🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activities such as network calls, shell executions, obfuscations, or credential risks. It appears to be a straightforward tool for image processing.

No network calls detected
No shell execution detected

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, indicating no immediate risk of command injection or similar attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: sanger.ac.uk>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ImageTileProcessor

Create a Python-based mini-application called 'TiledImageViewer' which leverages the 'ImageTileProcessor' package to efficiently manage and display large images. The goal of this application is to provide users with a tool that can handle massive images without consuming excessive memory, while also offering basic editing capabilities. Here’s a detailed breakdown of what your application should include:

1. **Lazy Tiling**: Implement a feature where the application loads only visible portions of an image into memory. This will be achieved using the 'ImageTileProcessor' package's lazy loading mechanism.
2. **Zoom and Pan**: Allow users to zoom in/out and pan across the image. Ensure that the application dynamically loads new tiles as the user navigates through different parts of the image.
3. **Basic Editing Tools**: Incorporate simple editing tools such as brightness/contrast adjustments, cropping, and rotation. These tools should work on the currently loaded tiles, and any changes should be applied lazily to ensure efficient memory usage.
4. **Save Functionality**: Provide an option to save the edited image. Since the image is processed in tiles, you'll need to implement logic to merge all tiles back together before saving.
5. **User Interface**: Design a simple yet intuitive GUI using Tkinter or another suitable library. The UI should have controls for zooming, panning, and accessing the editing tools.
6. **Performance Optimization**: Focus on optimizing the application to handle very large images efficiently. Use profiling tools to identify bottlenecks and apply appropriate optimizations.
7. **Documentation**: Write clear documentation explaining how to install the 'ImageTileProcessor' package, run the application, and use its features.

To utilize the 'ImageTileProcessor' package effectively, follow these steps:
- Import necessary modules from the package at the beginning of your script.
- Initialize the tiler with the path to the image file.
- Implement event listeners for zoom and pan actions that trigger tile loading based on the current view.
- For editing tools, apply changes to the tiles and update the display accordingly.
- When saving, ensure all tiles are merged correctly before writing the final image to disk.

By following these guidelines, you’ll create a powerful yet easy-to-use tool for handling large images.