add app and requirements

.gitignore

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+*.pdf

app.py

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+# Import the required libraries
+import gradio as gr
+import cv2 # OpenCV, to read and manipulate images
+import easyocr # EasyOCR, for OCR
+import torch # PyTorch, for deep learning  
+import pymupdf # PDF manipulation
+from transformers import pipeline # Hugging Face Transformers, for NER
+import os # OS, for file operations
+from glob import glob # Glob, to get file paths
+
+##########################################################################################################
+# Initiate the models
+
+# Easyocr model
+print("Initiating easyocr")
+reader = easyocr.Reader(['en'], gpu=torch.cuda.is_available(), model_storage_directory='.')
+
+# Use gpu if available
+print("Using gpu if available")
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+print(f"Using device: {device}")
+
+# Ner model
+print("Initiating nlp pipeline")
+nlp = pipeline("token-classification", model="dslim/distilbert-NER", device=device)
+
+##########################################################################################################
+## Functions
+
+# Define img_format
+img_format = "png"
+
+# Convert pdf to set of images
+def convert_to_images(pdf_file_path):
+
+    # Create a directory to store pdf images
+    pdf_images_dir = f'{pdf_file_path}_images'
+    os.makedirs(pdf_images_dir, exist_ok=True)
+
+    # DPI
+    dpi = 150
+
+    # Convert the PDF to images
+    print("Converting PDF to images...")
+    doc = pymupdf.open(pdf_file_path)  # open document
+    for page in doc:  # iterate through the pages
+        pix = page.get_pixmap(dpi=dpi)  # render page to an image
+        pix.save(f"{pdf_images_dir}/page-{page.number}.{img_format}")  # store image as a PNG
+
+    # Return the directory with the images
+    return pdf_images_dir
+
+# Do the redaction
+def redact_image(pdf_image_path, redaction_score_threshold):
+
+    # Loop through the images
+    print("Redacting sensitive information...")
+
+    print(f"Processing {pdf_image_path}...")
+    # Read the image
+    cv_image = cv2.imread(pdf_image_path)
+
+    # Read the text from the image
+    result = reader.readtext(cv_image, height_ths=0, width_ths=0, x_ths=0, y_ths=0)
+
+    # Get the text from the result
+    text = ' '.join([text for (bbox, text, prob) in result])
+
+    # Perform NER on the text
+    ner_results = nlp(text)
+
+    # Draw bounding boxes
+    for ((bbox, text, prob),ner_result) in zip(result, ner_results):
+
+        # Get the coordinates of the bounding box
+        (top_left, top_right, bottom_right, bottom_left) = bbox
+        top_left = tuple(map(int, top_left))
+        bottom_right = tuple(map(int, bottom_right))
+
+        # Calculate the centers of the top and bottom of the bounding box
+        # center_top = (int((top_left[0] + top_right[0]) / 2), int((top_left[1] + top_right[1]) / 2))
+        # center_bottom = (int((bottom_left[0] + bottom_right[0]) / 2), int((bottom_left[1] + bottom_right[1]) / 2))
+
+
+        # If the NER result is not empty, and the score is high
+        if len(ner_result) > 0 and ner_result['score'] > redaction_score_threshold:
+
+            # Get the entity and score
+            # entity = ner_result[0]['entity']
+            # score = str(ner_result[0]['score'])
+
+            # Apply a irreversible redaction
+            cv2.rectangle(cv_image, top_left, bottom_right, (0, 0, 0), -1)
+        # else:
+            # entity = 'O'
+            # score = '0'
+            
+        # # Draw the bounding box
+        # cv2.rectangle(cv_image, top_left, bottom_right, (0, 255, 0), 1)
+        # # Draw the entity and score
+        # cv2.putText(cv_image, entity, center_top, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+        # cv2.putText(cv_image, score, center_bottom, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+
+    # Save the redacted image
+    print(f"Saving redacted {pdf_image_path}...")
+    redacted_image_path = pdf_image_path.replace(f'.{img_format}', f'_redacted.{img_format}')
+    # Save the redacted image in png format
+    cv2.imwrite(redacted_image_path, cv_image)
+
+    return redacted_image_path
+
+# Convert the set of redacted images to a pdf
+def stich_images_to_pdf(redacted_image_files, input_pdf_name):
+
+    # Sort the redacted images
+    redacted_image_files.sort()
+
+    # Convert the redacted images to a single PDF
+    print("Converting redacted images to PDF...")
+    redacted_pdf_folder = "/tmp/gradio/redacted"
+    os.makedirs(redacted_pdf_folder, exist_ok=True )
+    redacted_pdf_path = f'{redacted_pdf_folder}/{input_pdf_name}_redacted.pdf'
+
+    doc = pymupdf.open()
+    for redacted_image_file in redacted_image_files:
+        img = pymupdf.open(redacted_image_file)  # open pic as document
+        rect = img[0].rect  # pic dimension
+        pdfbytes = img.convert_to_pdf()  # make a PDF stream
+        img.close()  # no longer needed
+        imgPDF = pymupdf.open("pdf", pdfbytes)  # open stream as PDF
+        page = doc.new_page(width = rect.width,  # new page with ...
+                        height = rect.height)  # pic dimension
+        page.show_pdf_page(rect, imgPDF, 0)  # image fills the page
+    doc.save(redacted_pdf_path)
+
+    # print(f"PDF saved as {redacted_pdf_path}")
+
+    return redacted_pdf_path
+
+def cleanup(redacted_image_files, pdf_images, pdf_images_dir, original_pdf):
+
+    # Remove the directory with the images
+    print("Cleaning up...")
+
+    # Remove the redacted images
+    for file in redacted_image_files:
+        os.remove(file)
+
+    # Remove the pdf images
+    for file in pdf_images:
+        os.remove(file)
+
+    # Remove the pdf images directory
+    os.rmdir(pdf_images_dir)
+
+    # Remove original pdf
+    os.remove(original_pdf)
+
+    return None
+
+# Func to control ui
+def predict(input_pdf_path, sensitivity):
+
+    print("Setting threshold")
+    # Convert sensitivity to threshold
+    redaction_score_threshold = (100-sensitivity)/100
+
+    # Get file name
+    print("Getting filename")
+    input_pdf_name = input_pdf_path.split('.')[-2]
+
+    # Convert the PDF to images
+    print("Converting pdf to images")
+    pdf_images_dir = convert_to_images(input_pdf_path)
+
+    # Get the file paths of the images
+    print("Gathering converted images")
+    pdf_images = glob(f'{pdf_images_dir}/*.{img_format}', recursive=True)
+    pdf_images.sort()
+
+    # Redact images
+    print("Redacting images")
+    redacted_image_files = []
+
+    for pdf_image in pdf_images:
+
+        redacted_image_files.append(redact_image(pdf_image, redaction_score_threshold))
+
+
+    # Convert the redacted images to a single PDF
+    print("Stitching images to pdf")
+    redacted_pdf_path = stich_images_to_pdf(redacted_image_files, input_pdf_name)
+
+    print("Cleaning up")
+    cleanup(redacted_image_files, pdf_images, pdf_images_dir, input_pdf_path)
+
+    return redacted_pdf_path
+
+##########################################################################################################
+
+contact_text = """
+# Contact Information
+
+👤  [Mitanshu Sukhwani](https://www.linkedin.com/in/mitanshusukhwani/)
+
+✉️  [email protected]
+
+🐙  [mitanshu7](https://github.com/mitanshu7)
+"""
+
+##########################################################################################################
+# Gradio interface
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+
+    # Title and description
+    gr.Markdown("# RedactNLP: Redact your PDF!")
+    gr.Markdown("## How redaction happens:")
+    gr.Markdown("""
+                1. The PDF pages are converted to images.
+                2. EasyOCR is run on the converted images to extract text.
+                3. "FacebookAI/xlm-roberta-large-finetuned-conll03-english" model does the token classification.
+                4. Non-recoverable mask is applied to identified elements.
+                """)
+    
+    # Input Section
+    pdf_file_input = gr.File(file_count='single', file_types=['pdf'], label='Upload PDF', show_label=True, interactive=True)
+    
+    # Slider for results count
+    slider_input = gr.Slider(
+        minimum=0, maximum=100, value=80, step=1, 
+        label="Sensitivity to remove elements. Higher is more sensitive, hence will redact aggresively."
+    )
+
+    # Submission Button
+    submit_btn = gr.Button("Redact")
+    
+    # Output section
+    output = gr.File(file_count='single', file_types=['pdf'], label='Download redacted PDF', show_label=True, interactive=False)
+
+    # Attribution
+    gr.Markdown(contact_text)
+
+    # Link button click to the prediction function
+    submit_btn.click(predict, [pdf_file_input, slider_input], output)
+
+
+################################################################################
+
+if __name__ == "__main__":
+    demo.launch()
+

requirements.txt

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+torch
+transformers
+opencv-python
+easyocr
+pymupdf
+gradio