semvec/pypi-packages huggingface dataset:
copyfolder 0.1.9
You can use this package to copy the content from one location to another location
Requires Python: >=3.8

# copyfolder

It is a python Command Line Interface (CLI) tool to copy a folder from one location to another location.

## Installation

```bash
pip install copyfolder
```

## Usage

```bash
copy <source> <destination>
```

## Example

```bash
copy /home/user/source /home/user/destination
```

## License

[MIT](https://choosealicense.com/licenses/mit/)
---

[![wakatime](https://wakatime.com/badge/user/451cc8bf-5caa-4a73-994a-c86238c69e56/project/018d7d2e-b957-462b-bcc7-e6fe7ad65ba2.svg)](https://wakatime.com/badge/user/451cc8bf-5caa-4a73-994a-c86238c69e56/project/018d7d2e-b957-462b-bcc7-e6fe7ad65ba2)
wikipedia-wrapper 0.0.1
API wrapper for wikipedia
Requires Python: >=3.6

# Wikipedia API Wrapper for Python

## Overview
This Python library provides a comprehensive API wrapper for interacting with the Wikipedia API. It offers more than 300 functions to access various information and data from Wikipedia.

### What is Wikipedia?
Wikipedia is a free online encyclopedia with articles in multiple languages, created and edited by volunteers around the world. The Wikipedia API allows developers to programmatically access and retrieve data from Wikipedia's vast collection of articles.

## Examples
```python
import wikipedia_py as wp

# Search for a specific page
results = wp.search_page("Python programming")
print(results)

# Get a summary of a page
summary = wp.get_summary("Python programming")
print(summary)

# Get the content of a page
content = wp.get_page_content("Python programming")
print(content)

# Access more than 300 functions offered by the library
...
```

## Installation
You can install the `wikipedia_py` library using pip:

```bash
pip install wikipedia_py
```

## Usage
1. Import the library:
```python
import wikipedia_py as wp
```

2. Use the functions provided by the library to interact with the Wikipedia API:
```python
# Example: Search for a page
results = wp.search_page("Machine learning")
print(results)
```

## Features
- Access to more than 300 functions for retrieving Wikipedia data
- Search for pages, get summaries, access page content, and more
- Easy installation and usage in Python projects

## Contribution
Contributions are welcome! If you have any suggestions, improvements, or new features to add, feel free to open an issue or submit a pull request.

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
pydantic_lambda_handler 1.0.1
Pydantic lambda handler modelled on FastAPI

![example workflow](https://github.com/harvey251/pydantic-lambda-handler/actions/workflows/main.yml/badge.svg) [![codecov](https://codecov.io/gh/harvey251/pydantic-lambda-handler/branch/main/graph/badge.svg?token=PX0GRFCYQE)](https://codecov.io/gh/harvey251/pydantic-lambda-handler) ![pypi package](https://img.shields.io/pypi/v/pydantic-lambda-handler?color=%2334D058&label=pypi%20package)

# Vision

Aim to create an easy-to-use decorator on a lambda for quick development of apis on AWS lambda using AWS Gateway.

Inspired by, and attempting to match the [Fast API](https://fastapi.tiangolo.com) api.

Currently, I'm working through the first steps to hit the basics.

This is very much at the beginning of the project, so I could do with all the help I can get :D

## MUST HAVE
* creates open api schema

API Gateway Lambda proxy integration
https://docs.aws.amazon.com/apigateway/latest/developerguide/set-up-lambda-proxy-integrations.html#api-gateway-simple-proxy-for-lambda-input-format

https://docs.aws.amazon.com/lambda/latest/dg/services-apigateway-tutorial.html

```commandline
pip install --target ./demo_app ../.
```
hartree-fock 3.2.1
PROVIDE HERE A DESCRIPTION OF YOUR APPLICATION
Requires Python: >=3.7
Keywords: triqs,hartree_fock

# TRIQS hartree_fock
An application based on TRIQS for using the Hartree-Fock method to solve lattice and impurity problems.

The documentation can be found at [triqs.github.io/hartree_fock](https://triqs.github.io/hartree_fock/).
bluevision 0.0.6
Bluesignal Vision AI project
Requires Python: >=3.8
Keywords: AI,Bluesignal,computer-vision

<div align=center>

![logo](assets/logo.png)

# bluevision

[![PyPI - Version](https://img.shields.io/pypi/v/bluevision.svg)](https://pypi.org/project/bluevision)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/bluevision.svg)](https://pypi.org/project/bluevision)
</div>


-----

**Table of Contents**

- [Installation](#installation)
- [Usage](#usage)
- [Test](#test)
- [License](#license)

## Installation

```console
pip install bluevision
```

## Usage

### Simple video demo
```python
from bluevision.demo import video_demo

video_demo(
    weights="yolov8s-coco.safetensors",
    video_path="sample.mp4",
    model_size="s",
    track=True,
    show=True,
    save_path="output.mp4",
)
```

### Video Inference Process

```python
import cv2
import supervision as sv
import bluevision as bv
from bluevision.utils import to_supervision_detections, make_labels

# Initialize
detector = bv.solutions.Detector(model=bv.solutions.detector.models.Yolov8(size='s'),
                                 nms=bv.utils.nms.soft_nms,
                                 weights="yolov8s.safetensors")
tracker = bv.utils.tracker.BYTETracker(track_thresh=0.15, match_thresh=0.9,
                                       track_buffer=60, frame_rate=30)
box_annotator = sv.BoundingBoxAnnotator(thickness=2)
label_annotator = sv.LabelAnnotator(text_scale=0.5, text_padding=2)

# Load sample video
vid = cv2.VideoCapture('sample.mp4')

# Start
while True:
    ret, original_image = vid.read()
    if not ret:
        break

    detections = detector(original_image)
    detections = tracker.update(detections)

    # Draw bboxes using supervision
    sv_detections = to_supervision_detections(detections)
    annotated_frame = box_annotator.annotate(
        scene=original_image,
        detections=sv_detections,
    )
    annotated_frame = label_annotator.annotate(
        scene=annotated_frame,
        detections=sv_detections,
        labels=make_labels(sv_detections)
    )

    cv2.imshow('annotated image', annotated_frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cv2.destroyAllWindows()
```

## BlueVision

### Solution

**[Object Detector](https://github.com/dh031200/BlueVision/tree/main/src/bluevision/solutions/detector)**

## Utils

**[NMS](https://github.com/dh031200/BlueVision/tree/main/src/bluevision/utils/nms)**
**[Object Tracker](https://github.com/dh031200/BlueVision/tree/main/src/bluevision/utils/tracker)**

## Test

```text
$ python test_with_time.py
Using device: mps
preprocess: 0.00225s, infer: 0.015508s, postprocess: 0.01313s, track: 0.00108s, draw: 0.000670s, total: 0.03462s, t-avg: 0.03576s
total frame : 1050
total elapsed time: 56.25610s
total inference time: 37.55273s
```

## License

`bluevision` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.