semvec/pypi-packages huggingface dataset:
topsis-himanshu-102303244 0.1.1
A Python package for Topsis method implementation
Requires Python: >=3.11

# TOPSIS-Himanshu-102303244

A Python package to implement the **Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)**. This command-line tool takes a dataset of alternatives and criteria, applies weights and impacts, and calculates a ranking score for each alternative.

## Installation

You can install this package via `pip`:

```bash
pip install topsis-himanshu-102303244
```

## Usage

The package can be used through the command line. It requires an input CSV or Excel file containing your data, a string of weights, a string of impacts, and the name of the output file.

### Command Syntax

```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

### Arguments

1. **InputDataFile**: Path to the `.csv` or `.xlsx` file.

- The file must contain **3 or more columns**.
- **First column**: Object/Model Name (e.g., M1, M2, M3). This column is not used in calculations but is preserved in the output.
- **Remaining columns**: Numeric values representing the criteria for each object.

2. **Weights**: Comma-separated numbers indicating the importance of each criterion (e.g., `"1,1,1,1"`).
3. **Impacts**: Comma-separated signs (`+` or `-`) indicating the desired direction of the criterion.

- `+`: Higher value is better (Profit).
- `-`: Lower value is better (Cost).

4. **ResultFileName**: Name of the output CSV file where results will be saved.

## Example

Let's assume we want to rank 5 different mobile phone models based on 4 criteria: **Price**, **Storage**, **Camera**, and **Looks**.

### 1. Input Data (`data.csv`)

| Model | Price | Storage | Camera | Looks |
| ----- | ----- | ------- | ------ | ----- |
| M1    | 250   | 16      | 12     | 5     |
| M2    | 200   | 16      | 8      | 3     |
| M3    | 300   | 32      | 16     | 4     |
| M4    | 275   | 32      | 8      | 4     |
| M5    | 225   | 16      | 16     | 2     |

- **Criteria Analysis**:
- **Price**: Lower is better (`-` impact).
- **Storage**: Higher is better (`+` impact).
- **Camera**: Higher is better (`+` impact).
- **Looks**: Higher is better (`+` impact).

### 2. Execution

Run the following command in your terminal:

```bash
topsis data.csv "1,1,1,1" "-,+,+,+" result.csv

```

- **Weights**: `1,1,1,1` (All criteria are equally important).
- **Impacts**: `-,+,+,+` (Price is negative impact, others are positive).

### 3. Output Data (`result.csv`)

The tool generates a new file `result.csv` containing the original data with two additional columns: **Topsis Score** and **Rank**.

| Model | Price | Storage | Camera | Looks | Topsis Score | Rank |
| ----- | ----- | ------- | ------ | ----- | ------------ | ---- |
| M1    | 250   | 16      | 12     | 5     | 0.534277     | 3    |
| M2    | 200   | 16      | 8      | 3     | 0.308368     | 5    |
| M3    | 300   | 32      | 16     | 4     | 0.691632     | 1    |
| M4    | 275   | 32      | 8      | 4     | 0.534737     | 2    |
| M5    | 225   | 16      | 16     | 2     | 0.492650     | 4    |

## License

This project is licensed under the MIT License.
pdfcoordex 0.1.2
Extract text, images, and tables from PDF files with analysis
Requires Python: >=3.8
Keywords: pdf extraction text images tables analysis

# pdfcoordex

Extract text, images, and tables from PDF files with automatic analysis.

## Installation

```bash
pip install pdfcoordex
```

## Features

- ✅ Extract all text content from PDFs
- ✅ Analyze embedded images (size, format, type)
- ✅ Extract tables with data preservation
- ✅ Save results to plain text file
- ✅ Simple and easy to use

## Quick Start

```python
from pdfcoordex import PDFCoordExtractor

# Create extractor
extractor = PDFCoordExtractor("document.pdf")

# Save to text file
extractor.save_to_text_file("output.txt")

# Close when done
extractor.close()
```

## Usage Examples

### Extract Single Page

```python
from pdfcoordex import PDFCoordExtractor

extractor = PDFCoordExtractor("document.pdf")

# Get page 1 data
page_data = extractor.extract_page(1)

print("Text:", page_data['text'])
print("Images:", len(page_data['images']))
print("Tables:", len(page_data['tables']))

extractor.close()
```

### Extract All Pages

```python
from pdfcoordex import PDFCoordExtractor

extractor = PDFCoordExtractor("document.pdf")

# Get all pages
all_pages = extractor.extract_all_pages()

for page_name, data in all_pages.items():
    print(f"{page_name}: {len(data['text'])} characters")

extractor.close()
```

### Using Context Manager

```python
from pdfcoordex import PDFCoordExtractor

# Automatically closes the PDF
with PDFCoordExtractor("document.pdf") as extractor:
    extractor.save_to_text_file("output.txt")
```

## Output Format

The text file output includes:

### Text Content
All text from each page as plain text.

### Image Analysis
- Image format (PNG, JPEG, etc.)
- Image dimensions (width x height)
- Orientation (landscape, portrait, square)
- Type classification (diagram, icon, chart)
- Color mode information

### Tables
Tables are formatted with pipe separators for easy reading:
```
Header1 | Header2 | Header3
Data1   | Data2   | Data3
```

## Requirements

- Python >= 3.8
- pdfplumber >= 0.10.3
- PyMuPDF >= 1.23.8
- Pillow >= 10.0.0

## License

MIT License

## Support

For issues and questions, please visit the GitHub repository.
hardstop 0.0.1
The mechanical brake for AI-generated commands. Pre-execution safety validation for Claude Code.
Requires Python: >=3.9
Keywords: hardstop, ai-safety, command-validation, security, claude-code, mcp, pre-execution, fail-closed, llm, bash, shell

# 🛑 Hardstop

> The mechanical brake for AI-generated commands

**Status:** Placeholder - Full release coming soon

## What is Hardstop?

Hardstop is a pre-execution safety system for AI-generated shell commands. It acts as a fail-closed verification layer, blocking dangerous patterns (like `rm -rf ~/`, reverse shells, credential exfiltration) before they execute.

**Two-layer defense:**
- **Pattern matching** — Instant regex-based detection
- **LLM analysis** — Semantic analysis for edge cases

## Key Features

- **Fail-closed design** — If safety check fails, command is blocked (not allowed)
- **Cross-platform** — Unix + Windows patterns
- **Command chaining** — Analyzes all parts of piped/chained commands
- **Audit logging** — All decisions logged

## Coming Soon

- Full Claude Code plugin release
- MCP server integration
- Configurable security policies

## Links

- [GitHub Repository](https://github.com/frmoretto/hardstop)

## License

CC-BY-4.0
docx2md 1.0.5
convert .docx to .md
Requires Python: >=3.10

# docx2md

Converts Microsoft Word document files (.docx extension) to Markdown files.

[Japanese](jp-README.md)

## 1. Install

```
pip install docx2md
```

## 2. How to use

```
usage: docx2md [-h] [-m] [-v] [--debug] SRC.docx DST.md

positional arguments:
  SRC.docx        Microsoft Word file to read
  DST.md          Markdown file to write

optional arguments:
  -h, --help      show this help message and exit
  -m, --md_table  use Markdown table notation instead of <table>
  -v, --version   show version
  --debug         for debug
```

## 3. Tables

A table is output as ```<table id="table(n)">```. ```id``` is the order of output, starting from 1.

If ```--md_table``` is specified, the output will use ```|```, but the title line item will be ```#``` fixed.

```
| # | # | # |
|---|---|---|
|a|b|c|
|d|e|f|
|g|h|i|
```

## 4. Pictures

Images will be output as ```<img id="image(n)">```. 
The ```id``` is output in order starting from 1.


## 5. Examples

* source: [example/example.docx](example/example.docx)
* result: [example/README.me](example/README.md), example/media/*

## 6. Elements that can be converted

* Tables (including merged cells)
* Lists (also with numbers as bullets)
* Headings
* Embedded images
* Page breaks (converted to ```<div class="break"></div>```)
* Line breaks within paragraphs (converted to ```<br>```)
* Text boxes (inserted in the body)

## 7. Elements that cannot be converted (only known ones)

* Table of Contents
* Text decoration (bold and etc...)

## 8. API

### 8.1. function

- docx2md.do_convert

```
>>> help(docx2md.do_convert)
Help on function do_convert in module docx2md.convert:

do_convert(docx_file: str, target_dir='', use_md_table=False) -> str
    convert docx_file to Markdown text and return it

    Args:
        docx_file(str): a file to parse
        target_dir(str): save images into target_dir/media/ if specified
        use_md_table(bool): use Markdown table notation instead of HTHML
    Returns:
        Markdown text(str)
```

### 8.2. class

- docx2md.DocxFile
- docx2md.DocxMedia
- docx2md.Converter

Refer to the do_convert implementation for the usage of each class.

```python
def do_convert(docx_file: str, target_dir="", use_md_table=False)  -> str:
    try:
        docx = DocxFile(docx_file)
        media = DocxMedia(docx)
        if target_dir:
            media.save(target_dir)
        converter = Converter(docx.document(), media, use_md_table)
        return converter.convert()
    except Exception as e:
        return f"Exception: {e}"
```

## 9. License

[MIT](LINCENSE)

## 10. Changelog

- 1.0.5 merge PR #7
- 1.0.4 fix issue #6
- 1.0.3 add API
- 1.0.2 change packaging system to pyproject.toml
novel-updates-scraper 0.1.8
An API to query the novel updates website 
Requires Python: >=3.9

## Usage
Get data from novel updates website 

Example:
```python
from Search import Search
from Book import Book
from genre_constants import *

search=Search(included_genres=[GENRES["GENRE_COMEDY"]])
titles=search.get_titles(5)
Book.make_book(titles[0])
```
verbex-sdk 0.1.0
Official Python SDK for the Verbex platform.
Requires Python: >=3.14
Keywords: verbex, sdk, api

# Verbex Python SDK

Learn how to build and manage intelligent AI Agents with the Verbex Platform.

Welcome to Verbex — where building intelligent AI Agents becomes simple and powerful. The platform enables businesses to create sophisticated AI Agents that understand, engage, and respond naturally to customer needs, enhancing customer service while maintaining efficiency at scale.

## Why Verbex

Verbex combines advanced AI technology with intuitive tools to help you create voice assistants that truly represent your brand. Whether you need 24/7 support, automation of routine tasks, or personalized user experiences, Verbex provides the foundation for conversational solutions that grow with your business.

## What is the Verbex SDK

The Verbex Python SDK is a clean, modular client for the Verbex API. It gives you a consistent interface for creating agents, configuring tools, managing calls, and more.

## Install

```bash
pip install verbex-sdk
```

## Getting an API key

1) Sign up on the Verbex Platform.
2) Open the API Keys page.
3) Click "Add new API key".
4) Name the key and click "Create".
5) Copy the key now. You will not be able to view it again.

## Quick start (create an agent)

```python
import verbex

client = verbex.Verbex(api_key="YOUR_API_KEY")

payload = {
    "agent_name": "Support Agent",
    "llm": {
        "llm_type": "simple",
        "model_provider": "openai",
        "model_name": "gpt-4",
        "system_prompt": "You are a helpful support agent.",
        "model_temperature": 0,
    },
    "tts": {
        "provider": "elevenlabs",
        "voice_id": "voice_id",
        "voice_name": "Default",
        "model_name": "eleven_turbo_v2_5",
        "voice_temperature": 0.2,
    },
    "stt": {"provider": "deepgram", "model": "nova-2-general"},
}

agent = client.ai_agents.create(payload)
```

## Documentation

See the `docs/` folder for endpoint guides and examples.

## Notes

- You can set `VERBEX_API_KEY` instead of passing the key to `Verbex()`.
- The default base URL is `https://api.verbex.ai`.


## Contributing

See `CONTRIBUTING.md` for guidelines on issues, pull requests, and development setup.
favro-cli 0.1.6
CLI for Favro project management
Requires Python: >=3.12

# favro-cli

Command line interface for Favro project management.

## Installation

```bash
pip install favro-cli
```

## Usage

```bash
# Authentication
favro login
favro logout
favro whoami

# Organizations
favro org list
favro org select <org-id>
favro org current

# Boards
favro board list
favro board show [board-id]
favro board view [board-id]
favro board select <board-id>
favro board current

# Cards
favro card list --board <board-id>
favro card show <card-id>
favro card create "Card name" --board <board-id>
favro card update <card-id> --name "New name"
favro card move <card-id> --column <column-id>
favro card assign <card-id> --add <user>
favro card tag <card-id> --add <tag>
favro card delete <card-id>

# Columns
favro column list --board <board-id>
favro column create "Column name" --board <board-id>
favro column rename <column-id> "New name" --board <board-id>
favro column move <column-id> <position> --board <board-id>
favro column delete <column-id> --board <board-id>
```

All commands support `--json` for machine-readable output.

Credentials are stored in `~/.config/favro-cli/config.toml`.