semvec/pypi-packages huggingface dataset:
gcmt 1.0.1
A simple CLI tool that uses LLMs to automatically generate meaningful commit messages from your staged changes.
Requires Python: >=3.9

# gcmt

A simple CLI tool that uses LLMs to automatically generate meaningful & conventional commit messages from your staged changes, while also allowing you to edit the message according to your instructions.

![gcmt demo](docs/assets/demo.gif)

Using the [Hugging Face Inference API's](https://huggingface.co/docs/api-inference/rate-limits) generous free tier, you can generate commit messages without incurring additional $$$.

## Installation

```bash
pip install gcmt
```

Be sure to set your Hugging Face access token as an environment variable:

```bash
export HF_TOKEN="<your-token>"
```

You can get an access token [here](https://huggingface.co/settings/tokens) after signing up for a Hugging Face account.

## Usage

```bash
# Generates a commit message for your staged changes
gcmt

# Generates a commit message and commits them automatically, no additional input required
gcmt --auto-commit

# Choose a different model to generate the commit message. List of models [here](https://huggingface.co/docs/api-inference/supported-models)
gcmt --model_name meta-llama/Meta-Llama-3-70B-Instruct
```

## Commands

After generating a commit message, you'll be prompted with several options:

- `c`: Commit the changes with the generated message
- `cp`: Copy the generated message to clipboard
- `e "<instructions>"`: Edit the commit message according to your instructions (e.g., `e "make it more concise"`)
- `r`: Re-generate a new commit message
- `a`: Abort the commit process

> 🎉 __Fun Fact__: All of the commit messages in this project were generated using gcmt :)
cracknuts-panel 0.0.18
Noteboot(like jupyter) widget for cracknuts.
Requires Python: >=3.12

<p align="center">
  <img src="https://github.com/cracknuts-team/cracknuts-panel/blob/main/docs/static/logo-panel.svg" alt=""/>
</p>
<p align="center">
    <a href="https://pypi.org/project/cracknuts-panel/"><img src="https://img.shields.io/pypi/v/cracknuts-panel.svg?color=%2334D058&label=pypi"  alt=""/></a>
    <a href="https://github.com/cracknuts-team/cracknuts-panel/blob/main/LICENSE"><img src="https://img.shields.io/pypi/l/cracknuts.svg"  alt=""/></a>
    <a href="https://github.com/cracknuts-team/cracknuts-panel/releases"><img alt="" src="https://img.shields.io/github/release/cracknuts-team/cracknuts-panel.svg"></a>
</p>

`Jupyter` widget for `cracknuts`.

## Installation

```shell
pip install cracknuts-panel
```

And then execute the following script. A successful output of the version number indicates a successful installation.

```python
import cracknuts_panel as cp
cp.version()
```
dlpml 0.1.0
Minimalist Machine Learning library
Requires Python: <4.0,>=3.12

# dlpml

`dlpml` is a minimalist machine learning library implemented in Python. It provides simple and efficient tools for data analysis and machine learning, including linear regression and logistic regression models.

## Features

- Linear Regression
- Logistic Regression
- Regularization
- Gradient Descent Optimization

## Installation

To install the required dependencies, use [Poetry](https://python-poetry.org/):

```sh
poetry install
```

## Usage (check notebooks)
### Linear Regression
```python
import pandas as pd
from dlpml.regression.linear_regressor import LinearRegressor

# Load dataset
data = pd.read_csv("data/ex_linear_regression_data1.csv", header=None)
X_train = data.iloc[:, [0]].to_numpy()
y_train = data.iloc[:, 1].to_numpy()

# Initialize and fit the model
model = LinearRegressor(alpha=0.01, iterations=10000, lambda_=0.01)
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_train)
```
### Logistic Regression
```python
import pandas as pd
from dlpml.classification.logistic_regressor import LogisticRegressor

# Load dataset
data = pd.read_csv("data/ex_logistic_regression_data1.csv")
X_train = data.iloc[:, 0:2].to_numpy()
y_train = data.iloc[:, 2].to_numpy()

# Initialize and fit the model
model = LogisticRegressor(alpha=0.01, iterations=10000, lambda_=0.01)
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_train)
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.
flarevel 5.0.1
Flarevel is a command-line interface (CLI) tool that quickly scaffolds Flask projects with a robust Model-View-Controller (MVC) architecture. It generates a well-organized project structure, including dedicated folders for models, views, controllers, and configurations, enabling developers to start building scalable and maintainable Flask applications in no time. Flarevel streamlines the setup process, saving you time and ensuring a clean, modular foundation for your projects.
Requires Python: >=3.7

# Flarevel

`flarevel` is a Python command-line tool that helps you quickly create a Flask project with a predefined folder structure. It generates the necessary files for a basic Flask application with SQLAlchemy support, a home page, and a user model.

## Features

- Generates a Flask project with a predefined structure.
- Includes a sample HomeController and User model.
- Includes basic project setup with templates and static assets.
- Automatically creates the necessary directories and files.
- Simple command-line interface to create the project.

## Installation

To install `flarevel`, you can use `pip`:

```bash
pip install flarevel
```

## Usage

To create a new Flarevel project, simply run the following command:

```bash
flarevel create <project_name>
```

You can install the necessary dependencies by running:
```bash
pip install -r requirements.txt
```

## Run the application

After generating the project, you can run it by executing the following command in the project directory:

```bash
python run.py
```

This will start the Flask development server, and you can view the project in your browser at http://localhost:5000


## Database Migration
The `migrate` command in Flarevel automates the process of handling database migrations. It uses Flask-Migrate under the hood to manage changes in the database schema.

### Usage

Run the following command in your project path terminal:

```bash
flarevel forge migrate
```

## Create Model

The `make:model` command allows you to easily create a new model file in your Flarevel project. It automatically generates a model with default fields (`id`, `created_at`, `updated_at`) and a structure ready for customization.

### Usage

Run the following command in your project path terminal:

```bash
flarevel forge make:model ModelName
```

## Create Controller

The `make:controller` command allows you to easily create a new controller file in your Flarevel project. It supports creating controllers with directories, making it easy to organize your application into submodules.

### Usage

Run the following command in your project path terminal:

```bash
flarevel forge make:controller ControllerName
```