semvec/pypi-packages huggingface dataset:
valorant-api-pillowless 0.0.3
Sync/Async Python wrapper for valorant-api.com without the pillow dependency
Requires Python: >=3.8

# valorant-api-pillowless
Sync/Async Python wrapper for valorant-api.com without the pillow dependency. Pillow has been removed in order to reduce the size of the requirements for this project. The primary purpose of this package is to be used in the [Valorant-Python-Wrapper](https://github.com/Whitelisted1/Valorant-Python-Wrapper) project. Please check out the original project [here](https://github.com/MinshuG/valorant-api).

[![pypi](https://img.shields.io/pypi/v/valorant-api-pillowless.svg)](https://pypi.python.org/pypi/valorant-api-pillowless/)
[![Downloads](https://static.pepy.tech/personalized-badge/valorant-api-pillowless?period=total&units=international_system&left_color=green&right_color=blue&left_text=Downloads)](https://pepy.tech/project/valorant-api-pillowless)

# Installation
<!-- `pip install git+https://github.com/MinshuG/valorant-api-pillowless` \
or \ -->
`pip install valorant-api-pillowless`

# Usages
```py
# this code is just for reference

from valorant_api import SyncValorantApi, AsyncValorantApi

#sync
api = SyncValorantApi(language="ru-RU")
agents = api.get_agents()

#Async
api = AsyncValorantApi(language="ru-Ru")
agents = await api.get_agents()

# searching
agent = agents.find_where(displayname="Raze", developerName="Gumshoe")
agent = agents.find_first(displayname="Raze")
```

# Requirements

* python-dateutil
* aiohttp
* requests
qje5vf 0.0.1
boodle oodle noodle poodle doodle moo
Requires Python: >=3.7

[![Python package](https://github.com/thammo4/qje5vf_DS5111su24_lab_01/actions/workflows/validations.yml/badge.svg)](https://github.com/thammo4/qje5vf_DS5111su24_lab_01/actions/workflows/validations.yml)

[![Python package](https://github.com/thammo4/qje5vf_DS5111su24_lab_01/actions/workflows/validations.yml/badge.svg)](https://github.com/thammo4/qje5vf_DS5111su24_lab_01/actions/workflows/validations.yml)
## Text Processing Functions

This project includes several text processing functions in `tokenize_text.py`:

- `clean_text(input_text)`: Converts text to lowercase and removes punctuation.
- `tokenize(input_text)`: Splits text into individual words.
- `count_words(input_text)`: Counts the occurrences of each word in the text.
- `count_lines(filename)`: Counts the number of lines in a file.
- `count_total_lines(filenames)`: Counts the total number of lines across multiple files.
- `count_total_words(filenames)`: Counts the total number of words across multiple files.
- `count_raven_occurrences(filename)`: Counts occurrences of the word 'raven' (case insensitive) in a file.

### Example Usage

Here's a simple example of how to use the `clean_text` and `count_words` functions:

```python
from tokenize_text import clean_text, count_words

text = "The Raven, by Edgar Allan Poe"
cleaned_text = clean_text(text)
word_counts = count_words(text)

print(f"Cleaned text: {cleaned_text}")
print(f"Word counts: {word_counts}")

# Output:
# Cleaned text: the raven by edgar allan poe
# Word counts: {'the': 1, 'raven': 1, 'by': 1, 'edgar': 1, 'allan': 1, 'poe': 1}


### Using `make all`

To automate the setup, text download, statistics generation, and testing processes, you can use the `make all` command. This command will:

1. Set up the Python virtual environment and install the required dependencies.
2. Download the specified text files from Project Gutenberg.
3. Generate statistics about the downloaded texts, including line and word counts for "The Raven" and total counts across all downloaded texts.
4. Run the test suite to ensure all functionalities are working correctly.

To use `make all`, simply run the following command in your terminal:

```bash
make all
person-detector 0.0.5
A high level API for detecting and tracking people.
Requires Python: >=3.10

# Person Detector

## Highly performant, and accurate, real-time person tracking with YOLOv10 and ByteTrack

### Command-Line Interface

#### Clone repo and navigate to project directory

```bash
git clone git@github.com:k3y5tr0k3/person-detector.git && cd person-detector
```

#### Install requirements

```bash
pip install -r requirements.txt
```

#### Example usage

##### Detections from Image

```bash
python -m src.main --image 'path/to/image.jpg' --detection --show
```

##### Detections from Video

```bash
python -m src.main --video 'path/to/video.mp4' --detection --show
```

##### Trajectory Tracking from Video
```bash
python -m src.main -video 'path/to/video.mp4' --tracking --show
```

##### For more info, use the help flag

```bash
python -m src.main --help
```

### Utility Functions

#### Convert video to images

```bash
python -m src.main --video2images 'path/to/video.mp4
```

### Generate Developer Documentation (Optional)

```bash
pdoc ./src
```

### Using as package in your project

#### Install with PIP

```bash
pip install py-person-detector
```

#### Example Usage

##### Detecting people in an image

```python
import pathlib
from person_detector.detector import Detector

detector = Detector(show=True)

image_path = pathlib.Path("path/to/image.jpg")
detector.detect_image(image_path)
```
NeuroUtils 0.2.0
Library for neural network projects organisation
Requires Python: >=3.8

================================================================
Description:
================================================================
NeuroUtils: Python library for Neural Network Utilities.

It is focused to automate some big operations and make them modular. Especially to make projects easily scalable and possible to experiment
with architectures, paramaters and data without worrying about saving results or data.

Early development! BUGS can occur!


================================================================
Instalation:
================================================================
You can either download repository and use it as standalone module in your python project, or install it via pip:

pip install NeuroUtils

or

pip install https://github.com/Ciapser/NeuroUtils

or if not working 

pip install git+https://github.com/Ciapser/NeuroUtils


================================================================
Disclaimer:
================================================================
Project is focused to create the framework to work with neural networks for educational purposes.
Which I am doing by creating it and using its functionality to organize work