Create data_preprocessing.py

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:
+        # Print error message for any kind of Exception
+        # print(f"Error processing image at index {index}: {e}")
+        return None
+
+# Create a ThreadPoolExecutor to parallelize image processing.
+num_workers = 8  # Adjust this number based on your machine's capabilities.
+imgur_text = []
+
+# Use ThreadPoolExecutor to create a pool of threads.
+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)