Sejal1908/email-classification-api

emailClassificationsupport_Sejal

A Python-based Flask API that detects and masks Personally Identifiable Information (PII) in email content and classifies the email into relevant categories using a machine learning model. This project is deployed via Hugging Face Spaces.

🚀 Demo Paste your email content into the API via a POST request, and get back a masked version with all sensitive entities hidden, alongside the predicted email category.

🔍 Features ✅ Regex and NER-based PII Masking (name, email, phone number, DOB, etc.)

🤖 ML-based Email Classification (e.g., General, Finance, Promotions)

📡 REST API built using Flask

🧪 Ready-to-test JSON response format

☁️ Hosted on Hugging Face Spaces with GitHub integration

📂 Project Structure

. ├── app.py # Main Flask app entrypoint ├── api.py # API routes and logic └── data/ └── emails.csv # the Data set ├── models.py # Classification logic and ML model ├── utils.py # PII masking functions ├── trainmodel.py # Script to train and save ML model ├── requirements.txt # Required Python dependencies ├── README.md # You're here! ├── space.yaml # Hugging Face deployment file ├── testinput.json # Sample input for testing └── model/ └── email_classifier.pkl # Pretrained ML model

🧠 Technologies Used Python 3.10

Flask

Scikit-learn

Regex + SpaCy (for PII detection)

Docker + Hugging Face Spaces

GitHub (CI/CD)

🔧 Installation (Run Locally)

# Clone the repository git clone https://github.com/Sejal1908/email-classification-api.git cd email-classification-api

Create virtual environment (optional but recommended)

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

Install dependencies

pip install -r requirements.txt

Run the app

python app.py

🧪 API Usage

I have used postman for this project

▶️ Endpoint POST /classify_email

▶️ Request Headers pgsql Copy code Content-Type: application/json

▶️ Request Body json Copy code { "email": "Contact Alice Wonderland at alice@wonder.land or +91 98765 43210. DOB: 01/01/1980." }

✅ Successful Response Format json Copy code { "inputemailbody": "Contact Alice Wonderland at alice@wonder.land or +91 98765 43210. DOB: 01/01/1980.", "listofmaskedentities": [ { "position": [0, 24], "classification": "fullname", "entity": "Contact Alice Wonderland" }, { "position": [28, 45], "classification": "email", "entity": "alice@wonder.land" }, { "position": [49, 64], "classification": "phonenumber", "entity": "+91 98765 43210" }, { "position": [71, 81], "classification": "dob", "entity": "01/01/1980" } ], "maskedemail": "[fullname] at [email] or [phonenumber]. DOB: [dob].", "categoryofthe_email": "General" }

⚙️ Hugging Face Deployment

Go to https://huggingface.co/spaces and click "Create Space"

Choose SDK as Docker

Select "Link to GitHub Repository"

Ensure you include a space.yaml or README.md for Hugging Face to build correctly

Push your code to GitHub — it auto-deploys!

Example space.yaml

This file tells Hugging Face Spaces how to run your app

sdk: docker python_version: "3.10" --------------------------------------------------------------------------------------- 🧠 Training the Classifier

You can train and save your ML model using the provided train_model.py script.

bash Copy code python train_model.py This will generate a classifier.pkl saved in the /model folder which your app will load at runtime.

DockerFile

Use official Python image

FROM python:3.10

Set environment variables

ENV PYTHONDONTWRITEBYTECODE 1 ENV PYTHONUNBUFFERED 1

Set working directory

WORKDIR /code

Install dependencies

COPY requirements.txt . RUN pip install --upgrade pip RUN pip install -r requirements.txt

Copy all project files

COPY . .

Expose port

EXPOSE 7860

Run the app

CMD ["python", "app.py"]

Docker Setup

1. 1.Build the Docker image: docker build -t email-classification-api .

1. 1.Run the Docker container: docker run -p 7860:7860 email-classification-api

1. 1.Access the application on http://localhost:7860. -------------------------------------------------------------------------------------------- 📜 License

This project is licensed under the MIT License --------------------------------------------------------------------------------------------

👩‍💻 Author Sejal Vhankade 🔗 Github 🔗 LinkedIn ✉️ sejal.vhankade@gmail.com