Dockerized English ↔ Swahili Translator -Dekut Walk Tghrough

http://localhost:5000/documentation

A simple, elegant web application for translating between English and Swahili using OpenAI's GPT API. Built with Flask and designed to be easily dockerized and deployed.

Features

• 🌍 Bidirectional translation (English ↔ Swahili)
• 🎨 Modern, responsive UI
• 🐳 Docker support
• 🚀 Production-ready with Gunicorn
• 💪 Health checks and error handling
• 📱 Mobile-friendly design

Prerequisites

• Docker and Docker Compose
• OpenAI API key

Quick Start

1. Clone and Setup

git clone <your-repo-url>
cd translator-app

2. Set up environment

# Copy the example environment file
cp .env.example .env

# Edit .env and add your OpenAI API key
nano .env

3. Run with Docker Compose

# Build and run
docker-compose up --build

# Or run in background
docker-compose up -d --build

The app will be available at http://localhost:5000

Manual Docker Build

Build the image

docker build -t translator-app .

Run the container

docker run -d \
  --name translator \
  -p 5000:5000 \
  -e OPENAI_API_KEY=your_api_key_here \
  translator-app

Pushing to Docker Hub

1. Tag your image

# Replace 'yourusername' with your Docker Hub username
docker tag translator-app yourusername/translator-app:latest
docker tag translator-app yourusername/translator-app:v1.0

2. Push to Docker Hub

# Login to Docker Hub
docker login

# Push the images
docker push yourusername/translator-app:latest
docker push yourusername/translator-app:v1.0

3. Run from Docker Hub

docker run -d \
  --name translator \
  -p 5000:5000 \
  -e OPENAI_API_KEY=your_api_key_here \
  yourusername/translator-app:latest

Project Structure

translator-app/
├── app.py                 # Main Flask application
├── requirements.txt       # Python dependencies
├── Dockerfile            # Docker configuration
├── docker-compose.yml    # Docker Compose configuration
├── .env.example         # Environment variables template
├── templates/
│   └── index.html       # Frontend template
└── README.md           # This file

API Endpoints

• GET / - Main application interface
• POST /translate - Translation API endpoint
• GET /health - Health check endpoint

Translation API Usage

curl -X POST http://localhost:5000/translate \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Hello, how are you?",
    "source_lang": "en",
    "target_lang": "sw"
  }'

Configuration

Environment Variables

• OPENAI_API_KEY - Your OpenAI API key (required)
• FLASK_ENV - Flask environment (default: production)

Customization

You can modify the following in app.py:

• Change the OpenAI model (currently using gpt-3.5-turbo)
• Adjust max tokens for translation
• Add more language pairs
• Modify translation prompts

Development

Local Development

# Install dependencies
pip install -r requirements.txt

# Set environment variable
export OPENAI_API_KEY=your_api_key_here

# Run the app
python app.py

Adding More Languages

To add more language pairs, modify the /translate endpoint in app.py and update the frontend accordingly.

Production Deployment

The Docker image uses Gunicorn for production deployment with:

• 2 worker processes
• 60-second timeout
• Health checks every 30 seconds
• Non-root user for security

Troubleshooting

Common Issues

1. API Key Error: Make sure your OpenAI API key is valid and has sufficient credits
2. Port Conflicts: Change the port mapping in docker-compose.yml if 5000 is occupied
3. Build Failures: Ensure Docker has sufficient resources allocated

Checking Logs

# Docker Compose logs
docker-compose logs -f

# Container logs
docker logs translator

License

resources:

https://docs.docker.com/engine/install/ubuntu/

https://docker-curriculum.com/

$ docker login --username=maryatdocker [email protected]

Password:
WARNING: login credentials saved in C:\Users\sven\.docker\config.json
Login Succeeded

# 1. Clone the repository (if code is hosted remotely)
git clone https://github.com/your-org/your-repo.git
cd your-repo

# 2. Pull the Docker image (from DockerHub or other registry)
docker pull your-dockerhub-username/your-image-name:tag

# Example:
# docker pull mycompany/app-backend:latest

# 3. Run the Docker container
docker run -d \
  --name your-container-name \
  -p 8000:8000 \               # map ports (host:container)
  -v $(pwd):/app \             # mount current dir to /app in container (optional)
  your-dockerhub-username/your-image-name:tag

# Example:
# docker run -d --name backend-app -p 8000:8000 mycompany/app-backend:latest

🔁 Basic Cleanup Commands

Stop All Running Containers

docker stop $(docker ps -q)

Remove All Stopped Containers

docker container prune -f

Remove Unused Docker Images

docker image prune -f

To remove all unused images:

docker image prune -a -f

Remove Unused Networks

docker network prune -f

Remove Unused Volumes

docker volume prune -f

Full Cleanup Force Cleanup

# Stop all running containers
docker stop $(docker ps -q)

# Remove all containers (running and stopped)
docker rm -f $(docker ps -aq)

# Remove all images
docker rmi -f $(docker images -q)

# Remove all volumes
docker volume rm -f $(docker volume ls -q)

# Remove all networks except default ones
docker network rm $(docker network ls | grep "bridge\|host\|none" -v | awk '{print $1}')

Verify Cleanup

docker ps -a           # No containers
docker images -a       # No images
docker volume ls       # No volumes
docker network ls      # Only default networks