semvec/pypi-packages huggingface dataset:
llmatex 0.1.0
CLI program for converting natural language text to LaTeX
Requires Python: <4.0,>=3.9

# llamatex

Convert natural language descriptions to LaTeX equations in your terminal, in under a second!

## Features

`llamatex` offers two primary modes of operation:
1. Single Instruction Mode: Generate LaTeX for a single equation description.
2. Clipboard Mode: Send LaTeX code from highlighted text into your clipboard.
3. Markdown File Editing Mode: Automatically replace natural language descriptions with LaTeX equations in a markdown file.

For equations that can be rendered in a single line, smoltex opens a new window displaying the rendered equation.

## Installation

```bash
pip install llamatex
```

For local generation support:

```bash
pip install llamatex[local]
```

Alternatively, you can build from source using `poetry`.

```bash
poetry install
```

## Usage

### Single Instruction mode

```bash
llamatex render -p "equation for the area of a circle"
```

Output:

```
Latex string: A = \pi r^2

>> completion time: 856.4 ms
```

### Clipboard mode

> [!WARNING]
> `llamatex` clipboard mode currently only supports MacOS

`llamatex` uses `pynput` to listen to your inputs and control the keyboard. To allow clipboard mode, allow your terminal/IDE to be added to Assessibility in System Settings.

Run `llamatex` in clipboard mode using the following command:

```bash
llamatex run
```

### Markdown File Editing Mode
1. Create a markdown file (e.g., `equations.md`) with placeholders:

```markdown
# Important Equations

1. Area of a circle:
{"area of a circle"}

2. Einstein's mass-energy equivalence:
{"einstein's famous equation"}
```
2. Run `llmatex`:

```bash
llamatex render -f equations.md
```

3. The file will be updated with LaTeX equations:

```markdown
# Important Equations

1. Area of a circle:
$A = \pi r^2$

2. Einstein's mass-energy equivalence:
$E = mc^2$
```

Use the hotkey `Cmd+Shift+L` (on macOS) to process the selected text. Press `esc` to exit the program.

## Configuration

### API Setup

1. Get a free API key from Groq console.
2. Set the environment variable

```bash
export GROQ_API_KEY=your-api-key-here
```

Available models:
- llama3: `llama3-8b`, `llama3-70b`, `llama3.1-8b`, `llama3.1-70b`
- gemma: `gemma-7b`, `gemma2-9b`
- mixtral: `mixtral-8x7b`
- Phi3 (local only): `phi-3-mini`

### Server selection
Choose between `groq` (default) or `llama-cpp` (for local generation) using the `-s` or `--server` option:

```bash
llamatex render -p "pythagorean theorem" -s llama-cpp
```
browser-gpt-api 0.1.0
Add your description here
Requires Python: >=3.8

# Browser-gpt-api
Interact with ChatGPT via an API running on a local server using Selenium under the hood.

## What is it?

A python tool to help you send prompts to ChatGPT webpage and get response via API calls. With Browser-GPT you can get _infinite_ amount of API calls without worrying about any credits. This works by automating the process of sending a prompt to ChatGPT and getting the response back using Selenium.

## How to Install & run:
This project uses [Rye Package manager](https://github.com/astral-sh/rye)

1. Clone the repository:
    ```terminal
    git clone https://github.com/vertexcover-io/browser-gpt-api.git
    cd src/browser_gpt_api
    ```
2. Install project dependencies using Rye:
    ```terminal
    rye sync
    ```
3. Create a copy of the environment variable template file and edit it according to your configuration:
    ```terminal
    cp .env.template .env
    ```
4. Download [Chrome Driver](https://developer.chrome.com/docs/chromedriver/downloads) based on your chrome version and OS.

5. Add the location of `chromedriver.exe` file as a `System variable` in Environmental variables.

6. In the main directory run `setup.py`:
    ```terminal
    python setup.py
    ```

7. To setup your local-server run `server.py`:
    ```terminal
    python server.py
    ```

8. Now you can access ChatGPT via the API endpoint : `http://127.0.0.1:8000/chat/completion`

## Stack
- **Rye**
- **Selenium**
- **FastAPI**
- **uvicorn**

## Pre-Commit
Set up pre-commit hooks to automatically check your code for linting and formatting issues before the commit. Run following command to install pre-commit hooks:
```terminal
rye run pre-commit install
```

## Format and Lint
This project uses [Ruff](https://github.com/astral-sh/ruff) for code formatting and linting. Use following commands to format and lint your code:
- Check for issues:
```terminal
ruff check 
```
- Format your code:
```terminal
ruff format
```
For more information refer to the [Ruff Documentation.](https://docs.astral.sh/ruff/)
barebonesnn 0.1.1
A neural network library built from scratch.
Requires Python: >=3.6

**BareBonesNN** is a lightweight neural network library implemented from scratch in Python, without relying on high-level machine learning libraries. This project aims to provide a clear and fundamental understanding of neural network concepts and operations, making it an excellent educational resource for learning and experimentation.

## Features

- **Basic Neural Network Components**: Core classes including `Value`, `Neuron`, `Layer`, and `MLP` (Multi-Layer Perceptron).
- **Automatic Differentiation**: Built-in backpropagation for gradient computation.
- **Customisable and Extensible**: Easily modify and extend the code to experiment with various neural network architectures.
- **Lightweight**: Minimal dependencies, focusing on core principles.

## Installation

To install BareBonesNN, use pip:

```bash
pip install barebonesnn==0.1.0
```

## Contributing

Contributions are welcome! If you have suggestions, bug reports, or feature requests, please open an issue or submit a pull request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
hw-hivepy 0.3.1
Small wrapper for HexWay Hive API
Requires Python: >=3.12

[![Run Tests](https://github.com/Cur1iosity/hivepy/actions/workflows/run-tests.yml/badge.svg)](https://github.com/Cur1iosity/hivepy/actions/workflows/run-tests.yml)
[![PyPI](https://img.shields.io/pypi/v/hw-hivepy)](https://pypi.org/project/hw-hivepy/)
[![Hexway](https://img.shields.io/badge/hexway-visit%20site-blue)](https://hexway.io)

# HivePy

Unofficial flexible library for [HexWay Hive](https://hexway.io/hive/) Rest API.

#### Tested on HexWay Hive 0.62.8

## Installation
```bash
pip install hw-hivepy
```

## Dependencies

- pydantic ~= 2.4
- requests ~= 2.31.0

## Usage
### API work modes
There are three available modes for the HiveApi:
- (**json**) returns raw json response
- (**raw_items**) returns raw json response with items only
- (**object** [default]) returns parsed json response as object

### Simple HiveClient
```python
from hivepy import HiveApi


def main() -> None:
    """Main function."""
    auth = {
        'url': 'http://127.0.0.1',
        'username': 'user',
        'password': 'password',
    }

    hive: HiveApi = HiveApi().connect(**auth)
    # hive.mode = 'json'  # Set mode
    
    # Getting projects and its issues
    hive.get_projects()
    hive.get_project(project_id='some-project-id')
    hive.get_issues(project_id='some-project-id')

    # Download binary file
    hive.get_file(project_id='some-project-id', file_id='some-file-id')


if __name__ == "__main__":
    main()


```
pyeyesim 0.4.0
Eye movement analysis and visualization package, with a focus on fixation map and scanpath similarity
Requires Python: >=3.8

# PyEyeSim

this is a Python library for eye-movement analysis, visualization and comparison, with a focus on scanpath comparison.

The ultimate goal of the library is to make advanced fixation map statistics (eg: entropy) and scanpath comparison  accesible (hidden markov model based, and saccade direction based).

The library also provides general descripitve statistics about eye-movements. It is intended to work with ordered fixation data. (a row for each fixation), that is loaded into a pandas dataframe.   

Additionaly, easy visualizations about the statistics (overall stats, stimulus based stats, within trial progrression) and heatmaps are also provided. 

three main scanpath similarity functionalities:

1. Within group similarity  (for a single group of observers in a single condition)
2. Between condition similarity (for single group of observers, observing the same stimuli in two conditions)
3. Between group similarity (for two groups of observers observing the same stimuli)


The library started to develop for use in art perception studies, therefore, there is an emphasis on stimulus based eye-movement comparison.


#### Installation:
in the terminal/anaconda prompt

`pip install pyeyesim`

OR 

if you cloned the library and are in the root folder of the library using the terminal (mac) or anaconda prompt (windows), you can install it, using the command: 
**" pip install -e . "**


#### Demo:
for examples of using the library, see the PyEyeDemoBasic.ipynb in the Notebooks folder


#### Dependencies:
![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white)
![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white) 
![SciPy](https://img.shields.io/badge/SciPy-%230C55A5.svg?style=for-the-badge&logo=scipy&logoColor=%white)
![Matplotlib](https://img.shields.io/badge/Matplotlib-%23ffffff.svg?style=for-the-badge&logo=Matplotlib&logoColor=black)

##### for full funcionality
  scikit-image
  hmmlearn
  xarray

##### on pip
https://pypi.org/project/pyeyesim/
adaptive-neighbourhoods 0.1.3
Python API for generating adapted and unique neighbourhoods for searching for adversarial examples.
Requires Python: >=3.6

<div align="center">

![](./docs/logo.png)

## Adaptive Neighbourhoods for the Discovery<br/> of Adversarial Examples

Python API for generating adapted and unique neighbourhoods for
searching for adversarial examples

[![PyPI](https://img.shields.io/pypi/v/adaptive-neighbourhoods?style=flat-square&color=green)](https://pypi.python.org/pypi/adaptive-neighbourhoods/)
[![GitHub license](https://img.shields.io/github/license/jaypmorgan/adaptive-neighbourhoods.svg?style=flat-square)](https://github.com/jaypmorgan/adaptive-neighbourhoods/blob/master/LICENSE)
[![Documentation Status](https://readthedocs.org/projects/adaptive-neighbourhoods/badge/?version=latest&style=flat-square)](https://adaptive-neighbourhoods.readthedocs.io/en/latest/?badge=latest)

</div>

## Installation & usage

This work is released on PyPi. Installation, therefore, is as simple as installing the package with pip:

```bash
python3 -m pip install adaptive-neighbourhoods
```

At this point, you're free to start generating neighbourhoods for your own dataset:

```python
from adaptive_neighbourhoods import epsilon_expand

neighbourhoods = epsilon_expand(
    x,  # your input data
    y)  # the integer encoded labels for your data
```

Move information on the variable parameters and general guidance on using this package can be found at: https://adaptive-neighbourhoods.readthedocs.io/en/latest/

## Contributing

All contributions and feedback are welcome!

There are three main remote mirrors used for hosting this project. If
you would like to contribute, please submit an
issue/pull-request/patch-request to any of these mirrors:

- Github: https://github.com/jaypmorgan/adaptive-neighbourhoods
- Gitlab: https://gitlab.com/jaymorgan/adaptive-neighbourhoods
- Source Hut: https://git.sr.ht/~jaymorgan/adaptive-neighbourhoods

## Citing this work

If you use this work in your research, please consider referencing our
article using the following bibtex entry:

```
@article{DBLP:journals/corr/abs-2101-09108,
  author    = {Jay Paul Morgan and
               Adeline Paiement and
               Arno Pauly and
               Monika Seisenberger},
  title     = {Adaptive Neighbourhoods for the Discovery of Adversarial Examples},
  journal   = {CoRR},
  volume    = {abs/2101.09108},
  year      = {2021},
  url       = {https://arxiv.org/abs/2101.09108},
  eprinttype = {arXiv},
  eprint    = {2101.09108},
  timestamp = {Sat, 30 Jan 2021 18:02:51 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2101-09108.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
device-format 0.1.1
This package is designed to generate regular expression formats based on device names.


# DEVICE FORMAT

  

This package is designed to generate regular expression formats based on device names. It simplifies the process of validating and parsing device identifiers by providing pre-defined regex patterns for various device types.

  

## Installation

  

You can install `device-format` via pip. Use the following command:

  

```markdown

pip install device-format

```

  

## Usage

  

Generate regular expression formats based on device names.

```markdown

from device_format import Device

device = Device("OVF-AAAAAAATEY", "olt")
print(device.get_device_format())

```

  
  

## Supported Device Names

- OLT

- OTB

- CA1

- CA2

- FTSBS

- CPE
ipfs-transformers 0.0.3
A wrapper around huggingface transformers, invoking an IPFS model manager.
Requires Python: >=3.8

# IPFS Huggingface Transformers

This is a model manager and wrapper for huggingface, looks up a index of models from an collection of models, and will download a model from either https/s3/ipfs, depending on which source is the fastest.

# How to use
~~~shell
pip install .
~~~

look run ``python3 example.py`` for examples of usage.

this is designed to be a drop in replacement, which requires only 2 lines to be changed

In your python script
~~~shell
from transformers import AutoModel
from ipfs_transformers import AutoModel
model = AutoModel.from_auto_download("bge-small-en-v1.5")  
~~~

or 

~~~shell
from transformers import AutoModel
from ipfs_transformers import AutoModel
model = AutoModel.from_ipfs("QmccfbkWLYs9K3yucc6b3eSt8s8fKcyRRt24e3CDaeRhM1")
~~~

or to use with with s3 caching 
~~~shell
from transformers import AutoModel
from ipfs_transformers import AutoModel
model = T5Model.from_auto_download(
    model_name="google-bert/t5_11b_trueteacher_and_anli",
    s3cfg={
        "bucket": "cloud",
        "endpoint": "https://storage.googleapis.com",
        "secret_key": "",
        "access_key": ""
    }
)
~~~


# IPFS Huggingface Bridge

for transformers.js visit:                          
https://github.com/endomorphosis/ipfs_transformers_js

for huggingface datasets python library visit:
https://github.com/endomorphosis/ipfs_datasets

for faiss KNN index python library visit:
https://github.com/endomorphosis/ipfs_faiss

for orbitdb_kit nodejs library visit:
https://github.com/endomorphosis/orbitdb_kit/

for fireproof_kit nodejs library visit:
https://github.com/endomorphosis/fireproof_kit

for python model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager/

for nodejs model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager_js/

for nodejs ipfs huggingface scraper with pinning services visit:
https://github.com/endomorphosis/ipfs_huggingface_scraper/

for ipfs huggingface agents visit:
https://github.com/endomorphosis/ipfs_agents

for ipfs huggingface accelerate visit:
https://github.com/endomorphosis/ipfs_accelerate

for ipfs_kit visit:
https://github.com/endomorphosis/ipfs_kit

for ipfs_kit_js visit:
https://github.com/endomorphosis/ipfs_kit_js

Author - Benjamin Barber
QA - Kevin De Haan
ipfs-model-manager 0.0.12
A wrapper around huggingface datasets, invoking an IPFS model manager.
Requires Python: >=3.8


# IPFS Model Manager

This is a model manager and wrapper for huggingface, and it maintains an index of models from collections of models store retrieved through local/https/s3/ipfs/orbitdb, then maintains a state of which what models are currently accesible, and it will choose what files should be cached through local/s3/ipfs/ based on configuration settings. 

# How to use
~~~shell
pip install .
~~~

look run ``python3 example.py`` for examples of usage.

this is designed to be a drop in replacement, which requires only 2 lines to be changed

In your python script
~~~shell
from ipfs_kit import ipfs_kit
from orbitdb_kit import orbitdb_kit 
from ipfs_model_manager import ipfs_model_manager as model_manager
from ipfs_model_manager import load_config()
from ipfs_model_manager import load_collection()
config = load_config()
collection = load_collection()
models = ModelManager()
ready = models.ready()
models.import_config(config)
models.import_collection(collection)
models.state()
~~~

or 

~~~shell
from ipfs_kit import ipfs_kit
from orbitdb_kit import orbitdb_kit 
from ipfs_model_manager import ipfs_model_manager as model_manager
from ipfs_model_manager import load_config()
from ipfs_model_manager import load_collection()
config = load_config()
config.s3cfg = {
        "bucket": "cloud",
        "endpoint": "https://storage.googleapis.com",
        "secret_key": "",
        "access_key": ""
    }
collection = load_collection()
models = ModelManager()
ready = models.ready()
models.import_config(config)
models.import_collection(collection)
models.state()
~~~

# IPFS Huggingface Bridge:

for huggingface transformers python library visit:
https://github.com/endomorphosis/ipfs_transformers/

for huggingface datasets python library visit:
https://github.com/endomorphosis/ipfs_datasets/

for faiss KNN index python library visit:
https://github.com/endomorphosis/ipfs_faiss

for transformers.js visit:                          
https://github.com/endomorphosis/ipfs_transformers_js

for orbitdb_kit nodejs library visit:
https://github.com/endomorphosis/orbitdb_kit/

for fireproof_kit nodejs library visit:
https://github.com/endomorphosis/fireproof_kit

for ipfs_kit python library visit:
https://github.com/endomorphosis/ipfs_kit/

for ipfs_kit_js nodejs library visit:
https://github.com/endomorphosis/ipfs_kit_js

for python model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager/

for nodejs model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager_js/

for nodejs ipfs huggingface scraper with pinning services visit:
https://github.com/endomorphosis/ipfs_huggingface_scraper/

for ipfs agents visit:
https://github.com/endomorphosis/ipfs_agents/

for ipfs accelerate visit:
https://github.com/endomorphosis/ipfs_accelerate/

Author - Benjamin Barber
QA - Kevin De Haan
orbitdb-kit 0.0.4
a python wrapper around nodejs orbitdb
Requires Python: >=3.8

# Orbitdb Kit

This Repository is meant to bridge python to an orbitdb instance running in nodejs. The orbitdb instance is also hosting a websocket server, and the python library uses its websocket connection to sync data to / from the orbitdb instance.


# IPFS Huggingface Bridge:

for huggingface transformers python library visit:
https://github.com/endomorphosis/ipfs_transformers/

for huggingface datasets python library visit:
https://github.com/endomorphosis/ipfs_datasets/

for faiss KNN index python library visit:
https://github.com/endomorphosis/ipfs_faiss

for transformers.js visit:                          
https://github.com/endomorphosis/ipfs_transformers_js

for orbitdb_kit nodejs library visit:
https://github.com/endomorphosis/orbitdb_kit/

for ipfs_kit python library visit:
https://github.com/endomorphosis/ipfs_kit/

for ipfs_kit nodejs library visit:
https://github.com/endomorphosis/ipfs_kit_js/

for python model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager/

for nodejs model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager_js/

for nodejs ipfs huggingface scraper with pinning services visit:
https://github.com/endomorphosis/ipfs_huggingface_scraper/

for ipfs agents visit:
https://github.com/endomorphosis/ipfs_agents/

for ipfs accelerate visit:
https://github.com/endomorphosis/ipfs_accelerate/

Author - Benjamin Barber
QA - Kevin De Haan
devices-format 0.3
This package is designed to generate regular expression formats based on device names.


# DEVICES FORMAT

  

This package is designed to generate regular expression formats based on device names. It simplifies the process of validating and parsing device identifiers by providing pre-defined regex patterns for various device types.

  

## Installation

  

You can install `devices-format` via pip. Use the following command:

  

```markdown

pip install devices-format

```

  

## Usage

  

Generate regular expression formats based on device names.

```markdown

from devices_format import Device

device = Device("OVF-AAAAAAATEY", "olt")
print(device.get_device_format())

```

  
  

## Supported Device Names

- OLT

- OTB

- CA1

- CA2

- FTSBS

- CPE
ipfs-kit 0.0.4
A wrapper around the Go ipfs and ipfs_cluster
Requires Python: >=3.8

# IPFS kit

This is a python library that installs and configures ipfs services through Kubo, ipfs_cluster_service based on the contents in the config file, for example whether the node is supposed to operate as a master, worker, or leecher.


# IPFS Huggingface Bridge:

for huggingface transformers python library visit:
https://github.com/endomorphosis/ipfs_transformers/

for huggingface datasets python library visit:
https://github.com/endomorphosis/ipfs_datasets/

for faiss KNN index python library visit:
https://github.com/endomorphosis/ipfs_faiss

for transformers.js visit:                          
https://github.com/endomorphosis/ipfs_transformers_js

for orbitdb_kit nodejs library visit:
https://github.com/endomorphosis/orbitdb_kit/

for ipfs_kit python library visit:
https://github.com/endomorphosis/ipfs_kit/

for ipfs_kit nodejs library visit:
https://github.com/endomorphosis/ipfs_kit_js/

for python model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager/

for nodejs model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager_js/

for nodejs ipfs huggingface scraper with pinning services visit:
https://github.com/endomorphosis/ipfs_huggingface_scraper/

for ipfs agents visit:
https://github.com/endomorphosis/ipfs_agents/

for ipfs accelerate visit:
https://github.com/endomorphosis/ipfs_accelerate/

Author - Benjamin Barber
QA - Kevin De Haan
ipfs-datasets 0.0.4
A wrapper around huggingface datasets, invoking an IPFS model manager.
Requires Python: >=3.8

# IPFS Huggingface Datasets

This is a model manager and wrapper for huggingface, looks up a index of models from an collection of models, and will download a model from either https/s3/ipfs, depending on which source is the fastest.

# How to use
~~~shell
pip install .
~~~

look run ``python3 example.py`` for examples of usage.

this is designed to be a drop in replacement, which requires only 2 lines to be changed

In your python script
~~~shell
from datasets import load_dataset
from ipfs_datasets import load_dataset
dataset = load_dataset.from_auto_download("bge-small-en-v1.5")  
~~~

or 

~~~shell
from datasets import load_dataset
from ipfs_datasets import load_dataset
dataset = load_dataset.from_ipfs("QmccfbkWLYs9K3yucc6b3eSt8s8fKcyRRt24e3CDaeRhM1")
~~~

or to use with with s3 caching 
~~~shell
from datasets import load_dataset
from ipfs_datasets import load_dataset
dataset = load_dataset.from_auto_download(
    dataset_name="common-crawl",
    s3cfg={
        "bucket": "cloud",
        "endpoint": "https://storage.googleapis.com",
        "secret_key": "",
        "access_key": ""
    }
)
~~~

# IPFS Huggingface Bridge:

for transformers python library visit:
https://github.com/endomorphosis/ipfs_transformers/

for transformers js client visit:                          
https://github.com/endomorphosis/ipfs_transformers_js/

for orbitdb_kit nodejs library visit:
https://github.com/endomorphosis/orbitdb_kit/

for fireproof_kit nodejs library visit:
https://github.com/endomorphosis/fireproof_kit

for Faiss KNN index python library visit:
https://github.com/endomorphosis/ipfs_faiss/

for python model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager/

for nodejs model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager_js/

for nodejs ipfs huggingface scraper with pinning services visit:
https://github.com/endomorphosis/ipfs_huggingface_scraper/


Author - Benjamin Barber
QA - Kevin De Haan