scripts/02_gen_dataframes.py

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()