# SetUpppy
SetUpppy: Your Project, Perfectly Structured!
## Description
SetUpppy is a Python package that helps you create a standard folder structure for end-to-end machine learning projects. This tool aims to make project setup faster, easier, and more organized.
## Features
- Automatically create a project folder structure
- Includes necessary configuration files and scripts
- Easy to use command-line interface
## Installation
To install SetUpppy, use pip:
```bash
pip install setupppy
```
## Usage
### Command Line
Once installed, you can use the `setupppy` command to create a new project structure. Here's how you can do it:
1. Open your terminal.
2. Navigate to the directory where you want to create your project.
3. Run the following command:
```bash
setupppy -n Project_Name
```
Replace `Project_Name` with the desired name of your project.
### Example
To create a new project called `my_ml_project`, you would run:
```bash
setupppy -n my_ml_project
```
### Project Structure
After running the command, the following folder structure will be created:
```
.
├── .github
│ └── workflows
│ └── .gitkeep
├── config
│ └── config.yaml
├── dvc.yaml
├── params.yaml
├── requirements.txt
├── research
│ └── trials.ipynb
├── setup.py
├── src
│ └── my_ml_project
│ ├── __init__.py
│ ├── components
│ │ ├── __init__.py
│ │ ├── data_ingestion.py
│ │ ├── prepare_base_model.py
│ │ ├── prepare_callbacks.py
│ │ ├── training.py
│ │ └── evaluation.py
│ ├── utils
│ │ ├── __init__.py
│ │ └── common.py
│ ├── config
│ │ ├── __init__.py
│ │ └── configuration.py
│ ├── pipeline
│ │ ├── __init__.py
│ │ ├── data_ingestion.py
│ │ ├── prepare_base_model.py
│ │ ├── training.py
│ │ ├── evaluation.py
│ │ └── predict.py
│ ├── entity
│ │ ├── __init__.py
│ │ └── config_entity.py
│ └── constants
│ └── __init__.py
├── templates
│ └── index.html
└── logs
└── running_logs.log
```
### Logging
SetUpppy includes a logging mechanism that logs the creation of directories and files. The logs are stored in `logs/running_logs.log`.
### Commands
- `setup -n Project_Name`: Creates the project structure with the specified project name.
- `setup help`: Prints this help documentation.
- `setupppy`: Displays a special welcome message.
### License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
### Author
Adarsh Kesharwani
Email: akesherwani900@gmail.com
### Contributing
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
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"description": "# SetUpppy\n\nSetUpppy: Your Project, Perfectly Structured!\n\n## Description\n\nSetUpppy is a Python package that helps you create a standard folder structure for end-to-end machine learning projects. This tool aims to make project setup faster, easier, and more organized.\n\n## Features\n\n- Automatically create a project folder structure\n- Includes necessary configuration files and scripts\n- Easy to use command-line interface\n\n## Installation\n\nTo install SetUpppy, use pip:\n\n```bash\npip install setupppy\n```\n\n## Usage\n\n### Command Line\n\nOnce installed, you can use the `setupppy` command to create a new project structure. Here's how you can do it:\n\n1. Open your terminal.\n2. Navigate to the directory where you want to create your project.\n3. Run the following command:\n\n```bash\nsetupppy -n Project_Name\n```\n\nReplace `Project_Name` with the desired name of your project.\n\n### Example\n\nTo create a new project called `my_ml_project`, you would run:\n\n```bash\nsetupppy -n my_ml_project\n```\n\n### Project Structure\n\nAfter running the command, the following folder structure will be created:\n\n```\n.\n\u251c\u2500\u2500 .github\n\u2502 \u2514\u2500\u2500 workflows\n\u2502 \u2514\u2500\u2500 .gitkeep\n\u251c\u2500\u2500 config\n\u2502 \u2514\u2500\u2500 config.yaml\n\u251c\u2500\u2500 dvc.yaml\n\u251c\u2500\u2500 params.yaml\n\u251c\u2500\u2500 requirements.txt\n\u251c\u2500\u2500 research\n\u2502 \u2514\u2500\u2500 trials.ipynb\n\u251c\u2500\u2500 setup.py\n\u251c\u2500\u2500 src\n\u2502 \u2514\u2500\u2500 my_ml_project\n\u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u251c\u2500\u2500 components\n\u2502 \u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u2502 \u251c\u2500\u2500 data_ingestion.py\n\u2502 \u2502 \u251c\u2500\u2500 prepare_base_model.py\n\u2502 \u2502 \u251c\u2500\u2500 prepare_callbacks.py\n\u2502 \u2502 \u251c\u2500\u2500 training.py\n\u2502 \u2502 \u2514\u2500\u2500 evaluation.py\n\u2502 \u251c\u2500\u2500 utils\n\u2502 \u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u2502 \u2514\u2500\u2500 common.py\n\u2502 \u251c\u2500\u2500 config\n\u2502 \u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u2502 \u2514\u2500\u2500 configuration.py\n\u2502 \u251c\u2500\u2500 pipeline\n\u2502 \u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u2502 \u251c\u2500\u2500 data_ingestion.py\n\u2502 \u2502 \u251c\u2500\u2500 prepare_base_model.py\n\u2502 \u2502 \u251c\u2500\u2500 training.py\n\u2502 \u2502 \u251c\u2500\u2500 evaluation.py\n\u2502 \u2502 \u2514\u2500\u2500 predict.py\n\u2502 \u251c\u2500\u2500 entity\n\u2502 \u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u2502 \u2514\u2500\u2500 config_entity.py\n\u2502 \u2514\u2500\u2500 constants\n\u2502 \u2514\u2500\u2500 __init__.py\n\u251c\u2500\u2500 templates\n\u2502 \u2514\u2500\u2500 index.html\n\u2514\u2500\u2500 logs\n \u2514\u2500\u2500 running_logs.log\n```\n\n### Logging\n\nSetUpppy includes a logging mechanism that logs the creation of directories and files. The logs are stored in `logs/running_logs.log`.\n\n### Commands\n\n - `setup -n Project_Name`: Creates the project structure with the specified project name.\n - `setup help`: Prints this help documentation.\n - `setupppy`: Displays a special welcome message.\n\n### License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n### Author\n\nAdarsh Kesharwani \nEmail: akesherwani900@gmail.com\n\n### Contributing\n\nContributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.\n",
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