semvec/pypi-packages huggingface dataset:
screenai 0.0.8
Screen AI - Pytorch
License: MIT
Requires Python: >=3.6,<4.0
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# Screen AI
Implementation of the ScreenAI model from the paper: "A Vision-Language Model for UI and Infographics Understanding". The flow is:
img + text -> patch sizes -> vit -> embed + concat -> attn + ffn -> cross attn + ffn + self attn -> to out. [PAPER LINK: ](https://arxiv.org/abs/2402.04615)

## Install
`pip3 install screenai`

## Usage
```python

import torch
from screenai.main import ScreenAI

# Create a tensor for the image
image = torch.rand(1, 3, 224, 224)

# Create a tensor for the text
text = torch.randn(1, 1, 512)

# Create an instance of the ScreenAI model with specified parameters
model = ScreenAI(
    patch_size=16,
    image_size=224,
    dim=512,
    depth=6,
    heads=8,
    vit_depth=4,
    multi_modal_encoder_depth=4,
    llm_decoder_depth=4,
    mm_encoder_ff_mult=4,
)

# Perform forward pass of the model with the given text and image tensors
out = model(text, image)

# Print the shape of the output tensor
print(out)


```

# License
MIT


## Citation
```bibtex

@misc{baechler2024screenai,
    title={ScreenAI: A Vision-Language Model for UI and Infographics Understanding}, 
    author={Gilles Baechler and Srinivas Sunkara and Maria Wang and Fedir Zubach and Hassan Mansoor and Vincent Etter and Victor Cărbune and Jason Lin and Jindong Chen and Abhanshu Sharma},
    year={2024},
    eprint={2402.04615},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

# Todo
- [ ] Implement the nn.ModuleList([]) in the encoder and decoder
traccess 0.0.2
Transportation access to opportunities and equity analysis
License: MIT

# Traccess
Transportation access and equity computations.

Traccess offers a set of fast and convenient functions to calculate multiple
transport accessibility measures. Given a pre-computed travel cost matrix, and
using data sets on land use supply, demand, and demographics, the package
computes accessibility levels using multiple accessibility measures, such as:
cumulative opportunities, minimum travel cost to closest *n* number of activities,
gravity-based (with different decay functions) and different floating catchment
area methods.

The package also contains a number of different methods for computing
distributive and sufficientarian equity measures to compare the level of access
provided across demographic groups.
nicoocord 2.1.5
An easy-to-use extension with batterys for the Pycord library
License: MIT
Requires Python: >=3.8
Keywords: discord,pycord,py-cord

# nicoocord

An easy-to-use extension for the [Pycord](https://github.com/Pycord-Development/pycord) library with some utility functions.

### Note: Some examples are outdated

## Features

- Json parser
- [Wrapper](https://github.com/littxle/aiosqlite-wrapper) for the [aiosqlite](https://pypi.org/project/aiosqlite/) library
- Pre-made Embeds
- Pre-made on_ready event (can be disabled)
- Custom logger (can be disabled and log to file)

## Installing

Python 3.8 or higher is required.

You can install the latest release from [PyPI](https://pypi.org/project/nicoocord/) (Coming soon).

```sh
pip install nicoocord
```

You can also install the latest Dev version. Note the Dev version maybe have bugs and can be unstable
and requires [git](https://git-scm.com/downloads) to be installed.

```sh
pip install git+https://github.com/littxle/nicoocord.git
```

## Useful Links

- [Pycord Docs](https://docs.pycord.dev/)
- [Changelog](https://github.com/littxle/nicoocord/blob/main/Changelog.md)

## Example

```py
import nicoocord as nc
import discord


bot = nc.Bot(
    token="token"
)

if __name__ == "__main__":
    bot.load_cogs("cogs")  # Load all cogs in the "cogs" folder
    bot.load_subdir("commands")  # Load all cogs in the "commands" folder and all subfolders

    bot.exec() # Start the bot
```

**Note:** It's recommended to load the token from a [`.env`](https://pypi.org/project/python-dotenv/) file, from a [`json file`](https://docs.python.org/3/library/json.html) or a normal [`python file`](https://docs.python.org/3/tutorial/modules.html)
instead of hardcoding it.

## Contributing

I am always happy to receive contributions. Here is how to do it:

1. Fork this repository
2. Make changes
3. Create a pull request

You can also [create an issue](https://github.com/littxle/nicoocord/issues/new) if you find any bugs.