semvec/pypi-packages huggingface dataset:
fhnw-i4ds-nlp-utils 0.1.27
Utilities for NLP courses taught at FHNW.
License: MIT

This library provides utilities for NLP courses taught at FHNW.

# Install 

```bash
sudo pip install fhnw_i4ds_nlp_utils
```

# Build

- Increase the version number in [setup.py](setup.py).

- Start a Docker container with the build/deploy tools:

```bash
docker run -e TZ=Europe/Zurich --name datascience-notebook --net=host -p 8888:8888 -v "$(pwd)":/home/jovyan/work -it --rm i4ds/datascience-notebook start-notebook.sh --NotebookApp.token=''
```

- Build and deploy the library to pypi:


```bash
docker exec -it datascience-notebook /bin/bash
cd work
rm -rf dist/*
python3 setup.py bdist_wheel
python3 -m twine upload dist/*
```

- Upload the latest changes to the git repository:

```bash
git commit -am "message"
git push
```
Silver-HandTrackingModule 1.0.0
Track the Hands from Web Cam as default
License: MIT
Keywords: hand handtracker handtrackermodule

Python Hand Tracking Module:
	This is a Hand Tracking Module, which tracks the position of different landmarks in the hand.
	Note: You can run the module as a standalone.

-------------------------------------------------------------
Github Repository : https://github.com/Cadris/HandTrackModule
-------------------------------------------------------------

Language Used : Python

Requirements(Do a pip install) :
---------------------------------
OpenCV
MediaPipe

Files :
-------
HandTrackingModule.py - Main Module. You Need to import this file to your File.
main.py - The Whole Thing. I used it to devolop the project.
Application2.py - Used this to show how to use the Hand Tracking Module.
README.md : Summon Michael Jackson.

My My pip list
---------------

(venv) D:\Projects\Python\HandTrack>pip list
Package               Version
--------------------- --------
absl-py               0.13.0  
attrs                 21.2.0  
cycler                0.10.0  
kiwisolver            1.3.2   
matplotlib            3.4.3   
mediapipe             0.8.7.1 
numpy                 1.21.2  
opencv-contrib-python 4.5.3.56
opencv-python         4.5.3.56
Pillow                8.3.1   
pip                   21.1.3  
protobuf              3.17.3  
pyparsing             2.4.7   
python-dateutil       2.8.2   
setuptools            56.0.0
six                   1.16.0
wheel                 0.37.0

Change Log
==========

1.0.0 (17-09-2021)
-------------------
- First Relase
fhnw-cas-nlp-utilities 0.1.26
Utilities for the CAS/DAS NLP course.
License: MIT

This library provides some basic utilities for the NLP class/lab of the CAS/DAS at FHNW.

# Install 

This package builds on Python 3.

```bash
sudo pip3 install fhnw_cas_nlp_utilities
```

# Build

Increase the version number in [setup.py](setup.py).


In order to extract the python script and upload it to pypi run

```bash
docker run -e TZ=Europe/Zurich --name datascience-notebook --net=host -p 8888:8888 -v "$(pwd)":/home/jovyan/work -it --rm i4ds/datascience-notebook start-notebook.sh --NotebookApp.token=''
docker exec -it datascience-notebook /bin/bash
cd work
rm -rf dist/*
python3 setup.py bdist_wheel
python3 -m twine upload dist/*
```

Upload everything to the git repository.
gpt-utils 1.4.0
Collection of utilities for use with OpenAI's GPT-3 language model
License: MIT

# gpt-utils

Helper tools for use with OpenAI's GPT-3 API.

The API key can be provided in three ways:
- In the environment variable `OPENAI_API_KEY`
- Via class attribute `GPT.api_key` of class `GPT` in module `gpt_utils.core` 
- Via keyword argument `api_key` in any method or function which obtains a response from OpenAI's servers 

## Disclaimer

These don't work perfectly.

## gpt_utils.utils

```
from gpt_utils import utils
```

`utils` contains simple functions which transform text in a specific way. Some functions are bidirectional; these take an optional **reverse** keyword argument which reverses the conversion's input and output.

### utils.case

```
>>> from gpt_utils.utils import case
```

Contains functions which modify the case of words in a string. These functions are meant to selectively preserve the case of proper nouns, etc. with minimal involvement. The first, `title()`, takes a `str` containing a word or phrase with proper capitalization for use in a heading or title: 

```
>>> case.title('the ant: an introduction')
'The Ant: An Introduction'
```

The second, `mid_sentence()`, adjusts the case of input words/phrases for insertion mid-sentence:

```
>>> case.mid_sentence('bicycles on inman street')
'bicycles on Inman Street'
>>> case.mid_sentence('An italian restaurant in somerville')
'an Italian restaurant in Somerville'
```

### utils.location

```
>>> from gpt_utils.utils import location
```

Currently contains one function, `fix_location()`, which takes an unformatted string containing a location and formats it properly for display:

```
>>> location.fix_location('lenox ma')
'Lenox, MA'
```

### utils.number

```
>>> from gpt_utils.utils import number
```

Contains two functions which convert numbers to strings.  The first, `number_to_str()`, takes an `int` or `float` and writes out the same number as a `str`:

```
>>> number.number_to_str(83)
'eighty-three'
>>> number.number_to_string(3.14)
'three point one four'
```

The second, `number_to_ordinal()`, takes an `int` and returns it in ordinal form as a `str`:

```
>>> number.number_to_ordinal(43)
'forty-third'
```

### utils.dechatify()

I have found that GPT tends to produce output of lower quality when it is prompted with text containing Internet/SMS vernacular. As a remedy, `dechatify()` converts text to standard/written English:

```
>>> utils.dechatify('how r u')
'How are you?'
>>> utils.dechatify('if the implementation is easy to explain then it might b a good idea')
'If the implementation is easy to explain, then it might be a good idea.'
```

### utils.generate_stem()

This function accepts a `str` containing a word/phrase and returns a sentence stem pairing the word/phrase with the appropriate form of the verb "to be":

```
>>> utils.generate_stem('GitHub')
'GitHub is'
>>> utils.generate_stem("pleiades")
'The Pleiades are'
```