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Text-to-Document-Generation/PDF-Redaction-API

sourceHugging Facemitupdated 5mo agoView on Hugging Face

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App README

PDF Redaction API 🔒

Automatically redact sensitive information from PDF documents using Named Entity Recognition (NER).

Features

—🤖 Powered by NER: Uses state-of-the-art Named Entity Recognition
—📄 PDF Support: Upload and process PDF documents
—🎯 Accurate Redaction: Correctly positioned black rectangles over sensitive text
—🚀 Fast Processing: Optimized OCR and NER pipeline
—🔧 Configurable: Adjust DPI and filter entity types

API Endpoints

`POST /redact`

Upload a PDF file and get it redacted.

Parameters:

—file: PDF file (required)
—dpi: OCR quality (default: 300)
—entity_types: Comma-separated entity types to redact (optional)

Example using cURL:

bash

curl -X POST "https://your-space.hf.space/redact" \
  -F "file=@document.pdf" \
  -F "dpi=300"

Example using Python:

python

import requests

url = "https://your-space.hf.space/redact"
files = {"file": open("document.pdf", "rb")}
params = {"dpi": 300}

response = requests.post(url, files=files, params=params)
result = response.json()

# Download redacted file
job_id = result["job_id"]
download_url = f"https://your-space.hf.space/download/{job_id}"
redacted_pdf = requests.get(download_url)

with open("redacted.pdf", "wb") as f:
    f.write(redacted_pdf.content)

`GET /download/{job_id}`

Download the redacted PDF file.

`GET /health`

Check API health and model status.

`GET /stats`

Get API statistics.

Response Format

json

{
  "job_id": "uuid-here",
  "status": "completed",
  "message": "Successfully redacted 5 entities",
  "entities": [
    {
      "entity_type": "PER",
      "entity_text": "John Doe",
      "page": 1,
      "word_count": 2
    }
  ],
  "redacted_file_url": "/download/uuid-here"
}

Entity Types

Common entity types detected:

—PER: Person names
—ORG: Organizations
—LOC: Locations
—DATE: Dates
—EMAIL: Email addresses
—PHONE: Phone numbers
—And more...

Local Development

Prerequisites

—Python 3.10+
—Tesseract OCR
—Poppler utils

Installation

bash

# Install system dependencies (Ubuntu/Debian)
sudo apt-get install tesseract-ocr poppler-utils

# Install Python dependencies
pip install -r requirements.txt

# Run the server
python main.py

The API will be available at http://localhost:7860

Using Docker

bash

# Build the image
docker build -t pdf-redaction-api .

# Run the container
docker run -p 7860:7860 pdf-redaction-api

Configuration

Adjust the DPI parameter based on your needs:

—150: Fast processing, lower quality
—300: Recommended balance (default)
—600: High quality, slower processing

Limitations

—Maximum file size: Dependent on Space resources
—Processing time increases with page count and DPI
—Files are automatically cleaned up after processing

Privacy

—Uploaded files are processed in-memory and deleted after redaction
—No data is stored permanently
—Use your own deployment for sensitive documents

Credits

Built with:

License

MIT License - See LICENSE file for details