semvec/pypi-packages huggingface dataset:
pyncbitk-runtime 30.0.0.0
Runtime component of the NCBI C++ Toolkit to use in PyNCBItk.
Requires Python: >=3.7
Keywords: bioinformatics, blast, sequence, alignment, ncbi

# 🧬🧰 PyNCBItk [![Stars](https://img.shields.io/github/stars/althonos/pyncbitk.svg?style=social&maxAge=3600&label=Star)](https://github.com/althonos/pyncbitk/stargazers)

*(Unofficial) [Cython](https://cython.org/) bindings and Python interface to the [NCBI C++ Toolkit](https://www.ncbi.nlm.nih.gov/toolkit).*

This package contains the runtime components of PyNCBItk to allow reusing them
across versions of the Cython bindings without having to rebuild. See the main
[`pyncbitk`](https://pypi.org/project/pyncbitk) package for more information.


## 🔧 Installing

The PyNCBItk runtime supports pre-built binaries for Linux x86-64 (based on `manylinux_2_34`),
and for MacOS x86-64 and Aarch64 (for MacOS 13.3+). At the moment, the package is only tested
on these platforms.

Compiling from scratch uses CMake and the [Conan C/C++ package manager](https://docs.conan.io/2/)
to build the official [`ncbi-cxx-toolkit-public` recipe](https://github.com/ncbi/ncbi-cxx-toolkit-conan)
and install the artifacts into a self-contained Python package. If compilation fails for your
platform, please open an issue on the [issue tracker](https://github.com/althonos/pyncbitk/issues).


## ⚖️ License

This library is provided under the [MIT License](https://choosealicense.com/licenses/mit/).
The NCBI C++ Toolkit is a "United States Government Work" and therefore lies in
the public domain, but may be subject to copyright by the U.S. in foreign
countries. Some restrictions apply, see the
[NCBI C++ Toolkit license](https://www.ncbi.nlm.nih.gov/IEB/ToolBox/CPP_DOC/lxr/source/doc/public/LICENSE).
freewvs 0.1.5
Search webroots for known vulnerable versions of web applications
Keywords: security, vulnerability, web

# freewvs

A local web vulnerability scanner.

freewvs is a tool to search webroots for known vulnerable versions of web applications.

## install

You can install [freewvs via pip](https://pypi.org/project/freewvs/):

```
pip install freewvs
```

Alternatively, you can run freewvs directly from the git source.

If you install via pip, you need to update the freewvs database first:

```
update-freewvsdb
```

## usage

Just run freewvs with a path, e.g.:

```
freewvs /var/www
```

The output will be something like this:

```
Joomla 3.9.11 (3.9.14) CVE-2019-19846 /var/www/example.org
nextcloud 14.0.1 (14.0.5) CVE-2019-5449 /var/www/cloud.example.org
MediaWiki 1.31.1 (1.31.6) CVE-2019-19709 /var/www/wiki.example.org
```

## faq

#### What does freewvs do?

It scans your webroot for known vulnerable versions of popular web applications.

#### What does the output tell me?

The output looks like this:

```
Joomla-3 3.9.11 (3.9.13) CVE-2019-18674 /home/joe/websites/joessite/
```

This says that in /home/joe/websites/joessite/, there's a Joomla installation of version
3.9.11. This version is vulnerable to CVE-2019-18674, and you should update it to
version 3.9.13.

#### CVE-2019-XXXX seems to be very minor, at least it doesn't affect me. Am I safe?

No, as freewvs only checks for the latest vulnerabilities. There may be other
vulnerabilities in your version not listed by freewvs. The only way to be sure is to
check the upstream changelog.

#### There is no version inside the brackets. What does that mean?

It means your web application has not released a security update. Often, this means the
software is no longer developed.

## contributions

See [CONTRIBUTIONS.md](CONTRIBUTIONS.md).

## misc

freewvs was developed by [schokokeks.org hosting](https://schokokeks.org/).

It's licensed under the 0BSD license.

[https://freewvs.schokokeks.org](https://freewvs.schokokeks.org)
fairscore-ai 1.0.0
Fair and Explainable AI Credit Scoring Terminal Application
Requires Python: >=3.10
Keywords: credit-scoring, explainable-ai, fairness, machine-learning, terminal-ui, financial-inclusion, shap, tui

# FairScore

**AI-Powered Credit Scoring System with Bloomberg Terminal-Style TUI**

FairScore is a fair, explainable, and inclusive credit scoring system that uses behavioral financial data instead of traditional credit bureau metrics. It features a Terminal User Interface (TUI) inspired by Bloomberg Terminal aesthetics.

## Features

- **4 Specialized ML Models**: Income Stability (LightGBM), Expense Discipline (Random Forest), Liquidity Buffer (Logistic Regression), Risk Detection (Isolation Forest)
- **FAIR-SCORE Formula**: Weighted ensemble producing 300-900 score range
- **Explainability**: SHAP-based explanations with waterfall visualizations
- **Dual Input Modes**: PDF parsing (Gemini AI) or manual data entry
- **Bloomberg Terminal UI**: Phosphor green on matte black aesthetic

## Installation

```bash
# Clone or download, then:
pip install -e .
```

## Quick Start

```bash
# Launch TUI
fairscore

# Train models (generates synthetic data first)
python -m fairscore.models.trainer

# Generate synthetic data only
python -c "from fairscore.data import DataGenerator; DataGenerator().generate_all()"
```

## Keyboard Shortcuts

| Key | Action |
|-----|--------|
| F1 | Help screen |
| F5 | Refresh display |
| Ctrl+O | Upload PDF |
| Ctrl+M | Manual entry |
| Tab | Navigate fields |
| Q | Quit |

## Score Categories

| Range | Category | Description |
|-------|----------|-------------|
| 750-900 | EXCELLENT | Best creditworthiness |
| 650-749 | GOOD | Above average |
| 500-649 | FAIR | Average |
| 300-499 | POOR | Below average |

## Project Structure

```
fairscore/
  __init__.py       # Package initialization
  app.py            # Main TUI application
  cli.py            # CLI entry points
  config.py         # Configuration management
  theme.py          # Bloomberg Terminal theme
  data/
    generator.py    # Synthetic data generation
    features.py     # Feature engineering
  models/
    income_stability.py    # LightGBM model
    expense_discipline.py  # Random Forest model
    liquidity_buffer.py    # Logistic Regression model
    risk_anomaly.py        # Isolation Forest model
    ensemble.py            # FAIR-SCORE computation
    trainer.py             # Training pipeline
  explainability/
    shap_explainer.py      # SHAP explanations
  parser/
    pdf_parser.py          # Gemini AI PDF parsing
  ui/
    dashboard.py           # Main dashboard
    model_matrix.py        # 4-panel model display
    score_gauge.py         # ASCII score gauge
    input_mode.py          # Input selection/forms
```

## Configuration

Set environment variable for PDF parsing:
```bash
set GOOGLE_API_KEY=your_gemini_api_key
```

## License

MIT License