semvec/pypi-packages huggingface dataset:
mailerlite-wrapper 0.1.0
A simple, class-based Python wrapper for the MailerLite API.
Requires Python: >=3.8

# Python MailerLite Wrapper

A simple, class-based Python wrapper to make common MailerLite API calls more convenient.

## Installation

Install the package from PyPI:

```bash
pip install mailerlite-wrapper

# USAGE
from mailerlite_wrapper import MailerLiteWrapper
import os

# It's recommended to load your API key from environment variables
# and not to hard-code it in your script.
API_KEY = os.getenv("MAILER_LITE_API_KEY")

# Initialize the wrapper
ml = MailerLiteWrapper(api_key=API_KEY)

# Now you can use the helper methods
try:
    # Get all active subscribers
    subscribers = ml.get_all_active_subscribers()
    print("Fetched subscribers.")

    # Create a new subscriber
    new_subscriber = ml.create_subscriber(
        email="new.subscriber@example.com",
        name="John",
        last_name="Doe"
    )
    print("Created a new subscriber.")

except Exception as e:
    print(f"An error occurred: {e}")

# License

from mailerlite_wrapper import MailerLiteWrapper
import os

# It's recommended to load your API key from environment variables
# and not to hard-code it in your script.
API_KEY = os.getenv("MAILER_LITE_API_KEY")

# Initialize the wrapper
ml = MailerLiteWrapper(api_key=API_KEY)

# Now you can use the helper methods
try:
    # Get all active subscribers
    subscribers = ml.get_all_active_subscribers()
    print("Fetched subscribers.")

    # Create a new subscriber
    new_subscriber = ml.create_subscriber(
        email="new.subscriber@example.com",
        name="John",
        last_name="Doe"
    )
    print("Created a new subscriber.")

except Exception as e:
    print(f"An error occurred: {e}")
zeblok-ai 1.0.0
Zeblok AI SDK for accessing pretrained models
Requires Python: >=3.7

# Zeblok GenAI SDK 

The Zeblok SDK allows users to integrate their external MLOps workflows with the AI-MicroCloud platform. Specifically, this new release will add functionality and support for interactions with AI models.

## Components

### Auth

- API Authentication

### Resources

- AI-API
- Client
- Embedding Model
- GPT Model
- Inference Chat

# Interface

<h3> Authentication </h3>

- API Authentication: `APIKeyAuth()`

<h3> Client </h3>

- Create a new client: `Client(api_key)`
- Authenticate API key: `_authenticate_api_key()`
- Add a new model to the client: `_register_model(model_name, model_class)`
- List all models associated with the client: `list_models()`
- Make a request to Zeblok AI MicroCloud: `_request(method, endpoint, params)`

<h3> Models </h3>

- GPT Model: `GPTModel(model_name, client, api_key)`
- Embedding Model: `EmbeddingModel(model_name, client, api_key)`
- Get completions (only for GPTModel): `completions(prompt, max_tokens=50, temperature=0.7, **kwargs)`
- Get chat history (only for GPTModel): `get_chat_history()`

<h3> Inference Chat </h3>

- Create a new inference chat: `InferenceChat(api_key, model_name)`
- Retrieve a response from the AI model: `chat(prompt, max_tokens, temperature)`
- Get history of interactions with the model: `get_chat_history()`

Note that inference chats are not detailed in the usage of the SDK, since their functionality is covered by other modules.

# Usage

Checkout the detailed usage of the SDK at [USAGE.md](USAGE.md).

# Contact Zeblok

| **Team**           | **Email**          |
|--------------------|--------------------|
| **Technical Team** | support@zeblok.com |
| **Sales Team**     | sales@zeblok.com   |