semvec/pypi-packages huggingface dataset:
bashvar-sentry 0.0.4
A robust Python module to safely source a bash script in a sandboxed environment and collect its variables.
Requires Python: >=3.7

# BashVar Sentry

**BashVar Sentry** is a Python utility module that securely extracts Bash variable declarations from scripts by **sourcing** them in a sandboxed environment. It supports sandboxing via `chroot`, `bwrap`, or `fakechroot` to reduce risk from untrusted content.

---

## 📦 Installation

Install from PyPI:

    pip install bashvar-sentry

---

## 🧰 Usage

### As a Python module

```python
from bashvar_sentry import source_and_get_vars

variables = source_and_get_vars(
    "example.sh",
    sandbox_method="auto",           # "auto", "chroot", "bwrap", "fakechroot", "empty"
    jail_dir="/",                    # Optional: target root dir for sandbox
    extra_env={"MYVAR": "from_python"},
    additional_args=["one", "two"]
)

print(variables)
```

---

## 📄 Bash Script Requirements

Your script must be syntactically valid (`bash -n` is run first).

---

### Example Script: `example.sh`

```bash
#!/bin/bash

ARG1=$1
ARG2=$2
ENV_CAPTURED="$MYVAR"

declare -a FRUITS=("apple" "banana split")
declare -A CONFIG=([host]="localhost" [port]="8080")
```

### Output

```python
{
  "ARG1": "one",
  "ARG2": "two",
  "ENV_CAPTURED": "from_python",
  "FRUITS": ["apple", "banana split"],
  "CONFIG": {"host": "localhost", "port": "8080"}
}
```

---

## 🔐 Sandbox Methods

| Method        | Isolation   | Root Required | Notes                      |
|---------------|-------------|---------------|----------------------------|
| `chroot`      | Full        | Yes       | Needs `/usr/sbin/chroot`  |
| `bwrap`       | Strong      | No            | Needs `bwrap` binary       |
| `fakechroot`  | Simulated   | No            | Must have `fakechroot`     |
| `empty`       | Minimal     | No            | Sets `PATH=""`             |
| `auto`        | Best fit    | No            | Picks the first available  |

---

## 🚫 Caveats

- Scripts are **sourced**, not executed. This means:
  - Side effects can persist in the current shell context.
  - Background jobs, subshells, etc., may behave differently.
- Environment is isolated if you use sandboxing. If not, it's up to you.

---

## ✅ Testing

Run tests with:

    python -m pytest

To test sandbox fallbacks, install:

- `/usr/sbin/chroot` and run as root (for chroot) - This auto skips if not root
- `bwrap`
- `fakechroot`

---

## 📄 License

Apache-2.0
energystats 0.5.2
A python statistical library for energy statistics methods.
Requires Python: >=3.7

# Energystats 

Energystats is a python library indending to implement the basic building tools of energy-statistics-based methods, 
which is a collection of non-parametric statistical methods that usually do not assume anything beyond the existence of the first and second moments.
For more information refer to Szekely and Rizzo[2023].


# Getting started

Energystats is on PyPi, you can download and install the package using 

```pip install energystats```
lazytrainer 0.1.3
Lazy ML: Because training models shouldn't need more energy than your morning coffee.
Requires Python: >=3.7

# LazyTrainer

**Train your regression models with zero drama.**  
LazyTrainer helps you get started with machine learning model training in the simplest way possible — no unnecessary complications.

---

## Why LazyTrainer?

This project was created by **MrFayntom** with a clear goal in mind:

> "I made LazyTrainer for beginners who want to train machine learning models but feel overwhelmed by all the complexity.  
> LazyTrainer wraps it up into a clean, lazy workflow so you can focus on *what matters* — learning and iterating."

---

## What it Does

- Automatically preprocesses your data
- Handles categorical encoding and scaling
- Trains a regression model (SGD for now)
- Evaluates performance (MAE, R², Accuracy)
- Plots predictions
- Saves the model, scaler, and encoders

---

## Installation

```bash
pip install lazyml
```
---

# LazyML – Quick Start Guide

For full details, visit the official GitHub repository:  
: [https://github.com/mrfayntom/LazyML](https://github.com/mrfayntom/LazyML)

---

## Minimal Workflow Example

```python
from lazyml.trainer import LazyTrainer

# Initialize the trainer
trainer = LazyTrainer(
    data_path="path/to/your/data.csv",          # Path to your CSV file
    target_col="TargetColumn",                  # Column name you want to predict
    features=["Feature1", "Feature2", "Feature3"],  # List of input feature column names
    categorical_cols=["Feature3"]               # List of categorical feature names (if any)
)

# Train the model
trainer.train()

# Evaluate the model
trainer.evaluate()

# Plot prediction summary
trainer.plot_summary()

# Save model, scaler and encoders
trainer.save()
trainer.save_scaler()
trainer.save_encoders()
```
— **Mr. Phantom**
botglue 0.0.6
botglue - Attempt to make Second Brain - Simple stand alone UI for RAG loader pipline/chatbot/api
Requires Python: <4.0,>=3.11

# botglue
 
[![CI](https://github.com/walnutgeek/botglue/actions/workflows/ci.yml/badge.svg)](https://github.com/walnutgeek/botglue/actions/workflows/ci.yml)

MLOps for building second brain

There is few principles we want to build botglue around


| Principle | Because |
| :-- | :-- |
| Standardize interface for different model vendors | Ability to substitute and store conversations uniformly |
| Automation of pipelines | Consistency  |
| Maintain teargeted conversation datasets | Capture requirements and provide data for finetuining and evaluation |
| Model evaluation and management | Informed decision making about trade off between open and closed models |
| Preference to open weight models and self hosting | Ability to finetune, privacy and lower cost |



## Architecture

* llore - (botglue-core) Tornado App that keep everything running. llore provides api and all other services built on top of it
    * MVP
        - [x] provide abstraction interface over LLM providers (local: ollama;  remote: openai,  claude;)
        - [x] maintain external document locations/versions and rebuild vector db collections on changes
        - [ ] augmentation pipelines and chatbot config
        - [x] completion api to interact with configured models and chatbots
    * later
        - [ ] store chatbot interactions in oumi conversation format per user if instructed
        - [ ] maintain chatbot library (includes: system prompt, finetuning and augmentation pipelines, data dependencies, and build schedule)
        - [ ] maintain conversation datasets and use them for tuning, RAG and testing
        - [ ] schedule and run pipelines: finetuning, RAG, chatbot evaluation 
        - [ ] monitor and run services: databases, queues, proxies, ui (llit)

* llit - streamlit UI 
    * MVP
        - [x] Chatbot interaction 
    * later
        - [ ] Inspect previous conversations or from library
        - [ ] Inspect data: sql, document db, vector db
        - [ ] Inspect logs from pipelines
        - [ ] Configuration: models, chatbot, pipelines


Got API and streamlit endpoints working, almost MVP!!!

![Screenshot_2025-06-08](Screenshot_2025-06-08.png)



* * *

## Project Docs

For how to install uv and Python, see [installation.md](installation.md).

For development workflows, see [development.md](development.md).

For instructions on publishing to PyPI, see [publishing.md](publishing.md).

* * *

*This project was built from
[simple-modern-uv](https://github.com/jlevy/simple-modern-uv).*