app.py

import gradio as gr
from transformers import pipeline
import os
import pandas as pd

# Load the text summarization pipeline
summarizer = pipeline("summarization", model="astro21/bart-cls")

chunk_counter = 0


def summarize_text(input_text):
    global chunk_counter
    chunk_counter = 0

    max_chunk_size = 1024
    chunks = [input_text[i:i + max_chunk_size] for i in range(0, len(input_text), max_chunk_size)]

    summarized_chunks = []
    chunk_lengths = []
    summarized_chunks_only = []

    for chunk in chunks:
        chunk_counter += 1
        summarized_chunk = summarizer(chunk, max_length=128, min_length=64, do_sample=False)[0]['summary_text']
        summarized_chunks.append(f"Chunk {chunk_counter}:\n{summarized_chunk}")
        summarized_chunks_only.append(summarized_chunk)

        chunk_lengths.append(len(chunk))

    summarized_text = "\n".join(summarized_chunks)
    summarized_text_only = "\n".join(summarized_chunks_only)

    # Save the merged summary to a file
    with open("summarized.txt", "w") as output_file:
        output_file.write(summarized_text_only)

    chunk_df = pd.DataFrame({'Chunk Number': range(1, chunk_counter + 1), 'Chunk Length': chunk_lengths})

    return summarized_text


# def read_file(file):
#     print(file[0].name)
#     with open(file[0].name, 'r') as file_:
#         content = file_.read()
#     return content


# def summarize_text_file(file):
#     if file is not None:
#         content = read_file(file)
#         return summarize_text(content)


input_type = gr.inputs.TextBox("textbox" , lines = 15)

# Name the outputs using the label parameter and provide a download option
demo = gr.Interface(fn=summarize_text, inputs=input_type,
                    outputs=[gr.Textbox(label="Summarized Text")],
                    title = "Text Summarization",
                    description = "Summarize text using BART",
                    theme = "huggingface",
                    allow_flagging="never",
                    live=True)

demo.launch(share=True)