semvec/pypi-packages huggingface dataset:
xml2pdf 0.2.1
A tool to convert XML files to PDF
Requires Python: >=3.6

# XML2PDF Converter

A Python library to convert XML files to PDF format. This tool can download XML files from URLs, clean them, and convert them to well-formatted PDFs.

## Features

- Download XML files from URLs
- Clean and normalize XML content
- Generate dynamic XSLT templates
- Convert XML to PDF with customizable styling
- Support for both local files and URLs

## Installation

### Using pip

```bash
pip install xml2pdf
```

### Using conda environment

```bash
# First install dependencies
conda install requests lxml weasyprint

# Then install xml2pdf
pip install xml2pdf
```

## Usage

### Converting XML from URL

```python
from xml2pdf import XMLToPDFConverter

# Initialize the converter
converter = XMLToPDFConverter()

# Convert XML from URL to PDF
url = "https://example.com/file.xml"
output_pdf = "output.pdf"
converter.url_to_pdf(url, output_pdf)
```

### Converting Local XML File

```python
from xml2pdf import XMLToPDFConverter

converter = XMLToPDFConverter()

# Convert local XML file to PDF
input_xml = "input.xml"
output_pdf = "output.pdf"
converter.file_to_pdf(input_xml, output_pdf)
```

## Dependencies

- Python 3.6+
- requests
- lxml
- weasyprint

## Error Handling

The library includes comprehensive error handling for:
- Network issues during XML download
- XML parsing errors
- File system operations
- PDF generation issues

## Contributing

Feel free to open issues or submit pull requests on GitHub.

## License

MIT License

## Author

- Hirthickkesh
- Email: hirthickkeshpr@gmail.com
- GitHub: https://github.com/Higgy-debug
earth-data-fetcher 0.1.2
Fetch remote sensing and reanalysis data from different sources
Requires Python: >=3.10

# Earth Data Fetcher

A simple package to fetch gridded earth data from different sources using the appropriate protocol. 

Supports the following protocols:
- HTTP[S]
- FTP
- S3
- THREDDS datasets
- CMEMS datasets

## To do

The majority of my datasets are hosted on CMEMS, so those will be prioritized.
The HTTP[S] protocol will be the next to be implemented since the SST and MLD (SODA) data are hosted on HTTP servers.
dvrd-smtp 1.1.7
Object oriented wrapper for smtplib
Requires Python: >=3.11

# README #

This README would normally document whatever steps are necessary to get your application up and running.

### What is this repository for? ###

* Quick summary
* Version
* [Learn Markdown](https://bitbucket.org/tutorials/markdowndemo)

### How do I get set up? ###

* Summary of set up
* Configuration
* Dependencies
* Database configuration
* How to run tests
* Deployment instructions

### Contribution guidelines ###

* Writing tests
* Code review
* Other guidelines

### Who do I talk to? ###

* Repo owner or admin
* Other community or team contact
minkarr 0.1.7
A minimal implementation of KaRR knowledge assessment method for Large Language Models (LLMs)
Requires Python: >=3.8

# Statistical Knowledge Assessment for Large Language Models
A minimal implementation of KaRR knowledge assessment method from the following paper:

> [**Statistical Knowledge Assessment for Large Language Models**](https://arxiv.org/abs/2305.10519),            
> Qingxiu Dong, Jingjing Xu, Lingpeng Kong, Zhifang Sui, Lei Li   
> *arXiv preprint ([arxiv_version](https://arxiv.org/abs/2305.10519))*   

This is a fork of the [official implementation](https://github.com/dqxiu/KAssess) released by the authors.

## How to use?

First, create a new virtual environment, then install [Pytorch-CUDA](https://pytorch.org/get-started/locally/), and finally install **minkarr** using the command:

```bash
pip install minkarr
```

Here is a simple example of **how to quantify the knowledge of a fact by an LLM using KaRR**
```python
from minkarr import KaRR
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "gpt2"
device = "cuda"
model = AutoModelForCausalLM.from_pretrained(model_name).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_name)

karr = KaRR(model, tokenizer, device)

# Testing the fact: (France, capital, Paris)
# You can find other facts by looking into Wikidata
fact = ("Q142", "P36", "Q90")

karr, does_know = karr.compute(fact)
print("Fact %s" % str(fact))
print("KaRR = %s" % karr)
ans = "Yes" if does_know else "No"
print("According to KaRR, does the model knows this fact? Answer: %s" % ans)
# Output:
# KaRR = 3.338972442145268
# According to KaRR, does the model knows this fact? Answer: No
```

## Difference with original repo

- Easy-to-use
- Cleaner code
- Minimalistic implementation: I kept only the portion of the code needed to compute KaRR and removed the rest
- This implementation can compute KaRR on a single fact (the original implementation went through all facts)

## Change storage location

Set the environment variable `STORAGE_FOLDER` to choose where to store the data that MinKaRR downloads.

## Citation
Cite the original authors using:
```
@misc{dong2023statistical,
      title={Statistical Knowledge Assessment for Large Language Models}, 
      author={Qingxiu Dong and Jingjing Xu and Lingpeng Kong and Zhifang Sui and Lei Li},
      year={2023},
      journal = {Proceedings of NeurIPS},
}
```
logo2Stripe 0.1.0
Convert images to black and white (no gray) to use as custom logos on Stripe credit cards.
Requires Python: >=3.7

# logo2Stripe

A PyPi library to convert images to black and white (no gray) to use as custom logos on Stripe credit cards. 

**Processing Pipeline:** Applies a slight Gaussian blur, resizes it to fit a maximum dimension of 1000×200 pixels, and centers it on a white canvas.

## Installation

1. Clone this repository or download the files.
2. In the same directory as `setup.py`, run:
   ```bash
   pip install .
   ```

   or to install in editable mode:

   ```bash
   pip install -e .
   ```

## Usage

```py
from logo2Stripe.converter import convert_to_black_and_white

# Convert an image to black & white and save it
convert_to_black_and_white("input_image.png", "output_image.png", threshold=128)
```

---

## How to Install and Use

1. **Install**  
   In your terminal, change directory (`cd`) into the location of `setup.py`, then run:
   ```bash
   pip install .
   ```

2. **Import and Call**  
   In your Python script or REPL:
    ```py
    from logo2Stripe.converter import convert_to_black_and_white

    convert_to_black_and_white("input.png", "output.png", threshold=128)
    ```