semvec/pypi-packages huggingface dataset:
restrosphere 0.1.0
RestroSphere — The AI that runs your restaurant.
Requires Python: >=3.10
Keywords: restaurant, ai, manager, automation, pos

# 🍽️ RestroSphere
## The AI that runs your restaurant.

Monitors your floor. Tracks your staff. Scores your applicants.
Sends you alerts. Never sleeps.
All from the dashboard or any chat app you already use.

## Quick Start
### Windows
```powershell
iwr -useb https://restrosphere.ai/install.ps1 | iex
```

### macOS / Linux  
```bash
curl -fsSL https://restrosphere.ai/install.sh | bash
```

Works on macOS, Windows & Linux. Installs Python and everything else automatically.

## What It Does
- **Runs 24/7** on your machine as a background service
- **Connects** to your POS, scheduling, and reservation systems
- **Monitors** labor vs sales in real time
- **Scores** job applicants automatically
- **Tracks** staff performance: tips, upsells, table turns
- **Sends** proactive alerts when something needs attention
- **Answers** any question about your restaurant via chat

## Commands
| Command | Description |
| :--- | :--- |
| `restrosphere init` | Set up your restaurant (Step-by-step) |
| `restrosphere run` | Start the agent (Interactive mode) |
| `restrosphere daemon start` | Run in background (24/7 Service) |
| `restrosphere daemon stop` | Stop background agent |
| `restrosphere daemon status` | Check if service is running |
| `restrosphere status` | See health of connected systems |
| `restrosphere skills list` | See active AI capabilities |

## What Gets Connected
- **POS**: Toast, Square, Clover
- **Scheduling**: 7shifts, HotSchedules, Deputy
- **Reservations**: OpenTable, Resy
- **Reviews**: Google, Yelp
- **Hiring**: Indeed webhooks

## Cost
Agent only calls AI when you message it or a scheduled job fires. Never runs in a loop.
Cost per restaurant: **~$8/month** in API fees.

---
© 2026 RestroSphere AI. Built with ❤️ for the restaurant industry.
gllm-multimodal-binary 0.3.16
A library containing multimodal components for Gen AI applications.
Requires Python: <3.13,>=3.11

# GLLM Multimodal

## Description
A library containing multimodal manager modules for handling modality-specific tasks.

## Installation

### Prerequisites

Mandatory:
1. Python 3.11+ — [Install here](https://www.python.org/downloads/)
2. pip — [Install here](https://pip.pypa.io/en/stable/installation/)
3. uv — [Install here](https://docs.astral.sh/uv/getting-started/installation/)

Extras (required only for Artifact Registry installations):
1. gcloud CLI (for authentication) — [Install here](https://cloud.google.com/sdk/docs/install), then log in using:
   ```bash
   gcloud auth login
   ```

### Install from Artifact Registry

This option requires authentication via the `gcloud` CLI.

```bash
uv pip install \
  --extra-index-url "https://oauth2accesstoken:$(gcloud auth print-access-token)@glsdk.gdplabs.id/gen-ai-internal/simple/" \
  gllm-multimodal
```

## Local Development Setup

### Prerequisites

1. Python 3.11+ — [Install here](https://www.python.org/downloads/)
2. pip — [Install here](https://pip.pypa.io/en/stable/installation/)
3. uv — [Install here](https://docs.astral.sh/uv/getting-started/installation/)
4. gcloud CLI — [Install here](https://cloud.google.com/sdk/docs/install), then log in using:

   ```bash
   gcloud auth login
   ```
5. Git — [Install here](https://git-scm.com/downloads)
6. Access to the [GDP Labs SDK GitHub repository](https://github.com/GDP-ADMIN/gl-sdk)

---

### 1. Clone Repository

```bash
git clone git@github.com:GDP-ADMIN/gl-sdk.git
cd gl-sdk/libs/gllm-multimodal
```

---

### 2. Setup Authentication

Set the following environment variables to authenticate with internal package indexes:

```bash
export UV_INDEX_GEN_AI_INTERNAL_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_INTERNAL_PASSWORD="$(gcloud auth print-access-token)"
export UV_INDEX_GEN_AI_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_PASSWORD="$(gcloud auth print-access-token)"
```

---

### 3. Quick Setup

Run:

```bash
make setup
```

### 4. Activate Virtual Environment

```bash
source .venv/bin/activate
```

## Local Development Utilities

The following Makefile commands are available for quick operations:

### Install uv

```bash
make install-uv
```

### Install Pre-Commit

```bash
make install-pre-commit
```

### Install Dependencies

```bash
make install
```

### Update Dependencies

```bash
make update
```

### Run Tests

```bash
make test
```

## Contributing

Please refer to the [Python Style Guide](https://docs.google.com/document/d/1uRggCrHnVfDPBnG641FyQBwUwLoFw0kTzNqRm92vUwM/edit?usp=sharing)
for information about code style, documentation standards, and SCA requirements.
deriva-ml 1.17.19
Utilities to simplify use of Dervia and Pandas to create reproducable ML pipelines
Requires Python: >=3.12

# DerivaML
Deriva-ML is a python library to simplify the process of creating and executing reproducible machine learning workflows
using a deriva catalog.


Complete on-line documentation for DerivaML can be found [here](https://informatics-isi-edu.github.io/deriva-ml/)

To get started using DerivaML, you can clone the [model template repository](https://github.com/informatics-isi-edu/deriva-ml-model-template), and modify it to suite your requirements.


## References