README.md

metadata

title: Email Classification and PII Masking API
emoji: 📧
colorFrom: blue
colorTo: purple
sdk: docker
sdk_version: latest
app_file: main.py
pinned: false
models:
  - Davlan/bert-base-multilingual-cased-ner-hrl
  - sentence-transformers/paraphrase-multilingual-mpnet-base-v2

Email Classification and PII Masking API

This FastAPI application provides an API for classifying emails and masking Personally Identifiable Information (PII) in text.

Features

• PII Detection and Masking
  • Full names
  • Email addresses
  • Phone numbers
  • Dates of birth
  • Aadhar numbers
  • Credit/Debit card numbers
  • CVV numbers
  • Card expiry dates
• Email Classification using MLP model
• Multilingual support using BERT-based models

Setup

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Download required model files:

• label_encoder.pkl
• pca.pkl
• mlp_model.pth Place these files in the same directory as main.py.

Usage

1. Start the FastAPI server:

uvicorn main:app --reload --host 0.0.0.0 --port 80

Note for Hugging Face Spaces: We explicitly bind to 0.0.0.0 and port 80, which is typically required by Spaces.

2. The API will be available at the Space's URL (e.g., https://your-username-your-space-name.hf.space).

3. API Endpoints:

• POST /classify: Classify and mask PII in email text
  • Input: JSON with input_email_body field

{
  "input_email_body": "Hello, my name is John Doe and my email is john.doe@example.com. Please help with my billing issue."
}

  - **Output:** JSON with masked text, detected entities, and classification

{
  "input_email_body": "Hello, my name is John Doe and my email is john.doe@example.com. Please help with my billing issue.",
  "list_of_masked_entities": [
    {
      "position": [
        16,
        24
      ],
      "classification": "full_name",
      "entity": "John Doe"
    },
    {
      "position": [
        39,
        60
      ],
      "classification": "email",
      "entity": "john.doe@example.com"
    }
  ],
  "masked_email": "Hello, my name is [full_name] and my email is [email]. I'm having trouble with my account.",
  "category_of_the_email": "Billing Issues"
}

API Documentation

Once the server is running on Hugging Face Spaces, the Swagger UI and ReDoc documentation endpoints might not be directly accessible via the standard /docs and /redoc paths in a "Static" Space setup. You would typically interact with the /classify endpoint directly via POST requests.

Project Structure

.
├── README.md
├── requirements.txt
├── main.py         # FastAPI application entry point
├── models.py       # ML model definitions and training logic
├── utils.py        # Utility functions for text processing
├── label_encoder.pkl # Label encoder for classification
├── pca.pkl         # PCA model for dimensionality reduction
└── mlp_model.pth   # Trained MLP model weights

Deployment on Hugging Face Spaces

To deploy this application on Hugging Face Spaces:

1. Create a new Space on https://huggingface.co/spaces.
2. Choose a Space name, select a license, and for Space Hardware, the "Free" tier should be sufficient for this type of API.
3. Crucially, under SDK, select "Static".
4. In your Space's settings, link your GitHub repository containing these files.
5. Hugging Face Spaces will automatically detect the requirements.txt and install the dependencies.
6. It will then look for an app_file specified in the frontmatter (main.py in this case) to run. For a "Static" Space running a FastAPI application, it will execute uvicorn main:app --host 0.0.0.0 --port 80.

Ensure all your model files (label_encoder.pkl, pca.pkl, mlp_model.pth) are present in your repository at the root level or in the same directory as main.py.