semvec/pypi-packages huggingface dataset:
dttimeframe 3.6.5
dtTimeFrame is a module that pack time track functions and subprocess call functions into one single class.

# dtTimeFrame

dtTimeFrame is a module that pack **time track functions** and **subprocess call functions** into one single class.

- Homepage: <https://github.com/SotongDJ/dtTimeFrame>

## Install dtTimeFrame

```bash
pip install dttimeframe
```

## Example codes

### tag()

```python
from dtTimeFrame.timeFrame import tag # type: ignore

# Initiation
Tool = tag()
Tool.log.name = "log.txt" # filename of the log file, store stdout info
Tool.error.name = "err.txt" # filename of the err file, store stderr info
Tool.extra.name = "log.json" # filename of the extra command record, store time stamp, commands and others info

# start logging and write header into log/err files
Tool.start()

# > your code insert here < 

# print time stamp with personalized msg
Tool.timeStamp("PREVIEW: show something you want to info")

# command runner, base on subprocess.call()
phrase_str = F"echo this command line"
Tool.runCommand(phrase_str)

# command runner with specific output file
phrase_str = F"echo this command line into specific output file: test.txt"
Tool.runCommand(phrase_str,export_file="test.txt")

# > your code insert here < 

# end logging and write footer into log/err files 
Tool.stop()
```

### detector()

```python
from dtTimeFrame.timeFrame import detector # type: ignore

file_be_process_handle = detector(print_func=print,call_func=print)
# "file_be_process_handle = detector(print_func=Tool.timeStamp,call_func=Tool.runCommand)"
# combine use with tag() 
file_be_process_handle.do(target_str="target.txt")
if file_be_process_handle.missing(): # if target.txt missing?
    with open(file_be_process_handle.doing_str,"w") as target_handle: # doing-target.txt
        target_handle.write("Hello world\n")
    file_be_process_handle.done() # rename doing-target.txt as target.txt

print(open(file_be_process_handle.target_str).read())
```
claude2openai 0.0.1
use claude like openai
Requires Python: >=3.7.1

# claude2openai
Claude2OpenAI is a repository that provides a simple way to use the Slack bot Claude API in the same format as OpenAI's pip package. This makes it easy to integrate Claude into your Slack workspace and leverage its capabilities to enhance your team's productivity and communication. With Claude2OpenAI, you can quickly and easily convert your existing Slack bot to use the OpenAI API format, without having to rewrite your code from scratch. Whether you're a developer looking to streamline your workflow or a team leader looking to improve your team's communication, Claude2OpenAI is a powerful tool that can help you achieve your goals.


# Installation
```
pip install --upgrade claude2openai
```
Install from source with
python setup.py install

# Usage
```bash
export SLACK_API_TOKEN=""
export BOT_ID=""
export CHANNEL_ID=""
```

Or set claude2openai to its value
```python
import claude2openai
claude2openai.slack_api_token = "xxx"
claude2openai.bot_id = "xxx"
claude2openai.channel_id = "xxx"
```

Then
```python
import claude2openai
# create a chat completion
chat_completion = claude2openai.ChatCompletion.create(model="claude", messages=[{"role": "user", "content": "Hello world"}])

# print the chat completion
print(chat_completion.choices[0].message.content)
```
streamlit-kpi 0.0.36
Streamlit component for displaying KPI's
Requires Python: >=3.7

## Test it Live !
<a href="https://aalteirac-kpi.streamlit.app/" title="3d-badge"><img src="https://static.streamlit.io/badges/streamlit_badge_black_white.svg"></a><br>

# streamlit-kpi

Streamlit component for displaying KPIs, easy layout to fit all your needs !

![ScreenShot](https://raw.githubusercontent.com/aalteirac/streamlit-kpi-app/master/screen.png)

## Installation instructions 

```sh
pip install streamlit-kpi
```

## Usage instructions

```python
import streamlit as st
from streamlit_kpi import streamlit_kpi

streamlit_kpi(key="first_kpi",height='200',title='Monthly Sales',value=25000,icon='fa-solid fa-globe',progressValue=100,unit='K€')

   """
    Parameters:

    key: Optional str, default is '', the streamlit component key, higly recommended 
    title: Mandatory str, the title...
    value: Mandatory str or int or float, the value...
    icon: Optional str, default is 'fa-solid fa-globe', any Font Awesome class (v6.3). See https://fontawesome.com/search?o=r&m=free
    icon: Optional str, default is '', the value unit appended after the value
    textAlign: Optional str, default is 'left', title and value alignement
    backgroundColor: Optional str, default is '#f3f3f3', widget background color can be str like blue, orange, transparent... or HEX color
    titleColor: Optional str, default is 'dark', title font color, can be str like blue, orange... or HEX color
    valueColor: Optional str, default is 'dark', value font color, can be str like blue, orange... or HEX color
    progressColor: Optional str, default is 'green', progress color, can be str like blue, orange... or HEX color
    iconColor: Optional str, default is 'black', icon font color, can be str like blue, orange... or HEX color
    animate: Optional bool, default is True, activate or not the animation
    animateDuration: Optional int, default is 2000, the animation duration in milliseconds
    progressValue: Optional int, default is 100, the progress bar completion, from 0 to 100
    iconOpacity: Optional int, default is 50, the opacity of the icon, from 0 to 100, 0 is invisible
    iconTop: Optional int, default is 24, the icon position from the top, from 0 to 100
    iconLeft: Optional int, default is 91, the icon position from the left, from 0 to 100
    showProgress: Optional bool, default is True, show or hide the progress bar
    showIcon: Optional bool, default is True, show or hide the icon
    height: Optional int, default is 250, the height of the whole widget

    """

```
baklabel 1.2.3
Baklabel delivers a daily label fragment for grandfathered backups
Requires Python: >=3.8


Baklabel is intended for use in automated scripts to deliver a sensible directory path
fragment (or label) each day to construct a grandfathered local backup.

Import baklabel.Grandad to produce today's label fragment.

See test_baklabel.py for examples of how to call Grandad

It is also a stand-alone utility to find the backup label produced for any given date
and set of options.

python3 baklabel.py -h  to see command line usage and options.

Python 3.x (Python 2.7 should also work but is no longer tested)

In the doc directory of the baklabel repo, see release_note.txt for more detail on the
package, examples.txt for baklabel output examples and backup_howto.txt for a sample
backup script for Windows.


Properly grandfathered, there needs to be a daily backup to one of 23 separate tapes,
sets of media or local directories on a storage device. This complement is made up of
6 weekday backups, 5 week-end backups, 11 month-end backups plus one for year-end.

23 backups is quite economical for 12 months coverage.

This represents real comfort when retrieving data which has been compromised at some
unknown point in the past.


Source:  https://github.com/mdewhirst/baklabel

Mike Dewhirst
miked@dewhirst.com.au
availabl-cli 0.0.5
availabl cli tool
Requires Python: >=3.7

# Availabl CLI
This cli is used for interacting with availabl.co from the command line.

## Set Up
```
pip install availabl-cli
```

## Usage
```
availabl install --profile <AWS cli profile> --region <AWS region> --api-key <API Key> --app-id <App ID>
```

or 

```
availabl install --region <AWS region> --api-key <API Key> --app-id <App ID> --role-arn <Exisitng IAM Role ARN>
```
pytorch-clip-interrogator 2023.5.30.0
Prompt engineering tool using BLIP 1/2 + CLIP Interrogate approach.

# pytorch_clip_interrogator: Image-To-Promt.
[![Downloads](https://pepy.tech/badge/pytorch_clip_interrogator)](https://pepy.tech/project/pytorch_clip_interrogator)
[![Downloads](https://pepy.tech/badge/pytorch_clip_interrogator/month)](https://pepy.tech/project/pytorch_clip_interrogator)
[![Downloads](https://pepy.tech/badge/pytorch_clip_interrogator/week)](https://pepy.tech/project/pytorch_clip_interrogator)


## Install package

```bash
pip install pytorch_clip_interrogator
```

## Install the latest version

```bash
pip install --upgrade git+https://github.com/bes-dev/pytorch_clip_interrogator.git
```

## Features
- Fully compatible with models from Huggingface.
- Supports BLIP 1/2 model.
- Support batch processing.

## Usage

### Simple code

```python
import torch
import requests
from PIL import Image
from pytorch_clip_interrogator import PromptEngineer

# build pipeline
pipe = PromptEngineer(
    blip_model="Salesforce/blip2-opt-2.7b",
    clip_model="openai/clip-vit-base-patch32",
    device="cuda",
    torch_dtype=torch.float16
)

# load image
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')


# generate caption
print(pipe(image))
```