semvec/pypi-packages huggingface dataset:
george-walrus 0.1.5
Backport compiler for Python 3.8 assignment expressions.
License: MIT
Requires Python: >=3.4
Keywords: walrus operator, assignment expression, back-port compiler

🎁 This repo is forked from https://github.com/pybpc/walrus for publishing their latest work on PyPi, since the PyPi has not been updated long. Thanks the original authors for their excellent work to support the walrus operator on early Python versions.

# NB: walrus is currently under reconstruction. It is highly recommended to directly install from the git repo or the pre-release distributions.

---

# walrus

[![PyPI - Downloads](https://pepy.tech/badge/python-walrus)](https://pepy.tech/count/python-walrus)
[![PyPI - Version](https://img.shields.io/pypi/v/bpc-walrus.svg)](https://pypi.org/project/bpc-walrus)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/bpc-walrus.svg)](https://pypi.org/project/bpc-walrus)

[![GitHub Actions - Status](https://github.com/pybpc/bpc-walrus/workflows/Build/badge.svg)](https://github.com/pybpc/bpc-walrus/actions?query=workflow%3ABuild)
[![Codecov - Coverage](https://codecov.io/gh/pybpc/bpc-walrus/branch/master/graph/badge.svg)](https://codecov.io/gh/pybpc/bpc-walrus)
[![Documentation Status](https://readthedocs.org/projects/bpc-walrus/badge/?version=latest)](https://bpc-walrus.readthedocs.io/en/latest/)

> Write *assignment expressions* in Python 3.8 flavour, and let `walrus` worry about back-port issues :beer:

&emsp; Since [PEP 572](https://www.python.org/dev/peps/pep-0572/), Python introduced *assignment expressions*
syntax in version __3.8__. For those who wish to use *assignment expressions* in their code, `walrus` provides an
intelligent, yet imperfect, solution of a **backport compiler** by replacing *assignment expressions* syntax with
old-fashioned syntax, which guarantees you to always write *assignment expressions* in Python 3.8 flavour then
compile for compatibility later.

## Documentation

&emsp; See [documentation](https://bpc-walrus.readthedocs.io/en/latest/) for usage and more details.

## Contribution

&emsp; Contributions are very welcome, especially fixing bugs and providing test cases.
Note that code must remain valid and reasonable.

## See Also

- [`pybpc`](https://github.com/pybpc/bpc) (formerly known as `python-babel`)
- [`f2format`](https://github.com/pybpc/f2format)
- [`poseur`](https://github.com/pybpc/poseur)
- [`relaxedecor`](https://github.com/pybpc/relaxedecor)
- [`vermin`](https://github.com/netromdk/vermin)
vector-database 0.0.2
A tool to build vector database quickly
License: MIT
Keywords: vector database

# Vector Database

## Description

This package is aiming to make python users to build their own vector database of diverse types of data. 

This package is in building process and will be released stable version soon.

## Try Alpha Version

```python
import vector_database.embdding as emb

image_embdding = emb.ImageEmbdding()
image_embdding.imageVectorDatabaseBuild('path/to/image_folder')
image_embedding.imageQuery()
```
glove-semantic-explorer 0.1.1
Easy to use CLI to set up embedding-explorer for deployment on a corpus with GloVe word embeddings
License: MIT
Requires Python: <4.0,>=3.9

# glove-semantic-explorer

Set up embedding explorer on a corpus with GloVe word embeddings with an easy-to-use CLI.

## Installation

You can install the CLI from PyPI:

> We recommend that you use a Linux/Unix system, preferably Debian when using this tool.
> Windows and MacOS could still work, but we do not guarrantee this.

```bash
pip install glove-semantic-explorer
```


## Usage

### 1. Train a model

You will need a corpus in the format of a bunch of `.txt` files in a folder.
Every line in a file should represent one sentence/passage.

To train a GloVe model on the corpus, run:

```bash
python3 -m glove_semantic_explorer train_model dat/ -o model/glove.kv
```

This will output a keyed vectors file to  `model/glove.kv`.

### 2. Run the Explorer

To run the explorer on the trained model locally, run:

```bash
python3 -m glove_semantic_explorer run_explorer -m model/glove.kv --port 8080
```

This will start embedding-explorer on the trained embedding model on port 8080.

### 3. Deploy!

You can deploy the application using `docker compose`.
The way this can be done with our CLI is by auto-generating a `Dockerfile`, a `compose.yaml` and a `main.py` file, that contains all the code for running the server.

To output this into a folder called `deployment/`, run the following command:

```bash
python3 -m glove_semantic_explorer generate_docker "your_project_name" -m model/glove.kv -p 8080 -o deployment/
```

> Beware that the model file only gets mounted to the container, and thus should not be removed, moved or renamed.

To deploy the app with docker compose run the following:

```bash
cd deployment/
sudo docker compose up
```

The app will then run on port 8080.
screenapi-cli 0.1.6
A cli interface to interact with screenapi
License: MIT
Requires Python: <4.0,>=3.10

# screenapi-cli

## Installation

```bash
python -m pip install screenapi-cli

screenapi-cli --help
```

## Step 1 - setup cli
```bash
screenapi-cli setup --api-url "<api-url>" --api-key "<api-key>" --max_workers 15
```

check all options with `screenapi-cli setup --help`

## Step 2 - start scraping

```bash
touch urls.txt # create a urls.txt file and add urls, one line == one url

screenapi-cli scrape urls.txt #
```
check all options with `screenapi-cli scrape --help`

## Step 3 - export to excel sheet
```bash
screenapi-cli export --input-dir "<path to scraper's output>" # check below
```

* example : scraped flipkart urls, and export execl file in default location
    ```bash
    screenapi-cli export --input-dir "flipkart" # `flipkart` will convert into `/path/to/screenapi-cli/config/output/flipkart` and --output-file will be `/path/to/screenapi-cli/config/output/flipkart.xlsx` and --sort-by will be `sl`
    ```

* example: custom input-dir , custom output-file
    ```bash
    screenapi-cli export --input-dir "~/Downloads/scraped" --output-file "./flipkart.xlsx"
    ```

## command: `screenapi-cli list`
```bash
[default]
output_dir = /path/to/output
max_workers = 15
api_url = *****************
api_key = *************************
namespace = ******-****-****-****-*********
export_dir = /path/to/export
```
check all options with `screenapi-cli export --help`