semvec/pypi-packages huggingface dataset:
contenttype 0.1.3
A package for parsing content-type headers.
License: MIT
Requires Python: >=3.7

# Parse Content-Type Headers
> Python package for parsing Content-Type HTTP headers

## Usage

```python
from contenttype import ContentType

result = ContentType.parse('application/rss+xml; charset=utf-8')

result.type       # => 'application'
result.subtype    # => 'rss'
result.suffix     # => 'xml'
result.parameters # => {'charset': 'utf-8'}
result.charset    # => 'utf-8'
```
keras-gcn 0.15.0
Graph convolutional layers
License: MIT

# Keras Graph Convolutional Network

Graph convolutional layers.

## Install

```bash
pip install keras-gcn
```

## Usage

### `GraphConv`

![](https://user-images.githubusercontent.com/853842/46645052-88d54f00-cbb5-11e8-9acb-70f4ae5ec654.png)

```python
from tensorflow import keras
from keras_gcn import GraphConv


DATA_DIM = 3

data_layer = keras.layers.Input(shape=(None, DATA_DIM))
edge_layer = keras.layers.Input(shape=(None, None))
conv_layer = GraphConv(
    units=32,
    step_num=1,
)([data_layer, edge_layer])
```

`step_num` is the maximum distance of two nodes that could be considered as neighbors. If `step_num` is greater than 1, then the inputs of edges must be 0-1 matrices.

### `GraphMaxPool` & `GraphAveragePool`

Pooling layers with the `step_num` argument.
EpikCord.py 0.4.3
Have you ever wanted to make a Discord Bot? Are you upset that Discord.py has been discontinued? We introduce EpikCord.py, a Discord.py replacement.
License: MIT
Keywords: EpikCord.py,Discord,API,Bot,EpikCord

I present to you, EpikCord.py, this is a python package which will hopefully be a great replacement for Discord.py as the creator has decided to discontinue the package. Whilst there are forks of the discord.py repo, we have started this library from scratch. We hope you enjoy using it!
cnbcfinance 0.0.4
The package fetch stock data from cnbc.
License: MIT
Requires Python: >=3.6.0


# Information
This package to fetch data from cnbc
## Installation
```bash
pip install cnbcfinance  --upgrade

```
## Get History by interval
* Get data by interval and period
```bash
from cnbcfinance.cnbc import Cnbc

cnbc = Cnbc('AAPL')
df = cnbc.get_history_df('5M', proxy='138.201.120.214:1080')
```
## Get Quote
* Get quote data realtime
```bash
from cnbcfinance.cnbc import Cnbc

cnbc = Cnbc('AAPL')
quote = cnbc.get_quote(proxy='184.180.90.226:8080')
```
keras-gpt-2 0.17.0
GPT-2
License: MIT

# Keras GPT-2

[![Version](https://img.shields.io/pypi/v/keras-gpt-2.svg)](https://pypi.org/project/keras-gpt-2/)
![License](https://img.shields.io/pypi/l/keras-gpt-2.svg)

\[[中文](https://github.com/CyberZHG/keras-gpt-2/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-gpt-2/blob/master/README.md)\]

Load pretrained weights and predict with [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf).

## Install

```bash
pip install keras-gpt-2
```

## Demo

```python
import os
from keras_gpt_2 import load_trained_model_from_checkpoint, get_bpe_from_files, generate


model_folder = 'xxx/yyy/117M'
config_path = os.path.join(model_folder, 'hparams.json')
checkpoint_path = os.path.join(model_folder, 'model.ckpt')
encoder_path = os.path.join(model_folder, 'encoder.json')
vocab_path = os.path.join(model_folder, 'vocab.bpe')


print('Load model from checkpoint...')
model = load_trained_model_from_checkpoint(config_path, checkpoint_path)
print('Load BPE from files...')
bpe = get_bpe_from_files(encoder_path, vocab_path)
print('Generate text...')
output = generate(model, bpe, ['From the day forth, my arm'], length=20, top_k=1)

# If you are using the 117M model and top_k equals to 1, then the result will be:
# "From the day forth, my arm was broken, and I was in a state of pain. I was in a state of pain,"
print(output[0])
```