---
license: mpl-2.0
language:
- en
base_model:
- LunaStev/FlowModel
tags:
- python
- lightweight
- machine-lerning
- framework
- ai
- model
- numpy
- torch
- beginner-friendly
- plugin-architecture
- flow-model
- torchvision
- ai-model
- '-ai-models'
- flow-models
- flowmodel
---

# 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.

---

## Table of Contents
1. [Introduction](#introduction)
2. [Installation](#installation)
3. [Directory Structure](#directory-structure)
4. [Usage](#usage)
    - [Training a Model](#training-a-model)
    - [Adding Plugins](#adding-plugins)
5. [Creating Plugins](#creating-plugins)
6. [Command-Line Interface](#command-line-interface)
7. [Contributing](#contributing)
8. [License](#license)

---

## 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.

---

## Installation

### Prerequisites
- Python 3.8 or higher
- `pip` package manager

### Steps
1. Clone the repository:
   ```bash
   git clone https://github.com/LunaStev/FlowModel.git
   cd FlowModel
   ```

2. Create and activate a virtual environment:
   ```bash
   python -m venv .venv
   source .venv/bin/activate  # For Unix/MacOS
   .venv\Scripts\activate   # For Windows
   ```

3. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

---

## Directory Structure
```plaintext
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
```

---

## Usage

### Training a Model
To train a model using FlowModel, run:
```bash
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.

---

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

1. **Create a new Python file in the `plugins/` directory**:
   ```bash
   plugins/my_plugin.py
   ```

2. **Define your plugin class**:
   ```python
   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.

---

## Command-Line Interface
FlowModel includes a simple CLI for interacting with the framework.

### Commands
- **Train**: Start the training process with plugins.
  ```bash
  python main.py train
  ```

---

## 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.

---

## License
FlowModel is released under the MPL-2.0 License. See [LICENSE](LICENSE) for details.

---

Happy experimenting with FlowModel! 🚀