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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):
# 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()
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