semvec/pypi-packages huggingface dataset:
datasend 0.1.0
The quickest way to share data with your team
Requires Python: >=3.7

# DataSend

**Work in Progress** - More will be announced soon.

## Description
DataSend is a Python package for quickly sharing data with your team. This project is currently under development.

## Installation

You can install the package using pip:

```
pip install datasend
```

Alternatively, you can install directly from the GitHub repository:

```
pip install git+https://github.com/yourusername/datasend.git
```

## Development Installation

To install the package for development:

1. Clone the repository:
   ```
   git clone https://github.com/yourusername/datasend.git
   ```

2. Navigate to the directory:
   ```
   cd datasend
   ```

3. Install in development mode:
   ```
   pip install -e .
   ```

## Usage

More details coming soon.

## License

TBD
ai-tools-ct 0.0.4
A package deisnged to help with general AI related developments, mainly data generation
Requires Python: >=3.9


# AI TOOLS CT

  
This repo is a collection of classes that will help users with ai tasks regarding the use of OpenAi for data generation. 

## Installation
``` $ pip install ai_tools_ct```

## Usage
At the moment, you will need an **openai api key** which you can find [here](https://platform.openai.com/docs/quickstart?api-mode=responses). 

```
from ai_tools_ct.gpt import Gpt
from ai_tools_ct.data_generator import DataGenerator as Dg

gpt = Gpt(api_key=your_api_key)
dg = Dg(gpt)

# for single prompt generation 
dg.single_generation(prompt="give me 10 random words")

# or do a bulk generation with multiple prompts
prompts = ["give me a random word", "give me a random number"]
targets = ["word", "number"]
system_prompts = ["you are a word generator", "you are a number generator"]
dg.bulk_generation(prompts = prompts, targets=targets, system_prompts=system_prompts)

# even do parrallel bulk generation for extra speediness
db.parallel_generation(prompts = prompts, targets=targets, system_prompts=system_prompts)

# access results either through python list of pandas df
dg.generation_results
dg.df_generation_results
```
CODEval-Benchmark 1.1.1
A Benchmark used to evaluate small large language models.

Title: CODEVAL<br>
Description: A LLM Benchmark that used to evaluate small Large Language Model.<br>
Version: 1.0.0<br>
Author: R Kiran Kumar Reddy<br>
Email: rkirankumarreddy599@gmail.com<br>
License: MIT<br>
URL: # Not Opensourced yet
clockclock24-py 1.0.1
Python port of ClockClock24 - A kinetic clock display
Requires Python: >=3.6
Keywords: clock, animation, tkinter

# ClockClock24 Python

Python port of the [ClockClock project](https://clockclock.com/).

> ClockClock 24 is both a kinetic sculpture and a functioning clock. Its hands veer from unpredictable, mechanical spinning, to perfect, synchronised alignment; to visually represent the abstract concept of time, and to report real time.

## 🛠 Technical information

This project is a Python port of the original JavaScript/React implementation, using:
- [Python](https://www.python.org/) (3.6 or higher)
- [Tkinter](https://docs.python.org/3/library/tkinter.html) for the graphical interface

## Installation

### Install from PyPI

The simplest way to install ClockClock24 is directly from PyPI:

```sh
pip install clockclock24-py
```

### Install from source

To install the package from source:

```sh
# Clone the repository
git clone https://github.com/tboy1337/ClockClock24-py.git
cd ClockClock24-py

# Install in development mode
pip install -e .

# Or install directly
pip install .
```

## Running the application

After installation, you can run the application in several ways:

```sh
# Using the entry point (available after pip installation)
clockclock24

# Or run the module directly
python -m clockclock24_py.main

# Or from the repository root
python run.py
```

## Usage

Once the application is running:
- Press the spacebar to trigger the animation
- The clock will automatically update to show the current time
- Resize the window to adjust the clock size

## License

This project is licensed under the MIT License - see the LICENSE.txt file for details.
mcp-jamie-test2 1.0.9
MCP server 测试使用
Requires Python: >=3.13

# MCP server test

```json
{
  "mcpServers": {
    "mcp-jamie-params": {
      "command": "uvx",
      "args": [
        "mcp-jamie-test2"
      ]
    }
  }
}
```
mlfz 0.1.3.3
Machine Learning From Zero: an educational machine learning library.
Requires Python: >=3.10

# mlfz
*Machine Learning From Zero: an educational machine learning library.*

Hi there! `mlfz` is my attempt to provide reference implementations of machine learning algorithms for educational purposes. The goal is not performance, but simplicity: you won't just use this library; you'll dig through the source code to understand how machine learning works on the inside. [Check the documentation](https://mlfz.readthedocs.io/), which is written like an interactive textbook on the internals of machine learning and neural networks.

If you find value in this project, support me by grabbing a copy of my [Mathematics of Machine Learning](https://tivadardanka.com/books/mathematics-of-machine-learning) book!

## Quickstart

You can install the package directly from pip:

```
pip install mlfz
```

However, I encourage you to clone the repository and install via
```
pip install -e .
```
from the directory. This way, any local change is reflected immediately, so you can play around with the code in, say, a Jupyter Notebook.

## Contributions

Contributions are welcome! If you think you could make this project better, feel free to submit a PR. To make the process smooth, here are the steps you should take.

1. Open an issue where we'll discuss your suggestions. If we are on the same page, you can start working on the PR. (And if we're not, you have saved yourself a ton of work.)
2. Fork the repository and create a feature branch where you'll prepare the proposed changes.
3. Open a PR to the `main` branch and tag me ([@cosmic-cortex](https://github.com/cosmic-cortex/)) as a reviewer.
4. I'll either leave comments and suggestions, or merge the PR.
text-to-nlp 0.1.4
A simple text preprocessing package for NLP

# Text to NLP
A simple and efficient Python package for text preprocessing in Natural Language Processing (NLP).

## Features
- Tokenizes text into words.
- Removes stop words (e.g., "the", "is").
- Removes punctuation marks (e.g., "!", ".", ",").
- Converts text to TF-IDF vectors for machine learning.

## Requirements
- Python 3.6+
- nltk>=3.8.1
- scikit-learn>=1.3.0

## Installation
```bash
pip install text-to-nlp
```

## Usage
```python
from text_preprocessor import TextPreprocessor

# Initialize the preprocessor
processor = TextPreprocessor()

# Preprocess a single text
text = "I am running, jumping happily!!!"
processed_text = processor.preprocess(text)
print("Processed text:", processed_text)  # Output: running jumping happily

# Convert multiple texts to TF-IDF
texts = ["I am running happily!", "Jumping is fun?", "Sad days are bad."]
tfidf_matrix, vectorizer = processor.to_tfidf(texts)
print("TF-IDF matrix shape:", tfidf_matrix.toarray().shape)  # Output: (3, 1000)
```
movieIsdk 0.0.3
SDK Python pour accéder à l’API MovieLens (Data Analysts/Data Scientists friendly)
Requires Python: >=3.12

# MovieLens SDK - `movieIsdk`

Un SDK Python simple pour interagir avec l’API REST MovieLens. Il est conçu pour les **Data Analysts** et **Data Scientists**, avec une prise en charge native de **Pydantic**, **dictionnaires** et **DataFrames Pandas**.

[![PyPI version](https://badge.fury.io/py/movieIsdk.svg)](https://badge.fury.io/py/movieIsdk)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

---

## Installation

```bash
pip install movieIsdk
```

---

## Configuration

```python
from movieIsdk import MovieClient, MovieConfig

# Configuration avec l’URL de votre API (Render ou locale)
config = MovieConfig(movie_base_url="https://moovie-backend-i.onrender.com")
client = MovieClient(config=config)
```

---

## Tester le SDK

### 1. Health check

```python
client.health_check()
# Retourne : {"status": "ok"}
```

### 2. Récupérer un film

```python
movie = client.get_movie(1)
print(movie.title)
```

### 3. Liste de films au format DataFrame

```python
df = client.list_movies(limit=5, output_format="pandas")
print(df.head())
```

---

## Modes de sortie disponibles

Toutes les méthodes de liste (`list_movies`, `list_ratings`, etc.) peuvent retourner :

- des objets **Pydantic** (défaut)
- des **dictionnaires**
- des **DataFrames Pandas**

Exemple :

```python
client.list_movies(limit=10, output_format="dict")
client.list_ratings(limit=10, output_format="pandas")
```

---

## Tester en local

Vous pouvez aussi utiliser une API locale :

```python
config = MovieConfig(movie_base_url="http://localhost:8000")
client = MovieClient(config=config)
```

---

## Public cible

- Data Analysts
- Data Scientists
- Étudiants et curieux en Data
- Développeurs Python

---

## Licence

MIT License

---

## Liens utiles

- API Render : [https://moovie-backend-i.onrender.com](https://moovie-backend-i.onrender.com)
- PyPI : [https://pypi.org/project/movieIsdk](https://pypi.org/project/movieIsdk)