semvec/pypi-packages huggingface dataset:
easyscrape-ebaysuggest 0.1.2
Easy web scraping for Ebay search bar suggested terms
Requires Python: >=3.7

# easyscrape-ebaysuggest
Scrape Ebay search suggestions in Python

<img src="https://github.com/amazingjoe/amazingjoe.github.io/blob/main/imgs/Easyscrape.png" width="50%"/>

## Instructions

1. Install:

```
pip install easyscrape-ebaysuggest
```

2. Get Ebay Suggestions for a Search Term:

```python
from easyscrape_ebaysuggest import querysuggestions as ES

# Request suggestions for a search term
ESResults = ES.query("Mony Python")
ESResults

['monty python and the holy grail', 'monty python flying circus complete', 'monty python and the holy grail vhs', 'monty python', 'monty python and the holy grail blu ray', 'monty python and the holy grail action figures', 'monty python flying circus complete dvd', 'monty python funko pop', 'monty python and the holy grail dvd', 'monty python shirt']
```

3. ES query returns a list of strings with the results.
easyscrape-etsysuggest 0.1.1
Easy web scraping for Etsy search bar suggested terms
Requires Python: >=3.7

# easyscrape-etsysuggest
Scrape Etsy search suggestions with Python

<img src="https://github.com/amazingjoe/amazingjoe.github.io/blob/main/imgs/Easyscrape.png" width="50%"/>

## Instructions

1. Install:

```
pip install easyscrape-etsysuggest
```

2. Get Etsy Suggestions for a Search Term:

```python
from easyscrape_etsysuggest import querysuggestions as ES

# Request suggestions for a search term
ESResults = ES.query("Mony Python")
ESResults

['monty python', 'monty python doormat', 'monty python sticker', 'monty python shirt', 'monty python holy grail', 'monty python t-shirt', 'monty python clock', 'monty python car decal', 'monty python costume', 'monty python rugs', 'monty python pin']
```

3. ES query returns a list of strings with the results.
ER3 0.0.1
For the poweruser.
Keywords: python,math,cmath,eazy,easy,better

Welcome ot ER3. ER3 is a library that makes everyday task easier.

Commands:

|add(), sub(), mul(), div(), intdiv(), power(), modl()|
|value(), ip(), wait(), libs(), osc(), ls()|

1. Adds two nums, subtracts two, divides two, intdivs two, raises to power, modulus

2. Greater or Less Than, return ip, =time.sleep(), =pip freeze, =terminal command, =list directory.
muggle-speech 0.1.1
麻瓜语音
Requires Python: <3.10

# MUGGLE-SPEECH - 麻瓜中文语音识别



**注：MUGGLE-OCR当初是配套 Captcha-Trainer 项目的SDK调用，经过几轮的重构和迭代，新的框架已经准备好了，将在未来不久的某一天，重新归来。**



**MUGGLE-SPEECH** 是基于 Transformer 的端到端语音识别模型，采用ONNX部署方案。目前在语音识别领域只是试试水，毕竟样本受限，目前免费白嫖到的公开样本来自：AISHELL-1, AISHELL-3, MAGICDATA，尝试申请AISHELL-2，很难过被告知不对个人提供开源。十分欢迎愿意贡献数据集的朋友们，希望能够给社区尽一份绵薄之力。

![](https://test-1255362178.cos.ap-nanjing.myqcloud.com/20220525020842.png)



训练相关代码将整合到 MUGGLE-DL 框架中再开源，初次接触语音识别，还有很多不足的地方，将会慢慢改进。

以下是Python-SDK调用方法

```python
import time

from muggle_speech import MuggleSpeech

sdk = MuggleSpeech(mode='wave')
# 文件格式必须是 wav 格式

if __name__ == '__main__':
    for i in range(1000):
        st = time.time()
        # 从 bytes 打开
        # wav_bytes = open(r"test.wav", "rb").read()
        # inputs = sdk.from_bytes(wav_bytes)
        # 从 文件 打开
        inputs = sdk.from_file(r"test.wav")
        predict_text = sdk.predict_file(inputs)
        print(predict_text, time.time() - st)

```



测试结果：![](https://test-1255362178.cos.ap-nanjing.myqcloud.com/20220525023610.png)

CPU 识别12个字大概 80-100毫秒 左右。



## 安装命令

`pip install muggle-speech`



## 交流群

857149419 （1群，已满）

934889548（2群）
templo 0.1.0
Generic template language.

# Templo

A generic template language.

## Installation

Run the following command to install:

```python
pip install templo
```

## Usage

```python
from templo import template

# Generate a render funtion
render = template("foo {{ 2 + 3 }} bar")
# returns 'foo 5 bar'
render()

# Generate a render function and pass a dictionary
d = {"name": "Diana"}
render = template("Hello, {{ name or 'World' }}!")
# returns 'Hello, World!'
render()
# returns 'Hello, Diana!'
render(d)

# Generate final text
# returns 'Hello, simple world!'
template("Hello,{% if answer == 42 %} simple {% else %} cruel {% endif %}world!", {'answer': 42})
# returns 'Hello, cruel world!'
template("Hello,{% if answer == 42 %} simple {% else %} cruel {% endif %}world!", {'answer': 73})
```