app.py

import contextlib
import logging
import random
import re
import time
from pathlib import Path

import gradio as gr
import nltk
from cleantext import clean
from doctr.io import DocumentFile
from doctr.models import ocr_predictor
from pdf2text import convert_PDF_to_Text

from summarize import load_model_and_tokenizer, summarize_via_tokenbatches
from utils import load_example_filenames, truncate_word_count

_here = Path(__file__).parent

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

logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)


def proc_submission(
    input_text: str,
    model_size: str,
    num_beams,
    token_batch_length,
    length_penalty,
    repetition_penalty,
    no_repeat_ngram_size,
    max_input_length: int = 1024,
):
    """
    proc_submission - a helper function for the gradio module to process submissions

    Args:
        input_text (str): the input text to summarize
        model_size (str): the size of the model to use
        num_beams (int): the number of beams to use
        token_batch_length (int): the length of the token batches to use
        length_penalty (float): the length penalty to use
        repetition_penalty (float): the repetition penalty to use
        no_repeat_ngram_size (int): the no repeat ngram size to use
        max_input_length (int, optional): the maximum input length to use. Defaults to 768.

    Returns:
        str in HTML format, string of the summary, str of score
    """

    settings = {
        "length_penalty": float(length_penalty),
        "repetition_penalty": float(repetition_penalty),
        "no_repeat_ngram_size": int(no_repeat_ngram_size),
        "encoder_no_repeat_ngram_size": 4,
        "num_beams": int(num_beams),
        "min_length": 4,
        "max_length": int(token_batch_length // 4),
        "early_stopping": True,
        "do_sample": False,
    }
    st = time.perf_counter()
    history = {}
    clean_text = clean(input_text, lower=False)
    max_input_length = 2048 if model_size == "base" else max_input_length
    processed = truncate_word_count(clean_text, max_input_length)

    if processed["was_truncated"]:
        tr_in = processed["truncated_text"]
        # create elaborate HTML warning
        input_wc = re.split(r"\s+", input_text)
        msg = f"""
        <div style="background-color: #FFA500; color: white; padding: 20px;">
        <h3>Warning</h3>
        <p>Input text was truncated to {max_input_length} words. That's about {100*max_input_length/len(input_wc):.2f}% of the submission.</p>
        </div>
        """
        logging.warning(msg)
        history["WARNING"] = msg
    else:
        tr_in = input_text
        msg = None

    _summaries = summarize_via_tokenbatches(
        tr_in,
        model_sm if model_size == "base" else model,
        tokenizer_sm if model_size == "base" else tokenizer,
        batch_length=token_batch_length,
        **settings,
    )
    sum_text = [f"Section {i}: " + s["summary"][0] for i, s in enumerate(_summaries)]
    sum_scores = [
        f" - Section {i}: {round(s['summary_score'],4)}"
        for i, s in enumerate(_summaries)
    ]

    sum_text_out = "\n".join(sum_text)
    history["Summary Scores"] = "<br><br>"
    scores_out = "\n".join(sum_scores)
    rt = round((time.perf_counter() - st) / 60, 2)
    print(f"Runtime: {rt} minutes")
    html = ""
    html += f"<p>Runtime: {rt} minutes on CPU</p>"
    if msg is not None:
        html += msg

    html += ""

    return html, sum_text_out, scores_out


def load_single_example_text(
    example_path: str or Path,
    max_pages=20,
):
    """
    load_single_example - a helper function for the gradio module to load examples
    Returns:
        list of str, the examples
    """
    global name_to_path
    full_ex_path = name_to_path[example_path]
    full_ex_path = Path(full_ex_path)
    if full_ex_path.suffix == ".txt":
        with open(full_ex_path, "r", encoding="utf-8", errors="ignore") as f:
            raw_text = f.read()
        text = clean(raw_text, lower=False)
    elif full_ex_path.suffix == ".pdf":
        logging.info(f"Loading PDF file {full_ex_path}")
        conversion_stats = convert_PDF_to_Text(
            full_ex_path,
            ocr_model=ocr_model,
            max_pages=max_pages,
        )
        text = conversion_stats["converted_text"]
    else:
        logging.error(f"Unknown file type {full_ex_path.suffix}")
        text = "ERROR - check example path"

    return text


def load_uploaded_file(file_obj, max_pages=20):
    """
    load_uploaded_file - process an uploaded file

    Args:
        file_obj (POTENTIALLY list): Gradio file object inside a list

    Returns:
        str, the uploaded file contents
    """

    # file_path = Path(file_obj[0].name)

    # check if mysterious file object is a list
    if isinstance(file_obj, list):
        file_obj = file_obj[0]
    file_path = Path(file_obj.name)
    try:
        if file_path.suffix == ".txt":
            with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
                raw_text = f.read()
            text = clean(raw_text, lower=False)
        elif file_path.suffix == ".pdf":
            logging.info(f"Loading PDF file {file_path}")
            conversion_stats = convert_PDF_to_Text(
                file_path,
                ocr_model=ocr_model,
                max_pages=max_pages,
            )
            text = conversion_stats["converted_text"]
        else:
            logging.error(f"Unknown file type {file_path.suffix}")
            text = "ERROR - check example path"

        return text
    except Exception as e:
        logging.info(f"Trying to load file with path {file_path}, error: {e}")
        return "Error: Could not read file. Ensure that it is a valid text file with encoding UTF-8 if text, and a PDF if PDF."


if __name__ == "__main__":
    logging.info("Starting app instance")

    logging.info("Loading summ models")
    with contextlib.redirect_stdout(None):
        model, tokenizer = load_model_and_tokenizer(
            "pszemraj/pegasus-x-large-book-summary"
        )
        model_sm, tokenizer_sm = load_model_and_tokenizer(
            "pszemraj/long-t5-tglobal-base-16384-book-summary"
        )

    logging.info("Loading OCR model")
    with contextlib.redirect_stdout(None):
        ocr_model = ocr_predictor(
            "db_resnet50",
            "crnn_mobilenet_v3_large",
            pretrained=True,
            assume_straight_pages=True,
        )
    name_to_path = load_example_filenames(_here / "examples")
    logging.info(f"Loaded {len(name_to_path)} examples")
    demo = gr.Blocks()
    _examples = list(name_to_path.keys())
    with demo:

        gr.Markdown("# Document Summarization with Long-Document Transformers")
        gr.Markdown(
            "TODO: Add a description of the model and how it works, and a link to the paper"
        )
        with gr.Column():

            gr.Markdown("## Load Inputs & Select Parameters")
            gr.Markdown(
                "Enter text below in the text area. The text will be summarized [using the selected parameters](https://huggingface.co/blog/how-to-generate). Optionally load an example below or upload a file."
            )
            with gr.Row():
                model_size = gr.Radio(
                    choices=["base", "large"], label="Model Variant", value="base"
                )
                num_beams = gr.Radio(
                    choices=[2, 3, 4],
                    label="Beam Search: # of Beams",
                    value=2,
                )
            gr.Markdown("Select an example, or upload a file (`.txt` or `.pdf`)")
            with gr.Row():
                example_name = gr.Dropdown(
                    _examples,
                    label="Examples",
                    value=random.choice(_examples),
                )
                uploaded_file = gr.File(
                    label="File Upload",
                    file_count="single",
                    type="file",
                )
            with gr.Row():
                input_text = gr.Textbox(
                    lines=4,
                    label="Input Text (for summarization)",
                    placeholder="Enter text to summarize, the text will be cleaned and truncated on Spaces. Narrative, academic (both papers and lecture transcription), and article text work well. May take a bit to generate depending on the input text :)",
                )
                with gr.Column(min_width=100, scale=0.5):
                    load_examples_button = gr.Button(
                        "Load Example",
                    )
                    load_file_button = gr.Button("Upload File")

        with gr.Column():
            gr.Markdown("## Generate Summary")
            gr.Markdown(
                "Summary generation should take approximately 1-2 minutes for most settings."
            )
            summarize_button = gr.Button(
                "Summarize!",
                variant="primary",
            )

            output_text = gr.HTML("<p><em>Output will appear below:</em></p>")
            gr.Markdown("### Summary Output")
            summary_text = gr.Textbox(
                label="Summary", placeholder="The generated summary will appear here"
            )
            gr.Markdown(
                "The summary scores can be thought of as representing the quality of the summary. less-negative numbers (closer to 0) are better:"
            )
            summary_scores = gr.Textbox(
                label="Summary Scores", placeholder="Summary scores will appear here"
            )

            gr.Markdown("---")
        with gr.Column():
            gr.Markdown("### Advanced Settings")
            with gr.Row():
                length_penalty = gr.inputs.Slider(
                    minimum=0.5,
                    maximum=1.0,
                    label="length penalty",
                    default=0.7,
                    step=0.05,
                )
                token_batch_length = gr.Radio(
                    choices=[512, 768, 1024, 1536],
                    label="token batch length",
                    value=1024,
                )

            with gr.Row():
                repetition_penalty = gr.inputs.Slider(
                    minimum=1.0,
                    maximum=5.0,
                    label="repetition penalty",
                    default=3.5,
                    step=0.1,
                )
                no_repeat_ngram_size = gr.Radio(
                    choices=[2, 3, 4],
                    label="no repeat ngram size",
                    value=3,
                )
        with gr.Column():
            gr.Markdown("### About the Model")
            gr.Markdown(
                "- [This model](https://huggingface.co/pszemraj/led-large-book-summary) is a fine-tuned checkpoint 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.Markdown(
                "- The two most important parameters-empirically-are the `num_beams` and `token_batch_length`. However, increasing these will also increase the amount of time it takes to generate a summary. The `length_penalty` and `repetition_penalty` parameters are also important for the model to generate good summaries."
            )
            gr.Markdown(
                "- 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."
            )
            gr.Markdown("---")

        load_examples_button.click(
            fn=load_single_example_text, inputs=[example_name], outputs=[input_text]
        )

        load_file_button.click(
            fn=load_uploaded_file, inputs=uploaded_file, outputs=[input_text]
        )

        summarize_button.click(
            fn=proc_submission,
            inputs=[
                input_text,
                model_size,
                num_beams,
                token_batch_length,
                length_penalty,
                repetition_penalty,
                no_repeat_ngram_size,
            ],
            outputs=[output_text, summary_text, summary_scores],
        )

    demo.launch(enable_queue=True)