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✨ add and update utils
Browse filesSigned-off-by: peter szemraj <[email protected]>
utils.py
CHANGED
@@ -1,12 +1,12 @@
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"""
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utils.py - Utility functions for the project.
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"""
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-
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import logging
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import re
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import subprocess
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from collections import defaultdict, deque
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from datetime import datetime
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from itertools import combinations, islice
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from pathlib import Path
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from typing import List
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@@ -25,6 +25,86 @@ STOPWORDS = set(
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def validate_pytorch2(torch_version: str = None):
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torch_version = torch.__version__ if torch_version is None else torch_version
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@@ -46,7 +126,7 @@ def get_timestamp(detailed=False) -> str:
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)
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def truncate_word_count(text, max_words=
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"""
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truncate_word_count - a helper function for the gradio module
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Parameters
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@@ -141,6 +221,42 @@ def textlist2html(text_batches):
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return text_html_block
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def extract_keywords(
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text: str, num_keywords: int = 3, window_size: int = 5, kw_max_len: int = 20
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) -> List[str]:
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"""
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utils.py - Utility functions for the project.
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"""
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import logging
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import os
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import re
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import subprocess
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from collections import defaultdict, deque
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from datetime import datetime, timedelta
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from itertools import combinations, islice
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from pathlib import Path
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from typing import List
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)
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def remove_stagnant_files(
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freq: str = "hourly",
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search_path: str = ".",
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substring="DocSumm",
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remove_suffix=".txt",
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):
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"""
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remove_stagnant_files - Remove files that have not been modified in a certain amount of time.
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:param str freq: frequency of file removal, defaults to "hourly"
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:param str search_path: location to search for files, defaults to "."
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:param str substring: substring to search for in file names, defaults to "DocSumm"
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:param str remove_suffix: suffix of files to remove, defaults to ".txt"
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:raises ValueError: if freq is not one of "hourly", "daily", or "weekly"
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"""
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current_time = datetime.now()
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search_path = Path(search_path)
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if freq == "hourly":
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time_threshold = current_time - timedelta(hours=1)
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elif freq == "daily":
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time_threshold = current_time - timedelta(days=1)
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elif freq == "weekly":
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time_threshold = current_time - timedelta(weeks=1)
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else:
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raise ValueError(
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"Invalid frequency. Supported values are 'hourly', 'daily', and 'weekly'."
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)
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files_to_remove = []
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potential_files = [
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f for f in search_path.iterdir() if f.is_file() and f.suffix == remove_suffix
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]
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logging.info(f"Found {len(potential_files)} files.")
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for candidate in potential_files:
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if (
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candidate.is_file()
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and substring in candidate.name
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and candidate.stat().st_mtime < time_threshold.timestamp()
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):
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files_to_remove.append(candidate)
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logging.debug(f"File {candidate} last modified at {candidate.stat().st_mtime}")
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logging.info(f"Removing {len(files_to_remove)} files.")
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for file_path in files_to_remove:
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file_path.unlink()
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logging.debug(f"Removed files: {files_to_remove}")
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def compare_model_size(model_name: str, threshold: int = 500) -> bool:
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"""
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compare_model_size - compare string representations of model size to a threshold
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:param str model_name: the model name to compare
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:param int threshold: the threshold to compare against in millions, defaults to 500
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:return: True if the model size is greater than the threshold, False or None otherwise
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"""
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pattern = r"(\d+)(M|G|k|b)?" # param regex
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matches = re.findall(pattern, model_name)
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if not matches:
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return None
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# Extract the parameter count and unit from the last match
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parameter_count, unit = matches[-1]
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parameter_count = int(parameter_count) # Convert to an integer
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# Convert to the standard form (M for million, G for billion, k for thousand)
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if unit == "G" or unit == "b":
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parameter_count *= 1000
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elif unit == "M":
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pass
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elif unit == "k":
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parameter_count /= 1000
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else:
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return None # Unknown
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return parameter_count > threshold
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def validate_pytorch2(torch_version: str = None):
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torch_version = torch.__version__ if torch_version is None else torch_version
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)
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def truncate_word_count(text, max_words=1024):
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"""
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truncate_word_count - a helper function for the gradio module
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Parameters
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return text_html_block
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def extract_batches(html_string, pattern=None, flags=None) -> list:
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"""
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Extract batches of text from an HTML string.
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Args:
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html_string (str): The HTML string to extract batches from.
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pattern (str, optional): The regular expression pattern to use. Defaults to a pattern that matches batches in the format provided.
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flags (int, optional): The flags to use with the regular expression. Defaults to re.DOTALL.
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Returns:
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list: A list of dictionaries where each dictionary represents a batch and has 'title' and 'content' keys.
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"""
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# Set default pattern if none provided
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if pattern is None:
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pattern = r'<h2 style="font-size: 22px; color: #555;">(.*?)</h2>\s*<p style="white-space: pre-line;">(.*?)</p>'
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# Set default flags if none provided
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if flags is None:
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flags = re.DOTALL
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try:
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# Find all matches in the string
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matches = re.findall(pattern, html_string, flags)
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# Convert matches to a list of dictionaries
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batches = [
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{"title": title.strip(), "content": content.strip()}
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for title, content in matches
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]
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return batches
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except re.error as e:
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logging.error(f"An error occurred while trying to extract batches: {e}")
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return []
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def extract_keywords(
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text: str, num_keywords: int = 3, window_size: int = 5, kw_max_len: int = 20
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) -> List[str]:
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