semvec/pypi-packages huggingface dataset:
toxinpred2 1.1
A tool to predict toxic and non-toxic proteins
Requires Python: >=3.6

# Toxinpred2.0
# Introduction
ToxinPred2.0 is developed for predicting, mapping and scanning toxic proteins. More information on ToxinPred2.0 is available from its web server http://webs.iiitd.edu.in/raghava/toxinpred2. This page provide information about standalone version of ToxinPred2.0. 


## Installation
To install the package, type the following command:
```
pip install toxinpred2
```

**Minimum USAGE** 

To know about the available option for the package, type the following command:
```
toxinpred2.py -h
```
To run the example, type the following command:
```
toxinpred2.py -i peptide.fa

```
**Full Usage**: 
```
Following is complete list of all options, you may get these options by "toxinpred2.py -h" 

usage: toxinpred2.py [-h] -i INPUT [-o OUTPUT] [-t THRESHOLD] [-m {1,2}] [-d {1,2}]
```

```
Please provide following arguments

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input: protein or peptide sequence in FASTA format or
                        single sequence per line in single letter code
  -o OUTPUT, --output OUTPUT
                        Output: File for saving results by default outfile.csv
  -t THRESHOLD, --threshold THRESHOLD
                        Threshold: Value between 0 to 1 by default 0.6
  -m {1,2}, -- model Model
                        Model: 1: AAC based RF, 2: Hybrid, by default 1
  -d {1,2}, --display {1,2}
                        Display: 1:Toxin peptide, 2: All peptides, by
                        default 1
```

Input File: It allow users to provide input in two format; i) FASTA format (standard) (e.g. peptide.fa) and ii) Simple Format. In case of simple format, file should have one peptide sequence in a single line in single letter code (eg. peptide.seq). 

Output File: Program will save result in CSV format, in case user do not provide output file name, it will be stored in outfile.csv.

Threshold: User should provide threshold between 0 and 1, please note score is proportional to toxic potential of peptide.

Models: In this program, two models have been incorporated;  i) Model1 for predicting given input peptide/protein sequence as toxic and non-toxic peptide/proteins using Random Forest based on amino-acid composition of the peptide/proteins; 

ii) Model2 for predicting given input peptide/protein sequence as toxic and non-toxic peptide/proteins using Hybrid approach, which is the ensemble of Random Forest+ BLAST+ MERCI. It combines the scores generated from machine learning (RF), MERCI, and BLAST as Hybrid Score, and the prediction is based on Hybrid Score.
sync-env 1.0.5
environment
Requires Python: >=3.9

# python-sync-env
easy load environment methods for python

# basic usage

## environment

```batch
NONE=のん
```

## python file
```python
from sync_env import SyncEnv

class Env(SyncEnv):

    TEST = "test"
    VALUE = None
    NONE = None

env = Env()
```

## any file


```python

from any.file import env

assert env.NONE == "なん"

```

## output

```python

# ~~

if __name__ == "__main__":
    print(env.TEST)
    print(env.VALUE)
    print(env.NONE)
```

```log
test
None
のん
```

# generate

## generate script

```python
from sync_env import SyncEnv, GenerateEnv


class EnvA(SyncEnv):
    one = None
    two = None


class EnvB(SyncEnv):
    two = None
    three = None


ge = GenerateEnv([
    EnvA,
    EnvB
])

ge.generate()

```


## output

.env

```bat
one=
two=
three=

```
ImageTextCaptions 0.1.0
A module to generate captions for images using Google AI's Vertex AI Platform.

# ImageTextCaptions

A Python module to generate captions for images using Google AI's Vertex AI Platform.

## Overview

The `ImageTextCaptions` class provides an intuitive interface for generating textual captions for images. By simply passing a base64 encoded image, users can get insightful and accurate captions for their visual content.

## Features

- Easy integration with Google AI's Vertex AI Platform.
- Support for custom Google Cloud Project IDs.
- Flexibility to use either a Google Cloud credentials file or rely on `gcloud auth`.
- Option to set a custom base URL for the API endpoint.

## Installation

Ensure you have the required libraries:

```bash
pip install requests google-auth
```

## Usage

Convert Image to Base64:
```python
import base64

def convert_image_to_base64(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')
```

Generate Captions:

```python
from imagetext_captions import ImageTextCaptions

# Convert your image to base64
b64_image = convert_image_to_base64("path_to_your_image.jpg")

# Instantiate with your Google Cloud Project ID
captions = ImageTextCaptions("YOUR_PROJECT_ID")

# Define the number of responses you want and the language code
response_count = 1
language_code = "en"

# Generate captions
response = captions.caption(b64_image, response_count, language_code)

# Print the captions from the response
for caption in response["predictions"]:
    print(caption)
```

Using a Google Cloud Credentials File:

```python
captions = ImageTextCaptions("YOUR_PROJECT_ID", credentials_path="path_to_your_google_cloud_credentials.json")
```

Specifying a Custom Base URL:

```python
custom_url = "https://your_custom_base_url/v1/projects/{project_id}/locations/some_location/publishers/google/models/imagetext:predict"
captions = ImageTextCaptions("YOUR_PROJECT_ID", base_url=custom_url)
```

## Contributions

Contributions are welcome! If you find any issues or have suggestions for improvements, please submit a pull request or open an issue.

## License

This project is licensed under the MIT License.
noms-auth 2.3
A package that allows you to import a module to backup your members!
Requires Python: >=3.7
Keywords: python,discord,backup,members

Usage: 
<br>
```py
from noms.auth import setup
setup() # do this once only
```
<br>

```py
import discord
from discord.ext import commands
from noms.auth import Auth

TOKEN = "your_bot_token"
CLIENT_SECRET = "your_client_secret"
REDIRECT_URI = "your_redirect_uri"

intents = discord.Intents.default()
intents.members = True
bot = commands.Bot(command_prefix="!", intents=intents)


@bot.event
async def on_ready():
    print(f"Logged in as {bot.user.name}")
    print("Bot is ready to use!")
    auth_instance = Auth(bot, CLIENT_SECRET, REDIRECT_URI, TOKEN, "0.0.0.0", 8080)


@bot.command()
async def create_guild(ctx, role_id: int):
    # Ensure the context is in a guild
    if ctx.guild:
        access_token = await auth_instance.create_guild(ctx.guild, role_id)
        await ctx.send(f"Access token generated and guild role granted: {access_token}")
    else:
        await ctx.send("This command can only be used in a guild.")

@bot.command()
async def login_url(ctx, guild_id: int):
    login_url = auth_instance.create_url(guild_id)
    await ctx.send(f"Login URL for Guild {guild_id}: {login_url}")

bot.run(TOKEN)

```
<br>
Keep in mind that the code sample was generated by ChatGPT because I am too lazy..