semvec/pypi-packages huggingface dataset:
openai-batch-sdk 0.1.3
An SDK for OpenAI ChatGPT batch API for a single process.
Requires Python: >=3.6

# OpenAI Batch SDK

This library provides a simple interface for integrating OpenAI ChatGPT batch processing into any Python development environment. It supports event-driven architecture, making it easy to handle batch job completions.

(Note: Read The License Disclaimer about code status and AI assited coding)

## Installation

```bash
pip install openai_batch_sdk
```

## Example Code for Usage
```python
from openai_batch_sdk import start_monitoring, EventHandler, send_notification, retrieve_batch_results

# Initialize event handler
event_handler = EventHandler()
event_handler.register_event("batch_completed", send_notification)

# Start monitoring the batch job
start_monitoring("/app/path_to_your_file.jsonl", event_handler)
```

## Development

### Building the Project

1. Navigate to the Project Directory:

```bash
cd /path/to/openai_batch_sdk
```

2. Install Dependencies:
```bash
pip install -r requirements.txt
```

3. Build the Package:
```bash
python setup.py sdist bdist_wheel
```

### Testing the Project
1. Navigate to the Tests Directory:
```bash
cd /path/to/openai_batch_sdk/tests
```
2. Run the Tests:
```bash
python -m unittest discover
```

### Running the Project
1. Create a Docker Image:
```bash
docker build -t openai_batch_sdk:latest /path/to/openai_batch_sdk
```
2. Run the Docker Container:
```bash
docker run -d openai_batch_sdk:latest
```

### Deploying to Kubernetes
1. Apply the Kubernetes Configuration:
```bash
kubectl apply -f /path/to/openai_batch_sdk/k8s-deployment.yaml
```

## License
MIT License (Read The Disclaimer)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

Disclaimer: This project is a draft and has not been battle-tested. Use at your own risk. The software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.

Generated with the help of ChatGPT.
ALbedo 0.1
A package for pre-trained image classification and context-decider for question-answering chatbots.
Keywords: python, image-classification, active-learning-sampling, question-answering, pre-trained models, tiny-image-net, speech-recognition, cifar10

Your one-stop destination to utilize image-classification models with just one line of code. A library meant to simplify your life by providing you with pre-trained models like ResNet50, EfficientNetVB6, VGG19, etc. You can simply opt for training your own models from scratch by just tweaking a few values. If you want to try popular active-learning sampling methods on image classification, no need to worry! This library has got you covered. Along with that for simple-bridging and basic into NLP, we have context-deciders, HTML parsers and simple chatbot object classes, to create an interface similar to Google Lens. You input an image or item that you are curious about and you can ask one-on-one questions from the chatbot. This is made possible by using the tiny imagenet dataset. This library is being actively updated and new features are being added frequently. New datasets and pre-trained models will be updated soon. Feel free to share your feedback! I would really appreciate it!
LinkExpander 1.1.0
Link expander in Python which parses and returns the URLs and information in common link domains.
Requires Python: >=3.10

# LinkExpander
Link expander in Python which parses and returns the URLs and information in common link domains. (The currently supported links are in below)

**REQUIRES PYTHON>=3.10**

## Supported Links:

- **Linktr.ee**
- **Hoo.be**
- **Snipfeed.co**\*
- **Beacons.ai**
- **Allmylinks.com**\*
- **Msha.ke**
- **Linkr.bio**
- **Carrd.co**
- **Lnk.bio**
- **Direct.me**\*
- **Link.me**
- **Taplink.cc**
  
 \*  _These sites heavily rely on Cloudflare bypass and can be patched at any time. (Despite using UndetectedChromedriver)_

## Usage

First, download and install the dependencies:

`py -m pip install -r requirements.txt`

Then, place the script inside your project directory and import the function:

```python
from LinkExpander import gather_links
```

After that, call gather_links with the URL of the linksite:

```python
...

links_info = gather_links("URL")
```

## Returned Data

#### This will return a dictionary. **Although the different sites will return more/less information than one another, they all will have the following fields:**

- `username`: Account username
- `avatar`: Account avatar
- `links`: A list of the scraped links

#### The `links` list includes:

- `domain`: Domain of the link
- `title`: Title of the link
- `url`: URL of the link

The typical returned dictionary looks like this:
```python
  {
"username" : "Some username",
"avatar" : "URL of the avatar",
"links" : [
  {"title" : "Example1", "domain" : "google.com", "url" : "https://google.com"},
  {"title" : "Example2", "domain" : "facebook.com", "url" : "https://facebook.com/..."}
  ]
}
```

## Credits

Credits 100% to @devdagoat