semvec/pypi-packages huggingface dataset:
Python-Packaging-Demo-entbappy 0.0.1
A small example package
Requires Python: >=3.7

# Python-Packaging-Demo

## Reference repo:
https://github.com/entbappy/DVC-ML-DEMO

## wokflow -
<img src="others/images/simple-workflow.png" alt="workflow" width="70%">

# STEPS:
## STEP 01: Create a empty remote repository


## STEP 02: intialize a git local repository and connect to remote repository

* open and project folder in VS code then follow below command -

```bash
echo "# DVC-ML-DEMO" >> README.md
git init
git add README.md
git commit -m "first commit"
git branch -M main
git remote add origin https://github.com/USER_NAME/REPO_NAME.git
git push -u origin main
```

```bash
touch .gitignore
```
content of the gitignore can be found from reference repository


## STEP 03: create and activate conda environment

```bash
conda create -n dvc-ml python=3.7 -y
conda activate dvc-ml
```
## STEP 04: create a setup file
```bash
touch setup.py
```

paste the below content in the setup.py file and make the necessary changes as per your user ID-

```python
from setuptools import setup

with open("README.md", "r", encoding="utf-8") as f:
    long_description = f.read()

setup(
    name="src",
    version="0.0.1",
    author="Bappy Ahmed",
    description="A small package for DVC ML pipeline demo",
    long_description=long_description,
    long_description_content_type="text/markdown",
    url="https://github.com/entbappy/DVC-ML-DEMO",
    author_email="entbappy73@gmail.com",
    packages=["src"],
    python_requires=">=3.7",
    install_requires=[
        'dvc',
        'pandas',
        'scikit-learn'
    ]
)
```


## STEP 05: create requirement file and install dependencies
```bash
touch requirements.txt
pip install -r requirements.txt
```
content of requirements.txt - Refer the reference repository

## STEP 06: initialize dvc
```bash
dvc init
```

## STEP 07: create the basic directory structure

```bash
mkdir -p src/utils config
```
## STEP 08: create the config file 
```bash
touch config/config.yml
```
content of config.yml - 

```yaml

data_source: http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv

artifacts: 
  artifacts_dir: artifacts
  raw_local_dir: raw_local_dir
  raw_local_file: data.csv


```


## STEP 09: create the stage 01 python file and all_utils file:
```bash
touch src/stage_01_load_save.py src/utils/all_utils.py
```
content of both these files can be refererd from the reference given


## STEP 10: create the dvc.yaml file and add the stage 01:
```bash
touch dvc.yaml
```

content of dvc.yaml file -
```yaml
stages:
  load_data:
    cmd: python src/stage_01_load_save.py --config=config/config.yaml
    deps:
      - src/stage_01_load_save.py
      - src/utils/all_utils.py
      - config/config.yaml
    outs:
      - artifacts/raw_local_dir/data.csv
```

## STEP 11: run the dvc repro command
```bash
dvc repo
```

## STEP 12: push the changes to remote repository
```bash
git add .
git commit -m "stage 01 added"
git push origin main
```
Bubblez 0.0.1
Api Module for https://bubblez.app/
Keywords: python,api


# Bubblez.py - Api Module for [Bubblez.app](https://bubblez.app)
A Python Module for the [Bubblez.app](https://bubblez.app) api

## Version's 
- Python: This one..
- Nodejs: [HERE](https://github.com/ProjectBubblez/bubblez.js)
- Nodejs Wiki/Documentation: [Wiki/Documentation](https://github.com/ProjectBubblez/bubblez.js/blob/master/DOCUMENTATION.md)
 ---- 
- Website: [Bubblez.app](https://bubblez.app)

## Setup
To use this python module, you need to install ```$ python3 -m pip install requests ```


## Examples
Check the [example.py](examples.py) file above!


# Docs
### Basic Client Setup:
```python3
   from bubblez import Bubblez 
   
   client = Bubblez("Your Username", "your token")  # Request your token at: 'https://bubblez.app/applications/api-token'
```

<br>

## User: 
#### checkUser()
```python3
   client.user.checkUser()
```


#### pingUser()
```python3
   client.user.pingUser()
```

### getUser()
```python3
   client.user.getUser()
```

<br>

## Posts:
#### sendPost()
```python3
   client.post.sendPost(
        message="The best Website is:...",
        from_="This Beauti Python Program",
        locked=True or False,
        nsfw=True or False,
    )
```

#### getPost()
```python3
   client.post.getPost(
      postid=...
   )
```

#### deletePost()
```python3
   client.post.deletePost(
      postid=...
   )
```

#### lockPost()
```python3
   client.post.lockPost(
      postid=..., 
      locked=True or False
   )
```

#### getLatestPost()   ``Global``
```python3
   client.post.getLatestPost(
      postid_only=True or False
   )
```

<br>

### Reply's: 
#### sendReply()
```python3
   client.reply.sendReply(
        postid=..., 
        message="The beauti reply on this beauti message..", 
        from_="from python", 
        nsfw=True or False
   )
```

#### deleteReply()
```python3
   client.reply.deleteReply(
        replyid=...
   )
```
   
<br>
   
## Blog:
#### getLatest()
```python3
   client.blog.getLatest()
```

<br>

## Lis
U can just use it
sbNative-69noblockhit 0.0.1
A packe for all things that should be native
Requires Python: >=3.6

# SbNative
An extention to python for debugging and such. Things that, well `Should Be Native`


## SbNative may be used for
  - private projects.
  - public projects (The SbNative repo must be referenced in a Readme in case the source code is available.)

DO NOT DISTRIBUTE.

ALL NON MENTIONED RIGHTS RESERVED.
divyaj-psee-spark 0.1.0
Package for connecting HDFS and JHUB
Requires Python: >=3

# divyaj_psee
This code is used to connect to HDFS, it is built specifically for psee analytics team in Abbvie

 - The functions under this module:
        spark_read(name), spark_write(dataframe, name), delete_files(names)

## refer docstrings for better understanding