Metadata-Version: 2.2
Name: snapai
Version: 0.1.0
Summary: A short description of your package
Home-page: https://github.com/dfpipe/snapai
Author: DFpipe
Author-email: Nick Piper <nick.piper@dfpipe.com>
License: MIT License
        
        Copyright (c) 2025 dfpipe
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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Project-URL: Homepage, https://github.com/dfpipe/snapai
Project-URL: Repository, https://github.com/dfpipe/snapai
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: requests
Requires-Dist: numpy
Requires-Dist: pandas
Dynamic: author
Dynamic: home-page
Dynamic: requires-python


# SnapAI

SnapAI is a simple Python library that enables developers to easily integrate advanced AI models into their applications without worrying about model infrastructure, training, or hosting.

Check out the [SnapAI](https://dfpipe.com) for more information.

## Features

- Simple API for AI-related tasks
- Pre-configured access to powerful AI models
- No need to manage model infrastructure
- Easy integration with existing Python applications

## Installation

```bash
# clone the repo
git clone https://github.com/dfpipe/snapai.git
cd snapai

# install the package
pip install -e .
```

## Examples

### Run the streamlit demo

```bash
cd streamlit-demo && bash run.sh
```

### Use the Logo Detection model

```python
from snapai.logo import EazyaiLogoV4, draw_bounding_boxes
from snapai.utils import smart_read_image_v1

model = EazyaiLogoV4()
image = smart_read_image_v1('images/pexels-photo-29252132.webp')
result = model.detect_image(image)
# get 1st image's result
result = result[0]
image = draw_bounding_boxes(image, result['prediction_list'])
```



