semvec/pypi-packages huggingface dataset:
reddit-gen 0.1.4
Generate Reddit Accounts Automatically
License: MIT
Requires Python: >=3.7,<4.0

# Reddit Account Generator


## Requirements
- [Google Chrome](https://www.google.com/chrome/)
- [Chrome Driver](https://chromedriver.chromium.org/downloads)
- *Optional, but recommended:* [MongoDB](https://www.mongodb.com/) (get a free database [here](https://www.mongodb.com/cloud/atlas/register)) 

## Getting started

### Step 1: Install the package
```sh
pip install -U reddit-gen
```

### Step 2: Configure your environment
```sh
reddit-gen --configure
```

## Usage

```
usage: reddit-gen [-h] [-d] [-s] [-i] [-j] [-p] [-n CREATE_N_ACCOUNTS]
                  [-c CONFIG_FILE] [-D] [-U] [--configure]
                  [--experimental-use-vpn]
                  [--check-subreddit-ban CHECK_SUBREDDIT_BAN] [-v]

options:
  -h, --help            show this help message and exit
  -d, --disable-headless
                        Disable headless mode
  -s, --solve-manually  Solve the captcha manually
  -i, --ip-rotated      The public IP address was changed by the user since
                        the last created account (to bypass the cooldown)
  -j, --use-json        Read from the local JSON database (pass if you're not
                        using MongoDB). A new local database will be created
                        if not found
  -p, --show-local-database-path
                        Prints the path to the local database, if exists
  -n CREATE_N_ACCOUNTS, --create-n-accounts CREATE_N_ACCOUNTS
                        Number of accounts to create (default: 1)
  -c CONFIG_FILE, --config-file CONFIG_FILE
                        Path to the config file. Defaults to
                        /Users/$USER/.redditgen.env
  -D, --debug           Debug mode
  -U, --update-database
                        Update accounts metadata (MongoDB-only)
  --configure           Configure your environment
  --experimental-use-vpn
                        Experimental feature (unstable)
  --check-subreddit-ban CHECK_SUBREDDIT_BAN
                        Check if your accounts are banned from a specific
                        subreddit (MongoDB-only)
  -v, --verbose         Print more logs
```

## Example

```sh
$ reddit-gen
# ───────────────────────────────── Starting... ─────────────────────────────────
# 2022-06-20 17:22:42.739 | INFO     | reddit_gen.generator:_signup_info:65 - Your account\'s email address: some_random_username@example.com
# 2022-06-20 17:22:42.739 | INFO     | reddit_gen.generator:_signup_info:67 - Username: some_random_username
# 2022-06-20 17:22:45.976 | DEBUG    | reddit_gen.generator:generate:196 - Solving captcha...
# 2022-06-20 17:24:12.579 | DEBUG    | reddit_gen.generator:generate:200 - Solved!
# 2022-06-20 17:24:38.841 | DEBUG    | reddit_gen.generator:generate:263 - Checking account info...
# 2022-06-20 17:24:39.069 | DEBUG    | reddit_gen.generator:generate:266 - Passed!
# 2022-06-20 17:24:39.069 | INFO     | reddit_gen.generato──r:generate:274 - Account verified!
# ───────────────────────────────────── Done! ───────────────────────────────────
```
zenoml-text-classification 0.0.2
Text Classification for Zeno
License: MIT
Requires Python: >=3.8,<3.11
Keywords: ml,testing

# Zeno View for Text Classification

## Development

First, build the frontend:

```bash
cd frontend
npm install
npm run build
```

## Install

Install the package from PyPI:

```
pip install zenoml_text_classification
```

And pass as the `task` argument to a Zeno `.toml` file.

### [Follow the documentation to get started](https://dig.cmu.edu/zeno/intro.html)
zenoml-image-classification 0.0.3
Image Classification for Zeno
License: MIT
Requires Python: >=3.8,<3.11
Keywords: ml,testing

# Zeno View for Image Classification

## Install

Install the package from PyPI:

```
pip install zenoml_image_classification
```

And pass as the `task` argument to a Zeno `.toml` file.

### [Follow the documentation to get started](https://dig.cmu.edu/zeno/intro.html)
pylems-codext 0.0.2
Python code traversal to XML
License: MIT
Keywords: pylems

### PyLEMS extension for code traversal 

This package is created for code traversal and model's equations and parameters storage. The idea behind it is to 
automatically traverse Python code and find the models and its parameters. Then, depending on the found model, 
alter its default values with the newly-found parameters. Next, create a PyLEMS model supplementing the new values. 
Finally, create the XML file(s) in the specified folder.


### Walk-through

Here is an example taken from `examples/50healthy_code.py`:
```
oscilator = models.ReducedSetHindmarshRose(r=[0.006], a=[1.0], b=[3.0], c=[1.0], d=[5.0], s=[4.0], xo=[-1.6], K11=[0.5],
                                           K12=[0.1], K21=[0.15], sigma=[0.3], mu=[2.2],
                                           variables_of_interest=["xi", "alpha"])
```




# Change Log
---

0.0.1 (02/09/2022)
---
First release that supports Python code traversal and HindmarshRose model.

### Added
- Code traversal excluding comments, imports, and empty lines
- Pattern matching for finding models using Regex
- Model's default parameter alteration with parameters found using Regex
- PyLEMS model creation
- XML storage in a specified folder
redis-client 1.0.1
A simple client for Redis
License: MIT
Requires Python: >=3.10,<4.0

# Redis client

Simple Redis client for python.

It has been made to simplify working with Redis in python.

## Usage
Instantiate `Cache()`, give the Redis `host`, `port` and `db`.

Then you can get a cached entry with `Cache.get_data_from_cache()` and add an entry to Redis with `Cache.save_data_to_cache()`

**⚠️The data send to cache NEEDS TO BE A DICTIONARY! ⚠️**

### Code example

```python
from redis_client.client import Cache
from time import sleep
from tpying import Dict

# Redis Configuration
cache = Cache(redis_host="localhost", redis_port=6379, redis_db=0, log_level="INFO")

def username_expander(username: str) -> Dict[str, str]:
    """Example of a function that require caching."""
    
    # Key that will be use to retrieve cached data
    # Note that I include the parameter 'username' in the key to make sure we only cache unique value.
    key = f"username_expander:{username}"
    
    # Check if the data is already caches
    cached_data = cache.get_data_from_cache(key)
    
    # Return it if yes
    if cached_data:
        return cached_data
    
    data = {"expanded_username": f"{username}_123"}
    
    # Save data to cache with an expiration time of 12 hours
    cache.save_data_to_cache(key, data, expiration_in_hours=12)
    
    return data
```