Update data_preprocessing.py

data_preprocessing.py

@@ -39,15 +39,12 @@ def ocr_image(index):
             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']))))