🌐 FastAPI Language Prediction API

Welcome to the FastAPI Language Prediction API! This project is a machine-learning-powered web service that predicts the language of a given sentence using an SVM classifier. It uses FastAPI for serving the model and TfidfVectorizer for text transformation.

🚀 Features

• Language Prediction: Detects the language of an input sentence.
• Clean Text Functionality: Automatically removes noise like HTML tags, URLs, stopwords, and accents.
• Machine Learning: Uses Support Vector Machine (SVM) to classify text.
• Swagger UI: Comprehensive API documentation using Swagger.
• Modular Code Structure: Well-organized codebase for easy maintainability and scalability.
• Docker Support: Seamlessly deploy the application using Docker.

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🏗 Project Structure

├── app/
│   ├── api/
│   │   ├── __init__.py    # Package initialization
│   │   └── v1/
│   │       ├── __init__.py    # API versioning
│   │       └── endpoints.py   # API routes and endpoints
│   ├── core/
│   │   ├── __init__.py    # Core initialization
│   │   └── config.py      # Application configuration settings
│   ├── models/
│   │   └── __init__.py    # Machine learning models (SVM, Tfidf, etc.)
│   ├── services/
│   │   ├── __init__.py    # Services initialization
│   │   └── prediction.py  # Business logic for language prediction
│   └── main.py            # FastAPI initialization and application bootstrap
├── dataset/
│   └── dataset.csv        # Training dataset (ignored in git)
├── Dockerfile             # Dockerfile for containerization
├── docker-compose.yml     # Docker Compose configuration
├── requirements.txt       # Python dependencies
└── README.md              # Project documentation (you're reading this!)

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🛠 Setup Instructions

Follow these steps to run the application locally or deploy it using Docker.

1. Clone the Repository

git clone https://github.com/Damieee/language-prediction-api
cd language-prediction-api

2. Setup Environment

Create a .env file in the root directory and add the following values:

AWS_ACCESS_KEY_ID=your-access-key
AWS_SECRET_ACCESS_KEY=your-secret-key
AWS_REGION=your-region

3. Install Dependencies

Make sure you have Python 3.8+ and install the required packages:

pip install -r requirements.txt

4. Run the Application Locally

To run the FastAPI app locally, execute:

uvicorn app.main:app --reload

The API documentation will be available at http://localhost:8000/docs.

5. Run with Docker

To deploy the application using Docker, run the following commands:

docker-compose up --build

This will build the Docker image and run the container. You can access the API at http://localhost:8000.

📊 Model Overview

• Text Cleaning: Removes HTML, URLs, accents, punctuation, and stopwords.
• TF-IDF Vectorization: Converts text to numerical data.
• SVM Classifier: Classifies the input text into one of the supported languages.
• Label Encoding: Encodes languages for the model.

🔥 API Endpoints

POST /predict_language/

Request:

{
  "sentence": "Enter the sentence here"
}

Response:

{
  "sentence": "Enter the sentence here",
  "predicted_language": "English"
}

📦 Deployment

This project is configured to run in production using Docker. In production, you should ensure the following:

• Environment Variables: Ensure that the .env file or Docker environment contains production-specific keys and configurations.
• Docker Compose: The Docker Compose configuration allows you to spin up the application easily in production environments.

🤝 Contributing

Feel free to open issues or submit pull requests for any feature improvements or bug fixes.

1. Fork the repository
2. Create a new feature branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -m 'Add feature')
4. Push to the branch (git push origin feature/your-feature)
5. Open a pull request

📚 Documentation & Resources

• FastAPI Documentation
• Scikit-learn Documentation

🔗 Project Links

📧 Contact

• Maintainer: Dare Ezekiel
• Email: [email protected]