semvec/pypi-packages huggingface dataset:
cmd-clever 0.1.0
A command-line tool for generating terminal commands using AI
Requires Python: >=3.7

# Cmd Clever

A command-line tool for generating terminal commands using AI.

## Installation

```bash
pip install cmd-clever
```

## Usage

### Command-line Arguments

```bash
# Basic usage with a query
cmd-clever 查找大于100MB的日志文件

# Specify API key and base URL
cmd-clever --api-key your-api-key --api-base your-api-base 查找最近修改的文件

# Use a different model ID
cmd-clever --model-id different-model-id 创建一个新的目录并将文件移动到其中

# Disable streaming output
cmd-clever --no-stream 查找包含特定文本的文件
```

### Interactive Mode

If you don't provide a query, Cmd Clever enters interactive mode:

```bash
cmd-clever
```

You can then input your queries one by one and get responses. Type "exit" or "quit" to leave interactive mode.

### Environment Variables

Cmd Clever looks for the following environment variables:

- `AGNO_API_KEY`: Your API key
- `AGNO_API_BASE`: Your API base URL

You can set these in your shell configuration (e.g., `.bashrc`, `.zshrc`) to avoid specifying them on each run:

```bash
export AGNO_API_KEY="your-api-key"
export AGNO_API_BASE="your-api-base"
```

## Features

- Accepts queries in Chinese
- Generates safe and reliable terminal commands
- Automatically adapts commands for Linux/macOS
- Warns about potentially dangerous operations
- Supports command explanations
- Interactive and one-off query modes

## License

MIT
smh-fastapi-backend-templates 0.1.1
A CLI tool for managing templates
Requires Python: <4.0,>=3.8

# smh-fastapi-backend-templates

## Overview
`smh-fastapi-backend-templates` is a CLI tool that enables seamless project scaffolding for FastAPI backend applications. It provides ready-to-use templates built with Poetry, ensuring an optimized development workflow with best practices and essential dependencies.

## Features
- **Python 3.12+ Support**: Ensures compatibility with the latest Python version.
- **Poetry-based Project Templates**: Each template is structured with Poetry for dependency and package management.
- **Automatic Environment Setup**: Installs dependencies via `poetry install` after template creation.
- **Dependency Management**: Comes pre-configured with FastAPI, SQLAlchemy, Pydantic, and more.
- **Automatic Poetry Installation**: If Poetry is not found, the CLI prompts for installation.
- **Interactive Template Selection**: Users can select from multiple available templates.
- **FastAPI Ready**: Optimized for building scalable FastAPI-based backend services.
- **Linting & Formatting**: Pre-configured with Ruff, Mypy, and Pre-commit hooks.

## Prerequisites
Ensure your system meets the following requirements:
- **Python 3.12+**
- **pip 25+**
- **Poetry** (Automatically installed if not found)

## Installation
You can install `smh-fastapi-backend-templates` via pip:
```sh
pip install smh-fastapi-backend-templates
```

## Usage
Once installed, you can access the CLI tool using the `smh-fastapi-backend-templates` command.

### List Available Templates
```sh
smh-fastapi-backend-templates list-templates
```

### Create a New Project
```sh
smh-fastapi-backend-templates create-project <project_name>
```
> **Note:** If no template is provided, an interactive prompt allows you to select one.

### Example Workflow
1. Install the package: `pip install smh-fastapi-backend-templates`
2. Run `smh-fastapi-backend-templates create-project my-fastapi-app`.
3. Choose a template from the list.
4. The project structure is created.
5. Dependencies are installed using `poetry install`.
6. Your FastAPI project is ready to use!

## Template Dependencies
Each template comes pre-installed with:
- **FastAPI** (with `standard` extras)
- **SQLAlchemy** for database management
- **Pydantic** for data validation
- **Alembic** for database migrations
- **Asyncpg & Psycopg2** for PostgreSQL integration
- **Httpx & Multipart Support** for async requests
- **Pre-commit, Mypy, Ruff** for code quality and linting
- **Nuitka** for optimized builds

## Contributing
1. Fork the repository.
2. Create a feature branch.
3. Submit a pull request.

## License
Apache License 2.0. See [LICENSE](LICENSE) for details.
yamlx 0.0.8
Allow variable and expression in YAML file
Requires Python: >=3.8

# yamlx

yaml内で変数や計算式を扱えるようにしたもの

## How to install

```
>> pip install yamlx
```

## How to use

存在しているキーの値を代入したいとき　->  \${key1.key2.key3 ...}

計算式を入れたいとき -> 普通に演算子を用いて書いてください(+,-,\*,/,%,//,\*\*)が使えます．\${...}も使えます．

```
# example.yaml

train :
  batch_size    : 16
  learning_rate : 0.001
  epochs        : 50

signal : 
  sample_rate : 16000
  hop_size : 256
  n_mels   : 80
  eps      : 1 / 100
  max_len  : ${signal.sample_rate} // ${signal.hop_size}

model :
  input_dim      : 128
  input_channel  : ${signal.n_mels}
  output_channel : 5
```

```
>> import yamlx
>> path = "./example.yaml"
>> data = yamlx.load(path)
>> print(data)
>> {'train': {'batch_size': 16, 'learning_rate': 0.001, 'epochs': 50}, 'signal': {'sample_rate': 16000, 'hop_size': 256, 'n_mels': 80, 'eps': 0.01, 'max_len': 62.0}, 'model': {'input_dim': 128, 'input_channel': 80, 'output_channel': 5}}  
```
pclp 0.1.0
PyTorch Causal Latent Programming for Multi-Label Learning

# PCLP - PyTorch Causal Latent Programming

[![PyPI Version](https://img.shields.io/pypi/v/pclp.svg)](https://pypi.org/project/pclp/)

A novel framework combining causal modeling and generative networks for multi-label learning.

## Features

- Bidirectional Generative Model (BGM)
- Structural Causal Models (SCM)
- Semi-supervised learning support
- Multi-GPU distributed training


## Installation

```bash
pip install pclp
dexlib 0.1.7
DEX interaction library for Base network
Requires Python: >=3.8

# DEX Interaction Library for Base Network

A Python library for interacting with Decentralized Exchanges (DEXes) on the Base network.

Repository: [https://github.com/atomic-235/dex](https://github.com/atomic-235/dex)

## Overview

This library provides tools and utilities for interacting with various decentralized exchanges on the Base network. It supports operations such as querying prices, executing trades, and managing liquidity positions.

## Installation

```bash
pip install dexlib
```

## Getting Started

1. Clone the repository
2. Install the required dependencies:
   ```bash
   pip install -r requirements.txt
   ```
3. Set up your environment variables in `.env` file
4. Import and use the library in your project

## Testing

The project includes a test suite. To run the tests:

```bash
pytest tests/
```

## License

This project is licensed under the MIT License.