FlowModel

FlowModel is a lightweight and extensible machine learning framework designed for beginners who want to explore AI development. With its modular plugin-based architecture, users can easily extend its functionality while keeping the core simple and maintainable.

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Table of Contents

1. Introduction
2. Installation
3. Directory Structure
4. Usage
   • Training a Model
   • Adding Plugins
5. Creating Plugins
6. Command-Line Interface
7. Contributing
8. License

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Introduction

FlowModel provides a simple entry point for experimenting with AI and machine learning. It allows users to start with a minimal framework and extend it by creating and adding plugins. The framework is designed to focus on simplicity, modularity, and extensibility.

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Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Steps

1. Clone the repository:

   git clone https://github.com/LunaStev/FlowModel.git
   cd FlowModel
   

2. Create and activate a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # For Unix/MacOS
   .venv\Scripts\activate   # For Windows
   

3. Install dependencies:

   pip install -r requirements.txt
   

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Directory Structure

FlowModel/
├── main.py             # Entry point for the application
├── plugins/            # Directory for plugins
│   ├── __init__.py     # Initializes the plugin package
│   ├── example_plugin.py # Example plugin
├── data/               # Placeholder for datasets
├── requirements.txt    # Python dependencies

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Usage

Training a Model

To train a model using FlowModel, run:

python main.py train

This will load any available plugins from the plugins/ directory and apply their logic during the training process.

Adding Plugins

To add a plugin, place a .py file with your plugin class in the plugins/ directory. FlowModel automatically detects and loads plugins at runtime.

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Creating Plugins

Plugins extend the functionality of FlowModel. To create a plugin:

1. Create a new Python file in the plugins/ directory:

   plugins/my_plugin.py
   

2. Define your plugin class:

   class MyPlugin:
       def __init__(self):
           print("MyPlugin initialized.")
   
       def modify_model(self, model):
           print("MyPlugin: Modifying the model.")
           return model
   
       def on_train_start(self):
           print("MyPlugin: Training started.")
   
       def on_train_end(self):
           print("MyPlugin: Training finished.")
   

3. Use your plugin during training: When main.py runs, it automatically loads your plugin and calls its methods.

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Command-Line Interface

FlowModel includes a simple CLI for interacting with the framework.

Commands

• Train: Start the training process with plugins.

  python main.py train
  

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Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch for your feature.
3. Commit your changes and push them.
4. Open a pull request.

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License

FlowModel is released under the MPL-2.0 License. See LICENSE for details.

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Happy experimenting with FlowModel! 🚀