semvec/pypi-packages huggingface dataset:
pipe-pai-tool 0.1.3
pipe-pai is a simple command-line tool that allows you to interact with OpenAI models through Unix pipes
Requires Python: >=3.13

# pipe-pai
pipe-pai is a simple command-line tool that allows you to interact with OpenAI models through Unix pipes.
Use it like this: `... | pai` - hence the name "pipe-pie".

## Installation

Install via pipx (recommended):

```bash
pipx install pipe-pai-tool
```

Or with pip:

```bash
pip install pipe-pai-tool
```

Or locally:
```
git clone git@github.com:krzysztofarendt/pai.git
cd pai
pipx install .
```

Ensure your OpenAI API key is set in the environment:

```bash
export OPENAI_API_KEY="your_api_key_here"
```

Optionally, set a default model using the PAI_MODEL environment variable:

```bash
export PAI_MODEL="gpt-4"
```

## Usage

Pipe a prompt to `pai` and receive the model's response on stdout:

```bash
echo "Write a haiku about the sea" | pai
```

Alternatively, specify a different model or API key:

```bash
echo "Tell me a joke" | pai --model gpt-4 --api-key $OPENAI_API_KEY
```

## Chat mode

By default, `pai` runs in the pipe mode with no chat history.
You can enter an interactive multi-turn chat by:
```bash
pai --chat
> Write a poem about the moon

--------------------
<AI response displayed here>
```

To exit chat mode, send an empty prompt press ctrl+c or type `q`, `exit`, or `quit`.
yufrosine 0.1
lightweight sentiment analysis tool for financial news
Requires Python: >=3.8

# Yufrosine

Yufrosine is a lightweight sentiment analysis toolkit for financial news and stock filtering. It includes tools for fetching and analyzing news articles using rule-based NLP methods like VADER and spaCy.

---

# Features

- Fetch articles from NewsAPI
- Analyze sentiment using VADER
- Filter by S&P 500 tickers

---

# Installation

```bash
pip install yufrosine

# Usage
from yufrosine import main
main.run_analysis()
time-measure-decorator 0.0.1
A tiny helper to measure execution time of a callable to ease your performance checks.
Requires Python: >=3.10

# time_measure
A tiny helper to measure execution time of a callable to ease your performance checks.

# Usage examples

Decorate your function or method with `async_measure_time` and see logs for execution time 
measures after your function is fnished:

```
from measure_decorator import async_measure_time

@async_measure_time
async def coro_that_runs_long(*args, **kwargs):
    ...
```

Whenever the `coro_that_runs_long` is done, you can see the following in your app logs:

`Func: coro_that_runs_long. Elapsed time in seconds: 0.10037329500482883`

Happy debugging!