semvec/pypi-packages huggingface dataset:
ahk-server 0.1.0
An AutoHotkey server allowing remote clients to execute ahk functionality
Requires Python: >=3.8.0
Keywords: ahk, autohotkey, windows, mouse, keyboard, automation, pyautogui, server, remote

# ahk-server

A server to allow remote execution of AutoHotkey using the Python [ahk](https://github.com/spyoungtech/ahk) wrapper. 
Uses fastapi.


## Installation

```bash
pip install ahk-server
```

Requires you have AutoHotkey installed. See [ahk readme](https://github.com/spyoungtech/ahk) for non-python dependencies.

## Usage

To start the server:

```bash
python -m ahk_server
```
This accepts two optional command line parameters: `--host` and `--port`. Alternatively, you can also configure the host 
and port by setting the environment variables `AHK_SERVER_HOST` and `AHK_SERVER_PORT`.

Alternatively still, you can also invoke the server using `uvicorn`
```bash
uvicorn ahk_server.app:app
```

## Standalone release

`ahk-server` is also available in a standalone exe release which can be found in the [releases page](https://github.com/spyoungtech/ahk-server/releases)

For connecting to the server, see the client project: [ahk-client](https://github.com/spyoungtech/ahk-client).


## Status

This project (and its client counterpart) is usable, but in **very** early stages of development. 
Notably, it does not currently include any authentication mechanisms for securing server connections, so use with caution.


TODO:

A noninclusive list of things that might come in the future:

- [ ] support some kind of basic authentication
- [ ] implement `run_script` functionality
- [ ] implement non-blocking functionality
- [ ] implement extension negotiation with clients
onepiece-classify 0.0.1
package to predict onepiece image character
Requires Python: >=3.12

# Machine Learning Project Documentation

## Project Overview
This repository contains programs for classifying images. The images used for this project are images of Onepiece anime characters. There are 18 predicted anime characters. This project is packaged in the form of a python package so that it can be used by the public.

## Installation Guide
### Requirements
It is recommended to run this application within a virtual environment. First thing first is clone the repository

```sh
git clone https://github.com/lombokai/onepiece-classifier.git
```
### Virtual Environment Setup
create a virtual environment

```sh
python3 -m venv <virtual environment name>
```

command line to activate your virtual environment in linux
```sh
source <virtual environment name>/bin/activate
```

command line to activate your virtual environment in windows
```sh
<virtual environment name>\Scripts\activate
```

install package requirements
```sh
pip install -r requirements/main.txt
```

## Usage
### Example Usage
This package provide image to predict in `assets` directory. If you want to try predict an image, run predict.py script with command bellow
```
python3 predict.py <image path>
```

command example if you are working in parent directory of this repo

```sh
python3 predict.py assets/luffy.png
```

try to predict different image with command above

### Run the Application
install onepiece-classify package
```sh
pip install onepiece-classify
```

acces predict method from onepiece-classify pakcage

```python
from onepiece_classify.infer import ImageRecognition
```

instantiate the class with your model path. Download trained model [here](https://drive.google.com/file/d/1M1-1Hs198XDD6Xx-kSWLThv1elZBzJ0j/view?usp=sharing) and make sure you specify model path parameter in the location of downloaded model

```python
predictor = ImageRecognition(<model path>)
```

then you just predict your image with `predict` method
```python
predictor.predict(<your image path>)
```


## Data Description
### Data Sources
Data obtained from Kaggle [here](https://www.kaggle.com/datasets/ibrahimserouis99/one-piece-image-classifie). The data contains a collection of 18 onepiece character images, and is saved in jpg, jpeg and png formats.

## Contributing
When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change.

## License

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
hyper-v 2.0.3
Удаленное управление Hyper-V через winrm
Requires Python: >=3.6

# Hyper-V winrm python

На данный момент можно создавать виртуальные машины только через заранее подготовленный шаблон виртуальноф машины.


## Installation

python3 -m pip install hyper-v

## Configuration

```
from hyperv import HyperV
import config

HV = HyperV(
    env_prefix=env_prefix,
    storage_root=storage_root,
    vm_template_pach=vm_template_pach,
    host=hv_host,
    user=hv_user,
    password=hv_password
    )


free_memory = HV.get_free_memory
print(free_memory)
```

* `storage_root` -  Директория, где будет создана папка, для Виртуальной машины
* `vm_template_pach` - Директория, где лежит шаблон Виртуальной машины
* `env_prefix`  -   Префикс, который будет добавлен к имени Виртуальной машины
* `host`    -   Hyper-V хост 
* `user`    - Имя пользователя для доступа по winrm
* `password`    - Пароль пользователя для доступа по winrm

## License

[GPL-3.0](LICENSE)
multiprocessed-socket 0.1.5
Creates a socket in a new process
Requires Python: >=3.6

Multiprocessed Socket

Copyright (c) 2018 The Python Packaging Authority

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
ragEvals 0.1.0
A library for evaluating Retrieval-Augmented Generation (RAG) systems
Requires Python: >=3.10

# RAG Evaluator

## Overview

RAG Evaluator is a Python library for evaluating Retrieval-Augmented Generation (RAG) systems. It provides various metrics to evaluate the quality of generated text against reference text.

## Installation

You can install the library using pip:

```bash
pip install rag-evaluator
```

## Usage

Here's how to use the RAG Evaluator library:

```python
from rag_evaluator import RAGEvaluator

# Initialize the evaluator
evaluator = RAGEvaluator()

# Input data
question = "What are the causes of climate change?"
response = "Climate change is caused by human activities."
reference = "Human activities such as burning fossil fuels cause climate change."

# Evaluate the response
metrics = evaluator.evaluate_all(question, response, reference)

# Print the results
print(metrics)
```

## Streamlit Web App

To run the web app:

- cd into streamlit app folder.
- Create a virtual env
- Activate
- Install all dependencies
- and run
```
streamlit run app.py
```

## Metrics

The following metrics are provided by the library:

- **BLEU**: Measures the overlap between the generated output and reference text based on n-grams.
- **ROUGE-1**: Measures the overlap of unigrams between the generated output and reference text.
- **BERT Score**: Evaluates the semantic similarity between the generated output and reference text using BERT embeddings.
- **Perplexity**: Measures how well a language model predicts the text.
- **Diversity**: Measures the uniqueness of bigrams in the generated output.
- **Racial Bias**: Detects the presence of biased language in the generated output.

## Testing

To run the tests, use the following command:

```
python -m unittest discover -s rag_evaluator -p "test_*.py"
```
## License

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

## Contributing

Contributions are welcome! If you have any improvements, suggestions, or bug fixes, feel free to create a pull request (PR) or open an issue on GitHub. Please ensure your contributions adhere to the project's coding standards and include appropriate tests.

### How to Contribute

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes.
4. Run tests to ensure everything is working.
5. Commit your changes and push to your fork.
6. Create a pull request (PR) with a detailed description of your changes.

## Contact

If you have any questions or need further assistance, feel free to reach out via [email](mailto:aianytime07@gmail.com).