🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network activity, shell execution, and code obfuscation. However, the metadata risk score is elevated due to the unavailability of the repository and the maintainer's new or inactive account status.

metadata risk score is high
repository not found
maintainer has a new or inactive account

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external communication.
Shell: No shell execution patterns detected, indicating no immediate signs of malicious activities like backdoors.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository not being found and the maintainer having a new or inactive account raises concerns.

🔬 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: ucl.ac.uk>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with PickMe-EM

Your task is to develop a mini-application called 'MembraneExplorer' that leverages the capabilities of the 'PickMe-EM' Python package. MembraneExplorer will serve as a user-friendly interface for researchers to analyze electron microscopy images, focusing on membrane segmentation and particle picking. The application should be designed with both novice and experienced users in mind, ensuring ease of use while providing advanced options for customization and analysis.

### Core Features:
1. **Image Upload**: Users should be able to upload their electron microscopy images directly from their computer or via a URL.
2. **Automatic Segmentation**: Utilize PickMe-EM's core functionality to automatically segment membranes within the uploaded images.
3. **Particle Picking**: After segmentation, allow users to automatically pick particles based on predefined criteria such as size, shape, and density.
4. **Customizable Parameters**: Provide options for users to adjust parameters for better segmentation and particle picking results.
5. **Result Visualization**: Display segmented membranes and picked particles overlaid on the original image, allowing users to zoom in/out and inspect areas of interest.
6. **Export Results**: Enable users to export their final results (segmented images and particle locations) in common file formats like PNG, TIFF, or CSV.
7. **Interactive Help**: Include a comprehensive help section explaining each feature and how to use them effectively.

### How to Use PickMe-EM:
- Import the necessary modules from PickMe-EM at the beginning of your script.
- Load the electron microscopy image using appropriate functions provided by PickMe-EM.
- Apply the segmentation algorithm to isolate membrane structures.
- Implement particle picking based on the segmented image.
- Use visualization tools within PickMe-EM to display the processed images and results.
- Finally, provide options to save the processed images and data for further analysis or reporting.

Develop MembraneExplorer to not only showcase the power of PickMe-EM but also to make advanced EM image analysis accessible to a broader audience.