semvec/pypi-packages huggingface dataset:
health-server 1.0.0
A simple health check server using FastAPI
Requires Python: >=3.12

# Health Server

A simple health check server built with FastAPI.

## Installation

1. Clone the repository.
2. Navigate to the `health-server` directory.
3. Install the dependencies using pip:

```bash
pip install .
```

## Running the server

To run the FastAPI application, use uvicorn:

```bash
uvicorn health_server:app --reload
```

This will start the server at `http://127.0.0.1:8000`.

## Health Check Endpoint

The health check endpoint is available at `/health`. You can access it using a web browser or a tool like `curl`:

```bash
curl http://127.0.0.1:8000/health
```

This endpoint will return a JSON response indicating the health status:

```json
{"status": "ok"}
cpp-binance-orderbook 0.0.3
Orderbook implementation using Pybind11 and C++
Requires Python: >=3.8

# Binance Unfazed

C++ Binance OrderBook that You can embed in Your python code!
Simulate whole market day of 3,389,470 OrderBookEntry in 109ms*

*Ran on i9-13980hx

## installation 
```bash
pip install cpp_binance_orderbook
```

```python
import cpp_binance_orderbook

def orderbook_callback(best_bid, best_ask, mid_price, orderbook_imbalance):
    # ...
    a, b, c, d = best_bid, best_ask, mid_price, orderbook_imbalance

if __name__ == '__main__':

    csv_path = "C:/Users/daniel/Documents/binance_archival_data/binance_difference_depth_stream_usd_m_futures_trxusdt_25-03-2025.csv"

    orderbook_session_simulator = cpp_binance_orderbook.OrderbookSessionSimulator()
    orderbook_session_simulator.processOrderbook(csv_path, orderbook_callback)
```

![Control-V](https://github.com/user-attachments/assets/a90a5dfc-88c9-4625-8c7e-5456468b6a41)
 
![Control-V (1)](https://github.com/user-attachments/assets/afe41fee-8f34-4493-aabb-be7d6e8f25e7)
yuconv 0.3
YuConv is very simple transliteration tool for Serbian language for Cyrillic2Latin and Latin2Cyrillic transliteration.
Requires Python: >=3.6

# YuConv

YuConv is very simple transliteration tool for Serbian language for Cyrillic2Latin and Latin2Cyrillic transliteration, based on srtools Python package.

## Features

### Text Transliteration

Transliterates text between Cyrillic and Latin characters and returns transliterated text.

### Text File Transliteration

Transliterates an input text files, and saves the transliteration result in an output text files.

### Word Documents Transliterations

Transliterates Word documents using python-docx package, and keeps formatting of a transliterated document.

## Transliteration Modes

* LatinToCyrillic Transliterates Latin to Cyrillic
* CyrillicToLatin Transliterates Cyrillic text to Latin

## Documentation

YuConv documentation is available on the [YuConv Wiki Page](https://github.com/DarkoMilosevic86/yuconv/wiki)

## YuConv Repository

To clone YuConv repository, use the following command:
```bash
git clone https://github.com/DarkoMilosevic86/yuconv.git
```


## Issues

To submit or review YuConv issues, visit:
[YuConv Issues Page](https://github.com/DarkoMilosevic86/yuconv/issues)
maxcompute-mcp-server 0.1.3
A MCP server for MaxCompute
Requires Python: >=3.10

# MaxCompute MCP Server

## Overview
A Model Context Protocol (MCP) server for MaxCompute.

## Components

### Tools
The server offers 4 core tools:

#### Query Tools
- `get_latest_partition`
  - Get the latest partition name for a specific table
  - Input:
    - `table_name` (string): Name of the table
  - Returns: The latest partition name

- `read_query`
   - Execute a SELECT query on the MaxCompute project, only SELECT query is allowed
   - Input:
     - `query` (string): The SELECT SQL query
   - Returns: Query results as array of objects

#### Schema Tools
- `list_tables`
   - List all tables in the MaxCompute project
   - No input required
   - Returns: Array of table objects with name and comment

- `describe_table`
   - Get the schema information for a specific table
   - Input:
     - `table_name` (string): Name of the table
   - Returns: Raw output of DESC command

## Build from source

- Code: [GitHub](https://github.com/datafe/maxcompute-mcp-server)

```bash
cd /path/to/maxcompute-mcp-server
uv pip install .
uv build
```

## Usage with Cline

- maxcompute endpoints can view in [endpoint](https://help.aliyun.com/zh/maxcompute/user-guide/endpoints)

- maxcompute projects can view in [projects](https://maxcompute.console.aliyun.com/cn-shanghai/project-list)

```bash
# Add the server to your cline_mcp_settings.json
"mcpServers": {
  "maxcompute-mcp-server": {
    "command": "uv",
    "args": [
      "run",
      "--with",
      "maxcompute-mcp-server",
      "maxcompute-mcp-server"
    ],
    "env": {
      "ALIBABA_CLOUD_ACCESS_KEY_ID": "",
      "ALIBABA_CLOUD_ACCESS_KEY_SECRET": "",
      "ALIBABA_CLOUD_MAXCOMPUTE_PROJECT": "",
      "ALIBABA_CLOUD_MAXCOMPUTE_ENDPOINT": "https://service.cn-shanghai.maxcompute.aliyun.com/api"
    }
  }
}
```
mcp-server-tat 0.1.5
mcp server for Tencent Cloud Automation Tools
Requires Python: >=3.10

# Tencent Cloud TAT MCP Server

An MCP server implementation for executing commands on Tencent Cloud instances using the TencentCloud Automation Tools (TAT) API.

## Features

- **RunCommand**: Asynchronously execute Shell/PowerShell commands on Tencent Cloud instances
- **QueryTask**: Retrieve command execution results and outputs
- **Supports both Linux and Windows**: Automatically handles command encoding based on OS type

## Tools

### RunCommand
Sync Execute commands on Tencent Cloud instances.

**Inputs**:
- `Region` (string): Tencent Cloud region (e.g. ap-beijing, ap-guangzhou)
- `InstanceId` (string): Target cloud server instance ID (format: ins-xxxxxxxx or lhins-xxxxxxxx)
- `Command` (string): Command to execute (will be automatically Base64 encoded)
- `SystemType` (string, optional): OS type (Linux/Windows, default: Linux)

### RunCommand
Async Execute commands on Tencent Cloud instances.

**Inputs**:
- `Region` (string): Tencent Cloud region (e.g. ap-beijing, ap-guangzhou)
- `InstanceId` (string): Target cloud server instance ID (format: ins-xxxxxxxx or lhins-xxxxxxxx)
- `Command` (string): Command to execute (will be automatically Base64 encoded)
- `SystemType` (string, optional): OS type (Linux/Windows, default: Linux)

### QueryTask
Retrieve asynchronous command execution results.

**Inputs**:
- `Region` (string): Tencent Cloud region
- `TaskId` (string): Task ID returned by RunCommand (format: invt-xxxxxx)

## Configuration

### Setting up Tencent Cloud Credentials

1. Obtain SecretId and SecretKey from Tencent Cloud Console
2. Set your default region (optional)

### Usage with Claude Desktop

Add this to your `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "tencent-tat": {
     "command": "uv",
    "args": [
      "run",
      "mcp-server-tat"]
      "env": {
        "TENCENTCLOUD_SECRET_ID": "YOUR_SECRET_ID_HERE",
        "TENCENTCLOUD_SECRET_KEY": "YOUR_SECRET_KEY_HERE",
        "TENCENTCLOUD_REGION": "YOUR_REGION_HERE"
      }
    }
  }
}
```

## Installation

```sh
pip install mcp-server-tat
```


## License

MIT License. See LICENSE for details.
nlpkwords 0.1.0
A package containing AI/ML codes
Requires Python: >=3.6
Keywords: nlp, machine learning, artificial intelligence, education

# NLPKWords

A Python package containing implementations of various AI/ML algorithms and NLP techniques for educational purposes.

## Features

- **NLP Processing**
  - Tokenization
  - Lemmatization
  - Bag of Words
  - TF-IDF
  - One-Hot Encoding

- **Neural Networks**
  - Sigmoid Activation
  - ReLU Activation
  - Tanh Activation
  - Simple MLP Implementation

- **Graph Algorithms**
  - BFS (Breadth-First Search)
  - DFS (Depth-First Search)
  - A* Search Algorithm
  - Best-First Search

- **Fuzzy Logic**
  - Fuzzy Set Operations
  - Complement Operations
  - Union and Intersection

- **Game Theory**
  - Nim Game Implementation

## Installation

```bash
pip install nlpkwords
```

## Usage

### NLP Processing
```python
from nlpkwords import tokenization, lemmatization

# Tokenization
tokens = tokenization.tokenize("Your text here")

# Lemmatization
lemmas = lemmatization.lemmatize("Your text here")
```

### Neural Networks
```python
from nlpkwords import sigmoid, relu, tanh

# Using different activation functions
output = sigmoid.forward(input_data)
output = relu.forward(input_data)
output = tanh.forward(input_data)
```

### Graph Algorithms
```python
from nlpkwords import bfs, dfs_recursive

# BFS Traversal
bfs.traverse(graph, start_node)

# DFS Traversal
dfs_recursive.traverse(graph, start_node)
```

## Requirements

- Python >= 3.6
- pandas >= 1.3.0
- nltk >= 3.6.0
- textblob >= 0.15.3
- scikit-learn >= 0.24.0
- numpy >= 1.20.0

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Author

Rish Dias - rishrdias672004@gmail.com

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.