semvec/pypi-packages huggingface dataset:
aioavasho 0.0.6
Python async client of Avasho API, TextToSpeach service of isahab.ir
Requires Python: >=3.9
Keywords: avasho, tts

# aioavasho
Python async client of Avasho API, TextToSpeech service of isahab.ir

Note: This is not an official client of Avasho or Sahab

# Installation
Install aioavasho using pip:
```bash
$ pip install aioavasho
```

# Usage Examples
## Short Speech
```python
import asyncio
import aioavasho


async def say_salam(
    avasho_client: aioavasho.AvashoClient, name: str
) -> aioavasho.ShortSpeechResponse:
    return await avasho_client.short_speech(
        f"سلام {name}", speaker=aioavasho.Speaker.BAHAR
    )


def main():
    loop: asyncio.AbstractEventLoop = asyncio.new_event_loop()
    avasho: aioavasho.AvashoClient = aioavasho.AvashoClient("TOKEN")
    salam: aioavasho.ShortSpeechResponse = loop.run_until_complete(
        say_salam(avasho, "سالار")
    )
    print(f"file path: {salam.get("filepath")}")


if __name__ == "__main__":
    main()
```

# License
MIT License - see [LICENSE](LICENSE) file for details
argus-htmx-frontend 0.18
HTTP Frontend as a django app for argus-server using HTMx
Requires Python: >=3.9

===================
argus-HTMx-frontend
===================

This frontend has been merged into `argus-server`_, as a django app.

See `argus-server`_ for more about argus.

Imports `django-htmx`_. See the `documentation for django-htmx`_
for details.

.. attention::

   This repo is has been merged into the repo at `argus-server`_.

   This is the final release, to update the README before archiving everything.

.. _django-htmx: https://github.com/adamchainz/django-htmx
.. _argus-server: https://github.com/Uninett/Argus
.. _documentation for django-htmx: https://django-htmx.readthedocs.io/en/latest/
tassskkkkkkkkk 0.0.2
交易系统
Requires Python: >=3.6

这个库用于实现模拟交易逻辑

第一层：Context,包含一个总账户和交易日期数据（self.portfolio = Portfolio(self)）
第二层：Portfolio,主账户的主要作用在于统计资产和增删子账户，不涉及到交易层面
第三层：SubPortfolio，子账户涉及交易顶层和一系列的金钱转换  （未完全测试）
第四层：Position，仓位层面，设计交易操作命令   （未完全测试）
lightcnn-pytorch 1.2.1
LightCNN for fast, accurate and lightweight face verification
Requires Python: >=3.8

# LightCNN

Fast and accurate face recognition model optimized for Central Asian faces. This PyTorch implementation provides an easy-to-use interface for face verification and feature extraction.

## Features
- LightCNN-V4 architecture optimized for Central Asian faces
- Fast and lightweight face verification
- Automatic face alignment and preprocessing
- Easy-to-use Python API
- Pre-trained model weights included

## Installation
```bash
pip install lightcnn-pytorch
```

## Quick Start
```python
from lightcnn_pytorch import LightCNN

# Initialize model (automatically downloads weights)
model = LightCNN()

# Verify two face images
similarity, is_same, confidence = model.verify("person1.jpg", "person2.jpg")
print(f"Similarity score: {similarity:.3f}")
print(f"Same person: {is_same}")
print(f"Confidence: {confidence:.3f}%")
```

## Usage

### Face Verification
```python
# Compare two face images
similarity, is_same, confidence = model.verify(image1, image2)
```

The `verify` method returns:
- `similarity`: Cosine similarity score (-1 to 1)
- `is_same`: Boolean indicating if images are of the same person
- `confidence`: Confidence score (0-100%)

### Feature Extraction
```python
# Get face embedding features
features = model.get_features(image)
```

### Input Formats
- File path (`str`)
- BGR image array (`numpy.ndarray`)
- RGB image array will be converted to BGR automatically

### Device Selection
```python
# Use specific device
model = LightCNN(device="cuda")  # or "cpu"
```

## Model Details
- **Architecture**: LightCNN-V4
- **Input**: 128x128 BGR image
- **Preprocessing**: `(x - 127.5) / 128.0`
- **Output**: 256-dimensional feature vector
- **Verification threshold**: 0.7
incredible-xwk-task 0.0.2
交易系统
Requires Python: >=3.6

这个库用于实现模拟交易逻辑

第一层：Context,包含一个总账户和交易日期数据（self.portfolio = Portfolio(self)）
第二层：Portfolio,主账户的主要作用在于统计资产和增删子账户，不涉及到交易层面
第三层：SubPortfolio，子账户涉及交易顶层和一系列的金钱转换  （未完全测试）
第四层：Position，仓位层面，设计交易操作命令   （未完全测试）
EcovacsTools 0.1.2
小工具

# EcovacsTools

EcovacsTools 是一个用于处理与Ecovacs相关的工具库，包含了消息发送、请求头处理和日期时间处理等功能。

## 目录

- [Ecovacs_message.py](#ecovacs_messagepy)
- [Ecovacs_header.py](#ecovacs_headerpy)
- [Ecovacs_date.py](#ecovacs_datepy)
- [Ecovacs_js.py](#ecovacs_jspy)

## Ecovacs_message.py

### 功能

- 发送文本消息到指定的Webhook URL。

### 使用方法

```python
from EcovacsTools.Ecovacs_message import Send_Text

webhook = "https://open.feishu.cn/open-apis/bot/v2/hook/XXXXXXXXXXXXX"
content = "Hello, World!"
response = Send_Text(webhook, content)
print(response)

```
## Ecovacs_header.py
- 将原始的请求头字符串转换为字典。
- 获取随机的User-Agent字符串。
### 使用方法
```python
from EcovacsTools.Ecovacs_header import copy_headers_dict, get_ua
headers_raw = "User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
headers_dict = copy_headers_dict(headers_raw)
print(headers_dict)

user_agent = get_ua()
print(user_agent)

```
## Ecovacs_date.py
### 功能
- 获取当前时间。
- 获取当前年、月、日。
- 将时间戳转换为日期时间格式。
- 将日期时间格式转换为时间戳。
- 获取昨天开始的时间戳。
- 计算两个时间的差值（秒）。
- 处理微博和优酷的时间格式。
- 生成最近N天的日期列表。
### 使用方法
```python
from EcovacsTools.Ecovacs_date import DateGo

# 获取当前时间
current_time = DateGo.now_data()
print(current_time)

# 获取当前年、月、日
current_ymd = DateGo.now_ymd()
print(current_ymd)

# 将时间戳转换为日期时间格式
timestamp = 1641024000
datetime_str = DateGo.timec_change_dtime(timestamp)
print(datetime_str)

# 将日期时间格式转换为时间戳
datetime_str = "2022-01-01 00:00:00"
timestamp = DateGo.dtime_to_timec(datetime_str)
print(timestamp)

# 获取昨天开始的时间戳
yesterday_timec = DateGo.yesterday_timec()
print(yesterday_timec)

# 计算两个时间的差值（秒）
time1 = 1641024000
time2 = 1641024060
time_diff = DateGo.difference_time(time1, time2)
print(time_diff)

# 处理微博时间格式
weibo_time = "1小时前"
formatted_time = DateGo.weibo_date(weibo_time)
print(formatted_time)

# 处理优酷时间格式
youku_time = "一天前"
formatted_time = DateGo.youku_date(youku_time)
print(formatted_time)

# 生成最近N天的日期列表
days = DateGo.gen_date(10)
print(days)

```



## Ecovacs_js.py

### 功能

- 将JavaScript中的`eval`压缩加密的代码还原成压缩前的代码。
### 使用方法

```python
from EcovacsTools.Ecovacs_js import parse_evaljs

# 压缩加密的JavaScript代码
compressed_js = "eval(function(p,a,c,k,e,d){e=function(c){return c.toString(36)};if(!''.replace(/^/,String)){while(c--){d[c.toString(a)]=k[c]||c.toString(a)}k=[function(e){return d[e]}];e=function(){return'\\w+'};c=1};while(c--){if(k[c]){p=p.replace(new RegExp('\\b'+e(c)+'\\b','g'),k[c])}}return p}('0 1(){2("3")}',4,4,'console|log|Hello|World'.split('|'),0,{}))"

# 还原压缩加密的代码
original_js = parse_evaljs(compressed_js)
print(original_js)
```


[//]: # (python setup.py sdist bdist_wheel)

[//]: # (twine upload dist/*)