semvec/pypi-packages huggingface dataset:
code-detox 1.1.1
A full-stack technical debt and security analyzer (PY, JS, HTML, CSS)
License: MIT
Requires Python: >=3.8
Keywords: technical-debt, code-quality, security, static-analysis, complexity

# 🔪 Code-detox

**Code-detox** is a precision CLI tool designed to dissect your codebase and reveal hidden technical debt. It performs a deep-dive audit across **Python, JavaScript, HTML, and CSS** files to provide a comprehensive health report and security audit.
---

## 🚀 Features

Code-detox analyzes your project's "rot" using several advanced metrics:

* **Multi-Language Analysis:** Deep-dive support for `.py`, `.js`, `.html`, and `.css`.
* **Maintainability Index (MI):** A composite score indicating how easy your code is to support.
* **Cyclomatic Complexity:** Measures the number of linearly independent paths through your source code.
* **Security Audit:** Automatic detection of hardcoded **API Keys, Passwords, and Tokens**.
* **Git Insights:** Tracks **Churn** (modification frequency) to identify "danger zones" in your history.
* **Redundancy Detection:** Identifies duplicated code blocks that increase maintenance costs.
* **Risk Scoring:** A proprietary formula that ranks files from **LOW** to **CRITICAL** based on debt.

---

## 🛠 How to Run

### 1. Installation


1. Installation
   ```bash
   pip install code-detox
   ```

2. Execution
   ```bash
   code-detox
   ```

3. Analyze a Specific Path
   ```bash
   Code-detox /path/to/your/project
   ```

##  Contributors

- [Visha Yadav](https://github.com/vishayadav) 
- [Viraj Ravani](https://github.com/v3ravani)


##  License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
rtichoke 0.1.28
interactive visualizations for performance of predictive models
License: MIT
Requires Python: >=3.9

# rtichoke

`rtichoke` is a Python library for visualizing the performance of predictive models. It provides a flexible and intuitive way to create a variety of common evaluation plots, including:

*   **ROC Curves**
*   **Precision-Recall Curves**
*   **Gains and Lift Charts**
*   **Decision Curves**

The library is designed to be easy to use, while still offering a high degree of control over the final plots. For some reproducible examples please visit the [rtichoke blog](https://uriahf.github.io/rtichoke-py/blog.html)!

## Installation

You can install `rtichoke` from PyPI:

```bash
pip install rtichoke
```

## Getting Started

To use `rtichoke`, you'll need two main inputs:

*   `probs`: A dictionary containing your model's predicted probabilities.
*   `reals`: A dictionary of the true binary outcomes.

Here's a quick example of how to create a ROC curve for a single model:

```python
import numpy as np
import rtichoke as rk

# Sample data for a model. Note that the probabilities for the
# positive class (1) are generally higher than for the negative class (0).
probs = {'Model A': np.array([0.1, 0.9, 0.4, 0.8, 0.3, 0.7, 0.2, 0.6])}
reals = {'Population': np.array([0, 1, 0, 1, 0, 1, 0, 1])}


# Create the ROC curve
fig = rk.create_roc_curve(
  probs=probs,
  reals=reals
)

fig.show()
```

## Key Features

*   **Simple API**: Create complex visualizations with just a few lines of code.
*   **Time-to-Event Analysis**: Native support for models with time-dependent outcomes, including censoring and competing risks.
*   **Interactive Plots**: Built on Plotly for interactive, publication-quality figures.
*   **Flexible Data Handling**: Works seamlessly with NumPy and Polars.

## Documentation

For a complete guide to the library, including a "Getting Started" tutorial and a full API reference, please see the **[official documentation](https://uriahf.github.io/rtichoke-py/)**.
contextflow 1.10.0
Large Language Models Tools
License: MIT
Requires Python: >=3.10

## Environment
To create a Python virtual environment, use the command:
```console
conda env create -f environment.yml
```

## Installation
```console
pip install contextflow
```

## Supported Models
The following LLM models are supported:
- Gemma Family
- Qwen Family
- YandexGPT Family

## LLM backends
The following LLM backends are supported:
- Llama.cpp Server API

## Run Llama.CPP Server backend
```console
llama.cpp/build/bin/llama-server -m model_q5_k_m.gguf -ngl 99 -fa -np 2 -c 8192 --host 0.0.0.0 --port 8000
```

## Install CUDA toolkit for Llama.cpp compilation
Please note that the toolkit version must match the driver version. The driver version can be found using the nvidia-smi command.
Аor example, to install toolkit for CUDA 12.4 you need to run the following commands:
```console
CUDA_TOOLKIT_VERSION=12-4
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt update
sudo apt -y install cuda-toolkit-${CUDA_TOOLKIT_VERSION}
echo -e '
export CUDA_HOME=/usr/local/cuda
export PATH=${CUDA_HOME}/bin:${PATH}
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
' >> ~/.bashrc
```

## Set GPU Max Temp
```console
nvidia-smi -pm 1
sudo nvidia-smi -gtt 80
nvidia-smi -q | grep Target
```
new-api-linux-do-oauth-token 0.0.0
A helper library to authenticate new-api with linux.do oauth and retrieve session
License: MIT
Requires Python: >=3.12

# new-api-linux-do-oauth-token

A helper library to authenticate new-api with linux.do oauth and retrieve session

---

[![Python CI](https://github.com/Sn0wo2/new-api-linux-do-oauth-token/actions/workflows/py.yml/badge.svg)](https://github.com/Sn0wo2/new-api-linux-do-oauth-token/actions/workflows/py.yml)
[![Release](https://github.com/Sn0wo2/new-api-linux-do-oauth-token/actions/workflows/release.yml/badge.svg)](https://github.com/Sn0wo2/new-api-linux-do-oauth-token/actions/workflows/release.yml)
[![CodeQL Advanced](https://github.com/Sn0wo2/new-api-linux-do-oauth-token/actions/workflows/codeql.yml/badge.svg)](https://github.com/Sn0wo2/new-api-linux-do-oauth-token/actions/workflows/codeql.yml)

[![GitHub release](https://img.shields.io/github/v/release/Sn0wo2/new-api-linux-do-oauth-token?color=blue)](https://github.com/Sn0wo2/new-api-linux-do-oauth-token/releases)

---

> [!] 项目当前正在开发, **不保证`v0`的向后兼容性**, 如果要在生产环境使用请等待`v1`发布

---

## Usage

> See example