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import os
import re
import pandas as pd
from bs4 import BeautifulSoup
def extract_puzzle_data(html):
"""extract data from HTML content"""
soup = BeautifulSoup(html, 'html.parser')
# Extract task description
task_description_section = soup.find('div', id='tabs-2') or ""
task_description = task_description_section.get_text(strip=True).replace('\xa0', ' ')
task_description = task_description.removeprefix("Backstory and Goal")
task_description = task_description.partition("Remember, as with all")[0]
categories = [cc.get_text(strip=True).replace('\xa0', ' ') for cc in soup.find_all('td', class_='answergrid_head')]
# Extract clues, and clean them
clues = []
for clue_div in soup.find_all('div', class_='clue'):
clue_raw = clue_div.get_text(strip=True).replace('\xa0', ' ')
# Remove numbers at the beginning of the string followed by a period and whitespace
cleaned_clue = re.sub(r'^\d+\.\s*', '', clue_raw)
clues.append(cleaned_clue)
# Extract label names such as from labelboxh
label_categories = dict(label_a=[])
for label in soup.find_all('td', class_='labelboxh'):
if label['id'].startswith("labelleftA"):
label_categories["label_a"].append(label.get_text(strip=True).replace('\xa0', ' '))
for letter in "bcd":
pattern = re.compile(f'label{letter}_ary' + r'\[\d+]\s*=\s*"([^"]+)";')
items = pattern.findall(html)
label_categories[f"label_{letter}"] = items
return dict(story=task_description,
clues=clues,
categories=categories,
**label_categories)
global_stories = set()
global_clues = set()
def process_one(difficulty, grid_size):
puzzle_data = []
with open(f'urls/{difficulty}{grid_size}.txt') as rr:
all_paths = [p.strip() for p in rr]
dir_path = f'htmls/{difficulty}{grid_size}/'
for c, puzzle_url in enumerate(all_paths):
filename = puzzle_url.removeprefix("https://logic.puzzlebaron.com/")
if c % 200 == 0:
print(f"{c=}")
file_path = os.path.join(dir_path, filename)
with open(file_path, 'r', encoding='utf-8') as file:
html_content = file.read()
data = extract_puzzle_data(html_content)
if len(global_clues.intersection(data['clues'])) >= 3:
continue
# elif len(global_clues.intersection(data['clues'])) >= 4:
# print("FAIL:", difficulty, grid_size)
# print(filename)
# print(global_clues.intersection(data['clues']))
# continue
# raise RuntimeError(global_clues.intersection(data['clues']))
global_clues.update(data['clues'])
data['grid_size'] = grid_size
data['difficulty'] = difficulty
data['url'] = puzzle_url
puzzle_data.append(data)
return puzzle_data
OUTPUT_DIR = "dataframes"
def main():
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
for grid_size in ['4x7', '4x6', '4x5', '4x4', '3x5', '3x4']:
for difficulty in ['challenging', 'moderate', 'easy']:
puzzle_data = process_one(difficulty, grid_size)
df = pd.DataFrame(puzzle_data)
jsonl_file_path = f'{OUTPUT_DIR}/{difficulty}{grid_size}.jsonl'
df.to_json(jsonl_file_path, orient='records', lines=True)
print(f'Data saved to {jsonl_file_path}', df.shape)
main()
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