🐳 DockerAizer

Transform your projects into container-ready configurations using AI-powered analysis.

Setup

1. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install required packages:

pip install streamlit litellm pathlib

Running the App

1. Start the application:

streamlit run dockeraizer.py

2. Open your browser to http://localhost:8501

How to Use

1. API Configuration

   • Enter your API key in the sidebar for your chosen LLM provider

2. Model Selection You have two options for selecting a model:

   a) Common Models Dropdown

   • Choose "Common Models" radio button
   • Select from popular models like:
     • OpenAI (gpt-4, gpt-3.5-turbo)
     • Anthropic (claude-3-opus, claude-3-sonnet)
     • Mistral (mistral-large, mistral-medium)
     • Google (gemini-pro)

   b) Custom Model Input

   • Choose "Custom Model" radio button
   • Enter any LiteLLM-supported model identifier
   • Examples:

     azure/gpt-4
     aws/bedrock/claude
     anthropic/claude-3-opus
     

3. Generate Docker Configs

   • Paste your project's directory path
   • Review the analysis and generated configurations

Supported Project Types

The tool can analyze projects containing:

• Python files (requirements.txt, setup.py)
• Node.js files (package.json)
• Java files (pom.xml, build.gradle)
• Go files (go.mod)
• Rust files (Cargo.toml)
• Generic configs (yaml, json)

⚠️ Important Notes

• Generated configurations are AI suggestions and need human review
• Always test configurations before deployment
• Keep your API keys secure
• Verify model compatibility with your API key
• Configurations may need adjustments for your specific needs

LiteLLM Model Support

For a complete list of supported models and providers, visit: LiteLLM's Provider Documentation