This project implements a machine translation model that translates text from Javanese to Indonesian using Bidirectional Long Short-Term Memory (Bi-LSTM) networks with an attention mechanism. The model is designed to handle the unique linguistic features of both languages, providing accurate translations.

1. Clone the Repository

   git clone https://github.com/kaniagalih/machine_translation.git

2. Create a Virtual Environment

   python3 -m venv mt_venv #on windows & linux 
   conda env create --prefix ./mt_venv --file requirements.yaml #using conda env 

3. Activate the Virtual Environment

   mt_venv/Scripts/activate #on windows
   conda activate ./mt_venv #using conda
   source .mt_venv/bin/activate #on linux 

4. Install the Required Packages

   pip install -r requirements.txt

5. Deactive Environment

   conda deactivate #conda 
   deactivate #windows & linux 

Run the application using Streamlit:

```bash
streamlit run app/dev/app.py
```

If you'd like to deploy the application using Docker, follow these steps:

1. Build the Docker Image

   docker build -t java-translator . | docker-compose up -d 

2. Run the Docker Container

   docker run -d -p 8501:8501 java-translator | if you do "docker-compose up -d" you don't need to run this command 

3. Access the Application After running the container, open your browser and go to the following URL:

   http://localhost:8501/

4. Stop the Docker

   docker stop | docker-compose down