app.py

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):
    # Calculate the number of tokens based on input length
    input_length = len(input.split())
    max_output_tokens = max(20, input_length // 2)  # Ensure output is less than half of the input
    min_output_tokens = max(10, input_length // 4)  # Ensure a meaningful summary

    # Generate summary with dynamic token limits
    output = text_summary(input, max_length=max_output_tokens, min_length=min_output_tokens, truncation=True)
    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", 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()