LangWithLang Translator

A context-aware translation tool that not only translates text but also provides cultural context, alternative translations, and pronunciation guides.

Features

• Translate between multiple languages
• Provides cultural context for idioms and expressions
• Suggests alternative translations for different contexts
• Includes pronunciation guides for important words
• Simple and intuitive user interface
• Meet Lang the Penguin, your translation companion!

Installation

Prerequisites

• Python 3.7 or higher
• A Groq API key (sign up at groq.com)

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/langwithlang-translator.git
   cd langwithlang-translator

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install the required packages:

   pip install -r requirements.txt

5. Create a .env file in the project root and add your Groq API key:

   GROQ_API_KEY=your-groq-api-key-here
   

Running the App

1. Make sure your virtual environment is activated.

2. Run the Streamlit application:

   streamlit run streamlit_translator.py

3. The application will open in your default web browser at http://localhost:8501.

Usage

1. Enter the text you want to translate in the input field.
2. Select the source language (default is English).
3. Select the target language you want to translate to.
4. Click the "Translate" button.
5. View the translation results, including cultural context, alternative translations, and pronunciation guides.

Adding Penguin Images

For the full experience, make sure the following image is in your project directory:

• Penguin.png

Customization

You can customize the application by:

• Modifying the CSS in the st.markdown section
• Adding more languages to the languages list in the ContextAwareTranslator class
• Changing the model by updating the model_name parameter in the ContextAwareTranslator class

Acknowledgements

• Powered by Streamlit
• Language model provided by Groq
• Integrated with LangChain