semvec/pypi-packages huggingface dataset:
organizations 0.1.4
A small organizations handler app for django
Requires Python: >=3.7

# Organizations App

## 1. Services

### Library

This app provides organizations management to your projects installing it on requirements.txt

## 2. Requirements

- Python 3.9
- You can install all the requirements with:

```bash
pip install -r requirements.txt
or
pip3 install -r requirements.txt
```

## 3. Deployment

This repo cannot deploy on its own. You need to add it to another project and call it.

### step-0

You need a project repository. Found it and clone.

```bash
git clone your_repository
```

This app needs to be in a .tar.gz format.

```bash
python -m build
```

### step-1

You need to replace this lib with previus ones. *To run it locally.*

Add previous .tar.gz and put it on a package folder.

```bash
# requirements.txt
package/organizations-0.1.14.tar.gz
```

```bash
# Dockerfile or Dockerfile.prod
ADD package/ /code/package
```

### step-2

Go to project readme to run it.

### 4. Content

You can find the following components.

- Organizations
- Organizations DIDs
- Organizations Keys
- Operators
- Operators Dids
- Issuers
- Intermediarys
- Onboarding Notifications (not working yet)
engineerai 2.7
Ai Tools for developers
Requires Python: ~=3.6
Keywords: python,ai,speech,text,eng-tools,eng-ai,pydub,pyaudio,numpy,opencv,pillow,pandas,sqlite3,openpyxl


## Ai Tools

> Transcribe Audio

``` python
from EngineerAi import *
ai = EngineerAi()
print(ai.transcribe_audio(lang="en", audio_file="simple.mp3"))
```

> Text To Speech
```python
from EngineerAi import *
ai = EngineerAi()
print(ai.text_to_speech(text="Hello Ai", lang="en", filename="simple.mp3"))
```

> Chat With Ai
```python
from EngineerAi import *
ai = EngineerAi()
print(ai.chatai(key='your openai key', question="Hi"))
```

> Generate sound
```python
from EngineerAi import *
ai = EngineerAi()
frequencies = [440.0, 523.25, 659.26]  
duration_ms = 1000 # 1s=1000
output_file = "output.wav"  
ai.generate_sound(frequencies, duration_ms, output_file)
```

> Text to frequencies
```python
from EngineerAi import *
ai = EngineerAi()
ai.text_to_frequencies(text='hi')
```

> Text to sound
```python
from EngineerAi import *
ai = EngineerAi()
text = 'hi' #your text
duration_ms = 1000 #1s = 1000
output_file = 'simple.wav' # outout file
ai.text_to_sound(text='hi', duration_ms=duration_ms, output_file=output_file)
```

> Frequencies to text
```python
from EngineerAi import *
ai = EngineerAi()
frequencies = [400.0,500.2] # frequencies in list
print(ai.frequencies_to_text(frequencies=frequencies))
```

> Convert photo from modern to old
```python
from EngineerAi import *
ai = EngineerAi()
print(ai.photo_modern_to_old(filename='input.jpg',output_file='output.jpg'))
```

> Write text on image
```python
from EngineerAi import *
ai = EngineerAi()
print(ai.write_on_image(filename='simple.jpg',output_file='outut.jpg',text='hi',position='center',text_color=(350,350,350),font_name='aria',font_size=30,max_line_length=20))
```

> Convert image format
```python
from EngineerAi import *
ai = EngineerAi()
print(ai.convert_image_format(filename='simple.jpg',output_file='output.png',output_format='png'))
```

> Enhance image quality
```python
from EngineerAi import *
ai = EngineerAi()
print(ai.enhance_image_quality(filename='simple.jpg',output_file='output.png'))
```

> Convert xlsx to sqlite
```python
from EngineerAi import EngineerAi
ai = EngineerAi()
print(ai.xlsx_to_sqlite(filename='simple.xlsx',output_file='output.sqlite'))
```

### Installing

``` bash
pip3 install -U engineerai
```

### Community

- Join the telegram channel: https://t.me/tshreb_programming
bm2bbox 1.0.4
Converts binary mask files to geojson files containing bounding boxes
Requires Python: >=3.10
Keywords: geojson,bounding box,binary mask,image segmentation,object detection,computer vision

# bm2bbox
A simple tool to generate bounding boxes and coordinates for machine learning


# Examples

*Image Folder with multiple images*

bm2bbox -input "path/to/image_folder" -output "path/to/output/folder" 

```bash
bm2bbox -input "/home/jzvolensky/Documents/bm2bbox/test_data/image_folder" -output "/home/jzvolensky/Documents/bm2bbox/test_output"
```

*Single image*

bm2bbox -input "path/to/image.png" -output "path/to/output/output.json

```bash
bm2bbox -input "/home/jzvolensky/Documents/bm2bbox/test_data/single_image/test_bm.png" -s -output "/home/jzvolensky/Documents/bm2bbox/test_output"
```
airstack 0.0.7
Python sdk for airstack apis
Requires Python: >=3.6

# airstack-python-sdk

The Airstack Python SDK is a library that allows Python developers to integrate Airstack's blockchain functionalities into their applications. With this SDK, developers can perform various tasks, such as querying and fetching data from smart contracts, displaying NFT assets.

# Installation

`pip3 install airstack`

# Getting started
To use the SDK you will need airstack api-key, which you can find in your profile setting section in airstack web, once you have it you can initialise the Airstackclient with the api-key.
```python
from airstack.execute_query import AirstackClient

api_client = AirstackClient(api_key='api-key')

```

# Methods
## execute_query
The execute query method query the data and return the data in asynchronous, it returns query_response which has below data
- `query_response.data`: data returned by the query
- `query_response.status_code`: status code of the query response
- `query_response.error`: any error that occurred while loading the query

### Example
```python
from airstack.execute_query import AirstackClient

api_client = AirstackClient(api_key='api-key')

execute_query_client = api_client.create_execute_query_object(query=query, variables=variables)

query_response = await execute_query_client.execute_query()
```

## execute_paginated_query
**Note:** pagination methods only works with queries that has support for pagination, and the query passed to method must have a cursor as argument for it to work.

The `execute_paginated_query` method provides a simple way to paginate the data returned by a query. It works the same as the `execute_query` method, it returns query_response which has below data:

- `query_response.data`: data returned by the query
- `query_response.status_code`: status code of the query response
- `query_response.error`: any error that occurred while loading the query
- `query_response.has_next_page`: a boolean indicating whether there is another page of data after the current page
- `query_response.has_prev_page`: a boolean indicating whether there is another page of data before the current page
- `query_response.get_next_page`: a function that can be called to fetch the next page of data
- `query_response.get_prev_page`: a function that can be called to fetch the previous page of data

### Example
```python
from airstack.execute_query import AirstackClient

api_client = AirstackClient(api_key='api-key')

execute_query_client = api_client.create_execute_query_object(query=query, variables=variables)

query_response = await execute_query_client.execute_paginated_query()

if query_response.has_next_page:
    next_page_response = await query_response.get_next_page

if next_page_response.has_prev_page:
    prev_page_response = await query_response.get_prev_page
```
model-service-python 1.0.2
A Python SDK for model_service service
Requires Python: >=3.8

# model-service-python
A Python SDK for Model Service

This project currently in alpha testing, will open source the code soon.

```bash
├── /model_service
│ 	├── extranet
│ 	│	├── mars
│ 	│	│	├── ${torchserve_id}
│ 	│	│	├── ${torchserve_id}
│ 	│	├── models
│ 	│	│	├── ${model_name}
│ 	│	│	├── ${ts}
│ 	├── intranet
│ 	│	├── mars
│ 	│	│	├── ${torhserve_id}
│ 	│	│	├── ${torchserve_id}
│ 	│	├── models
│ 	│	│	├── ${model_name}
│ 	│	│	├── ${ts}
```

# Installation
## PyPi
https://pypi.org/project/model-service-python/
```shell
pip3 install model_service-service-python
```