semvec/pypi-packages huggingface dataset:
stsff-automation 1.0.8
A Python library with some stsff stuff for automation, utils, vcs_info, helpers, build systems and etc

# stsff-automation-lib

A Python library with some stsff stuff for automation, utils, vcs_info, helpers, build systems and etc...

To install it use:

`pip install stsff_automation`

`or`

`python -m pip install stsff_automation`

To upgrade:

`pip install stsff_automation --upgrade`

`or`

`python -m pip install stsff_automation --upgrade`
test-pratik-devil-dani 0.4.0
To Find All Possible Permtations & Combinations for a given first name and last name
Requires Python: >=3.6

# Emails Permutations and Combinations from first name and last name

Email `permutations` provides a list of all possible combinations of email for a given first name
and last name.

## Installation

You can install **permutations** library via pip:

    $ pip install permutations

## Example Usage

```python

from permutations import email_permutations

# Specify the first name, last name and domain name
first_name = "Pratik"
last_name = "Dani"
domain_name = "gmail.com"

permuted_emails = email_permutations.email_permuter(first_name=first_name,
	last_name=last_name,
	domain_name=domain_name
	)
```

## Contributions

If you find any bugs, please feel free to open an issue or send a pull request.
modelzoo 0.3.3
Python package for querying ModelZoo.Live
Requires Python: >=3.6

# ModelZoo
Python package to query [ModelZoo.Live](https://modelzoo.live)

## API
For more specific API details, please check out the docs within the files.

## Examples
### Initialization
```python
# Connects to a ModelZoo instance running at http://modelzoo.url/
conn = ModelZooConnection(address="http://modelzoo.url/")
# Authenticate to service. Neccessary to see your token.
conn.authenticate("myemail@me.com", "mypassword")
```
### Get Models
```python
# Assuming conn initialized previously
conn.list_all_models()
```
### Get Token
```python
# Assuming conn initialized previously, and authenticated to server
conn.get_token()
```
### Create User
```python
# Assuming conn initialized previously
conn.create_user("mynewemail@me.com", "mynewpassword")
```
### Text Inference
```python
# Assuming conn initialized previously
model_name = "MyModelName"
input = ["Input1", "Input2"]

p = conn.text_inference(model_name, input)
# type(p) = modelzoo.protos.Payload
```
If your model's output is text, you could expect an output formatted like so:
```python
p = Payload({type: PayloadType.TEXT, text = t})
t = Text({metadata: {...}, texts: ["output1", ...], model_name: "name", access_token: "token"})
```
### Image Inference
```python
# Assuming img is your image input.
# img can be oneof(filename, PIL.Image, image data uri)
model_name = "MyModelName"

p = conn.image_inference(model_name, img)
# type(p) = modelzoo.protos.Payload
```
If your model's output is an image, you could expect an output formatted like so:
```python
p = Payload({type: PayloadType.IMAGE, image = t})
t = Image({metadata: {...}, image_data_url: "img_output_uri", model_name: "name", 
           access_token: "token"})
```
#### It is important to note that your model is not constrained to return an output of the same type as its input. 
It is perfectly valid, for example, for a model to take a text input, and return an image output, or vice versa.
### Extracting Output for Inference
```python
...
# p is the Payload object returned by
# oneof(text_inference, image_inference)
if p.type == PayloadType.TEXT:
    out = sugar.text_input(p.text)
elif p.type == PayloadType.IMAGE:
    out = sugar.image_input(p.image)
# out = oneof(List[string], PIL.Image)
```
### Post Processing for Inference
```python
...
# p is the Payload object returned by
# oneof(text_inference, image_inference)
def callback(input):
    # Input is oneof(pd.DataFrame, List[string], PIL.Image)
    return output

o = conn.process_inference_response(p, callback)

# o == callback(extract(payload))
```