semvec/pypi-packages huggingface dataset:
esimaccess-python 0.1.1
Python SDK for the Esimaccess API

# Esimaccess Python Library

Python SDK for the Esimaccess API, using httpx.

## Documentation

See the [API Docs](https://docs.esimaccess.com/).
## Installation

```
pip install esimaccess-python
```
## Usage

The library needs to be configured with your account's access code which is availible in your developer dashboard.
```python
from esimaccess_python import Package, authenticate

client = Package(authenticate("Access code"))
```


#### To Get All Data Packages
```python
print(client.list())
```

#### To Order Profiles
```python
print(client.order(transactionId="your_txn_id", amount=15000, packageInfoList=[{"packageCode": "7aa948d363", "count": 1, "price": 15000}]))
```

#### To Query All Allocated Profiles
```python
from esimaccess_python import PageParam

print(client.query(orderNo="B23120118131854", iccid="", pager=PageParam(pageNum=1, pageSize=20)))
```

#### To Cancel Profile
```python
print(client.cancel(iccid='8943108170000775671'))
```

#### To Suspend Profile
```python
print(client.suspend(iccid='8943108170000775671'))
```

#### To Unsuspend Profile
```python
print(client.unsuspend(iccid='8943108170000775671'))
```

#### To Revoke Profile
```python
print(client.revoke(iccid='8943108170000775671'))
```

#### To Get Balance
```python
print(client.balance())
```

#### To Top Up
```python
print(client.topup(iccid='89852245280001354019', topUpCode='TOPUP_CKH491', transactionId='top_up_for_existing_plan_CKH491', amount=15000))
```

#### To Set Webhook
```python
print(client.set_webhook(webhook='https://webhook.endpoint.site/unique-webhook'))
```

#### To Get Current Webhook
```python
print(client.query_webhook(webhook='https://webhook.endpoint.site/unique-webhook'))
```

#### To Send SMS
```python
print(client.sendSms(iccid='89852000261098474477', message='Your Message!'))
```
edunet 0.2.4
Simple Network Simulation For The Funs!
Requires Python: <4.0,>=3.9

[![codecov](https://codecov.io/gh/idjaw/edunet/graph/badge.svg?token=5MZIOFVHA9)](https://codecov.io/gh/idjaw/edunet)
![PyPI - Version](https://img.shields.io/pypi/v/edunet)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/edunet)
[![EduNet](https://github.com/idjaw/edunet/actions/workflows/python-app.yml/badge.svg?branch=main)](https://github.com/idjaw/edunet/actions/workflows/python-app.yml)

# EduNet
This application allows you to simulate different network operations in a simple plug-in manner.

The motivation with this project was to simply learn more about sockets, protocols, and making them all talk to one another.

The end result of this project is to be able to craft together any network entities and have them communicate as intended.

## Simple Supported Use Case

There is a built-in simple supported use case called SimpleHTTPNode.

Bringing this up will start a TCP listener on localhost on port 9999 accepting valid HTTP requests.
What will be sent back are valid HTTP responses.

## Complex Use Cases

As it will be supported soon, the intent is by using the core architecture,
we will be able to implement a router with basic features like:

- Routing table
- DHCP assignment
- Packet forwarding
- Network Access Translation (NAT)

## Simple Example to put it all together

### Installing
```shell
pip install edunet
```

### Usage
Going back to the Simple use case, as is you can simply do something like:

#### Starting service

```python
from edunet.core.applications.simple_http_application import SimpleHTTPApplication
from edunet.core.networking.handlers.simple_http_connection_handler import SimpleHTTPConnectionHandler
from edunet.core.networking.listeners.tcp_listener import TCPListener 

# create an HTTP application 
app = SimpleHTTPApplication()

# create a Connection Handler that takes an application
handler = SimpleHTTPConnectionHandler(app)

# A listener is needed to allow connections to come through
listener = TCPListener("127.0.0.1", 9999, handler)

listener.start()
```

#### Calling service
You can now communicate with it over HTTP by any means

### curl
```shell
curl -X GET http://127.0.0.1:9999
```

### python
```python
import urllib.request
response = urllib.request.urlopen("http://localhost:9999")
print(response.read().decode("utf-8"))
```
SQAM 0.1.0
Simplified Question Answering Machine
Requires Python: >=3.6

# **SQAM - Simplified Question Answering Machine**
Here is a [DEMO VERSION](https://www.cdsv.dev)

## Abstract

SQAM - Simplified Question Answering Machine is a light weight service for question answering using predefined question-answer pairs in a json database. The application tokenizes the user question using 384 dimensional SBERT embeddings and finds the first n-nearest predefined questions outputting the pair question-answer, the rank and the similarity score. This approach is very usefull for lightweight application where user will have limited number of domain specific questions. The example database consists of around 1000 predefined questions about Data Science, Machine Learning and Deep Learning. 

## Install

Clone the repo `git clone https://github.com/preslaff/SQAM.git` and install the dependencies with `pip install -r requirements.txt`

## How it works

SQAM uses the `app.py` flask application to embed, calculate the cosine similarity and then serve the closest to the user request, predefined question-answer pairs. When started, the app scans the directory where it resides for all `json` files and uses them as static database for question answering. Start the app with `python app.py` and then you can get answers with a front end of your choice or with curl ex.:

`curl -X POST http://127.0.0.1:5000/get_answers -H "Content-Type: application/json" -d '{"question": "What is Machine Learning?"}'`

## Offline use

You can use the app in offline mode (without Internet connection) if you save the embeddings:

`np.save('question_embeddings.npy', question_embeddings)`

and when the app is initialized and the model is cached load them with:

`question_embeddings = np.load('question_embeddings.npy')`

After this step, the app can operate in offline, standalone local mode.