https://github.com/chrisvivasai/translate-app
Multilingual Translation App
https://github.com/chrisvivasai/translate-app
ai huggingface nlp streamlit translation
Last synced: about 1 month ago
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Multilingual Translation App
- Host: GitHub
- URL: https://github.com/chrisvivasai/translate-app
- Owner: ChrisVivasAI
- Created: 2024-11-09T00:07:32.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-09T02:38:28.000Z (over 1 year ago)
- Last Synced: 2025-08-31T22:36:04.922Z (10 months ago)
- Topics: ai, huggingface, nlp, streamlit, translation
- Language: Python
- Homepage: https://translate-app-p7uuvfx3bzgpyykmp3b2b8.streamlit.app/
- Size: 89.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🌍 Multilingual Translation App
Welcome to the **Multilingual Translation App**! This application leverages Hugging Face's Inference API to provide seamless translation between multiple languages through a user-friendly Streamlit interface.
## 🚀 Features
- 🔄 **Dynamic Language Selection**: Choose source and target languages from a diverse set of options.
- ⚡ **Real-Time Translation**: Instantaneous translation results powered by Hugging Face's state-of-the-art models.
- 🖥️ **User-Friendly Interface**: Intuitive design for effortless navigation and interaction.
## 🖼️ Preview
### Main Interface
*The main interface of the Multilingual Translation App where users can enter text and select translation options.*
### Language Selection Screen
*Screen where users choose source and target languages for translation.*
## 🛠️ Setup and Deployment
Follow these steps to set up and deploy the application:
### 1. Clone the Repository
```bash
git clone https://github.com/ChrisVivasAI/translate-app
cd translation_app
```
### 2. Install Dependencies
Ensure you have Python 3.7 or higher installed. Then, install the required packages:
```bash
pip install -r requirements.txt
```
### 3. Obtain a Hugging Face API Token
1. Sign up or log in to your Hugging Face account.
2. Navigate to your API tokens page.
3. Create a new token with the necessary permissions.
### 4. Set Up Environment Variables
Create a `.env` file in the project root directory and add your Hugging Face API token:
```env
HUGGINGFACE_API_TOKEN=your_huggingface_api_token_here
```
*Note: Ensure the `.env` file is included in your `.gitignore` to prevent exposing sensitive information.*
### 5. Run the Application Locally
Start the Streamlit app:
```bash
streamlit run app.py
```
Access the app in your browser at [http://localhost:8501](http://localhost:8501).
### 6. Deploying to Streamlit Community Cloud
To deploy the app online:
1. Push your project to a GitHub repository.
2. Visit [Streamlit Community Cloud](https://streamlit.io/cloud).
3. Sign in with your GitHub account and select the repository.
4. Set the `HUGGINGFACE_API_TOKEN` in the app's Secrets section on Streamlit Community Cloud.
5. Deploy the app and share the generated link.
## 🤝 Contributing
Contributions are welcome! Feel free to fork the repository, make enhancements, and submit a pull request.
## 📄 License
This project is licensed under the MIT License. See the LICENSE file for details.
## 🙏 Acknowledgements
- Hugging Face for providing the Inference API and pre-trained models.
- Streamlit for the interactive web application framework.