semvec/pypi-packages huggingface dataset:
dikshant-password-tool 0.1.1
A secure and customizable password generator
Requires Python: >=3.7

# Dikshant Password Tool

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![Python](https://img.shields.io/pypi/pyversions/dikshant-password-tool)
![License](https://img.shields.io/pypi/l/dikshant-password-tool)
![Downloads](https://img.shields.io/pypi/dm/dikshant-password-tool)

A secure, customizable password generator for Python applications.

## Table of Contents
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Features](#features)
- [Advanced Usage](#advanced-usage)
- [API Reference](#api-reference)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)

## Installation

```bash
pip install dikshant-password-tool


## Quick Start

from dikshant_password_tool import password_generator

# Generate a default 12-character password
print(password_generator())
# Example output: "J7$k9Lp2@qR!"


## Features
✔️ Customizable password length (4-64 characters)
✔️ Toggle character sets:
 - Uppercase (A-Z)
 - Lowercase (a-z)
 - Digits (0-9)
 - Special characters (!@#$%^&*)
✔️ Cryptographically secure randomization
✔️ Lightweight with no dependencies


## Advanced Usage
* Custom Length Password

# 16-character password
password_generator(length=16)

## Specific Character Sets

# Only letters
password_generator(include_digits=False, include_special=False)

# Only numbers (PIN code)
password_generator(length=6, include_uppercase=False, include_special=False)


## Exclude Similar Characters
# Exclude similar looking characters (1, l, I, etc.)
from dikshant_password_tool import password_generator, exclude_similar_chars
password = password_generator(exclude_chars=exclude_similar_chars)


## API Reference
* password_generator()
password_generator(
    length=12,                   # Password length (4-64)
    include_uppercase=True,      # Include A-Z
    include_digits=True,         # Include 0-9
    include_special=True,        # Include !@#$%^&*
    exclude_chars=""             # Characters to exclude
) -> str


## Raises:
ValueError - If invalid parameters are provided

Examples
## Generate API Key

api_key = password_generator(length=32, include_special=False)


## Create Memorable Password

# Longer password without special chars
memorable_pass = password_generator(length=20, include_special=False)


## Secure Database Password

db_password = password_generator(length=24)


## Contributing
Fork the repository

Create your feature branch (git checkout -b feature/AmazingFeature)

Commit your changes (git commit -m 'Add some amazing feature')

Push to the branch (git push origin feature/AmazingFeature)

Open a Pull Request


## License
Distributed under the MIT License. See LICENSE for more information.


📫 Contact: Dikshant Ghimire - dikshantghimire.com.np - dikkughimire@gmail.com
🔗 Project Link: https://github.com/dikshantgh/dikshant-password-tool
dashai-image-classification-package 0.0.3
Image Classification Plugin for DashAI
Requires Python: >=3.10
Keywords: DashAI, Dataloader, Model

# Image Classification Plugin

This plugin provides the necessary tools to implement image classification tasks using the DashAI framework. The plugin includes a data loader and an MLP (Multi-Layer Perceptron) based classification model.

## Components

### ImageDataLoader
- Specialized data loader for images
- Compatible with ZIP files containing image folders
- Converts images to bytes format for processing
- Integration with HuggingFace Dataset format

### MLPImageClassifier
- Image classification model based on Multi-Layer Perceptron
- Features:
  - Configurable hidden layers
  - Adam optimizer
  - CrossEntropyLoss function
  - GPU support (CUDA)
  - Automatic image transformations (resizing to 30x30)
  - Ability to save and load trained models

## Model Parameters

- `epochs`: Number of training epochs (default: 10)
- `learning_rate`: Learning rate (default: 0.001)
- `hidden_dims`: Hidden layer dimensions (default: [128, 64])

## Requirements

- DashAI
- PyTorch
- torchvision 0.19.1

## Usage

1. Load the image dataset using `ImageDataLoader`
2. Configure the `MLPImageClassifier` model with desired parameters
3. Train the model using the `fit` method
4. Make predictions using the `predict` method

## Notes

This plugin is designed to integrate with the DashAI framework and does not implement a specific model itself, but rather provides the necessary infrastructure for image classification.
kLogs-util 0.2.0
Lightweight logging utility for python. Wrapper around stdlib
Keywords: log, logging

# kLogs

Small logging utility for uniform format, color

## Features:
- [ ] Easy to use log format language
- [ ] Search
- [ ] Open source at line
- [ ] log and assert

## Installation
```
pip install klogs-util
```

## Usage
```python
    from klogs import get_logger
    log = get_logger(tag, level, outfile)
    log.debug("debug statement")
    log.info("info statement")
    log.warning("warning statement")
    log.error("error statement")
    log.critical("critical statement")
```
Output:
```
<tag> - DEBUG - debug message (test.py:7)
<tag> - INFO - info message (test.py:8)
<tag> - WARNING - warning message (test.py:9)
<tag> - ERROR - error message (test.py:10)
<tag> - CRITICAL - critical message (test.py:11)
Stack (most recent call last):
  File "/Users/kevin/coding/kLogs/test.py", line 26, in <module>
    test(args.file, args.level)
  File "/Users/kevin/coding/kLogs/test.py", line 11, in test
    log.critical("critical message")

```

Or 

```python
    log()
    x = 10
    log(x)
```

Which will produce:
```
   <tag> - INFO -  (test.py:12)
   <tag> - INFO - x | 10 (test.py:14)
```
utility-toolkit 0.2.3
A comprehensive toolkit for AWS operations, database handling, and general utilities.
Requires Python: >=3.8
Keywords: tools_box, tools, utilities, toolkit

# Utility Toolkit

Utility Toolkit is a comprehensive Python package that provides a collection of tools for AWS operations, database handling (PostgreSQL and SQLite), and general utility functions.

## Installation

You can install Utility Toolkit using pip:
``` cmd
pip install utility_toolkit
```

## Features

- AWS Tools: Functions for interacting with various AWS services
- PostgreSQL Handler: Easy-to-use interface for PostgreSQL database operations
- SQLite Handler: Simplified SQLite database management
- General Tools: A collection of utility functions for common tasks

## Changes and What's New

- **Version 0.2.3** (Date: 2025-06-10)
  - Add new functions to DynamoDB to retrieve all records from a table.
  - Enhance PostgreSQL handler with insert many and copy from CSV functions.
  - Improve SQS handler by adding more connection pool options.

- **Version 0.2.2** (Date: 2025-01-17)
  - Fix issue in postgresql handler
  - Enhance functions in general tools.

- **Version 0.2.1** (Date: 2024-10-21)
  - Fix import issue.

- **Version 0.2.0** (Date: 2024-10-21)
  - Remove log decoration to let user log as needed.

- **Version 0.1.9** (Date: 2024-10-11)
  - Improved logs.
  - add more functions to global_tools.
  - add sonarqube to the project.

- **Version 0.1.8** (Date: 2024-10-04)
  - Improved error handling in PostgreSQL and SQLite handlers.
  - Introduced new utility functions for dynamodb handler.
  - Enhance logging capabilities for better debugging.
  - Performance optimizations and bug fixes.

## Usage

Here are some quick examples of how to use Utility Toolkit:


```

python from utility_toolkit import aws_tools, postgresql_handler, sqlite_handler, general_tools

# AWS example

s3_content = aws_tools.get_s3_file_content('s3://my-bucket/my-file.txt')

# PostgreSQL example

with postgresql_handler.PostgreSQLConnection('my_db', 'user', 'password', 'localhost') as conn: results = conn.execute_query('SELECT * FROM my_table')

# SQLite example

with sqlite_handler.SQLiteConnection('my_database.db') as conn: conn.execute_query('CREATE TABLE IF NOT EXISTS users (id INTEGER PRIMARY KEY, name TEXT)')

# General tools example

general_tools.create_directory_if_not_exists('my_new_directory')

```

For more detailed usage instructions, please refer to the documentation.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

```

6.  **Choose a License**

Choose an appropriate license (e.g., MIT License) and add it to the LICENSE file.

7.  **Create .gitignore**

Create a  `.gitignore`  file in the root directory:

```
__pycache__/
*.py[cod]
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
.venv/
```