semvec/pypi-packages huggingface dataset:
ai-credentials 0.1.2
A Python library for managing credentials with Rust backend
License: MIT
Requires Python: >=3.8
Keywords: credentials, security, password-manager

# ai_credentials

A Python library for managing credentials securely using a Rust backend.

## Installation

```bash
pip install ai_credentials
```

## Usage

```python
import ai_credentials

# Register credentials from .env file
ai_credentials.register_credentials('.env')

# Retrieve credentials for a specific username
password = ai_credentials.get_credentials('.env', 'user@example.com')
print(f'Retrieved password: {password}')
``` # Force refresh
sheet-image-finder 0.1.0
SheetImageFinder is a pure Python utility for extracting images from Excel .xlsx files — specifically from worksheets and cell addresses. It parses the internal XML structure of Excel files and maps embedded images to their anchor locations.
License: MIT
Requires Python: ==3.9.*

# 📦 SheetImageFinder

`SheetImageFinder` is a pure Python utility for extracting images from Excel `.xlsx` files — specifically from worksheets and cell addresses. It parses the internal XML structure of Excel files and maps embedded images to their anchor locations.

---

## ✨ Features

- ✅ Extract images anchored to specific cells (e.g., `H2`, `H3`)
- ✅ Supports file paths and `io.BytesIO` streams
- ✅ Lazy image loading — images loaded only when requested
- ✅ List image-bearing cells by sheet or across workbook
- ✅ Cross-platform temporary file handling
- ✅ `cleanup()` method for managing resources

---

## 📦 Dependency
No need for `openpyxl` or `openpyxl-image-loader`. Just have your `pillow` installed.

```bash
pip install pillow
```

•	Python 3.8+

## 📦 Installation

```bash
pip install sheet_image_finder
```

## 🚀 Usage Example

```python

from PIL import Image
from sheet_image_finder import XLSXImageFinder  # Replace with actual import path

# Load XLSX from file path
finder = XLSXImageFinder("/Users/ss/Downloads/SampleFile.xlsx")

# 1. List all image cells in 'EXTERIOR' sheet
image_cells = finder.list_image_cells("EXTERIOR")
print("Image cells in 'EXTERIOR':", image_cells)

# 2. Load and display an image at a specific cell
if 'H2' in image_cells:
    image = finder.get_image("EXTERIOR", "H2")
    if image:
        image.show()
    else:
        print("No image found at EXTERIOR!H2")
else:
    print("H2 not in image cells for EXTERIOR")

# 3. List all image-containing cells across all sheets
all_images = finder.list_all_sheet_image_cells()
print("\nAll image cells in workbook:")
for sheet, cells in all_images.items():
    print(f"  {sheet}: {cells}")

# 4. Clean up temporary files
finder.cleanup()
```

## 🧰 API Reference

`XLSXImageFinder(xlsx_source)`
	•	xlsx_source: Path to .xlsx file or io.BytesIO stream

⸻

`get_image(sheet_name, cell_address)` → `PIL.Image` or `None`

Loads and returns the image anchored at cell_address in sheet_name.

⸻

`list_image_cells(sheet_name)` → `List[str]`

Returns a list of cell addresses that contain images in the given sheet.

⸻

`list_all_sheet_image_cells()` → `Dict[str, List[str]]`

Returns all sheets and their image-bearing cells:
```
{
    'EXTERIOR': ['H2', 'H3'],
    'APPLIANCES': ['H4']
}
```

## 📘 License

MIT License — free to use, modify, and distribute.

## 🧠 Author’s Note

This library bypasses heavyweight Excel parsing and directly inspects the underlying structure of .xlsx files. It’s optimized for performance, memory, and flexibility — ideal for image extraction in automation pipelines or reporting tools.