semvec/pypi-packages huggingface dataset:
ms2lipid 0.1.8
ms2lipid is a Python library for predicted lipid class by using ms2 spectrum.
Requires Python: <3.13,>=3.9

# ms2lipid

ms2lipid is a Python library for predicted lipid class by using ms2 spectrum.

## Installation

Use the package manager pip to install ms2lipid.

```bash
pip install ms2lipid
```

## Usage

```python
import ms2lipid

# predicted lipid subclass by ms2 spectrum
predicted_result_df = ms2lipid.predclass(data_path, ms2spectrum_column_name, precurcerion_column_name, ionmode)

```

## License

[MIT](https://choosealicense.com/licenses/mit/)
simple-hifigan 0.1.1
A simple way to use HiFi-GAN.
Requires Python: >=3.6

# Simple HiFiGAN

Please cite [the HiFiGAN paper](https://arxiv.org/abs/2010.05646) when using this.

A simple wrapper for the this [HiFiGAN Implementation](
    https://github.com/ming024/FastSpeech2/blob/master/hifigan
). This wrapper just adds an interface to the model.

## Installation

```bash
pip install git+https://github.com/MiniXC/simple_hifigan.git
```

## Usage

```python
from simple_hifigan import Synthesiser

synthesiser = Synthesiser()

# create a mel spectrogram
mel_spectrogram = synthesiser.wav_to_mel(
    some_audio, some_sample_rate
)

# multiple mel spectrograms
mel_spectrograms = synthesiser.wavs_to_mels(
    some_audios, some_sample_rate
)

# mel spectrogram to audio
audio = synthesiser(mel_spectrogram)
wvmos 1.0
This package is written for MOS score prediction based on fine-tuned wav2vec2.0 model
Requires Python: >=3.9.0

# WV-MOS
MOS score prediction by fine-tuned wav2vec2.0 model

**Keywords:** MOS-Net, MB-Net, PESQ, STOI, speech quality

## Getting started
The package installation was tested with python3.9

```bash
pip install git+https://github.com/AndreevP/wvmos
```
## Inference

```python
from wvmos import get_wvmos
model = get_wvmos(cuda=True)

mos = model.calculate_one("path/to/wav/file") # infer MOS score for one audio 

mos = model.calculate_dir("path/to/dir/with/wav/files", mean=True) # infer average MOS score across .wav files in directory
```

## Citation and Acknowledgment
This work was done for the deep learning course in Skolteh university by Pavel Andreev, Nikolay Patakin, Oleg Desheulin, Alexander Kagan and Arthur Bulanbaev.
More details are described in paper https://arxiv.org/abs/2203.13086
lexica-api 1.6.0
The python package for api.qewertyy.dev
Requires Python: >=3.8
Keywords: Python, API, Bard, Google Bard, Large Language Model, Chatbot API, Google API, Chatbot, Image Generations, Latent Diffusion, State of Art, Image Reverse Search, Reverse Image Search

## Here are some projects which utilizes the LexicaAPI.

### AverageAI
- **Name:** [AverageAI](https://ai.qewertyy.dev)
- **Description:** Image Generations and LLMs.

### AverageImages
- **Name:** [AverageImages](https://images.qewertyy.dev)
- **Description:** Search Images on google and bing.

### Upscale
- **Name:** [Upscale](https://upscale.qewertyy.dev)
- **Description:** Upscale Images.

### AverageNews
- **Name:** [AverageNews](https://news.qewertyy.dev)
- **Description:** News App.

### Social-DL
- **Name:** [Social-DL](https://social-dl.vercel.app)
- **Description:** Download Videos/Images from social media.

### TelegramBots
[Miko](https://github.com/Awesome-Tofu/miko-bot), [AntiNSFWBot](https://telegram.me/ProtectYourGroupsRobot), [Sung](https://github.com/Dhruv-Tara/Sung), [GameInfoBot](https://github.com/barryspacezero/Telegram-GameInfoBot), [YaeMiko](https://github.com/Infamous-Hydra/YaeMiko), [FilterBot](https://github.com/Codeflix-Bots/AutoFilter), [News](https://github.com/SOMEH1NG/TechNewsDigest) [etc..](https://github.com/search?q=https%3A%2F%2Fapi.qewertyy.dev&type=code)
## Usages
LLM's
```python
from lexica import Client, languageModels

def main(prompt: str) -> dict:
    client = Client()
    response = client.palm(prompt,languageModels.gemini)
    return response

if __name__ == "__main__":
    print(main("hello world"))
```

Upscale an image.
```python
from lexica import Client

def main(image: bytes) -> bytes:
    client = Client()
    imageBytes = client.upscale(image)
    with open('upscaled.png', 'wb') as f:
        f.write(imageBytes)

if __name__ == "__main__":
    image = open('examples/images/image.png', 'rb').read()
    main(image)
```

Anti-NSFW
```python
from lexica import Client

def main(image_url: str) -> dict:
    client = Client()
    response = client.AntiNsfw(image_url)
    if response['content']['sfw'] == True:
        return "This image is safe for work."
    else:
        return "This image is not safe for work."

if __name__ == "__main__":
    print(main("https://graph.org/file/13e95c6cc932530823391.png"))
```
wallid-certishop 0.1.1
A Python library for managing verifiable credentials with WalliD CertiShop.
Requires Python: >=3.6

## Overview

The WalliD CertiShop lib provides an interface for interacting with the WalliD API. It allows users to create Certificate Authorities, manage templates, issue verifiable certificates, and verify certificates through a simplified and structured approach.

## Features

- Create and manage Certificate Authorities (CAs)
- Create and manage templates
- Issue verifiable credentials
- Verify credentials
- Error handling for API requests

## Installation

To use the WalliD library, run the following code in your terminal

```bash
pip install wallid-certishop
````

## Usage

### Initialize the WalliD Client

```bash
from certishop import WalliD

api_url = "https://demo.eidcmp.wallid.io/api/v1"
api_token = "your_api_token"

wallid = WalliD(api_url, api_token)

```

### Create a Certificate Authority

```bash
wallet_address = "0xYourWalletAddress"
admin_email = "admin@example.com"
response = wallid.create_ca(wallet_address, admin_email)
print(response)
```

### Create a Template

```bash
cid = "cid_value"
name = "template_name"
wa = "0xYourWalletAddress"
frontend_props = {
    "components": [
        {"id": "component_id", "type": "text"}
    ],
    "currentLayout": "current_layout"
}
response = wallid.create_template(cid, name, wa, frontend_props)
print(response)

```

### Issue a Certificate

```bash
cid = "cid_value"
tid = "tid_value"
waAdmin = "0xYourWalletAddress"
data = [{"key": "value"}]
email = "user@example.com"
response = wallid.issue_credential(cid, tid, waAdmin, data, email)
print(response)
```

### Verify a Certificate

```bash
credential_id = "credential_id"
tid = "tid_value"
guid = "guid_value"
response = wallid.verify_credential(credential_id, tid, guid)
print(response)
```

## Error Handling

The library includes error handling to manage API response errors gracefully. When an error occurs, it raises an exception with a descriptive error message.

## Tests

The library includes a suite of unit tests to verify functionality. You can run the tests using:
```bash
python -m unittest discover
```
## Contributing

Contributions are welcome! If you would like to contribute to the WalliD library, please create a pull request or open an issue for discussion.

## License

### Instructions

1. **Update Links and Tokens:** Make sure to replace any placeholder text (like `your-repository-name`, `your_api_token`, etc.) with actual values relevant to your project.
2. **Add any additional sections:** You may want to include additional sections, such as "Known Issues," "Future Improvements," or "FAQ," depending on your project's complexity.
them4tch 0.1.0
Una biblioteca para consultar los curso de Hack4u

# Cursos Hack4U
Biblioteca Python para consultar curso de S4vitar, es una práctica de su curso de Python Ofensivo

## Cursos disponibles
- Personalización Linux
- Introducción Linux
- Python Ofensivo
- Introducción al Hacking

## Instalación
Instalar el paquete con `pip3`

```
pip3 install them4tch
```
iris-insee-utils 0.1.1
Get code IRIS from GPS points or addresses
Requires Python: >=3.10

[![codecov](https://codecov.io/gh/adrienpacifico/iris_insee_utils/branch/master/graph/badge.svg?token=ZZItjohsp9)](https://codecov.io/gh/adrienpacifico/iris_insee_utils)  
Note: This library is currently in beta and may contain bugs. Please use it with caution.

This repository provides an easy solution to determine which IRIS zone (Ilots Regroupés pour l'Information Statistique) corresponds to a set of GPS coordinates. It also includes utilities to plot maps with IRIS contours, enrich IRIS-specific data, and manage external data containing GPS points.

### Install
`pip install iris_insee_utils`

### Usage:

 ```python
>>>from iris_insee_utils import gps_to_code_iris

>>>gps_to_iris(lat, long, iris_year=2018)
'920200101'
 ```

Working with a dataframe with longitude and latitude:

```python
>>> df_gps_to_iris(df, long_col, lat_col, iris_year=2020)
                               Lieu longitude latitude  IRIS  CODE_IRIS            NOM_IRIS
0               Mairie de Marseille     5.369   43.296  0301  132020301        Quai du Port
1  Site-Mémorial du Camp des Milles     5.382   43.503  0905  130010905  Les Milles Village
```

Plot a map with data that enriches the IRIS level:

```python
plot_folium_map(
    2020,
    "Paris",
    df_enrich=df_enrich,
    df_enrich_iriscol_colname="IRIS",
    df_enrich_select_cols=["Nombre de ménges","Nombre de personnes"]
)
```
![alt text](img/image.png)


```python
plot_folium_map(2018, "Limoges", df_oi=df_bus_stops)
```
![alt text](img/image-3.png)



More information about IRIS:
https://www.insee.fr/fr/information/5008701?sommaire=5008710#titre-bloc-32

Related project: 
https://github.com/Oslandia/pyris