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40a04d4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 | #!/usr/bin/env python3
"""
humanizer.py - remove bulleted lists, markdown bold indicators, titles, and
various other obviously-AI-written textual features, and replace them with more
human-like connective text.
This module provides `humanize(text, ...)`, which preserves the basic content
of the original text, but with a more human-like straight-prose expression. See
function docstring for arguments and explanations.
"""
import re
import random
import argparse
from typing import List
INTRO_PHRASES = [
"On {topic}, ",
"On the {topic} issue, ",
"When it comes to {topic}, ",
"As for {topic}, ",
"Another thing is {topic}, ", # yep, it's a comma splice! We're human.
"People often claim that {topic}, but ",
"People might say {topic}, but "
]
def strip_markdown(text: str) -> str:
text = re.sub(r"\*\*(.*?)\*\*", r"\1", text)
text = re.sub(r"\*(.*?)\*", r"\1", text)
return text
def is_bullet(line: str) -> bool:
# Matches:
# * item
# - item
# • item
# 1. item
# 1) item
return bool(
re.match(r"^\s*(?:[*\-•]|(?:\d+[.)]))\s+", line)
)
def extract_bullet_text(line: str) -> str:
return re.sub(r"^\s*(?:[*\-•]|(?:\d+[.)]))\s+", "", line).strip()
def choose_intro(topic: str) -> str:
phrase = random.choice(INTRO_PHRASES)
return phrase.format(topic=topic.strip().lower())
def collapse_list(items: List[str]) -> str:
if len(items) == 1:
return items[0]
if len(items) == 2:
return f"{items[0]} and {items[1]}"
return ", ".join(items[:-1]) + f", and {items[-1]}"
def lowercase_initial(text: str) -> str:
"""
Lowercase the first alphabetic character in `text`.
Leaves leading quotes/whitespace/punctuation intact.
"""
chars = list(text)
for i, ch in enumerate(chars):
if ch.isalpha():
chars[i] = ch.lower()
break
return "".join(chars)
def normalize_inline_bullets(text: str) -> str:
"""
Turn inline bullet markers into real line-starting bullets.
Example:
"pay: * Sales taxes... * Property taxes..."
becomes:
"pay:\n* Sales taxes...\n* Property taxes..."
"""
# Put a newline before any bullet marker that is preceded by whitespace,
# but avoid changing bullets that are already at the start of a line.
text = re.sub(r"(?m)(?<!^)\s+([*\-•])\s+", r"\n\1 ", text)
# Also handle numbered bullets like " 1) foo" or " 1. foo"
text = re.sub(r"(?m)(?<!^)\s+(\d+[.)])\s+", r"\n\1 ", text)
return text
def humanize_chunk(text: str) -> str:
text = normalize_inline_bullets(text)
text = strip_markdown(text)
lines = text.splitlines()
output: List[str] = []
current_sentence = None
tail_items: List[str] = []
for raw_line in lines:
line = raw_line.strip()
if not line:
continue
if is_bullet(line):
item = extract_bullet_text(line)
# Heading bullet: flush previous sentence first
if ":" in item:
if current_sentence:
if tail_items:
clean_items = [
lowercase_initial(ti.rstrip("."))
for ti in tail_items
]
current_sentence += " " + collapse_list(clean_items)
tail_items = []
output.append(current_sentence)
title, rest = item.split(":", 1)
body = lowercase_initial(rest.strip())
current_sentence = choose_intro(title) + body
else:
# Sub-bullet: belongs to current heading
if current_sentence:
tail_items.append(item)
else:
# orphan bullet (rare, but handle)
output.append(item)
else:
# Normal line flushes everything
if current_sentence:
if tail_items:
clean_items = [
lowercase_initial(ti.rstrip("."))
for ti in tail_items
]
current_sentence += " " + collapse_list(clean_items)
tail_items = []
output.append(current_sentence)
current_sentence = None
output.append(line)
# Final flush
if current_sentence:
if tail_items:
clean_items = [
lowercase_initial(ti.rstrip("."))
for ti in tail_items
]
current_sentence += " " + collapse_list(clean_items)
output.append(current_sentence)
result = "\n\n".join(output)
result = re.sub(r"[ \t]+", " ", result)
return result
def humanize(text: str) -> str:
paragraphs = re.split(r"\n\s*\n", text.strip())
cleaned = [humanize_chunk(p) for p in paragraphs]
return "\n\n".join(cleaned)
def parse_args():
parser = argparse.ArgumentParser(
description="Interactive 'humanizer': replaces obviously AI-written "
"content with more human-like comment.")
parser.add_argument(
"--verbose",
action="store_true",
help="Print verbose output for debugging."
)
return parser.parse_args()
if __name__ == "__main__":
random.seed(123)
args = parse_args()
sample = """
* **Free Healthcare:** Undocumented immigrants generally do not receive free, comprehensive healthcare.
* **Other Benefits:** The vast majority of federally funded public benefits require legal status.
* **No Taxes:** This is a common misconception.
* Sales taxes
* Property taxes
* Federal and state income taxes
"""
print(f"\nSample humanized version:\n{humanize(sample)}")
s = input("\nEnter text or filename (ending in .txt): ")
while s and s != "done":
if s.endswith(".txt"):
with open(s, encoding='utf-8') as f:
s = f.read()
humanized = humanize(s)
print(f"\nHumanized version: {humanized}")
s = input("Enter text: ")
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