semvec/pypi-packages huggingface dataset:
InterOptimus 0.0.4
High througput simulation making crystalline interfaces
Requires Python: >=3.10

An efficient python package for Interface Simulation.
1. Visualizing lattice matching information by polar projection figure;
2. Symmetry analysis to screen out identical matching and termination conditions;
3. Structure pre-optimization by MLIP-predicted interface energy.

Install
`pip install .`

InterOptimus integrated machine learning interatomic potentials (MLIPs) including `grace-2l` `chgnet` `mace` `orb-models` `sevenn`. Details can be find in https://matbench-discovery.materialsproject.org/

As these MLIP packages are not compatible in the same python environment, to use them flexibly to calculate atomic energies of interface structures in InterOptimus, we created their corresponding docker images with their required python environments and packages respectively, and achieve usages of these MLIPs through requesting their docker containers for energy prediction or structure optimization results. Our images are uploaded in the Alibaba Cloud. Therefore, to use our package, you need to register an Alibaba Cloud account at https://account.alibabacloud.com/ and install docker. You need to finish the steps below:

1. After you register your Alibaba Cloud account, go to the `Container Registry/Instances` page, follow the instruction to register for a totally free `Instance of Personal Edition`, and get your countainer registry [username] and [password] which you will need to login in to the docker registry.
![image](https://github.com/user-attachments/assets/bd4240f8-f9d2-4f36-990b-579963a7462a)
2. Then, following the instruction at https://cr.console.aliyun.com/cn-shenzhen/instances/mirrors set the docker Image Accelerator.
3. Finally, execute the `docker login` command provided in your own `Container Registry/Instances` page, and try to run tutorial.ipynb.

If you have successfully run all the commands in tutorial.ipynb. Congrates! Explore your own interfaces now!
JoyIT-st7789vw 1.0.1
Library to use the SBC-LCD01 with the Raspberry Pi
Requires Python: >=3.7

# JoyIT_st7789vw
Library to use the SBC-LCD01 with the Raspberry Pi

## Installation
You can install this library from PyPI.
To install it for the current user on your Raspberry Pi, use the following command:
```
pip install JoyIT_st7789vw
```

## Example
You need to clone this repository on your Raspberry Pi to be able to execute the example code.
```
git clone https://github.com/joy-it/JoyIT_st7789vw
```
Afterward, you need to move into the directory and activate the virtual environment with the following commands.
```
cd JoyIT_st7789vw
python -m venv --system-site-packages env
source env/bin/activate
```
Then you can execute the example with the following command.
```
python3 examples/example.py
```
pyrtc-volcengine-third 0.1.0
字节跳动火山引擎实时语音大模型Python SDK，非官方
Requires Python: >=3.10

# pyrtc-volcengine-third
字节跳动火山引擎实时语音大模型Python SDK，非官方


## 安装

1. 仅SDK
```
pip install pyrtc-volcengine-third
```

2. 包含简单的人声识别
```
pip install "pyrtc-volcengine-third[vad]"
```

## 用法

```
import asyncio
from pyrtc_volcengine import DialogSession

ws_connect_config = {
    # 具体配置参考官方文档
}

async def main() -> None:
    session = DialogSession(config.ws_connect_config)
    await session.start()

if __name__ == "__main__":
    asyncio.run(main())
 
```

### Example_1: rtc-terminal

实现的功能与官方示例 realtime_dialog 基本相同，增加了降噪和人声判断

在官方示例中，如果使用笔记本自带的输入输出设备进行外放时，录音会录制播放的声音造成无限循环对话，故实际生产使用时，有两种思路解决

#### 思路1: 硬件分离
播放声音时中断录制，除非手动停止播放和开启录音；同理，开启录制时自动停止播放

#### 思路2: 回声消除
录制时去除自身设备播放的重复音源数据，例如使用 Python-Acoustic-Echo-Cancellation-Library[https://github.com/Keyvanhardani/Python-Acoustic-Echo-Cancellation-Library]


### Example_2: rtc-terminal-llm

在 rtc-terminal 的基础上，增加了 *意图识别* 部分的逻辑，支持在意图识别中转联网搜索或使用工具获取信息，需要自己继承不同事件Handler，并定制逻辑


#### 1. ASREndedHandler

对应 ASRResponse 事件，返回对输入语音识别的文本结果，在这一步进行意图识别，并标记缓存闲聊结果

#### 2. TTSHandler(可选)

接收语音数据时，意图识别结束如果是闲聊可以直接播放，否则丢弃。主要是为了当意图识别早于 TTSResponse 事件结束，并且是闲聊结果，可以提前播放语音。
但由于内置语音通常比较短和快，更简单的做法则不做复杂判断，只加到缓存队列，在 TTSEndedHandler 中再判断是否播放或丢弃。

#### 3. TTSEndedHandler

对应 TTSEnded 事件，判断丢弃缓存还是播放

#### 4. ChatEndedHandler

闲聊的文本或 ChatTTSText 的文本，同样需要根据意图识别的结果判断是否要丢弃


在生产应用中，还需要加上更复杂的聊天记录以便于供 意图识别 和其他 Agent 使用