semvec/pypi-packages huggingface dataset:
itela-autoqa 0.0.2
iTelaSoft AI QA Automation CLI Tool
Requires Python: >=3.8

# iTelaSoft AutoQA

`itela-autoqa` is a CLI tool designed to help software QA engineers automate and accelerate their testing workflows using AI. The tool captures a screenshot of a webpage, sends it for analysis, and provides actionable insights to improve QA processes.

## Features
- **Automated QA Analysis**: Quickly analyze any webpage for potential QA issues.
- **AI-Powered**: Utilize the power of AI to enhance and speed up the testing process.
- **Fast Execution**: Get instant feedback for your test cases and page analysis.

## Installation
```bash
pip install itela-autoqa
```
Make sure you have Playwright installed and configured:

```bash
playwright install
```

## Usage

To run the tool, use the following command:

```bash
itela-autoqa --url <website_url> --description "Description" --apikey YOUR_OPENAI_API_KEY
```

### Arguments:
- `--url`: The URL of the webpage you want to analyze.
- `--description`: A short description or context for the analysis. This could describe the type of tests being performed or what you expect from the result.
- `--apikey`: OpenAI api key.

### Example:

```bash
itela-autoqa --url https://example.com --description "Homepage accessibility test" --apikey YOUR_OPENAI_API_KEY
```

## Output

The tool will:
1. Capture a screenshot of the provided URL.
2. Send the screenshot for AI analysis.
3. Print out the analysis result.

## License

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

## Contribution

Contributions, issues, and feature requests are welcome. Feel free to open a pull request or submit an issue.

---

**Created by iTelaSoft AI Team**  
Enhancing the future of QA testing with AI.
flexenvconfig 1.0.0
An abstract configuration object used to facilitate building flexible configs from environment variables
Requires Python: <4.0,>=3.11

# flexenvconfig
A common pattern that I use across many of my Python apps/packages/etc is to use configuration classes that fetch my configs from the environment.  Because each project may use different configurations, and different parts of a project (the database, for example) might have specific configurations,
I created this package which provides an abstract class as a base to build flexible environmental configuration objects.

Here is an example:
```python
from flexenvconfig import BaseFlexEnvConfig


class MongoDBConfig(BaseFlexEnvConfig):
    def __init__(self) -> None:
        self.MONGO_DB_NAME = MongoDBConfig.get_env("MONGO_DB_NAME", "testdb")
        self.MONGO_DB_HOST = MongoDBConfig.get_env("MONGO_DB_HOST", "localhost")
        self.MONGO_DB_PORT = int(MongoDBConfig.get_env("MONGO_DB_PORT", "12345"))
        self.MONGO_DB_USERNAME = MongoDBConfig.get_env("MONGO_DB_USERNAME", "mongouser")
        self.MONGO_DB_PASSWORD = MongoDBConfig.get_env("MONGO_DB_PASSWORD", "mongopassword")
        self.OPTIONAL_CONFIG = MongoDBConfig.get_env("OPTIONAL_CONFIG")

    def validate(self) -> bool:
        return all([self.MONGO_DB_NAME, self.MONGO_DB_HOST, self.MONGO_DB_PORT, self.MONGO_DB_USERNAME, self.MONGO_DB_PASSWORD])
```

`BaseFlexEnvConfig`'s constructor defines the configs which gets sourced from the environment when implemented.  When implemented, the `validate` method is used to validate that a config object contains the required values.

## Development
You'll need poetry and Python 3.11+.  Clone the repo and then run `poetry install`

Then you'll need to install the pre-commit hooks: `poetry run pre-commit install`

You can run the test suite using `make test` and you can manually lint the project using `make lint`
bm3d 4.0.3
BM3D for correlated noise
Requires Python: >=3.9

# Python wrapper for BM3D denoising - from Tampere with love

Python wrapper for BM3D for stationary correlated noise (including white noise) for color,
grayscale and multichannel images and deblurring.

BM3D is an algorithm for attenuation of additive spatially correlated
stationary (aka colored) Gaussian noise. This package provides a wrapper
for the BM3D binaries for use for grayscale, color and other multichannel images
for denoising and deblurring.

This implementation is based on
- Y. Mäkinen, L. Azzari, A. Foi, 2020, "Collaborative Filtering of Correlated Noise: Exact Transform-Domain Variance
for Improved Shrinkage and Patch Matching", in IEEE Transactions on Image Processing, vol. 29, pp. 8339-8354.
- K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, 2007, "Image Denoising by Sparse 3-D Transform-Domain Collaborative
Filtering", in IEEE Transactions on Image Processing, vol. 16, pp. 2080-2095.

This package provides a BM3D interface for the "bm4d" denoising package. Please see the 
[bm4d](https://pypi.org/project/bm4d) package for supported platforms.

The package is available for non-commercial use only. For details, see LICENSE.

For examples, see the examples folder of the full source (bm3d-***.tar.gz) from https://pypi.org/project/bm3d/#files , 
which also includes the example noise cases demonstrated in the paper.

Authors: \
    Ymir Mäkinen <ymir.makinen@tuni.fi> \
    Lucio Azzari \
    Alessandro Foi
bm4d 4.2.5
BM4D for correlated noise
Requires Python: >=3.9

# Python wrapper for BM4D denoising - from Tampere with love, again

Python wrapper for BM4D for stationary correlated noise (including white noise).

BM4D is an algorithm for attenuation of additive spatially correlated
stationary (aka colored) Gaussian noise for volumetric data.
This package provides a wrapper for the BM4D binaries for Python for the 
denoising of volumetric and volumetric multichannel data. For denoising of 
images/2-D multichannel data, see also the [bm3d](https://pypi.org/project/bm3d)  package.

These newer binaries (v4+) are designed only for dealing with additive Gaussian
white or correlated noise. Special features like embedded handling of Rice noise
and adaptive groupwise variance estimation are supported by the legacy binaries
v3.2 at https://webpages.tuni.fi/foi/GCF-BM3D/ .

This implementation is based on
- Y. Mäkinen, L. Azzari, A. Foi, 2020, "Collaborative Filtering of Correlated Noise: Exact Transform-Domain Variance
  for Improved Shrinkage and Patch Matching", in IEEE Transactions on Image Processing, vol. 29, pp. 8339-8354.
- M. Maggioni, V. Katkovnik, K. Egiazarian, A. Foi, 2013, "Nonlocal Transform-Domain Filter for Volumetric Data Denoising
  and Reconstruction", in IEEE Transactions on Image Processing, vol. 22, pp. 119-133.
- Y. Mäkinen, S. Marchesini, A. Foi, 2022, "Ring Artifact and Poisson Noise Attenuation via Volumetric Multiscale
  Nonlocal Collaborative Filtering of Spatially Correlated Noise", in Journal of Synchrotron Radiation, vol. 29, pp. 829-842.

The package contains the BM4D binaries compiled for:
- Windows (Win11, MinGW-64)
- GNU/Linux (GCC 11.3.0, 64-bit)
- Mac OSX (El Capitan, 64-bit): no longer maintained and will be removed from future releases.
- macOS (Sonoma, 64-bit ARM)

The package is available for non-commercial use only. For details, see LICENSE.

Basic usage:
```python
y_hat = bm4d(z, sigma)  # white noise: include noise std
y_hat = bm4d(z, psd)  # correlated noise: include noise PSD (size of z)
```

For usage examples, see the examples folder of the full source (bm4d-***.tar.gz) from https://pypi.org/project/bm4d/#files .


Contact: Ymir Mäkinen <ymir.makinen@tuni.fi>