semvec/pypi-packages huggingface dataset:
pronet 0.1.1
A Python package for visualizing protein interaction networks from STRING database.
Requires Python: >=3.8

 pronet

`pronet` is a Python library for fetching protein interactions from STRING database, 
building protein networks, visualizing them, and generating reports.

1- Installation

```bash
pip install pronet

2-  Usage

Here is a simple example of how to use pronet: 
from pronet import get_string_interactions, build_network, generate_report

proteins = ["BRCA1", "MDM2", "EGFR"]
data = get_string_interactions(proteins)
G = build_network(data, proteins, show_plot=True, layout="circular")
generate_report(G, proteins, data, save_path="protein_report.txt")

3-  Requirements

Python >= 3.8

requests

networkx

matplotlib
 
4- Features

Fetch interactions for a list of proteins using STRING API.

Build and visualize protein interaction networks using NetworkX and Matplotlib.

Generate detailed reports including main proteins, top interactions, and network statistics.

Choose different network layouts: spring, circular, or shell.

License

MIT License

Copyright (c) 2025 Sarah

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
promtree 0.1.3
PDF를 Markdown으로 변환하여 저장하는 라이브러리
Requires Python: ==3.12.10

# PromTree

PromTree is a Python library for converting PDF documents to Markdown format. It uses OCR and layout detection to accurately preserve document structure and formatting.

## Features

- Convert PDF documents to Markdown with preserved formatting
- Automatic layout detection using Surya OCR
- Support for text, images, and section headers
- Command-line interface and Python API
- Optional cleanup of intermediate files

## Requirements

- Python 3.12.10
- PyMuPDF 1.26.5
- Surya OCR 0.17.0
- PyTorch 2.9.0
- Transformers 4.57.1
- Additional dependencies listed in `requirements.txt`

Note: First run will download Surya OCR models from Hugging Face (approximately 1-2GB). An internet connection is required for initial setup.

## Installation

### Install from PyPI

```bash
pip install promtree
```

### Install from source

```bash
git clone https://github.com/Team-PromTree/promtree.git
cd promtree
pip install -e .
```

### Install dependencies only

```bash
pip install -r requirements.txt
```

## Usage

### Command Line Interface

Basic conversion:

```bash
promtree path/to/document.pdf
```

Specify output file:

```bash
promtree path/to/document.pdf --output-md output.md
```

Enable cleanup mode (removes intermediate files):

```bash
promtree path/to/document.pdf --cleanup
```

### Python API

```python
from promtree import PromTree

# Basic conversion
PromTree(pdf_path="document.pdf")

# With custom output path
PromTree(pdf_path="document.pdf", output_md="output.md")

# With cleanup enabled
PromTree(pdf_path="document.pdf", cleanup=True)
```

## How It Works

PromTree processes PDF documents through a four-stage pipeline:

1. **PDF to PNG Conversion**: Converts each PDF page to high-resolution images
2. **Text Extraction**: Extracts text elements and images with bounding box coordinates
3. **Layout Detection**: Analyzes page layout using Surya OCR to identify sections and structure
4. **Markdown Assembly**: Combines extracted text with layout information to generate formatted Markdown

## Output Structure

For a PDF file named `document.pdf`, PromTree generates:

```
document/
|-- images/           # Extracted images from PDF
|-- layouts/          # Layout detection results
|   |-- results.json
|   |-- *_layout.png  # Annotated images showing detected regions
|-- outputs/          # PNG images of each page
|-- output.md         # 최종 결과물
|-- output.txt        # Intermediate text extraction results
```

Final Markdown output: `output.md` (or custom path specified with `--output-md`)

Use `--cleanup` flag to automatically remove intermediate directories after conversion.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Repository

https://github.com/Team-PromTree/promtree

## Authors

Team.PromTree (effort-result@naver.com)
upils 0.12.0
Unified Python Utils. Requires python 3.10 and later
Requires Python: <4.0,>=3.10

# Unified Python Utils

Collection of python library utils

## Installation

```poetry add upils```

## How to use the logger

First, set up the logger. It accepts string like "INFO" or int 20

```logger = configure_logger(20)```

And then you can just use it like other logger

```
logger.info("info")
logger.warning("warning")
```

### Add extra information
How to use the extra logging:

```
logger.bind(user="ajung", desc="smart").error(
    "Inline binding of extra attribute"
)
```

The output will be

```
{"level": "ERROR", "time": {"repr": "2023-10-04 12:03:53.043106+07:00", "timestamp": 1696395833.043106}, "message": "Inline binding of extra attribute", "file": {"name": "app.py", "path": "/Users/user/Documents/projects/de-projects/service/statistics-services/app.py"}, "line": 47, "exception": null, "extra": {"ajung": "smart"}}
```