AI Analysis
Final verdict: SUSPICIOUS
The package shows no direct malicious activities, but the incomplete maintainer profile and obfuscated code raise concerns about its authenticity and potential long-term support.
- Incomplete maintainer profile
- Obfuscated code
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution detected, indicating the package does not perform system-level commands.
- Obfuscation: The code snippets show typical patterns of model evaluation and prediction which are common in machine learning libraries, but the partial and obfuscated nature raises some concern.
- Credentials: No clear signs of credential harvesting or secret handling were detected in the provided code snippets.
- Metadata: The maintainer has an incomplete profile and seems to be new or inactive, raising some suspicion but not definitive evidence of malice.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 8.0
Found 4 obfuscation pattern(s)
""" preds = [] model.eval() logger.debug("Predicting %d tiles on device=%s", len(adtype=np.float32) model.eval() with torch.inference_mode(): x = (d_state_dict(state) model.eval() _MODEL_CACHE[key] = model logger.info(f"Model loaropoutprob=0, ) model.eval() return model @pytest.fixture def dummy_256_image():
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: leeds.ac.uk>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository mayatek1/afMLevel appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 afmlevel
Create a desktop application using Python that leverages the 'afmlevel' package to analyze and level Atomic Force Microscopy (AFM) images. This application will allow users to upload AFM images, apply machine learning-based leveling algorithms to correct for surface topography distortions, and visualize the results. Here’s a detailed plan on how to proceed: 1. **Setup Environment**: Ensure you have Python installed along with the necessary libraries such as 'afmlevel', 'Pillow' for image handling, and 'matplotlib' for visualization. 2. **User Interface Design**: Use PyQt5 or Tkinter to create a user-friendly interface where users can browse and upload their AFM images. 3. **Image Preprocessing**: Implement a feature to preprocess the uploaded images before applying any leveling algorithm. This might include resizing, normalization, and noise reduction. 4. **Leveling Algorithms**: Utilize 'afmlevel' to apply its machine learning-based leveling algorithms. Allow users to choose from different leveling methods available in the package, and provide options to tweak parameters if needed. 5. **Visualization and Comparison**: Display the original and leveled images side by side for easy comparison. Include tools to zoom in/out, pan, and measure specific areas of interest. 6. **Save Results**: Provide functionality to save the processed images either as new files or to overwrite existing ones. 7. **Advanced Features**: Consider adding advanced features like batch processing multiple images at once, exporting results in various formats, or integrating with cloud storage services for seamless data management. 8. **Documentation and Support**: Write comprehensive documentation for your application and consider setting up a support system for users who encounter issues. By following these steps, you'll develop a powerful tool for researchers and engineers working with AFM images, making it easier to analyze and correct for surface distortions.