semvec/pypi-packages huggingface dataset:
NepalStockMonitor 0.7.2
Gets the market details of the provided company. Only for Nepal's stock market
License: MIT
Requires Python: >=3

# Nepal Stock Monitor

A python script that gets the stock information as per the company symbol
provided by the user from [Mero Lagani](https://merolagani.com/LatestMarket.aspx). This
script supports for the stock information of Nepal only.

<img src="https://github.com/ghanteyyy/NepalStockMonitor/raw/main/NepalStockMonitor/included_files/1.png">

# Installation

NepalStockMonitor is available on PyPI. You can install it through pip:

`pip install NepalStockMonitor`

# Usage

- If you want GUI window then Run **NepalStockMonitor.py** directly

- To get data only

  ```python
  from NepalStockMonitor import monitor

  data = monitor('Company Symbol', show_gui=False)  # Returns stock information of the given company symbol
  print(data.details)  # Printing the stock information from above returned data
  ```
ftr-classifier2 2.2
Classifies English, Dutch and German sentences in terms of how they refer to the future.
License: MIT
Requires Python: >3.6.*


        This is a fork of https://pypi.org/project/ftr-classifier/ which has been updated to work with newer python versions up to 3.10
        A funktion was added, that can classify single strings(classify_str())
        This is an implementation of the key-word analysis techniques described in Robertson et al. (TKTK). 
        
        It is designed to classify future-time-referring sentences in English, Dutch, and German in terms of whether 
        they use the accepted "future" tense morphemes (i.e. will/shall/be going to (English); zullen/gaan (Dutch); or werden (German)),
        use the present tense to refer to future time, or use an expression which specifically marks the probability of the utterance, 
        i.e. "It could rain tomorrow", or "It will probably rain tomorrow." This classifier is without doubt overfit to the data it is designed to be used on,
        and will not perform well out-of-sample. For instance, it assumes all sentences it encounters refer to future time, and that most make predictions,
        (as ooposed to talk about schedules, or intentions). It is designed to be used exclusively with data generated using
        the  Future Time Reference questionnaire described in Robertson et al (TKTK), and available for free to academic researchers,
        downloadable link to come.
        
        Any interested researchers should feel free to contact myself (Cole Robertson) at cbjrobertson@gmail.com, or open an issue for discussion
        on the project git repository.
yescaptcha 0.1.0
YesCaptcha SDK for Python
License: MIT
Requires Python: >=3.5.0

# yescaptcha-python

This is SDK for YesCaptcha.

## Installation

```
pip3 install yescaptcha
```

## Usage

Below are some simple exmaples for Recaptcha, Image2Text:

### NoCaptchaTaskProxyless

```python
from yescaptcha.task import NoCaptchaTaskProxyless
from yescaptcha.client import Client

CLIENT_KEY = <YOUR_CLIENT_KEY>
website_url = 'https://www.google.com/recaptcha/api2/demo'
website_key = '6Le-wvkSAAAAAPBMRTvw0Q4Muexq9bi0DJwx_mJ-'

client = Client(client_key=CLIENT_KEY, debug=True)
task  = NoCaptchaTaskProxyless(website_key=website_key, website_url=website_url)
job = client.create_task(task)
print('result', job.get_solution())
```

### ImageToText

```python
from utils import resize_base64_image_from_file # see examples/utils.py
from yescaptcha.task import ImageToTextTask
from yescaptcha.client import Client

CLIENT_KEY =  <YOUR_CLIENT_KEY>
IMAGE_PATH = <IMAGE_PATH>

client = Client(client_key=CLIENT_KEY)
task  = ImageToTextTask(resize_base64_image_from_file(IMAGE_PATH))
job = client.create_task(task)
print('result', job.get_solution_text())
```

Please check [http://yescaptcha.readthedocs.org](http://yescaptcha.readthedocs.org) to get more details.