{"id":19820386,"url":"https://github.com/drcbeatz/doggo-gpt-mini","last_synced_at":"2026-04-10T12:31:41.816Z","repository":{"id":251660001,"uuid":"837629439","full_name":"DrCBeatz/doggo-gpt-mini","owner":"DrCBeatz","description":"LLM translation app deployed on AWS (Terraform). Dockerized backend behind ALB on EC2; S3+CloudFront frontend; CI/CD with GitHub Actions. 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It uses Ollama to run a small open-source LLM (Llama 3.1 8b) locally, but can be deployed with any LLM supported by Ollama.\n\nThe app also performs context retrieval by searching for English/DoggoLingo terms in a CSV file (`data/doggo_dictionary.csv`) and adds them to the prompt context. The CSV file can be modified to customize the translations.\n\n## Requirements\n\n- Git\n- Docker\n- Docker Compose\n\n## Local Installation\n\n1. **Clone the repository**:\n \n```bash\ngit clone https://github.com/DrCBeatz/doggo-gpt-mini.git\n```\n\n2. **Navigate to the project directory**:\n\n```bash\ncd doggo-gpt-mini\n```\n\n3. **Build and run the containers**:\n\n```bash\ndocker-compose up -d --build\n```\n\n4. **Pull the Llama 3.1 8b model**:\n\n```bash\ndocker-compose exec ollama ollama pull llama3.1:8b\n```\n\n5. **Access the app**:\n\nOpen your web browser and go to:\n\n```bash\nhttp://localhost:5000\n```\n\n## Usage\n\n1. Select the translation direction by clicking on the input field that says 'English to Doggo' (can be changed to 'Doggo to English').\n2. Enter the message to be translated in the field 'Enter your message...'.\n3. Click the 'Translate Message' button.\n\nYou can try traslating the following English messages to DoggoLingo:\n\n- `Hello friends!`\n- `Hello human, I am a dog!`\n- `I like to eat chicken nuggets, and drink Pepsi, not Coke. Bark bark!`\n\n\n## Deployment\n\n### Deploying on EC2\n\n1. ***Launch an EC2 instance:***\nFollow [AWS documentation](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EC2_GetStarted.html \"AWS EC2 Documentation\") to set up an EC2 instance.\n\nRecommended EC2 settings:\n- Choose the Amazon Linux 2023 Amazon Machine Image (Free tier elgigble).\n- Choose the c5.2xlarge Instance type (8 vCPUs, 16 Gb RAM) for adequate processing power.\n- Choose EC2 Spot Instances (under Advanced details in the AWS Management Console) to save 90% on costs.\n- Choose at least 10Gb of storage for the root volume under 'Configure Storage'.\n\n2. ***Conect to your instance:*** \nSSH into your EC2 instance, or use AWS EC2 Connect in the AWS Management Console.\n\n3. ***Install Docker and Docker Compose***:\n```bash\nsudo yum update -y\nsudo yum install docker -y\nsudo service docker start\nsudo systemctl enable docker\nsudo usermod -a -G docker ec2-user\nsudo chmod 666 /var/run/docker.sock\nsudo yum install git -y\nsudo curl -L \"https://github.com/docker/compose/releases/latest/download/docker-compose-$(uname -s)-$(uname -m)\" -o /usr/local/bin/docker-compose\nsudo chmod +x /usr/local/bin/docker-compose\n```\n\n4. ***Clone the repository and set up the application***:\n```bash\ngit clone https://github.com/DrCBeatz/doggo-gpt-mini.git\ncd doggo-gpt-mini\ndocker-compose up -d --build\ndocker-compose exec ollama ollama pull llama3.1:8b\n```\n\n5. ***Configure security groups:***\nEnsure your EC2 instance's security groups allow inbound traffic on port 5000.\n\n6. ***Access the app***:\nOpen your browser and navigate to your EC2 instance’s public IP address on port 5000.\n\n## License\n\nThis project is licensed under the MIT License.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdrcbeatz%2Fdoggo-gpt-mini","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdrcbeatz%2Fdoggo-gpt-mini","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdrcbeatz%2Fdoggo-gpt-mini/lists"}