semvec/pypi-packages huggingface dataset:
orionbot 1.2.1
orionbot - a simple chatbot training framework

# OrionBot: A Python bot training framework
OrionBot is a Python library that allows you to easily train and run chatbots, fully customizable.

## Installation
To install OrionBot, simply use pip:
```bash
pip install orionbot
```

## Usage

To use OrionBot, simply import the `Chatbot` class and create a new instance with a name:
```python
from orionbot import Chatbot
bot = Chatbot("My Bot")
```

### Training
You can train your bot by calling the `train` method with an input text and a response text:

```python
bot.train("Hi", "Hello there!")
```

You can also train your bot from a file:
```python
bot.train_from_file("training_data.json")
```

Or, you can train your bot from the default file:
```python
bot.train_from_default_file()
```

### Getting Responses
To get a response from your bot, simply call the `get_response` method with an input text:
```python
response = bot.get_response("Hi")
print(response)  # "Hello there!"
```

If your bot does not understand the input text, it will return a default response. You can set this response by calling the `set_no_response_message` method:

```python
bot.set_no_response_message("I'm sorry, I didn't understand that.")
```

### Training Toggle

You can enable or disable training by calling the `enable_training` or `disable_training` methods:

```python
bot.enable_training()
bot.disable_training()
```

## Examples

Here are some examples of how to use OrionBot:

### Example 1: Simple greeting bot
```python
from orionbot import Chatbot

bot = Chatbot("Greeting Bot")

bot.train("Hi", "Hello there!")
bot.train("Hello", "Hi there!")
bot.train("Hey", "Hiya!")

print(bot.get_response("Hi"))  # "Hello there!"
print(bot.get_response("Hey"))  # "Hiya!"
print(bot.get_response("What's up?"))  # "I'm sorry, I didn't understand that."
```
### Example 2: Movie recommendation bot
```python
from orionbot import Chatbot

bot = Chatbot("Movie Bot")

bot.train("What's a good movie to watch?", "I recommend watching The Shawshank Redemption.")
bot.train("What's your favorite movie?", "My favorite movie is The Godfather.")
bot.train("Have you seen Inception?", "Yes, Inception is a great movie!")

print(bot.get_response("What's a good movie to watch?"))  # "I recommend watching The Shawshank Redemption."
print(bot.get_response("What's your favorite movie?"))  # "My favorite movie is The Godfather."
print(bot.get_response("Have you seen The Matrix?"))  # "I'm sorry, I didn't understand that."
```

You can find more information on our [GitHub repository](https://github.com/LyubomirT/OrionBot)

## Table of Contents
 
 - [Overview](README.md)
 - [Training Guide](TRAINING.md)
 - [The Chatbot class](CHATBOTCLASS.md)
 - [Preprocessing Method](PREPROCESSING.md)
 - [Training Method](TRAINMETHOD.md)
 - [Train from an existing dataset](DATASETTRAINING.md)
 - [Training Mode](TRAININGMODE.md)
 - ["No Response Message"](NORESPONSE.md)
aenigma 0.1.1
The Aenigma library calculates the Relative Complexity Score (RCS) of each sentence in a given body of text
Requires Python: >=3.10

# Aenigma

The Aenigma library calculates the Relative Complexity Score (RCS) of each sentence in a given body of text. It achieves this by calculating the Flesch-Kincaid score of every individual sentence within the body of text, then comparing them against the mean score.
augrs 1.0.0
An automate metasploit script generator
Requires Python: >=3.10

# Auto Generate Metasploit Resouce Script

This Program run an exploit with automate search for open port using only excellent rank in Metasploit Module.
Generate shell / bash scripts to use in security automate test

## Getting Started
### Prerequisites
~~~
- Python >= 3.10
- python3-nmap
- pymetasploit3
- pyfiglet
~~~

### Installing

~~~
pip install augrs 
~~~

 #### Nmap Installing
 
 This project use Nmap to search for an open port you must install Nmap in your local to use this function
 [Download the Free Nmap Security Scanner for Linux/Mac/Windows](https://nmap.org/download.html)

#### MSFRPC Installing

This project use MSFRPC API and msfconsole you must install and start RPC server before running the program
In case of you don't have MSFRPC or msfconsole instance
We provide the docker image that contain both you may follow the step below
1. Install docker desktop  [Get Docker](https://docs.docker.com/get-docker/)
2. Pull docker image for running MSFRPC and msfconsole
~~~
docker pull wisarud2k/ubuntu-metasploit
~~~
3. Start docker image and mapping port using port 55553 and 443
~~~
docker run -it -p 55553:55553 -p 443:443 --name={your own container name} wisarud2k/ubuntu-metasploit:latest
~~~
4. Run program with MSFRPC password "test"
~~~
augrs
~~~
After first docker run you can start your own docker container with
~~~
docker start -i {your own container name}
~~~
## Usage
Start the program in console with command
~~~
augrs
~~~
### Run exploit in program:
1. Use Nmap module
2. Go to Exploit module
3. Use Search for exploit
4. Run all exploit and wait for report
5. The report will create in exploit_report folder

### create shell or bash script for running exploit
1. Use Nmap module
2. Go to Exploit module
3. Use Generate resource script
4. select the environment to create script (linux or window)
5. name the script and enter 
6. The script will create in rc folder

### Options manage
1. Use Options module
2. select options that want to edit
3. edit you options

## License

This project is licensed under the MIT License - see the LICENSE.md file for details.
csv-sqlite-converter 0.0.1
A convenient tool for transferring files between SQLite database and csv files and vice versa.
Requires Python: >=3.10
Keywords: csv,sqlite,converter

# CSV-SQLite Converter
The CSV-SQLite Convert allows to store data both from SQLite database in csv files and from csv files in a SQLite database.

## Installation
You can install CSV-SQLite Converter from PyPi (in the future).
    
    python -m pip install csv_sqlite_converter

The converter supports Python 3.10 and higher.

## How to use
The converter is a command line tool. To use it, run:

    python -m csv_sqlite_converter

And the following should appear:
    
    >>> Choose one of the following:
            "db" to transfer data from a database to csv files
            "csv" to transfer data from csv files to a database
            "stop" to stop
        > 

where:

### "db" 
allows to transfer data from a database to csv files. **It needs two paths**: to the database and folder where csv files can be stored. After choosing this option the following appears:
    
    >>> Copy path to database: <provide/here/path/to/database.db>
    >>> Copy path to directory where folder with csv files will be stored: <provide/path/to/a/folder>
    >>> Data from [name_of_table] table are now stored in [name_of_table].csv.
    >>> Data from [name_of_another_table] table are now stored in [name_of_another_table].csv.
    >>> ...

### "csv"
allows to transfer data from csv files to a database. **It needs two paths**: to csv files with data and the database to write to. However, csv files **must follow a very precise structure**:
```
.
+-- csv1.csv
+-- csv2.csv
+-- csv3.csv
```
where: 
1. the name of the root folder matches the name of the database file
2. names of csv files match names of tables in the database files (ex. students.csv holds data for a table named students)

After choosing this option the following appears:

    >>> Copy path to database: <provide/here/path/to/database.db>
    >>> Copy path to directory with csv files: <provide/path/to/a/folder/with/csv/files>
    >>> File [name_of_csv_file] processed successfully.
    >>> File [name_of_another_csv_file] processed successfully.
    >>> ...   

### "stop"
stops the execution of the package.
GoldStar 1.0.1
API for website.goldstar.cf
Requires Python: >=3.6

# GoldStar


# API DOCS
Link to api docs: [API DOCS](https://docs.goldstar.cf)

## Installation
```
pip install goldstar
```
## example 
Server Count Post :
```python
from goldstar import goldstar
from discord.ext import commands

client = commands.Bot(command_prefix="!") 
dbl = goldstar(client,"token of goldstar")

@client.event
async def on_ready():
  x = await dbl.serverCountPost()
  print(x)

client.run("token")
```

Search bot: 
```python
from goldstar import goldstar

client = commands.Bot(command_prefix="!") 
dbl = goldstar(client,"token of goldstar")
id=botid

a = dbl.search(id)
print(a)

```
All functions in api:
```angular2html
1: serverCountPost()
2: search()
3: hasVoted()
```