semvec/pypi-packages huggingface dataset:
fastapi-jwt-authlib 0.9.3
A FastAPI JWT authentication library
Requires Python: <4.0,>=3.13

# fastapi-jwt-authlib

[![PyPI version](https://badge.fury.io/py/fastapi-jwt-authlib.svg)](https://badge.fury.io/py/fastapi-jwt-authlib)

---

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

---

## Описание проекта

Библиотека для аутентификации JWT в FastAPI. Она предоставляет удобные инструменты для работы с JWT токенами, обеспечивая безопасность и простоту интеграции в ваши FastAPI приложения.

## Установка

```bash
pip install fastapi-jwt-authlib
```

## Пример использования

main.py: 

```python 
from fastapi_jwt_authlib.auth import AuthJWT

AuthJWT.config(
    secret_key=settings.SECRET_KEY,
    cookie_access_key=settings.COOKIE_ACCESS_KEY,
    cookie_refresh_key=settings.COOKIE_REFRESH_KEY,
    cookie_refresh_path=f"{settings.ROOT_PATH}/auth/refresh",
)
```

auth.py:

```python
from fastapi import APIRouter, HTTPException
from fastapi.security import HTTPBasicCredentials
from fastapi_jwt_authlib.auth import JWTUserData
from fastapi_jwt_authlib.depends import (
    AuthAccessDepends,
    AuthDepends,
    AuthRefreshDepends,
)

router = APIRouter()


@router.post("/login")
def login(user: HTTPBasicCredentials, auth: AuthDepends):
    if user.username != "admin" or user.password != "admin":
        raise HTTPException(status_code=401, detail="Incorrect username or password")

    roles = ["admin"]
    data_jwt = JWTUserData(user=user.username, roles=roles)
    auth.generate_and_store_access_token(data_jwt)
    auth.generate_and_store_refresh_token(data_jwt)

    return {"msg": "Successful login"}


@router.delete("/logout")
def logout(auth: AuthDepends):
    auth.unset_cookies()
    return {"msg": "Successful logout"}


@router.post("/refresh")
def refresh(auth: AuthRefreshDepends):
    roles = ["admin"] if is_admin(auth.user) else []
    data_jwt = JWTUserData(user=auth.user, roles=roles)
    auth.jwt.generate_and_store_access_token(data_jwt)
    return {"msg": "The token has been refresh"}


@router.get("/protected")
def protected(auth: AuthAccessDepends):
    return {"user": auth.user}
```
sleep-model 0.1.3
Your package description
Requires Python: ==3.8.*

# Machine Learning-Guided Mapping Sleep-Promoting Volatiles in Aromatic Plants

## Project Description
This repository provides a machine-learning pipeline for identifying sleep-promoting volatile organic compounds (VOCs) from aromatic plants, including pretrained base models and a stacking predictor for quick inference.
![image](https://github.com/user-attachments/assets/2fbe4d84-0f63-40aa-b340-3f0d605319bc)


## Dependency
The code has been tested in the following environment:

|  Package    | Version  |
|  ----  | ----  |
| Python  | 3.8.16 |
| Conda  | 23.5.0 |
| RDKit  | 2023.3.1 |
| Scikit-learn  | 1.0.2 |

# How to Use

## Installation

### Option A: From PyPI (simplest)
```
pip install sleep-model
```

### Option B: Conda environment (recommended for RDKit/DeepChem)
```bash
conda env create -f environment.yaml -n sleep_model
conda activate sleep_model
# install this project from source (editable or regular)
python -m pip install -e .
# python -m pip install .
```

### Option C: uv (fast installer; lockfile included)
```bash
python -m pip install uv
py -m venv .venv
.\.venv\Scripts\Activate.ps1  # Windows PowerShell
uv sync
```
## File Structure
```
├── data/                   # Input data files
├── data_analysis/          # Data processing and analysis
├── models/                 # Pretrained base model files for Stacking model training
│   ├── RF/
│   │   ├── rf_MACCSkeys_random_0.ipynb
│   │   ├── rf_RDkit_random_0.ipynb
│   ├── SVM/
│   │   ├── svm_MACCSkeys_random_3.ipynb
│   ├── XGB/
│   │   ├── xgb_MACCSkeys_random_0.ipynb
│   │── stacking_predict.ipynb
├── predict_smiles.py 
└── README.md

These four models (rf_MACCSkeys, rf_RDkit, svm_MACCSkeys, xgb_MACCSkeys) are the base models that we use to train the final stacking model.
```

## Predicting

### Command-line (console script)
After installation, a console command is available:
```bash
sleep-predict --smiles "CC(=O)OC1=CC=CC=C1C(=O)O"
```

### As a Python module
```bash
python -m predict_smiles --smiles "CC(=O)OC1=CC=CC=C1C(=O)O"
```

### Batch prediction from CSV
Predict for a CSV file containing a SMILES column (default column name: `smiles`):
```bash
python predict_smiles.py --csv example/input.csv --out example/preds.csv
```
Customize the SMILES column name and encoding when needed (e.g., column `SMILES`):
```bash
python predict_smiles.py --csv example/input.csv --out example/preds.csv --smiles-column SMILES --input-encoding utf-8
```

### Notes
- Models and training data are loaded from the installed package resources (project `models/` and `data/GABAA.csv`). Ensure they are present if running from source.
- If the console command is not found on Windows, re-activate your environment or run the module form.

## Troubleshooting
- RDKit/DeepChem wheels can be environment-specific. If installation via `pip` fails, prefer the Conda-based installation.
- If you see a file-not-found error for `models/` or `data/GABAA.csv`, run from the project root or install the project so resources are available in the environment.
light-mlt 0.1.3
Lightweight preprocessing and reversible Modular Linear Tokenization (MLT) utilities for categorical and continuous data.
Requires Python: >=3.9


[![PyPI version](https://img.shields.io/pypi/v/light-mlt?color=orange)](https://pypi.org/project/light-mlt/)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![GitHub](https://img.shields.io/badge/github-light--mlt-black?logo=github)](https://github.com/tcharliesschmitz/light-mlt)



# 🧩 Module: `light_mlt.py`

Lightweight, reproducible preprocessing pipeline for tabular datasets — integrating **categorical encoding via Modular Linear Tokenization (MLT)** and **continuous feature scaling**.  
Designed for **efficient fit/transform workflows** with full reversibility and schema persistence.

> 📘 **Reference**  
> Schmitz, T. (2025). *light-mlt: Modular Linear Tokenization for Scalable Categorical Encoding*.  
> DOI: [10.5281/zenodo.17467914](https://doi.org/10.5281/zenodo.17467914)

---

## 🔖 Key Features

- Deterministic preprocessing for categorical and continuous data  
- Append-only vocabulary management (`cats_map.pkl`)  
- Fully reversible categorical encoding via **MLT**  
- Continuous feature scaling with `StandardScaler`  
- Persistent schema for consistent transformations  
- Minimal dependencies: `numpy`, `pandas`, `scikit-learn`

---

## 📦 Artifacts

Each `fit()` operation generates or updates the following artifacts (default directory: `light_mlt_artifacts/`):

| File | Description |
|------|--------------|
| `schema.pkl` | Schema metadata (columns, types, MLT config) |
| `scaler.pkl` | Trained `StandardScaler` for continuous columns |
| `cats_map.pkl` | Append-only `{label → id}` mapping for categorical features |
| `mlt_params.pkl` | MLT parameters (`p`, `n`, `M`, `Minv`) per column |
| `preprocessed.csv` | Optional transformed dataset export |

---

---

## 🧪 Detailed Examples

Below are complete examples demonstrating how to use `light_mlt.py` for preprocessing, transformation, and reversibility.

---

### **1️⃣ Basic Usage**

```python
import pandas as pd
from light_mlt import fit_transform, inverse_transform

# Example dataset
df = pd.DataFrame({
    "city": ["São Paulo", "Curitiba", "São Paulo", "Florianópolis"],z
    "vehicle": ["Truck", "Car", "Car", "Bus"],
    "mileage": [12.4, 25.8, 31.5, 18.7],
})

print("=== Original Data ===")
print(df)

# --- Step 1: Fit + Transform ---
df_t, path, token_cols, report = fit_transform(
    df,
    categorical_cols=["city", "vehicle"],
    continuous_cols=["mileage"],
    dir="light_mlt_artifacts/"
)

print("\n=== Transformed Data ===")
print(df_t.head())

print("\nGenerated token columns:", token_cols)
print("Report:", report)

# --- Step 2: Inverse Transform ---
df_rec = inverse_transform(df_t, dir="light_mlt_artifacts/")
print("\n=== Reconstructed Data ===")
print(df_rec)
twistdl 1.1.0
A comprehensive system monitoring tool similar to htop
Requires Python: >=3.7

# System Monitor

A comprehensive Python package for system monitoring similar to htop/top, providing detailed information about CPU, memory, disk, network, and processes.

## Features

- **CPU Monitoring**: Usage percentage, frequency, core statistics, and load average
- **Memory Monitoring**: Virtual memory, swap usage, and detailed memory statistics
- **Disk Monitoring**: Partition usage, I/O statistics, and disk activity
- **Network Monitoring**: Interface information, bandwidth usage, and network statistics
- **Process Monitoring**: Top processes by CPU/memory usage with detailed information
- **Interactive Dashboard**: Live-updating terminal interface similar to htop
- **Programmatic API**: Easy access to all system information via Python API

## Installation

```bash
pip install twistdl
intelligent-work-assistant 0.2.1
Personal Intelligent Work Assistant running on local computer
Requires Python: >=3.12

# Intelligent Work Assistant

A personal intelligent work assistant application running on your local computer, built with Streamlit.

## Description

Intelligent Work Assistant (IWA) is a Python-based application designed to help you with your daily work tasks. It provides an interactive web interface powered by Streamlit for easy interaction.

## Features

- Local execution for privacy and security
- Web-based user interface using Streamlit
- Lightweight and easy to use

## Requirements

- Python >= 3.12
- Streamlit >= 1.50.0

## Installation

Install the package:
```bash
pip install intelligent-work-assistant
```

## Download models
1. Download Embedding model
```
modelscope download --model "OpenVINO/Qwen3-Embedding-0.6B-int8-ov" --local_dir Qwen3-Embedding-0.6B-int8-ov
```

2. Download Text-Generation model
```
modelscope download --model "OpenVINO/Qwen3-0.6B-int8-ov" --local_dir Qwen3-0.6B-int8-ov
```

## How to Run
```bash
iwa --model <Text-Generate model> --embed-model <Embedding model>
```
Assume that you downloaded **Qwen3-Embedding-0.6B-int8-ov** and **Qwen3-0.6B-int8-ov** model, the command should be
```
iwa --model Qwen3-0.6B-int8-ov --embed-model Qwen3-Embedding-0.6B-int8-ov
```

This will launch the Streamlit application in your default web browser.

## Project Structure

```
IntelligentWorkAssistant/
├── src/
│   └── intelligent_work_assistant/
│       ├── __init__.py
│       ├── __main__.py
│       ├── main.py          # Entry point and launcher with CLI arguments
│       ├── model.py         # OpenVINO LLM wrapper with streaming support
│       └── st_main.py       # Main Streamlit multi-page application
├── tests/
│   └── __init__.py
├── threads-history/         # Chat history storage (auto-created)
├── pyproject.toml           # Project configuration and dependencies
└── README.md
```

### Key Files

- **main.py**: Command-line interface entry point that validates models and launches Streamlit
- **model.py**: OpenVINO GenAI wrapper class for LLM inference with streaming capabilities
- **st_main.py**: Streamlit application with multi-page navigation, chat interface, and history management

## Development

To contribute or modify the application:

1. Install in editable mode: `pip install -e .`
2. Make your changes to the code
3. Test by running `iwa` command
4. The changes will be reflected immediately without reinstallation

## License

MIT
primula 0.7
A code coverage tool for Vim script
Requires Python: >=3.10

Primula
=======

Primula is a code coverage tool for `Vim script`_.

.. image:: https://img.shields.io/pypi/v/primula
   :target: https://pypi.org/project/primula

.. image:: https://github.com/hattya/primula/actions/workflows/ci.yml/badge.svg
   :target: https://github.com/hattya/primula/actions/workflows/ci.yml

.. image:: https://ci.appveyor.com/api/projects/status/if338u3j55a8kekm?svg=true
   :target: https://ci.appveyor.com/project/hattya/primula

.. image:: https://codecov.io/gh/hattya/primula/branch/master/graph/badge.svg
   :target: https://codecov.io/gh/hattya/primula

.. _Vim script: https://www.vim.org/


Installation
------------

.. code:: console

   $ pip install primula


Requirements
------------

- Python 3.10+
- coverage 5.0+
- Vim 7.4+


Usage
-----

run
~~~

.. code:: console

   $ primula run vim --clean -Nnu vimrc -S /path/to/script.vim -c q
   $ primula report -m

The contents of ``vimrc`` as follow:

.. code:: vim

   if $PROFILE !=# ''
     execute 'profile start' $PROFILE
     profile! file ./*
   endif


combine
~~~~~~~

.. code:: console

   $ vim --clean -Nn --cmd "profile start profile.txt" --cmd "profile! file ./*" -S /path/to/script.vim -c q
   $ primula combine profile.txt
   $ primula report -m


Configuration
-------------

.coveragerc
~~~~~~~~~~~

.. code:: ini

   [primula]
   environ = PROFILE
   profile = profile.txt


cond
  It controls whether following condition commands to be included as
  statements.

  - ``:elsei[f]``
  - ``:cat[ch]``
  - ``:fina[lly]``

  They are counted by Vim 8.1.309+.
  
  Default: ``True``

end
  It controls whether following end commands to be included as statements.

  - ``:en[dif]``
  - ``:endw[hile]``
  - ``:endfo[r]``
  - ``:endt[ry]``
  - ``:endf[unction]``

  Default: ``False``

environ
  An environment variable name.

  Default: ``PROFILE``

profile
  A profile output path.

  Default: ``profile.txt``


License
-------

Primula is distributed under the terms of the Apache License, Version 2.0.
libnmap2 1.0.0
A Python package for generating random emoji as PNG image files
Requires Python: >=3.7
Keywords: emoji, image, generator, png, graphics

# Emoji Generator

A Python package for generating random emoji characters as PNG image files with transparent backgrounds.

## Features

- 🎲 **Random Emoji Selection**: Access to thousands of emoji characters
- 🖼️ **PNG Image Generation**: Convert emoji to properly sized PNG files
- 🌈 **Transparent Backgrounds**: Clean images with alpha transparency
- 🔧 **Cross-Platform**: Works on Windows, macOS, and Linux
- 📐 **Optimal Sizing**: Automatic font size adjustment for best appearance

## Installation

```bash
pip install multiutility
create-rl-app 0.0.8
Bootstrap a new Reinforcement Learning project
Requires Python: >=3.11

# create-rl-app

[![Python Version](https://img.shields.io/badge/python-3.11%2B-blue.svg)](https://python.org)
[![Jaxnasium Version](https://badge.fury.io/py/jaxnasium.svg)](https://github.com/ponseko/jaxnasium)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

A CLI application to bootstrap reinforcement learning applications within the Jaxnasium ecosystem. Quickly scaffold new RL projects for either developing environments with baseline algorithms or for altering existing baselines.

## What it does

`create-rl-app` is a command-line tool that helps you quickly set up new reinforcement learning projects using the Jaxnasium framework. It creates a well-structured project template with:

- 🚀 **Quick Setup**: Get a new RL project running in seconds
- 🏗️ **Helpful Templates**: Templates for environments and algorithms for you to start with.
- ⚡ **Performance Optimized**: Sets you up with PureJaxRL compatible agents and environments for performance and GPU scalability.

## Useage

### uvx (Recommended)

```bash
uvx create-rl-app <project_name>
cd <project_name>
uv run train_example.py
```

### pipx

```bash
pipx run create-rl-app <project_name>
cd <project_name>
# Create a new environment (e.g. conda, venv, etc.)
# source .../bin/activate
python train_example.py
```

### Or Install Globally

```bash
uv tool install create-rl-app
```

```bash
pip install create-rl-app
```

## Dependencies

None.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
floriday-supplier-client 0.3.0
Main - Floriday Suppliers API
Keywords: Swagger, Main - Floriday Suppliers API

    ﻿Every endpoint requires at least the &#x60;role:app&#x60; scope and the header &#x60;X-Api-Key&#x60; populated with your given API-key. Most endpoints also require an additional scope which is listed in their descriptions.  - 🗝 [Authorization server (staging)](https://idm.staging.floriday.io/oauth2/ausmw6b47z1BnlHkw0h7/.well-known/oauth-authorization-server) - 🗝 [Authorization server (live)](https://idm.floriday.io/oauth2/aus3testdcf2vyfs70i7/.well-known/oauth-authorization-server) - 📚 [Documentation](https://developer.floriday.io/docs/welcome) - ▶ [Coding screencast: getting started (NL)](https://www.youtube.com/watch?v&#x3D;fdqzP7_Y_s8)  ---  _The current state of this version 2024v1 is **Main**._ * This version will be deprecated after October 2024. * This version will be removed after April 2025.  ---  🔗 2023v2: [Customers API](https://api.staging.floriday.io/customers-api-2023v2/swagger/index.html) | [Suppliers API](https://api.staging.floriday.io/suppliers-api-2023v2/swagger/index.html) 🔗 2024v1: [Customers API](https://api.staging.floriday.io/customers-api-2024v1/swagger/index.html) | [Suppliers API](https://api.staging.floriday.io/suppliers-api-2024v1/swagger/index.html) 🔗 2024v2: [Customers API](https://api.staging.floriday.io/customers-api-2024v2/swagger/index.html) | [Suppliers API](https://api.staging.floriday.io/suppliers-api-2024v2/swagger/index.html)   # noqa: E501