utils.py

"""
    utils.py - Utility functions for the project.
"""
import logging
import os
import re
import string
import subprocess
from collections import defaultdict, deque
from datetime import datetime, timedelta
from itertools import combinations, islice
from pathlib import Path
from typing import List

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

import torch
from natsort import natsorted
from nltk.tokenize import word_tokenize
from rapidfuzz import fuzz

# Define stopwords
STOPWORDS = set(
    "a about above after again against all am an and any are aren't as at be because been before being below between both but by can't cannot could couldn't did didn't do does doesn't doing don't down during each few for from further had hadn't has hasn't have haven't having he he'd he'll he's her here here's hers herself him himself his how how's i i'd i'll i'm i've if in into is isn't it it's its itself let's me more most mustn't my myself no nor not of off on once only or other ought our ours ourselves out over own same shan't she she'd she'll she's should shouldn't so some such than that that's the their theirs them themselves then there there's these they they'd they'll they're they've this those through to too under until up very was wasn't we we'd we'll we're we've were weren't what what's when when's where where's which while who who's whom why why's with won't would wouldn't you you'd you'll you're you've your yours yourself yourselves".split()
)


def remove_stopwords(text: str, stopwords: list = STOPWORDS) -> str:
    """
    remove_stopwords - Remove stopwords from a string.

    :param str text: text to remove stopwords from
    :param list stopwords: list of stopwords to remove, defaults to STOPWORDS
    :return, str: text with stopwords removed
    """
    words = word_tokenize(text)
    filtered_words = []

    for word in words:
        word = word.strip(string.punctuation)  # remove punctuation

        if word.lower() not in stopwords:
            filtered_words.append(word)

    filtered_text = " ".join(filtered_words)

    return filtered_text


def remove_stagnant_files(
    freq: str = "hourly",
    search_path: str = ".",
    substring="DocSumm",
    remove_suffix=".txt",
):
    """
    remove_stagnant_files - Remove files that have not been modified in a certain amount of time.

    :param str freq: frequency of file removal, defaults to "hourly"
    :param str search_path: location to search for files, defaults to "."
    :param str substring: substring to search for in file names, defaults to "DocSumm"
    :param str remove_suffix: suffix of files to remove, defaults to ".txt"
    :raises ValueError: if freq is not one of "hourly", "daily", or "weekly"
    """
    current_time = datetime.now()
    search_path = Path(search_path)

    if freq == "hourly":
        time_threshold = current_time - timedelta(hours=1)
    elif freq == "daily":
        time_threshold = current_time - timedelta(days=1)
    elif freq == "weekly":
        time_threshold = current_time - timedelta(weeks=1)
    else:
        raise ValueError(
            "Invalid frequency. Supported values are 'hourly', 'daily', and 'weekly'."
        )

    files_to_remove = []
    potential_files = [
        f for f in search_path.iterdir() if f.is_file() and f.suffix == remove_suffix
    ]
    logging.info(f"Found {len(potential_files)} files.")
    for candidate in potential_files:
        if (
            candidate.is_file()
            and substring in candidate.name
            and candidate.stat().st_mtime < time_threshold.timestamp()
        ):
            files_to_remove.append(candidate)
        logging.debug(f"File {candidate} last modified at {candidate.stat().st_mtime}")
    logging.info(f"Removing {len(files_to_remove)} files.")
    for file_path in files_to_remove:
        file_path.unlink()
    logging.debug(f"Removed files: {files_to_remove}")


def compare_model_size(model_name: str, threshold: int = 500) -> bool:
    """
    compare_model_size - compare string representations of model size to a threshold

    :param str model_name: the model name to compare
    :param int threshold: the threshold to compare against in millions, defaults to 500
    :return: True if the model size is greater than the threshold, False or None otherwise
    """
    pattern = r"(\d+)(M|G|k|b)?"  # param regex

    matches = re.findall(pattern, model_name)
    if not matches:
        return None

    # Extract the parameter count and unit
    parameter_count, unit = matches[-1]
    parameter_count = int(parameter_count)

    # Convert to the standard form (M for million, G for billion, k for thousand)
    if unit == "G" or unit == "b":
        parameter_count *= 1000
    elif unit == "M":
        pass
    elif unit == "k":
        parameter_count /= 1000
    else:
        return None  # Unknown

    return parameter_count > threshold


def validate_pytorch2(torch_version: str = None) -> bool:
    """
    validate_pytorch2 - validate that the PyTorch version is 2.0 or greater

    :param str torch_version: the PyTorch version to validate, defaults to None
    :return: True if the PyTorch version is 2.0 or greater, False otherwise
    """

    torch_version = torch.__version__ if torch_version is None else torch_version

    pattern = r"^2\.\d+(\.\d+)*"

    return True if re.match(pattern, torch_version) else False


def get_timestamp(detailed=False) -> str:
    """
    get_timestamp - get a timestamp for the current time
    :param bool detailed: whether to include seconds and microseconds, defaults to False
    :return: str, the timestamp
    """
    return (
        datetime.now().strftime("%b%d%Y_%H%M%S%f")
        if detailed
        else datetime.now().strftime("%b%d%Y_%H")
    )


def truncate_word_count(text: str, max_words=1024) -> dict:
    """
    truncate_word_count - truncate a text to a maximum number of words
    :param str text: the text to truncate
    :param int max_words: the maximum number of words to keep, defaults to 1024
    :return: dict, the processed text
    """
    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 load_examples(src, filetypes=[".txt", ".pdf"]):
    """
    load_examples - a helper function for the gradio module to load examples
    :param str src: the path to the examples
    """
    src = Path(src)
    src.mkdir(exist_ok=True)

    pdf_url = (
        "https://www.dropbox.com/s/y92xy7o5qb88yij/all_you_need_is_attention.pdf?dl=1"
    )
    subprocess.run(["wget", pdf_url, "-O", src / "all_you_need_is_attention.pdf"])
    examples = [f for f in src.iterdir() if f.suffix in filetypes]
    examples = natsorted(examples)
    # 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, "base", 2, 1024, 0.7, 3.5, 3])

    return text_examples


def load_example_filenames(example_path: str or Path):
    """
    load_example_filenames - a helper function for the gradio module to load examples
    Returns:
        dict, the examples (filename:full path)
    """
    example_path = Path(example_path)
    # load the examples into a list
    examples = {f.name: f for f in example_path.glob("*.txt")}
    return examples


def textlist2html(text_batches: List[str]) -> str:
    """textlist2html - convert a list of text summaries into a single HTML string"""
    # Step 1: Generate each summary batch as a string of HTML
    formatted_batches = [
        f"""
        <div style="
            margin-bottom: 20px;
            font-size: 18px;
            line-height: 1.5em;
            color: #333;
        ">
            <h2 style="font-size: 22px; color: #555;">Batch {i}:</h2>
            <p style="white-space: pre-line;">{s}</p>
        </div>
        """
        for i, s in enumerate(text_batches, start=1)
    ]

    # Step 2: Join all the summary batches together into one string
    joined_batches = "".join(formatted_batches)

    # Step 3: Wrap the summary string in a larger div with background color, border, and padding
    text_html_block = f"""
    <div style="
        border: 1px solid #ddd;
        border-radius: 5px;
        padding: 20px;
    ">
    {joined_batches}
    </div>
    """

    return text_html_block


def extract_batches(html_string: str, pattern=None, flags=None) -> list:
    """
    Extract batches of text from an HTML string.

    Args:
        html_string (str): The HTML string to extract batches from.
        pattern (str, optional): The regular expression pattern to use. Defaults to a pattern that matches batches in the format provided.
        flags (int, optional): The flags to use with the regular expression. Defaults to re.DOTALL.

    Returns:
        list: A list of dictionaries where each dictionary represents a batch and has 'title' and 'content' keys.
    """
    # Set default pattern if none provided
    if pattern is None:
        pattern = r'<h2 style="font-size: 22px; color: #555;">(.*?)</h2>\s*<p style="white-space: pre-line;">(.*?)</p>'

    # Set default flags if none provided
    if flags is None:
        flags = re.DOTALL

    try:
        # Find all matches in the string
        matches = re.findall(pattern, html_string, flags)

        # Convert matches to a list of dictionaries
        batches = [
            {"title": title.strip(), "content": content.strip()}
            for title, content in matches
        ]

        return batches
    except re.error as e:
        logging.error(f"An error occurred while trying to extract batches: {e}")
        return []


def extract_keywords(
    text: str, num_keywords: int = 3, window_size: int = 5, kw_max_len: int = 20
) -> List[str]:
    """
    Extracts keywords from a text using a simplified TextRank algorithm.

    Args:
        text: The text to extract keywords from.
        num_keywords: The number of keywords to extract. Default: 3
        window_size: The number of words considered for co-occurrence. Default: 5
        kw_max_len: The maximum length of a keyword (truncate longer keywords to max). Default: 20
    Returns:
        A list of strings, where each string is a keyword extracted from the input text.
    """
    logger = logging.getLogger(__name__)
    # Remove stopwords and tokenize the text into words
    words = [
        word
        for word in re.findall(r"\b\w{3,}\b", text.lower())
        if word not in STOPWORDS
    ]

    # Create a graph of word co-occurrences within a moving window of words
    cooccur = defaultdict(lambda: defaultdict(int))
    deque_words = deque(maxlen=window_size)
    for word in words:
        for w1, w2 in combinations(deque_words, 2):
            cooccur[w1][w2] += 1
            cooccur[w2][w1] += 1
        deque_words.append(word)

    # Assign scores to words using a simplified TextRank algorithm
    scores = defaultdict(float)
    for _ in range(10):
        new_scores = defaultdict(float)
        for word, co_words in cooccur.items():
            new_scores[word] = 0.15 + 0.85 * sum(
                cooccur[word][other] / sum(cooccur[other].values()) * scores[other]
                for other in co_words
            )
        scores = new_scores

    # Sort the words by score and return the top num_keywords keywords
    keywords = sorted(scores, key=scores.get, reverse=True)[:num_keywords]
    logger.debug(f"All keywords: {keywords}")
    # Use fuzzy matching to remove similar keywords
    final_keywords = []
    for keyword in keywords:
        if not any(fuzz.ratio(keyword, other) > 70 for other in final_keywords):
            final_keywords.append(keyword[:kw_max_len])
    logger.debug(f"Keywords (max len. {kw_max_len}):\t{final_keywords}")
    return final_keywords


def saves_summary(
    summarize_output, outpath: str or Path = None, add_signature=True, **kwargs
) -> Path:
    """
    saves_summary - save the summary generated from summarize_via_tokenbatches() to a text file

    summarize_output: output from summarize_via_tokenbatches()
    outpath: path to the output file
    add_signature: whether to add a signature to the output file
    kwargs: additional keyword arguments to include in the output file
    """
    logger = logging.getLogger(__name__)
    sum_text = [f"{s['summary'][0]}\n" for s in summarize_output]
    sum_scores = [f"\n - {round(s['summary_score'],4)}" for s in summarize_output]
    scores_text = "\n".join(sum_scores)
    full_summary = "\n".join(sum_text)

    keywords = "_".join(extract_keywords(full_summary, kw_max_len=4))
    logger.debug(f"kw:\t{keywords}")
    outpath = (
        Path.cwd() / f"DocSumm_{keywords}_{get_timestamp()}.txt"
        if outpath is None
        else Path(outpath)
    )
    logger.info(f"Saving summary to:\t{outpath.name}")
    with open(
        outpath,
        "w",
        encoding="utf-8",
    ) as fo:
        fo.writelines(full_summary)
        fo.write("\n\n")
        if add_signature:
            fo.write("\n\n---\n\n")
            fo.write("Generated with the Document Summarization space :)\n\n")
            fo.write("https://hf.co/spaces/pszemraj/document-summarization\n\n")
    with open(
        outpath,
        "a",
        encoding="utf-8",
    ) as fo:
        fo.write("\n")
        fo.write(f"## Section Scores:\n\n")
        fo.writelines(scores_text)
        fo.write("\n\n")
        fo.write(f"Date: {get_timestamp()}\n\n")
        if kwargs:
            fo.write("---\n\n")
            fo.write("## Parameters:\n\n")
            for key, value in kwargs.items():
                fo.write(f"{key}: {value}\n")
    return outpath