semvec/pypi-packages huggingface dataset:
fireworks-livekit-voice-agent 0.1.5
Fireworks AI Livekit Voice Agent Plugin
Requires Python: >=3.9.0
Keywords: audio, fireworks, livekit, realtime, stt, voice agent, webrtc

# Fireworks AI Livekit Voice Agent Plugin

Fireworks AI Livekit Plugin for voice agent with [Fireworks AI](https://fireworks.ai/).

## Installation

```bash
pip install fireworks-livekit-voice-agent
```

## Pre-requisites

You'll need an API key from Fireworks AI. It can be set as an environment variable: `FIREWORKS_API_KEY`
ner-analyzer-cli-v2 0.3
NER analyzer package with CLI and export options
Requires Python: >=3.7

# ner_analyzer_cli_v2

A simple Named Entity Recognition (NER) analyzer package with a command-line interface (CLI) and export options for JSON and CSV. Powered by [spaCy](https://spacy.io/) and supporting multiple languages.

## Features

- Analyze text for named entities using spaCy.
- Supports multiple languages (`en`, `fr`, `de`, `es`, `pt`).
- Export results to JSON or CSV files.
- Shows entity frequency statistics.
- Easy-to-use CLI interface.

## Installation

First, install the package (after cloning or downloading):

```bash
pip install ner_analyzer_cli_v2
```

You must also download the relevant spaCy language model(s) as needed. For example:

```bash
python -m spacy download en_core_web_sm
python -m spacy download fr_core_news_sm
# For other languages, see: https://spacy.io/models
```

## CLI Usage

```bash
ner-analyze "Your text to analyze here"
```

### Options

- `--output <file>`: Specify output file (`.json` or `.csv`).
- `--lang <code>`: Specify language code (`en`, `fr`, `de`, `es`, `pt`). Default is `en`.

#### Example

Analyze some text and export results:

```bash
ner-analyze "Barack Obama was born in Hawaii." --output results.json
ner-analyze "Emmanuel Macron est le président français." --lang fr --output results.csv
```

## Output Example

### Console

```
Entities:
[
  {
    "text": "Barack Obama",
    "label": "PERSON"
  },
  {
    "text": "Hawaii",
    "label": "GPE"
  }
]

Frequency:
{'PERSON': 1, 'GPE': 1}
```

### JSON

```json
{
  "entities": [
    {"text": "Barack Obama", "label": "PERSON"},
    {"text": "Hawaii", "label": "GPE"}
  ],
  "frequency": {"PERSON": 1, "GPE": 1}
}
```

### CSV

```
Entity,Label
Barack Obama,PERSON
Hawaii,GPE

Label,Count
PERSON,1
GPE,1
```

## API Usage

You can also use the `NERAnalyzer` class directly in your Python scripts:

```python
from ner_analyzer.cli import NERAnalyzer

analyzer = NERAnalyzer(lang_model="en_core_web_sm")
entities = analyzer.analyze_text("Angela Merkel was Chancellor of Germany.")
print(entities)
```

## License

MIT License

## Author

[Amal Alexander](https://www.linkedin.com/in/amal-alexander-305780131/)
mlguard 0.1.2
A diagnostic toolkit for evaluating machine learning models. MLGuard provides plug-and-play checks for class imbalance, overfitting, multicollinearity, bias detection, and more — built on top of scikit-learn, pandas, and statsmodels.
Requires Python: >=3.7

# MLGuard 

Lightweight Python library for automated machine learning model checks:

- Bias Detection (group fairness check)
- Class Imbalance Detection
- Multicollinearity Detection
- Overfitting/Underfitting Detection (using learning curves)

---


## visit Docs 
https://mlguard-tool.tiiny.site/


## Installation

```bash
pip install mlguard
vis_cli 0.5.0
Vis visualizes fuzzy tabular data, no script required
Requires Python: <4.0,>=3.12

# Vis

Visualize fuzzy tabular data without leaving the terminal.

## Install

```bash
pip install vis_cli
```

## Features

### Histograms

```shell
awk 'BEGIN { for (i = 0; i < 1000; i++) print rand() * 100 }' | vis hist --kde
```

<p align="center"><img src="https://raw.githubusercontent.com/hcgatewood/vis/main/assets/vis_hist.png" alt="Vis histograms" width="550"/></p>

### Scatter plots

```shell
echo -e '1 2\n1.5 3\n2 1\n3 1.5\n2 2' | vis scatter --trend
```

<p align="center"><img src="https://raw.githubusercontent.com/hcgatewood/vis/main/assets/vis_scatter.png" alt="Vis scatter plots" width="550"/></p>

### Line plots

```shell
seq 0 0.1 10 | awk '{print $1, sin($1)}' | vis line --xlab "Time" --ylab "sin(t)"
```

<p align="center"><img src="https://raw.githubusercontent.com/hcgatewood/vis/main/assets/vis_line.png" alt="Vis line plots" width="550"/></p>

## More examples

### Histogram: Kubernetes Pod Ages

```shell
kubectl get pods --all-namespaces | vis hist --col 5 --sep '   ' --unit day --kde --xlab 'Pod age (days)'
```

<p align="center"><img src="https://raw.githubusercontent.com/hcgatewood/vis/main/assets/k8s_hist_a.png" alt="Vis histogram Kubernetes pod age" width="550"/></p>

### Histogram: Kubernetes CPU utilization

```shell
kubectl top nodes | vis hist --static --col 2 --bins 10 --xmax 100 --xlab 'CPU util' --kde
```

<p align="center"><img src="https://raw.githubusercontent.com/hcgatewood/vis/main/assets/k8s_hist_b.png" alt="Vis histogram Kubernetes CPU utilization" width="550"/></p>

### Scatter plot: Kubernetes pod CPU vs memory limits

```shell
kubectl resource-capacity --pods | grep -v '\*.*\*' | vis scatter --static --cols 4 6 --xlab "CPU limits" --ylab "Memory limits" --trend
```

<p align="center"><img src="https://raw.githubusercontent.com/hcgatewood/vis/main/assets/k8s_scatter.png" alt="Vis scatter plot Kubernetes pod CPU vs memory limits" width="550"/></p>

## Help pages

```text
$ vis --help
Usage: vis [OPTIONS] COMMAND [ARGS]...

  A fuzzy tabular data visualization tool.

Options:
  -h, --help  Show this message and exit.

Commands:
  clean    Clean the data from a file or stdin and print it to stdout.
  hist     Create a histogram from numerical data.
  line     Create a line plot from tabular data.
  scatter  Create a scatter plot from tabular data.
```