semvec/pypi-packages huggingface dataset:
AmaProductReviews 0.1.2
Analyze Amazon product reviews
Requires Python: <4.0,>=3.8

# Project Description: Amazon Product Review Analysis
This Python script aims to analyze Amazon product reviews by extracting reviews from a specific product's URL and performing sentiment analysis on those reviews.

## Functions Overview:

1. **get_url_review_page(UrlProduct, i)**:
   - Constructs the URL for a specific page of product reviews.

2. **get_rating(UrlProduct)**:
   - Retrieves the overall rating of the product.

3. **get_product_name(UrlProduct)**:
   - Extracts the name of the product from its URL.

4. **get_global_rating(UrlProduct)**:
   - Fetches the global rating of the product.

5. **get_image_url(UrlProduct)**:
   - Obtains the URL of the product's image.

6. **extract_reviews(UrlProduct)**:
   - Extracts reviews from a specific page of product reviews.

7. **extract_all_reviews(UrlProduct)**:
   - Extracts all reviews from multiple pages of product reviews.

8. **clean_comment(comment)**:
   - Cleans the text of a comment by converting it to lowercase, removing non-alphabetic characters, digits, and stop words.

9. **sentiment_analysis_textblob(text)**:
   - Performs sentiment analysis using TextBlob and returns the sentiment label (Positive, Negative, or Neutral).

10. **comment_dataFrame(UrlProduct)**:
    - Constructs a DataFrame containing cleaned comments and their corresponding sentiments.

11. **sentiment_by_comment(df)**:
    - Calculates the count of comments by sentiment.

## Installation Requirements:

- Python 3.x
- Packages listed in the `requirements.txt` file. Install them using `pip install -r requirements.txt`.
- [en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl#sha256=86cc141f63942d4b2c5fcee06630fd6f904788d2f0ab005cce45aadb8fb73889](https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl#sha256=86cc141f63942d4b2c5fcee06630fd6f904788d2f0ab005cce45aadb8fb73889)
- beautifulsoup4
- selenium
- pandas
- nltk
- spacy
- textblob
- streamlit
- plotly
- requests
- wordcloud
- webdriver_manager

## Team Members:

- El Ouankrimi Ali
- Olamine Zakaria
- Oubella Abdallah
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lynxkite-client 5.4.1
Python API for LynxKite
Requires Python: >=3.6

# LynxKite Python API

This is the official Python client API for [LynxKite](https://lynxkite.com/) from Lynx Analytics.
See the LynxKite documentation for usage instructions.

- [LynxKite Python API tutorial](https://lynxkite.com/docs/tutorial-python/)
- [LynxKite Python API reference](https://lynxkite.com/docs/latest/python-api/)
nerdd-kafka 0.2.1
Run a NERDD module as a Kafka service

# NERDD Kafka

Run a [NERDD module](https://github.com/molinfo-vienna/nerdd-module) as a Kafka 
service that consumes input molecules and produces prediction tuples.


## Installation

```bash
pip install -U nerdd-kafka
```
  
## Usage

When a class inherits from ```nerdd_module.AbstractModel``` (see 
[NERDD Module Github page](https://github.com/molinfo-vienna/nerdd-module)), it can be 
used to create a Kafka service. 

```bash 
# run a Kafka service for NerddModel on localhost:9092
run_nerdd_server package.path.to.NerddModel

# modify broker url, input topic and batch size
run_nerdd_server package.path.to.NerddModel \
  --broker-url my-cluster-kafka-bootstrap.kafka:9092 \
  --input-topic examples \
  --batch-size 10

# more information via --help
run_nerdd_server --help
```

If the model class is called ```ExamplePredictionModel```, the server will read input 
tuples from the input topic ```example-prediction-inputs``` in batches of size 100
and write results to the ```results``` topic. The batch size specifies the number
of input tuples that are given to the model at once.
easy-code-to-text 0.1.6
A Python package to concatenate code files into a single text file.


# easy_code_to_text Project

A Python package to concatenate code files into a single document, with support for multiple programming languages and customizable ignore patterns.

## Features

- Support for multiple programming languages including Python, JavaScript, HTML, CSS, Java, and YAML.
- Customizable ignore patterns to exclude specific files or directories.
- Generates a single document with clear demarcation for each file's path and language.

## Installation

The package can be installed via pip:

```
pip install easy-code-to-text
```

## Configuration

### Ignore File

Create an ignore file (e.g., `.codeToTextIgnore`) in your project root with patterns to ignore, example:

```
# Your environment files
.venv

# Your private files
.env

# Python cache and logs files
__pycache__
*.log

# Your output and ignore file
my_project_output.txt
.codeToTextIgnore
```

## Usage

After installation, you can use the package as follows:

```py
from code_to_text.code_to_text import read_and_combine_files

read_and_combine_files(input_directory='your_code_directory',
                       output_file='your_output_file.txt',
                       ignore_file_path='your_ignore_file.txt')
```

## Contributing

We welcome contributions! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch for each feature or improvement.
3. Submit a pull request with a comprehensive description of changes.

## License

This project is open source and available under the MIT License. Benjamin QUINET.
ZexusDigital-Scraper 0.1
Instagram Scraping
Requires Python: >=3.6

# Instagram Scraper

This project contains a simple Python application that scrapes data from Instagram. This application retrieves posts of a specific user and displays basic information about those posts.

## Installation

To run the project, you need to have Python installed. Then, you can follow these steps to run the project:

1. Clone the repository:
```bash
git clone https://github.com/ZexusDigital/instagram-scraper.git
```

3. Navigate to the project directory:
```bash
cd instagram-scraper
```

5. Install the required dependencies:
```bash
pip install -r requirements.txt
```

6. Run the main script:
```bash
python main.py
```
dicio-py 2.5
Script para buscar o significado de palavras no dicionário online Dicio.
Requires Python: >=3.6

# dicio-py
Um dicionário com interface de terminal (Dessa vez feito em Python para ser multiplataforma)

> Este script faz um web scraping no site: https://www.dicio.com.br/ e formata o retorno em um agradável texto em terminal. 
>Ele é basicamente um remake do meu script: dicio-cli, que foi feito usando shell script. Gosto bastante dele, mas precisava de um dicionário cli para usar no windows, então criei esta versão usando python.

Palavras de "[ludovici philippus](https://github.com/ludovici-philippus/dicio-py)", feito a três meses atrás segundo Github. Fiz algumas modificações no código, aperfeiçoando-o.

## Instalação
A instalação desse script pode ser feita por meio dos seguintes comandos:

### [Linux ou Mac]
```bash
git clone https://github.com/Jetrom17/dicio-py && cd dicio-py && sudo mv dicio-py /usr/local/bin && cd .. && rm -rf dicio-py && sudo chmod +x /usr/local/bin/dicio-py && clear && echo 'Feito!'
```

### [Windows]

```py
pip install dicio-py
```

Depois disso basta partir para o uso!

## Uso
O uso é bastante simples, basta digitar: `dicio-py [PALAVRA]`, onde [PALAVRA] é a palavra que você quer pesquisar.


![](https://raw.githubusercontent.com/Jetrom17/dicio-py/master/dicio-py.png)
epub-simplifier 0.0.5
A command-line tool to simplify language in EPUB books
Requires Python: >=3.8
Keywords: epub, language, simplification, tool

# EPUB Simplifier

![Test](https://github.com/bubenkoff/epub-simplifier/actions/workflows/test.yml/badge.svg)
[![PyPI Version](https://img.shields.io/pypi/v/epub-simplifier.svg)
](https://pypi.python.org/pypi/epub-simplifier)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/epub-simplifier)
](https://pypi.python.org/pypi/epub-simplifier)
[![Coverage](https://img.shields.io/coveralls/bubenkoff/epub-simplifier/main.svg)
](https://coveralls.io/r/bubenkoff/epub-simplifier)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

## Description
EPUB Simplifier is a command-line tool designed to convert text within EPUB files into a simplified language at a selected proficiency level. This tool aims to make literature more accessible to language learners by simplifying complex language structures and vocabulary.

> [!WARNING]
> It uses OpenAI API and requires an API key to work, which is not provided with the package. It can also be quite expensive to use, depending on the size of the book.

## Motivation
The idea for this tool was inspired by the need to make literature more accessible to language learners. Many language learners struggle to read books in their target language due to the complexity of the language used in literature. This tool addresses this issue by simplifying the language used in books to make them more accessible to language learners at different proficiency levels.

In particular, for the Dutch language, there is a lack of literature in accessible language in electronic format. Having the text on an e-book allows using built-in dictionaries and other tools to help comprehensively understand the text.

## Installation
To install EPUB Simplifier, follow these steps:

``
pip install epub-simplifier
``

## Usage
After installation, you can use the EPUB Simplifier tool directly from your command line. The basic command structure is as follows:

``
export OPENAI_API_KEY=your_api_key
``

Optionally, set the organization ID if you have one:

``
export OPENAI_ORG_ID=your_org_id
``

Run the command:

``
epub-simplify --help
``

## Example Command

``
epub-simplify original_book.epub simplified_book.epub Dutch B1
``

This command will read `original_book.epub`, simplify its contents, and save the result as `simplified_book.epub`.

## Requirements
The dependencies will be installed automatically during the package installation process.

## Feedback and Contributions
Your feedback and contributions are welcome! If you encounter any issues or have suggestions for improvements, please feel free to open an issue on the GitHub repository or submit a pull request with your changes.

## License
MIT