.launch()