semvec/pypi-packages huggingface dataset:
dg-chatgpt 0.1.5
Wrapper for OpenAI API, intended to be used for a discord bot powered by chatGPT.
Requires Python: >=3.7

# DevGround

## ChatGPT API wrapper

This is a wrapper for the [ChatGPT API](https://platform.openai.com/docs/introduction) by [DevGround](https://devground.cz/).

### Model

gpt-3.5-turbo

### License

It is licensed under the MIT license.

### Language

Written in Python 3.11

### Requirements

requires environment variables:

- OPENAI_API_KEY

### Installation

install the package with pip:

```bash
pip install dg_chatgpt_api
```

### Usage

```python
from dg_chatgpt_api import ChatGPTAPI as api

api.reconnect()
api.add_user("user_id", "username")
api.add_conversation("user_id")
message = {
    "role": "user",
    "content": "message"
}
response = api.get_response("user_id", message)
```
project-base-AB3NzaC1yc-setuptools 1.1.6
Fit population genetic models of demography and selection using diffusion approximations to the allele frequency spectrum
Requires Python: >=3.7

**Diffusion Approximation for Demographic Inference**

dadi implements methods for demographic history and selection inference from genetic data, based on diffusion approximations to the allele frequency spectrum. One of dadi's main benefits is speed: fitting a two-population model typically takes around 10 minutes, and run time is independent of the number of SNPs in your data set. dadi is also flexible, handling up to three simultaneous populations, with arbitrary timecourses for population size and migration, plus the possibility of admixture and population-specific selection.

Originally dadi was initially developed by  Ryan Gutenkunst in the labs of Scott Williamson and Carlos Bustamante (https://bustamantelab.stanford.edu/) in Cornell's Department of Biological Statistics and Computational Biology. Ryan is now faculty in Molecular and Cellular Biology at the University of Arizona, and his group continues to work on dadi and other topics (http://gutengroup.mcb.arizona.edu).

If you use dadi in your research, please cite RN Gutenkunst, RD Hernandez, SH Williamson, CD Bustamante "Inferring the joint demographic history of multiple populations from multidimensional SNP data" PLoS Genetics 5:e1000695 (2009).

**Getting started**

See the manual (https://dadi.readthedocs.io) and the example files in the source distribution. Full API documenation is available (https://dadi.readthedocs.io/en/latest/api/dadi/) and is in the source distribution, under doc/api/dadi/index.html .

Also, please sign up for our mailing list, where we help the community with ∂a∂i. Please do search the archives before posting. Many questions come up repeatedly. (http://groups.google.com/group/dadi-user)

The easiest way to install dadi is via conda, `conda install -c conda-forge dadi`.

**Notebook examples**

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/git/https%3A%2F%2Fbitbucket.org%2Fgutenkunstlab%2Fdadi%2Fsrc%2Fmaster/HEAD)
pyvpd 0.1.2
Vanishing Point Detector

# PyVPD: Vanishing Point Detector

This is a Python + Scikit-Image implementation of the Vanishing point detection in images

## Installation
```
pip install pyvpd
```

## Usage

```python
import cv2
from pyvpd import VPDetector

image = cv2.imread("io/input/1.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

vp_detector = VPDetector()
vps_3d, vps_2d = vp_detector.detect(image)
print("vps_3d:", vps_3d)
print("vps_2d:", vps_2d)
```

## Results

Input image:

![Input image](./io/input/1.jpg)

First vanishing point:

![First vanishing point](./io/output/vp1.png)

Second vanishing point:

![Second vanishing point](./io/output/vp2.png)

## Acknowledgments
We made necessary changes on top of [Automated Rectification of Image](https://github.com/chsasank/Image-Rectification) to fit our design. Many thanks to Sasank Chilamkurthy for releasing the code.

Implements the modified version of the following paper:  

[Chaudhury, Krishnendu, Stephen DiVerdi, and Sergey Ioffe. "Auto-rectification
of user photos." 2014 IEEE International Conference on Image Processing (ICIP).
 IEEE, 2014.](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42532.pdf)
Codebugger 0.0.1
Codebugger explains the buggy code in simple terms and offers ways to fix the code.
Keywords: python,debugger,gpt


# Codebugger

Codebugger explains the buggy code in simple terms and offers ways to fix the code.
This takes away hours of frustration by helping you understand the root cause of the error, helping to clear up any misconceptions, and saving you time. Debugger uses gpt language model from Openai.
python-pymetrics-assessment-client 2.0.0
pymetrics Assessment API
Keywords: OpenAPI,OpenAPI-Generator,pymetrics Assessment API

### This is pymetrics&#39;s public API. The API can be used to get information on candidates as part of a job application workflow, or for employee career pathing and development. The typical use case for this is to support an externally initiated assessment for a candidate job application. This is often done \&quot;inline\&quot; with the candidate&#39;s application, or asynchronously after the candidate submits their application. This data can then be used for career pathing and employee development in subsequent stages.  The expected sequence of API calls is: * &#x60;Generate OAuth Token&#x60; with the OAuth Client ID and Secret you&#39;ve been provided * &#x60;Get Assessment Configurations&#x60; to determine which configured assessment templates are available * &#x60;Create Assessment Order&#x60; for a selected Assessment and candidate job application * &#x60;Get Assessment Order&#x60; to receive the recommendation results and reports, once they are available  # noqa: E501