semvec/pypi-packages huggingface dataset:
workflow-builder 0.11
A tool for building and executing workflows
Requires Python: >=3.6

WorkflowBuilder

Introduction
-------------------------
WorkflowBuilder is a tool for building and executing workflows. It allows users to define configuration classes, task classes, and workflows to create complex workflows, which can be easily managed and executed.

Installation
-------------------------
Ensure you have Python 3.6+ installed, then install WorkflowBuilder via pip:
pip install workflow_builder

Usage Example
-------------------------
### Configuration Class
First, define a configuration class to store the configuration information needed by the workflow. Here we define a configuration class named `ExampleConfiguration` that contains a `greeting` field.
python
from workflow_builder import configclass, Configuration

# Parameters specify aliases
@configclass('example', 'example_config')
class ExampleConfiguration(Configuration):
    greeting: str

### Task Classes
Next, define task classes. Each task class must inherit from the `Task` class and implement the `preprocess` and `work` methods.

#### ExampleTask1
`ExampleTask1` is a specific task class that retrieves `ExampleConfiguration` from the workflow's configuration manager before execution and returns a dictionary containing the processing results in the `work` method.
python
class ExampleTask1(Task):
    def __init__(self, task_config: TaskConfig):
        task_config.name = ''
        # task_config.asynchronize = True
        self.config = None

    def preprocess(self, workflow: Workflow):
        self.config = workflow.config_manager[ExampleConfiguration]

    def work(self, workflow):
        return {
            'example_task1': self.config.greeting + ' World'
        }

#### ExampleTask
`ExampleTask` is another task class that receives output from `ExampleTask1` in its `work` method and prints it.
python
class ExampleTask(Task):
    def __init__(self):
        ...

    def preprocess(self, workflow: Workflow):
        ...

    def work(self, example_task1):
        print(example_task1)

### Workflow
Finally, add the task classes to the workflow and create and execute the pipeline.
python
tasks = [
    ExampleTask1,
    ExampleTask
]

wf = Workflow(tasks)

# Manually specify configs
# workflow = Workflow(tasks, configs=configs)

# Load toml from the current directory by default:
# workflow = Workflow(tasks, configs_path='.')

pipeline = wf.create_pipeline()
# Execute pipeline
pipeline()

Configuration File
-------------------------
By default, the workflow loads toml configuration files from the current directory. You can specify other paths using the `configs_path` parameter.

### test.toml Example
[example]
greeting = "Hello"

Running
-------------------------
Ensure the configuration file exists, then run the following command to execute the workflow:
python your_script.py
autograde-viz 0.0.1.8
D3 Autograding Utility
Requires Python: >=3.11

# autograde-viz

## Setup
`conda create --name <env_name> --file requirements.txt`
`conda activate <env_name>`

## Create package release
`python3 -m build`
`python3 -m twine upload --repository pypi dist/*` (or the newly built files in `dist/`)
username: `__token__`
password: api token
ScribeLog 0.1.0
A lightweight Python library for logging messages to text files.
Requires Python: >=3.6

# ScribeLog

[Read this in English](README.md) | [Leer esto en español](README_es.md)

ScribeLog is a lightweight Python library for logging messages to text files. It offers path validation and optional timestamp support for your log entries.

## Features

- Log messages to a specified `.log` file.
- Validate the file path and extension.
- Add timestamps to log entries.
- Simple configuration and usage.

## Installation

To install ScribeLog, run:

```bash
pip install ScribeLog
```

## Usage

```python
from ScribeLog import ConfigLog, Log, GetPathLogin

# Configure the log file
ConfigLog("/path/to/yourfile.log")

# Log a message with timestamp
Log("This is a test message.")

# Log a message without timestamp
Log("Another test message.", time=False)

# Retrieve the current log file path
print(GetPathLogin())
```

## Error Handling

If the log file path is invalid or doesn't meet the requirements, a `FileError` will be raised. For example:

```python
from ScribeLog import ConfigLog, FileError

try:
    ConfigLog("/invalid/path/to/file.txt")
except FileError as e:
    print(f"An error occurred: {e}")
```

## License

ScribeLog is licensed under the MIT License. See the LICENSE file for details.
lumivor 0.1.8
Seamlessly Integrating AI with the Web
Requires Python: >=3.11

<img src="./static/lumivor.png" alt="Lumivor Logo" width="full"/>

<br/>

[![Documentation](https://img.shields.io/badge/Documentation-📕-blue)](https://lumivor-labs.com)
[![Twitter Follow](https://img.shields.io/twitter/follow/lumivor_labs?style=social)](https://x.com/lumivor_labs)

Seamlessly Integrating AI with the Web

We enable AI systems to interact with websites by pinpointing and isolating essential interactive elements for smooth navigation.

To learn more about the library, check out the [documentation 📕](https://lumivor-labs.com).

# Quick start

With pip:

```bash
pip install lumivor
```

(optional) install playwright:

```bash
playwright install
```

Spin up your agent:

```python
from langchain_openai import ChatOpenAI
from lumivor import Agent
import asyncio

async def main():
    agent = Agent(
        task="Find a one-way flight from Bali to Oman on 12 January 2025 on Google Flights. Return me the cheapest option.",
        llm=ChatOpenAI(model="gpt-4o"),
    )
    result = await agent.run()
    print(result)

asyncio.run(main())
```

And don't forget to add your API keys to your `.env` file.

```bash
OPENAI_API_KEY=
```

For other settings, models, and more, check out the [documentation 📕](https://lumivor-labs.com).

## Examples

For examples see the [examples](examples) folder

# Contributing

Contributions are welcome! Feel free to open issues for bugs or feature requests.

## Local Setup

To learn more about the library, check out the [local setup 📕](https://lumivor-labs.com/development/local-setup).

---

<div align="center">
  Made with ❤️ in Zurich and San Francisco
</div>
lbox-clients 1.1.2
This module contains client sdk uses to conntect to the Labelbox API and backends
Requires Python: >=3.9
Keywords: ai, edu, labelbox, labeling, llm, machinelearning, ml

# lbox-clients

This is an example module which can be cloned and reused to develop modules under the `lbox` namespace.

## Module Status: Experimental

**TLDR: This module may be removed or altered at any given time and there is no offical support.**

Please see [here](https://docs.labelbox.com/docs/product-release-phases) for the formal definition of `Experimental`.