semvec/pypi-packages huggingface dataset:
UzbekTagger 1.1
Part of Speech Tagger for Uzbek Language.
Requires Python: >=3.7

# UzbekTagger

**UzbekTagger** is a part-of-speech (POS) tagging library for text in the Uzbek language. This library allows you to automatically assign grammatical categories (such as noun, verb, adjective, etc.) to each word in a given text.

### Article: [UzbekTagger: The rule-based POS tagger for Uzbek language](https://www.researchgate.net/publication/370418960_UzbekTagger_The_rule-based_POS_tagger_for_Uzbek_language)
### GitHub link:  https://github.com/MaksudSharipov/UzbekTagger

##  Features

- Tokenizes and tags Uzbek sentences with grammatical categories
- Supports basic POS tagging (NOUN, VERB, ADJ, PRON, etc.)
- Simple and lightweight interface

##  Installation:

You can install the library using pip:

```python
pip install UzbekTagger
```

## Example:
```python

from UzbekTagger import pos

print(pos.tagger("Men bugun uyga ertaroq ketdim. Boshqa bolalar bugun maktabdan kechroq kelar ekan."))

```

## Result:

```python 
 Men/PRON bugun/ADV uyga/NOUN ertaroq/ADV ketdim/VERB ./PUNCT

 Boshqa/ADV bolalar/NOUN bugun/ADV maktabdan/NOUN kechroq/VERB kelar ekan/VERB ./PUNCT
```
maybankpdf2json 0.1.52
A package for extracting JSON data from Maybank PDF account statements
Requires Python: >=3.6
Keywords: maybank, pdf, JSON, account statements, Email delivery statement, Malayan Banking

# Maybank PDF Account Statement to JSON

This package provides functionality to extract and process data from Maybank account statement PDFs. It allows users to read PDF files and extract json data from them.

## Installation

To install the package, clone the repository and run the following command:

```
pip install maybankpdf2json
```

## Usage

Here is a simple example of how to use the package to extract data from a Maybank PDF statement:

```python
import os
from maybankpdf2json import MaybankPdf2Json

# Path to your PDF file and its password
example_path = os.path.join(os.path.dirname(__file__), "test.pdf")
example_password = "12345"  # Replace with your actual PDF password

with open(example_path, "rb") as f:
    extractor = MaybankPdf2Json(f, example_password)
    data = extractor.json()
    print(data)

    # Output example:
    # [
    #   {
    #     "date": "01/01/2024",
    #     "desc": "Deposit from client",
    #     "trans": 50.0,
    #     "bal": 1050.0
    #   },
    #   {
    #     "date": "02/01/2024",
    #     "desc": "Purchase - Office Supplies",
    #     "trans": -20.0,
    #     "bal": 1030.0
    #   }
    # ]
```

## Testing

To run the tests, navigate to the project directory and execute:

```
pytest tests/
```

## Makefile Usage

This project includes a `Makefile` with helpful commands:

- To run tests:
  ```sh
  make test
  ```
- To build and release the package (requires proper credentials):
  ```sh
  make release
  ```

## Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any enhancements or bug fixes.

## License

This project is licensed under the MIT License. See the LICENSE file for more details.
aliasify-cli 1.0.3
Aliases Keeper - Manage and synchronize your aliases
Requires Python: >=3.7

# Aliasify CLI

[![PyPI version](https://badge.fury.io/py/aliasify-cli.svg)](https://badge.fury.io/py/aliasify-cli)
[![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

Aliasify CLI is a powerful command-line tool for managing and synchronizing your Unix shell aliases across different systems. It helps you maintain a consistent shell environment by organizing aliases into groups and syncing them with your dotfiles repository.

## Features

- 🚀 Create, edit, and delete aliases with ease
- 📁 Organize aliases into groups
- 🔄 Sync aliases with your dotfiles repository
- 🎯 Support for multiple shell types (bash, zsh, fish, etc.)
- 💻 Cross-platform compatibility
- 🔧 Simple and intuitive CLI interface

## Quick Start

### Installation

```bash
# Create and activate a virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install the package
pip install aliasify-cli
```

### Basic Usage

```bash
# Create a new alias
aliasify create myalias "my command" --group "My Group"

# List all aliases
aliasify show

# Edit an alias
aliasify edit myalias "new command" --group "New Group"

# Delete an alias
aliasify delete myalias

# Sync with dotfiles repository
aliasify sync --direction local-to-remote
```

## Documentation

- [Installation Guide](docs/installation.md)
- [User Guide](docs/user-guide.md)
- [Configuration](docs/configuration.md)
- [Advanced Usage](docs/advanced-usage.md)
- [Contributing](docs/contributing.md)
- [FAQ](docs/faq.md)

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Author

Developed by [Islam Zekry](https://github.com/theizekry)
cargo-aidoc 0.1.3
`cargo-aidoc` is a Python script that leverages Large Language Models (LLMs) to automatically generate documentation comments for Rust code items like functions, structs, enums, etc.
Requires Python: ==3.11.12

# cargo-aidoc

`cargo-aidoc` is a Python script that leverages Large Language Models (LLMs) to automatically generate documentation comments for Rust code items like functions, structs, enums, etc.

## Features

*   Analyzes Rust source files using `tree-sitter`.
*   Identifies documentable items (functions, structs, enums, etc.).
*   Uses LLMs (configurable via `mirascope`) to generate documentation comments.
*   Integrates with Git to prevent overwriting uncommitted changes (can be overridden with `--force`).
*   Configurable via `cargo-aidoc.toml`.
*   Supports dry runs to preview generated documentation.

## Installation

This project uses `uv` for package management.

```
uv tool install cargo-aidoc
```

## Usage

Run the script pointing it to the root of your Rust crate:

```bash
cargo aidoc /path/to/your/rust/crate
```

**Options:**

*   `CRATE_PATH`: (Required) The path to the Rust crate directory.
*   `-q`, `--quiet`: Disable verbose output.
*   `--dry-run`: Generate documentation but do not write changes to the files.
*   `--force`: Ignore the Git uncommitted changes check and modify files directly.
*   `-c`, `--clean-cache`: Remove the `.cargo-aidoc` cache directory after execution.

**Example:**

```bash
# Generate docs for a crate, showing verbose output, but don't modify files
cargo aidoc . --quiet --dry-run

# Generate and write docs, forcing overwrite even with uncommitted changes
cargo aidoc . --force
```

## Configuration

The script looks for a `cargo-aidoc.toml` file in the root of the target crate. If it doesn't exist, a default one will be created.

**Default `cargo-aidoc.toml`:**

```toml
[llm]
# Configuration passed to mirascope.llm.call
# See mirascope documentation for options
provider = "openai"
model = "gpt-4o"
call_params = {} # e.g., {"temperature": 0.5}

[doc]
# Glob pattern relative to the crate root for source files
source = ["src/**/*.rs"]
# Types of items to document
item_types = ["fn", "struct", "enum", "union", "trait", "macro", "static"]
```

You can customize the LLM provider, model, call parameters, source file pattern, and the types of Rust items you want to document.

## Dependencies

*   [click](https://pypi.org/project/click/): For creating the command-line interface.
*   [toml](https://pypi.org/project/toml/): For parsing the configuration file.
*   [tree-sitter](https://pypi.org/project/tree-sitter/): The core parsing library.
*   [tree-sitter-rust](https://pypi.org/project/tree-sitter-rust/): Rust grammar for tree-sitter.
*   [mirascope](https://github.com/Mirascope/mirascope): For interacting with LLMs.
*   [termcolor](https://pypi.org/project/termcolor/): For colored terminal output.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request.

## License

The MIT License (MIT) under Computer-Aided Programming Group at Purdue University, see `LICENCE`
llm-fragments-site-text 0.1
Run URLs through the Trafilatura to get plain text
Requires Python: >=3.9

# llm-fragments-site-text

A fragment loader for [LLM](https://llm.datasette.io/) that converts websites into markdown plaintext using [Trafilatura](https://trafilatura.readthedocs.io/).

[![PyPI](https://img.shields.io/pypi/v/llm-fragments-site-text.svg)](https://pypi.org/project/llm-fragments-site-text/)
[![Changelog](https://img.shields.io/github/v/release/daturkel/llm-fragments-site-text?include_prereleases&label=changelog)](https://github.com/daturkel/llm-fragments-site-text/releases)
[![Tests](https://github.com/daturkel/llm-fragments-site-text/actions/workflows/test.yml/badge.svg)](https://github.com/daturkel/llm-fragments-site-text/actions/workflows/test.yml)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/daturkel/llm-fragments-site-text/blob/main/LICENSE)

## Installation

Install this plugin in the same environment as [LLM](https://llm.datasette.io/):

```bash
llm install llm-fragments-site-text
```

## Usage

Use `-f 'site:URL'` to fetch and convert a webpage to plaintext markdown with metadata. This plugin uses Trafilatura to extract clean text content and metadata from websites, and formats it as markdown.

Example:

```bash
llm -f 'site:https://example.com/article' "What is this article about?"
```

The output includes:
- Site name (if available)
- Title (if available)
- Author (if available)
- Date (if available)
- Description (if available)
- Main content (as markdown with links preserved)

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

```bash
cd llm-fragments-site-text
python -m venv venv
source venv/bin/activate
```

Install the dependencies and test dependencies:

```bash
pip install -e '.[test]'
```

To run the tests:

```bash
pytest
```

## Dependencies

- [httpx](https://www.python-httpx.org/): For making HTTP requests
- [trafilatura](https://trafilatura.readthedocs.io/): For extracting clean text and metadata from websites
- [llm](https://llm.datasette.io/): The LLM CLI tool this plugin extends
par2cmdline-turbo 1.3.0
Produce, verify and repair par2 files.
Requires Python: >=3.8

# par2cmdline-turbo.py

This package provides [par2cmdline-turbo](https://github.com/animetosho/par2cmdline-turbo) as a Python package for MacOS, Linux, and Windows; for Python 3.8 and later. Any feedback is welcome!

> [!NOTE]
> This distribution of `par2cmdline-turbo` is currently not affiliated with the official `par2cmdline-turbo` project.

## Motivation

`pip` is a convenient way to install small utilities, even if they aren't pure Python. It also means you can declare them as dependencies and have `pip` or other Python package managers handle the dependency for you. This is may be convenient if you live exclusively in Python-land, but still want to use the very excellent `par2cmdline-turbo`.

## Install

    $ pip install par2cmdline-turbo

## Build

    $ git checkout https://github.com/brenthuisman/par2cmdline-turbo.py.git
    $ pip wheel ./par2cmdline-turbo.py

Optionally, you can "cross-compile" the wheel for other platforms. It actually just downloads the right binary from the `par2cmdline-turbo` releases page. Just set the `TURBO_PLATFORM` environment variable with a supported platform. For instance:

    $ TURBO_PLATFORM=linux-x64 pip wheel ./par2cmdline-turbo.py

Supported platforms: linux-amd64, linux-arm64, linux-armhf, macos-arm64, macos-x64, win-arm64, win-x64, win-x86. You can use `generate-wheels.sh` to generate them all.

## Usage

After install, `par2` should be in your path and usable as upstream `par2cmdline`. See [their usage documentation](https://github.com/Parchive/par2cmdline?tab=readme-ov-file#using-par2cmdline).

## Dependencies

At build-time, you need `7z[.exe]` in your `PATH` when you are building Windows wheels.

## Credits

- Thanks to `@animetosho` for creating ParPar and taking the time to wrap it up as `par2cmdline-turbo`.
- Thanks to `@agriyakhetarpal` for giving me the idea and code with their [hugo-python-distributions](https://github.com/agriyakhetarpal/hugo-python-distributions).
nonebot-plugin-emojilike-automonkey 0.1.1
nonebot2贴猴插件
Requires Python: <4.0,>=3.10
Keywords: nonebot, nonebot2, emoji, like

# nonebot_plugin_emojilike_automonkey
>复刻自 github.com/fllesser/nonebot-plugin-emojilike/ 的自动贴猴插件
dqff 0.1.0
tbd
Requires Python: <4.0,>=3.9

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dqtl 0.1.0
tbd
Requires Python: <4.0,>=3.9

## Hi there 👋

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