semvec/pypi-packages huggingface dataset:
open-exchange 0.2.0
Open Exchange Python SDK.
Requires Python: <4.0.0,>=3.6.2

# Open Exchange Python SDK

The Open Exchange Python SDK provides access to the Open Exchange REST API from applications written in Python 3.6
and later.

## Documentation

The REST API documentation is available at [https://openexchange.readme.io/](https://openexchange.readme.io/).

## Installation

You can install the package via pip:

```bash
pip install open-exchange
```

## Usage

See the [examples][0] directory for examples of how to use the SDK.

```python
import open_exchange
from open_exchange.types.data import rental_comps_fetch_params, rental_comps_response
from typing import List

# get API KEY from environment variable OPEN_EXCHANGE_API_KEY
client = open_exchange.OpenExchangeClient()

addresses: List[rental_comps_fetch_params.Address] = [
    {
        "street": " 5201 S 44th St",
        "city": "Omaha",
        "state": "NE",
        "postal_code": "68107",
        "token": "client-provided-token-1",
    }
]

filters: rental_comps_fetch_params.Filters = {
    "bedrooms_total": {
        "relative": 1,
    }
}

# get rental comps for a iterable of addresses with filters
result: rental_comps_response.Result
for result in client.data.rental_comps.fetch(addresses=addresses, filters=filters):
    assert result.token == "client-provided-token-1"

    print("Subject property details:", result.subject_property_details)
    print("Number of rental comps:", len(result.rental_comps))
    print("Rental comps:", result.rental_comps)
```

## Logging

We use the Python standard library [`logging`](https://docs.python.org/3/library/logging.html) module.

## Requirements

Python 3.6 or higher.


[0]: https://github.com/opendoor-labs/open-exchange-python/tree/main/examples
youtubedlapi-server-infusiblecoder 3.7.9
An API server based on yt-dlp

[![PyPI](https://img.shields.io/pypi/v/youtubedlapi-server-infusiblecoder)](https://pypi.org/project/youtubedlapi-server-infusiblecoder/)
[![Downloads](https://static.pepy.tech/badge/youtubedlapi-server-infusiblecoder)](https://pepy.tech/project/youtubedlapi-server-infusiblecoder)
[![Downloads](https://static.pepy.tech/badge/youtubedlapi-server-infusiblecoder/month)](https://pepy.tech/project/youtubedlapi-server-infusiblecoder)
[![Downloads](https://static.pepy.tech/badge/youtubedlapi-server-infusiblecoder/week)](https://pepy.tech/project/youtubedlapi-server-infusiblecoder)
[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://img.shields.io/badge/License-MIT-blue.svg)



youtubedlapi-server-infusiblecoder
=====================

A REST API server for getting the info for videos from different sites, powered by [yt-dlp](https://github.com/yt-dlp/yt-dlp)_.
The installation instructions and the documentation are available at [Read the Docs](https://youtubedlapi-server-infusiblecoder.readthedocs.io/)_.

About
-----

``youtubedlapi-server-infusiblecoder`` is released to the public domain, read the [License](https://raw.githubusercontent.com/syedusama5556/youtubedlapi-server-infusiblecoder/master/LICENSE.md) for more info.


NEW Example Usage
-----
``uvicorn youtubedlapi_server_infusiblecoder.app:app --host 127.0.0.1 --port 9191 --workers 1 --log-level info``

or

``uvicorn youtubedlapi_server_infusiblecoder.app:app --host 0.0.0.0 --port 9191 --workers 1 --log-level info``

or for running in bacground 

``nohup uvicorn youtubedlapi_server_infusiblecoder.app:app --host 0.0.0.0 --port 9191 --workers 1 --log-level info &``

Run in BG with A Repeted Job To Restart Server When Its Down
------------------------------------------------------------

create a script named 'bgapi.sh' add below code in the file 

```#!/bin/bash
while true
do
uvicorn youtubedlapi_server_infusiblecoder.app:app --host 0.0.0.0 --port 9191 --workers 1 --log-level info
sleep 2500
done
```
save it then run this command to run it in the BG

``nohup ./bgapi.sh &``



Old Example Usage
-----

``youtubedlapi-server-infusiblecoder -p 8000 --host 127.0.0.1 --number-processes 1``

or

``youtubedlapi-server-infusiblecoder -p 9191 --host 0.0.0.0 --number-processes 1``

or for running in bacground 

``nohup youtubedlapi-server-infusiblecoder -p 9191 --host 0.0.0.0 --number-processes 1 &``

Run in BG with A Repeted Job To Restart Server When Its Down
------------------------------------------------------------

create a script named 'bgapi.sh' add below code in the file 

```#!/bin/bash
while true
do
youtubedlapi-server-infusiblecoder -p 9191 --host 0.0.0.0 --number-processes 1
sleep 2500
done
```
save it then run this command to run it in the BG

``nohup ./bgapi.sh &``
fastapi-jwt-auth-md 1.1.0
FastAPI extension that provides JWT Auth support (secure, easy to use and lightweight)
Requires Python: >=3.6

<h1 align="left" style="margin-bottom: 20px; font-weight: 500; font-size: 50px; color: black;">
  FastAPI JWT Auth
</h1>

![Tests](https://github.com/IndominusByte/fastapi-jwt-auth/workflows/Tests/badge.svg)
[![Coverage Status](https://coveralls.io/repos/github/IndominusByte/fastapi-jwt-auth/badge.svg?branch=master)](https://coveralls.io/github/IndominusByte/fastapi-jwt-auth?branch=master)
[![PyPI version](https://badge.fury.io/py/fastapi-jwt-auth.svg)](https://badge.fury.io/py/fastapi-jwt-auth)
[![Downloads](https://static.pepy.tech/personalized-badge/fastapi-jwt-auth?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=Downloads)](https://pepy.tech/project/fastapi-jwt-auth)

---

**Documentation**: <a href="https://indominusbyte.github.io/fastapi-jwt-auth" target="_blank">https://indominusbyte.github.io/fastapi-jwt-auth</a>

**Source Code**: <a href="https://github.com/IndominusByte/fastapi-jwt-auth" target="_blank">https://github.com/IndominusByte/fastapi-jwt-auth</a>

---

## Features
FastAPI extension that provides JWT Auth support (secure, easy to use and lightweight), if you were familiar with flask-jwt-extended this extension suitable for you, cause this extension inspired by flask-jwt-extended 😀

- Access tokens and refresh tokens
- Freshness Tokens
- Revoking Tokens
- Support for WebSocket authorization
- Support for adding custom claims to JSON Web Tokens
- Storing tokens in cookies and CSRF protection

## Installation
The easiest way to start working with this extension with pip

```bash
pip install fastapi-jwt-auth
```

If you want to use asymmetric (public/private) key signing algorithms, include the <b>asymmetric</b> extra requirements.
```bash
pip install 'fastapi-jwt-auth[asymmetric]'
```

## License
This project is licensed under the terms of the MIT license.
HoiSaAI 0.0.2
HoiSaAI is an open-source artificial intelligence (AI) project designed to harness the power of distributed computing for solving complex problems efficiently
Requires Python: >=3.11

# HoiSaAI

## What is HoiSaAI

HoiSaAI is an open-source artificial intelligence (AI) project designed to harness the power of distributed computing for solving complex problems efficiently. Leveraging cutting-edge machine learning algorithms and distributed computing frameworks, HoiSaAI tackles a wide range of challenges, from data analysis and predictive modeling to natural language processing and computer vision.

At its core, HoiSaAI aims to democratize access to AI capabilities by providing a scalable and adaptable platform that can be easily deployed across various environments, including cloud infrastructures, on-premises clusters, and edge devices. By distributing computational tasks across multiple nodes, HoiSaAI enables parallel processing, allowing users to tackle large-scale datasets and computationally intensive tasks with ease.

Key components of HoiSaAI include:

- Distributed Computing Infrastructure: A robust framework for distributing computational tasks across multiple nodes, optimizing resource utilization, and improving overall performance.
- Machine Learning Algorithms: State-of-the-art machine learning models and algorithms to solve diverse AI problems, including classification, regression, clustering, and reinforcement learning.
- Scalable Data Processing: Tools and utilities for efficient data ingestion, preprocessing, feature extraction, and transformation, ensuring seamless integration with existing data pipelines.
- Model Training and Evaluation: Facilities for training, tuning, and evaluating machine learning models using distributed computing resources, with support for hyperparameter optimization and model validation.
- Real-time Inference: APIs and endpoints for deploying trained models into production environments, enabling real-time inference and decision-making at scale. Whether you're a data scientist, AI researcher, or developer, HoiSaAI provides a flexible and extensible platform for experimenting with AI techniques, building custom solutions, and deploying AI-powered applications in diverse domains, such as healthcare, finance, retail, and beyond.

Join our community today and contribute to the future of AI innovation with HoiSaAI!

## Installation

Install jax and jaxlib from <https://jax.readthedocs.io/en/latest/installation.html>

```
pip install -U hoisaai
```

## Citing JAX

To cite this repository:

```
@software{
    hoisaai,
    author = {Mike Mo Shun Cheng},
    title = {HoiSaAI},
    url = {https://github.com/mikecheng18/hoisaai},
    version = {0.0.1},
    year = {2024},
}
```
llmaudit 0.1.3
A CLI tool to find LLM usage accross your repos
Requires Python: >=3.8

# LLM Audit README

## Overview
LLM Audit is a CLI tool designed to scan GitHub repositories or local directories for the usage of Large Language Models (LLMs). It aggregates usage statistics, identifies top LLM providers, and generates a comprehensive report.

This tool was built in response to security engineers needing a streamlined way to gather and report on the use of LLMs across codebases in their organization. We quickly built LLM Audit to replace those custom scripts.


## Installation

To install LLM Audit, ensure you have Python 3.8 or higher installed, then run:

`pip install llmaudit`



## Usage

LLM Audit can be used in two modes: scanning GitHub repositories or scanning local directories.

### Scanning GitHub Repositories

To scan GitHub repositories, use the `github` command followed by the repositories you wish to scan. You can specify additional options such as a temporary directory for cloning, whether to keep the cloned repos, and a timeout for cloning.


`llmaudit github [--repos <repo1> <repo2>] [--temp-dir <dir>] [--keep] [--timeout <seconds>]`



- `--repos`: Names of the GitHub repositories to scan. Scans all repos if not specified.
- `--temp-dir`: Temporary directory to clone repositories into. Default is `llm_usage_temp`.
- `--keep`: Keep the temporary folder after cloning. By default, the folder is deleted.
- `--timeout`: Timeout for cloning each repository in seconds. Default is 300 seconds.

### Scanning Local Directories

To scan local directories, use the `local` command followed by the paths to the directories you wish to scan.


`llmaudit local --repos <path1> <path2> ...`


- `--repos`: Paths to the local directories to scan. Required.

## Report Generation

After scanning, LLM Audit generates an HTML report detailing the usage of LLMs across the scanned repositories or directories. The report includes total usage counts, usage by LLM provider, top owners, and statistics by repository.

The report is saved in the `results` directory with a filename that includes the current date, e.g., `llm_usage_report_DD-MM-YYYY.html`.

## Security and Privacy

LLM Audit does not send any data externally. All processing and report generation are done locally on your machine.


## Supported Provider SDKs and Languages

- **Mistral** : Python, JS/TS
- **OpenAI** : Python, JS/TS
- **Anthropic**: Python, JS/TS

## Contributing

Contributions to LLM Audit are welcome! Expanding provider/language and platform support are top of mind, but any other suggestions are welcome!  

## License

LLM Audit is licensed under the Apache Software License. See the LICENSE file for more details.

## Contact

For questions or support, please contact founders@promptarmor.com.
jaraco.crypto 4.0.1
Cryptography support by jaraco
Requires Python: >=3.8

.. image:: https://img.shields.io/pypi/v/jaraco.crypto.svg
   :target: https://pypi.org/project/jaraco.crypto

.. image:: https://img.shields.io/pypi/pyversions/jaraco.crypto.svg

.. image:: https://github.com/jaraco/jaraco.crypto/actions/workflows/main.yml/badge.svg
   :target: https://github.com/jaraco/jaraco.crypto/actions?query=workflow%3A%22tests%22
   :alt: tests

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
    :target: https://github.com/astral-sh/ruff
    :alt: Ruff

.. image:: https://readthedocs.org/projects/jaracocrypto/badge/?version=latest
   :target: https://jaracocrypto.readthedocs.io/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/skeleton-2024-informational
   :target: https://blog.jaraco.com/skeleton

A set of cryptographic routines and utilities
implemented in pure Python.

================
Acknowledgements
================

Many of the ideas and much of the code has been adapted from or inspired by
several other projects, including:

- The code repository was originally a fork of the `ctypescrypto project
  <http://code.google.com/p/ctypescrypto/>`_. The code history includes the
  full history of the ancestral project.
- The blowfish code was adapted from a module contributed to the community
  by Michael Gilfix.