README.md

README.md

@@ -1,140 +1,160 @@
----
-license: mpl-2.0
----
-
-# 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! 🚀
-
+---
+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! 🚀