semvec/pypi-packages huggingface dataset:
pht-station 0.2.2
Python library for handling containerized PHT trains
Requires Python: >=3.10,<4.0

[![Build](https://github.com/PHT-EU/station-backend/actions/workflows/Build.yml/badge.svg)](https://github.com/PHT-EU/station-backend/actions/workflows/Build.yml)
[![Tests](https://github.com/PHT-EU/station-backend/actions/workflows/tests.yml/badge.svg)](https://github.com/PHT-EU/station-backend/actions/workflows/tests.yml)
[![codecov](https://codecov.io/gh/PHT-Medic/station-backend/branch/master/graph/badge.svg?token=SWJRH1V44S)](https://codecov.io/gh/PHT-Medic/station-backend)

# PHT Station Backend

This project contains the implementation of the station API, workers for training models, as well as the configuration
for the station airflow instance and related workers. A FastAPI REST API als well as a command line tool for train and
station management can be found in the `station` directory.

## Setup development environment

Checkout the repository and navigate to project directory.

```bash
git clone https://github.com/PHT-Medic/station-backend.git
```

```bash
cd station-backend
```

### Prerequisites

Make sure the following ports required for the services are open or change the port mappings in the `docker-compose.yml`
file.

- Postgres: `5432`
- Redis: `6379`
- Minio: `9000` & `9001` (Console)
- Airflow: `8080`
- Blaze FHIR server: `9090`
- API: `8000`

### Start services

Start the services for development using docker-compose.

```shell
docker compose up -d
```

Check the logs of the services to see if everything is running as expected.

```shell
docker compose logs -f
```

### Configure station config for running the API in development mode

Copy the `station_config.yml.tmpl` file in the root directory to `station_config.yml` and adjust the values (especially
configuring addresses and credentials for the central api)

```yaml
# Configure authentication for central services
central:
  api_url: ""
  # Robot credentials for accessing central services, these can be obtained in the central UI
  robot_id: "central-robot-id"
  robot_secret: "central-robot-secret"
  private_key: "/path/to/private_key.pem"
  # optional password for private key
  private_key_passphrase: "admin"

######### some lines omitted #########

# Configures the address and credentials for the central container registry
registry:
  address:
  password:
  user:
  project:

```

### Install python dependencies

Install dependencies using [poetry](https://python-poetry.org/). This will also create a virtual environment for the
project.

```shell
poetry install --with dev
```

### Run the station API

To run the station API with hot reloading, run the following command:

```bash
poetry run python station/app/run_station.py
```
dinohub-seedwork-be 0.1.56
A seedwork for DinoHub service
Requires Python: >=3.10,<4.0

# DINO hub seedwork

This is a package contains
  * DDD (Domain Driven Design) artifact
  * Functional programming with Returns
  * Basic helpers function

## Install

`dinohub-seedwork-be` is available on PyPi:

```
pip install dinohub-seedwork-be
```

See at here: https://pypi.org/project/dinohub-seedwork-be/
TrueCallerScraper 1.0.0
TrueCallerScraper is a Python script that automates the extraction of profile names from the TrueCaller app using ADB and OCR
Requires Python: >=3.6
Keywords: sample setuptools development Phone number search,TrueCaller,Missing digits,Automation,Python script,Data extraction,ADB,Android,Linux,Hacking,Python programming,Phone number lookup,Contact information,Reconnect,Missed calls

# TrueCallerScraper

TrueCallerScraper is a Python script that automates the extraction of profile names from the TrueCaller app using ADB (Android Debug Bridge) and OCR (Optical Character Recognition).

## Features

- Search phone numbers on TrueCaller and extract profile names.
- Capture screenshots and perform OCR to extract profile names.
- Save profile names to an output file.
- Fully functional and ready to use.

## Prerequisites

To run this script, you need to have the following tools and packages installed:

- Python 3
- ADB (Android Debug Bridge)
- pytesseract
- Pillow

## Installation

1. Clone this repository to your local machine.
2. Install the required Python packages by running the following command:

   ```bash
   pip install -r requirements.txt
   ```

3. Ensure that ADB (Android Debug Bridge) is properly installed and set up on your machine. If you haven't installed ADB yet, you can refer to external sources like XDA Developers for detailed instructions on installing ADB for your specific operating system.

Note: Setting up ADB is outside the scope of this guide, and it's important to have ADB properly installed and configured before running the TrueCallerScraper script.


## Usage

1. Connect your Android device to your computer and ensure that USB debugging is enabled.

2. Open the TrueCaller app on your Android device and navigate to the desired chat or search page.

3. Update the script with the appropriate coordinates and settings for your device, if necessary.

4. Run the script using the following command:

```bash
python truecaller_scraper.py
```

The script will enter phone numbers, search on TrueCaller, capture screenshots, extract profile names, and save them to an output file.

## Customization
1. You can customize the script by modifying the following:

2. Adjusting the coordinates for tapping on the screen to match your device's layout.
3. Adding additional functionalities or error handling based on your requirements.

## Contribution
Contributions to this project are welcome! If you have any suggestions, improvements, or bug fixes, please feel free to open an issue or submit a pull request.
cyberbullying-detection 2.0
An package to detect the cyberbullying content in the text

# CYBERBULLYING TEXT CONTENT DETECTOR

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)                 
[![Python 3.9](https://img.shields.io/badge/python-3.9-blue.svg)](https://www.python.org/downloads/release/python-390/)   


## Functionality of the CyberBullying detector

- Used to detect cyberbullying content in the text.
- Helpful for social media platforms to detect cyberbullying content.
- If the text is cyberbullying content, it will return the kind of cyberbullying as age, gender, religion, ethinicity and others
- If the text is not cyberbullying return not cyberbullying.

## Usage

- Make sure you have Python installed in your system.
- Run Following command in the CMD.
 ```
  pip install cyberbullying-detction
  ```
  ```
  import nltk
  nltk.download('stopwords')
  nltk.download('punkt')
  nltk.download('wordnet')
  ```
## Example

 ```

import cyberbullying_detection as cd
x = "confronting the primary school bully and then breaking his arm with my pencilcase"
cb = cd.cyberbullying(x)
cb.find()

The content belongs to age type
  ```
online-stats 2023.6
Online algorithm for mean, variance, and covariance.
Requires Python: >=3.6
Keywords: online algorithm,online mean,online variance,online covariance,online statistics

*online-stats* — Online algorithm for mean, variance, and covariance
====================================================================

The *online-stats* Python package contains a single function
`online_stats.add_sample()` which updates a sample mean given a new sample, as
well as optionally the sample variance and the sample covariance matrix.

The package has no dependencies, as the `online_stats.add_sample()` function
works with any input data type that supports in-place addition and fancy
slicing.

Usage
-----

```py
>>> import numpy as np
>>>
>>> # the package
>>> import online_stats
>>>
>>> # online algorithm for the mean
>>> # start from zero
>>> mu = np.zeros(4)
>>>
>>> # generate samples and compute their mean
>>> for i in range(1000):
...     x = np.random.normal([0.1, 0.3, 0.5, 0.7])
...     online_stats.add_sample(i, x, mu)
...
>>> # the mean is computed in place
>>> print(mu)
[0.08804402 0.25896929 0.44891264 0.73418769]
>>>
>>> # compute the variance
>>> mu = np.zeros(4)
>>> var = np.zeros(4)
>>> for i in range(1000):
...     x = np.random.normal([0.1, 0.3, 0.5, 0.7], [0.2, 0.4, 0.6, 0.8])
...     online_stats.add_sample(i, x, mu, var=var)
...
>>> print(mu)
[0.09854301 0.29509305 0.4777673  0.70008311]
>>> print(var**0.5)
[0.19900518 0.4012857  0.59267129 0.81856542]
>>>
>>> # compute the covariance matrix
>>> mu = np.zeros(4)
>>> cov = np.zeros((4, 4))
>>> for i in range(100_000):
...     x = np.random.multivariate_normal([0.1, 0.3, 0.5, 0.7],
...             [[0.2, 0.02, 0.04, 0.06],
...              [0.02, 0.4, 0.06, 0.08],
...              [0.04, 0.06, 0.6, 0.10],
...              [0.06, 0.08, 0.10, 0.8]])
...     online_stats.add_sample(i, x, mu, cov=cov)
...
>>> print(mu)
[0.10095607 0.30486108 0.50113141 0.69912377]
>>> print(cov)
[[0.20101406 0.02105503 0.0382198  0.06220174]
 [0.02105503 0.39909545 0.06192678 0.0791239 ]
 [0.0382198  0.06192678 0.59960537 0.1082596 ]
 [0.06220174 0.0791239  0.1082596  0.80071002]]

```