semvec/pypi-packages huggingface dataset:
create-server-app 1.0.6.9
A CLI tool to create a server app project.

# Create Server App

This project is a template for creating a server application using modern web technologies. It provides a basic structure and setup to help you get started quickly.

## Features

- Easy setup and configuration
- Template routes using flask
- Environment-based configuration

## Getting Started

### Prerequisites

- Python v3.8

### Installation

1. Install using pip:
    ```sh
    pip install create-server-app
    ```
2. Create the server-app:
    ```sh
    create-server-app <name of your project>
    ```
    then done

### Running the Server

To start the server in development mode:
```sh
python app.py
```
or
```sh
py app.py
```

The server will be running at `http://localhost:3000`.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
spam-api 0.3
API para la detección de spam usando machine learning

##Nuevo! pueden instalar la API como una libreria directamente con "pip install spam-api" desde la terminal

# Spam Detection API

Esta es una API simple creada con **FastAPI** que permite detectar si un mensaje es **spam** o **no spam** usando un modelo de machine learning entrenado con **Naive Bayes**. La API permite cargar nuevos datos de entrenamiento, asÃ­ como realizar predicciones sobre mensajes.

## Requisitos

- **Python 3.7+**
- Las siguientes dependencias:

```bash
fastapi
uvicorn
pydantic
joblib
scikit-learn
python-multipart
pandas
```

Instalacion
Clona este repositorio o descarga el cÃ³digo fuente.

Crea un entorno virtual (opcional pero recomendado):

```bash
python -m venv venv
source venv/bin/activate  # En Linux/MacOS
venv\Scripts\activate     # En Windows
```
**Instala las dependecias**

```bash
pip install -r requirements.txt
```

Ejecutar la API
Para correr la API, usa Uvicorn:

```bash
uvicorn anti_spam:app --reload
```

Esto iniciarÃ¡ la API en http://127.0.0.1:8000.


**Endpoints**

/detect_spam/ (POST)
Este endpoint recibe un mensaje de texto y devuelve si es spam o no.

Ejemplo de request:

```json
{
    "text": "Congratulations! You have won a free gift card."
}
```
Ejemplo de respuesta:

```json
{
  "is_spam": true
}
```

/train_model/ (POST)
Este endpoint permite agregar nuevos datos de entrenamiento (texto y etiqueta) al modelo.

Ejemplo de request: (hay que asegurarse de que cada dato de entrenamiento tenga el texto y la etiqueta correspondiendte. 1 = SPAM, 0 = NO SPAM)

```json
[{
    "text": "spam message!",
    "label": 1
  },
  {
    "text": "normal message.",
    "label": 0
}]
```

Ejemplo de respuesta:

```json
{
  "message": "Datos cargados correctamente, el modelo se ha entrenado."
}
```

Cargar un archivo CSV
Puedes cargar un archivo CSV con datos de entrenamiento usando el siguiente endpoint:
/upload_data/ (POST)


**Nota**
Este proyecto utiliza un modelo de Naive Bayes para clasificaciÃ³n de texto, que ha sido entrenado previamente con algunos datos de ejemplo. Puedes agregar mÃ¡s datos de entrenamiento y reentrenar el modelo en cualquier momento.
bank-calculator 0.1.2
A simple Python package that can calculate loan payments and dividends
Requires Python: >=3.12

# Bank Calculator

Project 1 - Python Terminal Game

11-24-24

This is a savings and loan calculator that I started on my own but am now incorporating into a portfolio 
project for Codecademy CS certification

This code will be fleshed out to be a more interactive application that is used in a terminal.

Most recent iteration now has a title screen and a welcome message that explains what the application
is about. The underlying logic is unchanged as I wrote the print statements as functions in a separate file
to be imported into the main file wherein I just had to create print objects and print functions.

11-25-24

At this point I think I've done as much as I can with this little program. It probably went a bit faster than the course designers intended. But being that I had a week head start and decided it fit the criteria of the assignment, I decided to just build on what I already had.

11-25-24 Repo Update

I am renaming this repo so that instead of focusing on one portfolio project, I can use it to store them all. Renaming to "Codecademy Portfolio Projects". 

As such, I am restructuring the folder system on the tree so that each one is labeled for the appropriate project.

12-3-24

I am renaming the repo again. I decided that it was going to be necessary to learn how to ship actual packages. That being said, I figured trying to store multiple, possibly unreleated, packages in a single repo. So I will be re-dedicating this one to the bank calculator project, and starting new repos for subsequent projects.

Somehow 'import logging' got removed. Re-added
jina-segmenter 0.1.0
A text segmentation tool using Jina AI API
Requires Python: >=3.6
Keywords: jina, text segmentation, nlp, ai

# Jina Segmenter

一个基于 Jina AI API 的智能文本分段工具。它能够智能地将长文本分割成合适大小的片段，同时保持语义的完整性。

## 特性

- 智能文本分段，保持语义完整性
- 自动计算和优化分片大小
- 支持自定义最大分片大小
- 返回每个分片的 token 数量
- 简单易用的 API

## 安装

```bash
pip install jina-segmenter
```

## 使用方法

首先，你需要设置 Jina AI 的 API key：

```python
import os
os.environ['JINA_API_KEY'] = 'your_jina_api_key'
```

然后你就可以使用分段功能：

```python
from jina_segmenter import segment_text

text = "你的长文本..."
chunks = segment_text(text)  # 默认最大分片大小为 1500 tokens

# 查看分片结果
for i, chunk in enumerate(chunks, 1):
    print(f"片段 {i} (tokens: {chunk['tokens']}):")
    print(chunk['text'])
    print("-" * 30)
```

你也可以自定义最大分片大小：

```python
chunks = segment_text(text, max_chunk_size=1000)
```

## 获取 API Key

1. 访问 [Jina AI](https://jina.ai/)
2. 注册并登录你的账号
3. 在控制台中创建新的 API key

## 依赖

- Python >= 3.6
- requests >= 2.31.0

## 许可证

MIT License

## 作者

WSY (wangshuyue@gmail.com)
musicas-cristianas 0.1.2
Paquete para obtener datos de canciones cristianas


---

### Musicas disponibles:
- Divina Existencia [4.26 min]
- El Ladrón [4.98 min]
- Conquistado Por Jesús [3.78 min]

---

### Instalación
Instala el paquete usando `pip3`:
```bash
pip3 install musicas-cristianas
```

---

### Uso básico

#### Listar todas las canciones
```python
from musicas_cristianas import list_musicas

list_musicas()
```

#### Obtener una cancion por nombre
```python
from musicas_cristianas import search_musica_by_name

print(search_musica_by_name("Divina Existencia"))
```

#### Calcular duración total de las canciones
```python
from musicas_cristianas import total_duration

print(f"Duración total: {total_duration()} horas")
```

---
mcp-dice 0.1.2
MCP server enabling LLMs to roll dice
Requires Python: >=3.10

# mcp-dice: A MCP Server for Rolling Dice

![screenshot](https://github.com/user-attachments/assets/ff7615b8-46ba-4be5-8287-8e1bf348ae28)


A Model Context Protocol (MCP) server that enables Large Language Models (LLMs) to roll dice. It accepts standard dice notation (e.g., `1d20`) and returns both individual rolls and their sum.

## Features

- Supports standard dice notation (e.g., `1d20`, `3d6`, `2d8+1`)
- Returns both individual rolls and the total sum
- Easy integration with Claude Desktop
- Compatible with MCP Inspector for debugging

## Installation

```shell
# Install using uv
uv pip install mcp-dice
```

## Usage

### Basic Command Line Usage

```shell
# Using uvx
uvx mcp-dice
```

### Input Format

The server accepts a JSON object with a `notation` field:
```json
{
  "notation": "2d6+3"
}
```

Example responses:
```json
{
  "rolls": [
    3,
    1
  ],
  "sum": 4,
  "modifier": 3,
  "total": 7,
  "notation": "2d6+3",
  "timestamp": "2024-12-03T16:36:38.926452"
}
```

## Claude Desktop Configuration

### Location
- macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
- Windows: `%APPDATA%/Claude/claude_desktop_config.json`

### Examples

<details>
<summary>macOS Configuration</summary>

```json
{
  "mcpServers": {
    "dice_service": {
      "command": "uvx",
      "args": ["mcp-dice"]
    }
  }
}
```

</details>

<details>
<summary>WSL Configuration</summary>

```json
{
  "mcpServers": {
    "dice": {
      "command": "wsl",
      "args": [
        "-e",
        "zsh",
        "-lc",
        "uvx mcp-dice"
      ]
    }
  }
}
```

Note: Replace `zsh` with your login shell.
</details>

## Development and Debugging

### Installing Development Dependencies

```shell
# Clone the repository
git clone https://github.com/yourusername/mcp-dice
cd mcp-dice

# Install development dependencies
uv pip install -e ".[dev]"
```

### Running Tests

```shell
uv run pytest
```

### Using MCP Inspector

The [MCP Inspector](https://github.com/modelcontextprotocol/inspector) is a useful tool for debugging your MCP server. Install and run it using npm:

```shell
npx @modelcontextprotocol/inspector uvx mcp-dice
```

### Claude Desktop Configuration for Development

<details>
<summary>macOS Configuration (local dev)</summary>
```json
{
  "mcpServers": {
    "dice_service": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "path/to/mcp-dice-repo mcp-dice"
      ]
    }
  }
}
```
</details>

<details>
<summary>Windows (WSL) Configuration (local dev)</summary>

```json
{
  "mcpServers": {
    "dice": {
      "command": "wsl",
      "args": [
        "-e",
        "zsh",
        "-lc",
        "uv run --directory path/to/mcp-dice-repo mcp-dice"
      ]
    }
  }
}
```

Note: Replace `zsh` with your login shell.
</details>