app.py

import logging
import re
from pathlib import Path

import gradio as gr
import nltk
from cleantext import clean

from summarize import load_model_and_tokenizer, summarize_via_tokenbatches

_here = Path(__file__).parent

nltk.download("stopwords")  # TODO=find where this requirement originates from

import transformers

transformers.logging.set_verbosity_error()
logging.basicConfig()


def truncate_word_count(text, max_words=512):
    """
    truncate_word_count - a helper function for the gradio module
    Parameters
    ----------
    text : str, required, the text to be processed
    max_words : int, optional, the maximum number of words, default=512
    Returns
    -------
    dict, the text and whether it was truncated
    """
    # split on whitespace with regex
    words = re.split(r"\s+", text)
    processed = {}
    if len(words) > max_words:
        processed["was_truncated"] = True
        processed["truncated_text"] = " ".join(words[:max_words])
    else:
        processed["was_truncated"] = False
        processed["truncated_text"] = text
    return processed


def proc_submission(
    input_text: str,
    num_beams,
    token_batch_length,
    length_penalty,
    repetition_penalty,
    no_repeat_ngram_size,
    max_input_length: int = 512,
):
    """
    proc_submission - a helper function for the gradio module
    Parameters
    ----------
    input_text : str, required, the text to be processed
    max_input_length : int, optional, the maximum length of the input text, default=512
    Returns
    -------
    str of HTML, the interactive HTML form for the model
    """

    settings = {
        "length_penalty": length_penalty,
        "repetition_penalty": repetition_penalty,
        "no_repeat_ngram_size": no_repeat_ngram_size,
        "encoder_no_repeat_ngram_size": 4,
        "num_beams": num_beams,
        "early_stopping": True,
        "min_length": 10,
        "do_sample": False,
    }

    history = {}
    clean_text = clean(input_text, lower=False)
    processed = truncate_word_count(clean_text, max_input_length)
    if processed["was_truncated"]:
        tr_in = processed["truncated_text"]
        msg = f"Input text was truncated to {max_input_length} words (based on whitespace)"
        logging.warning(msg)
        history["WARNING"] = msg
    else:
        tr_in = input_text
        history["was_truncated"] = False

    _summaries = summarize_via_tokenbatches(
        tr_in,
        model,
        tokenizer,
        batch_length=token_batch_length,
        **settings,
    )
    sum_text = [s["summary"][0] for s in _summaries]
    sum_scores = [f"\n - Section {i}: {round(s['summary_score'],4)}" for i, s in enumerate(_summaries)]

    history["Summary Text"] = "\n\t".join(sum_text)
    history["Summary Scores"] = "\n".join(sum_scores)
    history["Input"] = tr_in
    html = ""

    for name, item in history.items():
        html += (
            f"<h2>{name}:</h2><hr><b>{item}</b><br><br>"
            if "summary" not in name.lower()
            else f"<h2>{name}:</h2><hr><b>{item}</b>"
        )

    html += ""

    return html


def load_examples(examples_dir="examples"):
    src = _here / examples_dir
    src.mkdir(exist_ok=True)
    examples = [f for f in src.glob("*.txt")]
    # load the examples into a list
    text_examples = []
    for example in examples:
        with open(example, "r") as f:
            text = f.read()
            text_examples.append([text, 2, 1024, 0.7, 3.5, 3])

    return text_examples


if __name__ == "__main__":

    model, tokenizer = load_model_and_tokenizer("pszemraj/led-large-book-summary")
    title = "Long-Form Summarization: LED & BookSum"
    description = "A simple demo of how to use a fine-tuned LED model to summarize long-form text. [This model](https://huggingface.co/pszemraj/led-large-book-summary) is a fine-tuned version of [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384) on the [BookSum dataset](https://arxiv.org/abs/2105.08209). The goal was to create a model that can generalize well and is useful in summarizing lots of text in academic and daily usage."

    gr.Interface(
        proc_submission,
        inputs=[
            gr.inputs.Textbox(
                lines=10,
                label="input text",
                placeholder="Enter text to summarize, the text will be cleaned and truncated to 512 words on Spaces. Narrative, academic (both papers and lecture transcription), and article text work well.",
            ),
            gr.inputs.Slider(
                minimum=1, maximum=6, label="num_beams", default=4, step=1
            ),
            gr.inputs.Slider(
                minimum=512,
                maximum=2048,
                label="token_batch_length",
                default=1024,
                step=512,
            ),
            gr.inputs.Slider(
                minimum=0.5, maximum=1.1, label="length_penalty", default=0.7, step=0.05
            ),
            gr.inputs.Slider(
                minimum=1.0,
                maximum=5.0,
                label="repetition_penalty",
                default=3.5,
                step=0.1,
            ),
            gr.inputs.Slider(
                minimum=2, maximum=4, label="no_repeat_ngram_size", default=3, step=1
            ),
        ],
        outputs="html",
        examples_per_page=4,
        title=title,
        description=description,
        article="The model can be used with tag [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). See the model card for details on usage & a notebook for a tutorial.",
        examples=load_examples(),
        cache_examples=False,
    ).launch(
    )