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reddit_political_subs / data_preprocessing.py

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	'''
	This code outlines the data pre-processing work done (text: cleaning/ tokenizing and images: extracting text from the image using pytesseract).
	The dataset in the repo is the processed one.
	'''

	! pip install datasets q

	from datasets import load_dataset

	dataset = load_dataset("mo-mittal/reddit_political_subs", trust_remote_code=True)

	# Install Tesseract OCR
	!sudo apt install tesseract-ocr

	# Install pytesseract
	!pip install pytesseract

	import pytesseract
	from concurrent.futures import ThreadPoolExecutor

	# this assumes 'dataset' is already loaded and contains the earlier data. I had done the preprocessing on an earlier version of the current dataset.

	#### you can also check how many CPU cores are available for the threadpool processor

	# import os
	# num_cores = os.cpu_count()
	# print(f"Number of available CPU cores: {num_cores}")


	def ocr_image(index):
	try:
	# Retrieve the image and associated URL from the dataset.
	image_pil = dataset['train'][index]['image']
	url = dataset['train'][index]['url']

	# Check if 'image_text' is empty and if the word 'imgur' is in the 'url' field.
	if (dataset['train'][index]['image_text'] == '') and ('imgur' in url):
	text = pytesseract.image_to_string(image_pil)
	print(f'Sublime! Processed img at {index}')
	return text
	except Exception as e:
	return None

	# Create a ThreadPoolExecutor to parallelize image processing.
	num_workers = 8 # Adjust this number based on your machine's capabilities.
	imgur_text = []

	with ThreadPoolExecutor(max_workers=num_workers) as executor:
	# Use map to apply 'ocr_image' function to each index.
	results = list(executor.map(ocr_image, range(len(dataset['train']))))

	# Now 'results' contains the OCR results or None for each image.
	# Filter out None values and add the text to 'imgur_text' list.
	imgur_text.extend(filter(None, results))

	# 'imgur_text' now contains all the OCR results. This can take quite a while to process! (About 7503 images and 12 hours for an 8 core CPU)

	## for text cleaning

	import pandas as pd

	df = pd.DataFrame(dataset['train'].remove_columns(['image']))
	# df.head()

	results_list = list(results)

	for i, text in enumerate(results_list):
	if text is not None:
	df.loc[i, 'image_text'] = text

	# df.to_csv('reddit_political_subs.csv', index=False)
	import pandas as pd

	df = pd.read_csv('reddit_political_subs.csv')

	import nltk
	nltk.download('stopwords')

	import re
	from nltk.corpus import stopwords
	import string

	def clean_text(text):
	text = re.sub(r'\\n', ' ', text)
	text = re.sub(r'\\x..', '', text)
	text = re.sub(r'[@\|\\]', '', text)

	text = text.translate(str.maketrans('', '', string.punctuation))
	stop_words = set(stopwords.words('english'))
	text = ' '.join([word for word in text.split() if word not in stop_words])

	text = ' '.join(text.split())
	text = text.lower()

	return text

	df['image_text'] = df['image_text'].apply(clean_text)