semvec/pypi-packages huggingface dataset:
MPCP2.0 2.0
The multi-purpose command prompt 2.0 (MPCP2.0) is a programme meant to perform general and important tasks autonomously. From orgnising your desktop, to encrypting your files and decrypting your files with keys as well as autonomously making a report with an excel file.
License: MIT
Keywords: Multi-purpose

This is a python project by Bryan K.K Mametja
The multi-purpose command prompt 2.0 (MPCP2.0) is a programme meant to perform general and important tasks autonomously.
From orgnising your desktop, to encrypting your files and decrypting your files with keys as well as autonomously making a report with an excel file.


Change Log
==========
2.0 (2022/10/04)
-----------------
-First Release
tplcentral 0.1.2
3PL REST API Client
License: MIT

TPL Python API
==============

A Python wrapper for the 3PL Central REST API

Usage
-----

set up a `.env` file in the root of the project:

.. code-block:: python

    BASE_URL="https://secure-wms.com" 
    AUTH_PATH="AuthServer/api/Token"
    CLIENT_ID="your-id"
    CLIENT_SECRET="your-secret"
    FACILITY_ID=99
    TPL_KEY="your-tpl-key"
    GRANT_TYPE="client_credentials"
    USER_LOGIN_ID="name-this-whatever-you-want"
    ROUTING_INFO_ACCOUNT_NUMBER="ups-account-number"

Verify that you can access the api by running 

.. code-block:: python
    
    python3 -m verify_credentials



Then, run the following get a list of methods you can try out, with a handy run-in-place CLI:


.. code-block:: shell
    
    python3 -m examples


.. code-block::

    Which endpoint do you want to try?
    1) cancel_test_order

    2) create_test_item

    3) create_test_order

    4) get_carriers_list

    5) get_order

    Enter a number:



License
-------

MIT

See LICENSE for more details
fastflows 0.1.2
FastFlows is a FastAPI server & command line tool to comunicate with Prefect 2.0 as a Workflow manager (and in future other systems like Pygeoapi, for example) to deploy, run, track flows and more.
License: MIT
Requires Python: >=3.8,<4.0

## Run FastFlow server

FastFlows is a FastAPI server & command line tool to comunicate with Prefect 2.0 as a Workflow manager to deploy, run, track flows and more.

To start work with FastFlows you should define at least 2 environment variables:

```console

    # Prefect API Server address
    PREFECT_URI=http://localhost:4200

    # Path to folder with flows
    FLOWS_HOME=flows

```

If you want to define variables with env prefix, for example, like 'LOCAL_PREFECT_URI' or 'DEV_PREFECT_URI' you can use environment variable 'ENV_NAME'

If Fastflow will see 'ENV_NAME' variable in environment - it will search for variables with prefix defined in this ENV_NAME, for example:

if ENV_NAME = 'LOCAL'

Fastflows will read variables like LOCAL_PREFECT_URI and LOCAL_FLOWS_HOME,

if ENV_NAME = 'dev', then fastflow will expect variables like 'dev_PREFECT_URI' and 'dev_FLOWS_HOME'

### How to run FastFlows with Prefect cluster

#### with Kubernetes

- [Kube instructions](docker/README.md)
- [Kubernetes configuration](docker/kube-infra/)

#### with Docker-Compose

- [Docker-Compose readme](docker/README.md)
- [Docker-compose YAML](docker/docker-compose.yml)

### Run cli

```console

    fastflows --help

```

### Flows Deployment

#### Auto deployment

Deployment of Flows can be done by FastFlows automatically: if there is a new flow or changes in FLOWS_HOME directory - FastFlow create new deployment. To disable auto deployment set env variable to 0

```console

    FASTFLOWS_AUTO_DEPLOYMENT = 0

```
clip-gaze 0.3.0
Compare image prompts using CLIP
License: MIT
Keywords: clip,art,art analysis

# clip-gaze

An art analysis tool powered by CLIP.

## Motivation

Diffusion models (such as [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release)) used [OpenAI's CLIP](https://openai.com/blog/clip/) in order to perform textual analysis of their training data.
Precisely what these machine learning systems actually learned from their training data is
opaque.
This tool helps us understand how CLIP, and therefore the models that use CLIP,
see images.

## What does it do?

Given an image and a series of (text) phrases it calculates the *relative* likelihood of each phrase to be a good description of the image.
Note that this is not the same thing as "given a text phrase, calculate the accuracy of that phrase".

## An example

Let's show it the painting ["Brücke über die Marne bei Creteil" by Cézanne](https://commons.wikimedia.org/wiki/File:Cezanne_bruecke-ueber-die-marne-bei-creteil.jpg).
If we download the 2,175 × 1,713 pixel version of the painting and open it (e.g. using `PIL.Image.open` from the package `pillow`) as `image` we can then pass it to the `gaze` command.

```python
# Assuming you have already saved the image
import clip_gaze
clip_gaze.gaze(image, clip_gaze.MOVEMENTS)
```

Just looking at the highest probability outputs for `clip_gaze.ARTISTS_BY_TRAINING_PREVALENCE`, `clip_gaze.Movements`, and `clip_gaze.SURFACES` we see:

```python
{'artist': ['by paul cézanne (82%)'], 'movement': ['tonalism movement (16%)'], 'surface': ['on canvas (86%)']}
```

So the terms "by paul cézanne", "tonalism movement", and "on canvas" are the most likely to describe the input image.

For more examples and fuller documentation please see the [project's page on github](https://github.com/hmillerbakewell/clip-gaze).

## How does it work?

CLIP is a tool provided by OpenAI that calculates the similarity between an image and some text.
This is a machine learning system trained on an enormous amount of data,
and that data will contain biases (intentional and unintentional).
**It is not a source of truth, but a useful tool to give you ideas about where to search next.**

This tool works by downloading CLIP onto your computer and running it locally.
This is not an easy task for all computers, especially older ones.
See the "Arguments for `gaze`" section of the project's README for a way to change memory load.

## Biases

This software is built on a machine learning system, and the biases in this tool come in two parts:

1. CLIP itself comes with its own biases, and we refer the user to OpenAI's own work on explaining and mitigating that bias
2. The lists of chosen phrases

The lists used in this software are primarily from Wikipedia and from the training data that CLIP used.
Neither of these sources are perfect, and care should be taken when using this software to account for these biases where possible.
Although the lists are long (e.g. the list of 6000 artists) there are no claims of completeness or relative importance made.