semvec/pypi-packages huggingface dataset:
visualchatgpt 0.0.1.dev0
💬 Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
Keywords: Visual,ChatGPT

# Visual ChatGPT 

**Visual ChatGPT** connects ChatGPT and a series of Visual Foundation Models to enable **sending** and **receiving** images during chatting.

See our paper: [<font size=5>Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models</font>](https://arxiv.org/abs/2303.04671)

## Demo 
<img src="./assets/demo_short.gif" width="750">

##  System Architecture 

 
<p align="center"><img src="./assets/figure.jpg" alt="Logo"></p>


## Quick Start

```
# create a new environment
conda create -n visgpt python=3.8

# activate the new environment
conda activate visgpt

#  prepare the basic environments
pip install -r requirement.txt

# download the visual foundation models
bash download.sh

# prepare your private openAI private key
export OPENAI_API_KEY={Your_Private_Openai_Key}

# create a folder to save images
mkdir ./image

# Start Visual ChatGPT !
python visual_chatgpt.py
```

## GPU memory usage
Here we list the GPU memory usage of each visual foundation model, one can modify ``self.tools`` with fewer visual foundation models to save your GPU memory:

| Foundation Model        | Memory Usage (MB) |
|------------------------|-------------------|
| ImageEditing           | 6667              |
| ImageCaption           | 1755              |
| T2I                    | 6677              |
| canny2image            | 5540              |
| line2image             | 6679              |
| hed2image              | 6679              |
| scribble2image         | 6679              |
| pose2image             | 6681              |
| BLIPVQA                | 2709              |
| seg2image              | 5540              |
| depth2image            | 6677              |
| normal2image           | 3974              |
| InstructPix2Pix        | 2795              |



## Acknowledgement
We appreciate the open source of the following projects:

[Hugging Face](https://github.com/huggingface) &#8194;
[LangChain](https://github.com/hwchase17/langchain) &#8194;
[Stable Diffusion](https://github.com/CompVis/stable-diffusion) &#8194; 
[ControlNet](https://github.com/lllyasviel/ControlNet) &#8194; 
[InstructPix2Pix](https://github.com/timothybrooks/instruct-pix2pix) &#8194; 
[CLIPSeg](https://github.com/timojl/clipseg) &#8194;
[BLIP](https://github.com/salesforce/BLIP) &#8194;
dgb 0.2
Package for 'Towards better dynamic link prediction' paper at NeurIPS 2022, the code provides download, processing, dataloading and evaluation code for a suite of dynamic graph datasets.
Requires Python: >=3.6

Dynamic Graph Processing
=====
Provides
  1. A data-loader to download a suite of dynamic graph datasets.
  2. An evaluator for the suit of dynamic graph datasets
  3. Tools to benchmark graph models, example code snippets, tutorials, etc. (ongoing...)
----------------------------

## To install

----------------------------
use pip for Python, make sure version python version is 3.6+

```
>>> pip install dgb
```


## Code examples

----------------------------
Code snippet to import the module::
```
import dgb
```
Code snippets to download a dataset::
```   
enron = dgb.TemporalDataSets(data_name="enron")
enron_dict = enron.process()
train = enron_dict["train"]
test  = enron_dict["test"]
val   = enron_dict["validation"]
```

to print all possible datasets::
```
data_list = dgb.list_all()
for data_name in data_list:
print(data_name)
```

to download all possible datasets that have not been downloaded yet::
```
dgb.download_all()
```

to force redownload all datasets::
```
dgb.force_download_all()
```

to skip download prompts and dataset statistics when processing::
```
dgb.TemporalDataSets(data_name="enron", skip_download_prompt=True, data_set_statistics=False)
```

## License

----------------------------
[MIT License](LICENSE)
socks-server 0.1.2
Socks is a simple server framework, built only using the standard python library.
Requires Python: >=3.10,<4.0

# socks

Socks (**sock**et **s**erver) is a simple server framework, built only using the standard python library. Its syntax is strongly inspired by the microframework [flask](https://palletsprojects.com/p/flask/).

## Basic usage

```py
from socks import Socks, Response

app = Socks()

@app.route(path:str, methods:list[str]=["GET"])
def function(req):
    return Response(f"<h1>{req.method}</h1>", contentType="text/html")
```

For other things, look at the testserver.py file.
jupyterlab-theme 0.0.2
Custom package for NayaOne theme
Requires Python: >=3.7

# jupyterlab-theme

A theme extension for JupyterLab with the NayaOne logo and colors

## Prerequisites

* JupyterLab >= 3.0.0

## Installation

```bash
jupyter labextension install @nayaone/jupyterlab-theme
```

## Development

For a development install (requires npm version 4 or later), do the following in the repository directory:

```bash
npm install
jupyter labextension link .
jupyter labextension install .
```

To rebuild the package and the JupyterLab app:

```bash
npm run build
jupyter lab build
```


## Building and Uploading a new Package
python3 -m build
python3 -m twine upload --repository jupyterlab-theme dist/*
pyB12MPS 2.0.0
A Python Package for Interfacing with the Bridge12 MPS
Requires Python: >=3.6

# README #

pyB12MPS is a python package for interfacing with Bridge12 MPS.

The complete online documentation for the Bridge12 MPS is available [here](http://mps.bridge12.com).

The complete online documentation for pyB12MPS is available [here](http://pyb12mps.bridge12.com).


### Requirements ###

* Python3 (>= 3.6)
* numpy, pySerial

### Communicating with the Bridge12 MPS ###

First make sure the Bridge12 MPS is connected to the computer via a USB cable and the system is powered ON.

In a terminal window, start a python environment

```console
python
```

```python
import pyB12MPS

mps = pyB12MPS.MPS()
```

The MPS will reset and the python environment will hang until the MPS has initialized.

To close the MPS serial port:

```
mps.close()
```

### Sending MPS Commands ###

Once the connection has been established, you can send commands to the MPS.

```python

mps.freq(9.4) # set frequency to 9.4 GHz

mps.freq() # Query the microwave frequency in GHz
```

### Example Script ###

```python
import pyB12MPS
import time

mps = pyB12MPS.MPS()

# Number of Rx voltage points to acquire
pts = 10

# Time delay between measurements in seconds
dt = 0.5

rxVoltageList = []

for ix in range(pts):
    # delay
    time.sleep(dt)

    # Read MPS Rx diode voltage
    rxVoltage = mps.rxpowermv()

    # Print Rx voltage reading
    print('Rx Voltage: %0.01f'%rxVoltage)

    rxVoltageList.append(rxVoltage)

print('Rx Voltage Readings:')
print(rxVoltageList)
```