🤖 AI Analysis

Final verdict: SAFE

The package is assessed as safe with minimal risks identified. It shows no signs of malicious activity, though some basic obfuscation and metadata indicators suggest it's from a potentially newer developer.

No network or shell risks detected
Some obfuscation and metadata indicators present

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating no suspicious system command executions.
Obfuscation: The observed pattern is likely related to model evaluation and GPU usage, which is common in machine learning applications but could indicate basic obfuscation techniques.
Credentials: No suspicious patterns for credential harvesting were detected.
Metadata: The maintainer has only one package, which may indicate a new or less active account.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

featureModel = model.model.eval() featureModel = featureModel.cuda() if useGPU

✓ 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: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Franz Ehrlich" 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 SleePyPhases

Create a mini-application named 'SleepPhaseAnalyzer' using the Python package 'SleePyPhases'. This application will allow users to analyze their sleep phases through a series of deep learning models provided by the package. The application should be user-friendly, allowing for easy input of sleep data and displaying results in a clear, understandable format. Here are the key steps and features for the application:

1. **Data Input**: Users should be able to upload their sleep data in CSV format. The CSV file should contain at least two columns: timestamp and heart rate.
2. **Data Preprocessing**: Use SleePyPhases to preprocess the uploaded data. This includes normalization and segmentation into sleep phases (REM, NREM, Wake).
3. **Model Training**: Utilize SleePyPhases to train a deep learning model on the preprocessed data if the user wishes to customize their analysis. Provide default models as well.
4. **Analysis Output**: Display the sleep phase analysis in a graphical format, showing the duration and percentage of time spent in each phase. Additionally, provide a summary of sleep quality based on the analysis.
5. **Customization Options**: Allow users to adjust parameters such as the sensitivity of phase detection or choose different models for analysis.
6. **Export Results**: Users should have the option to export their analysis results as a PDF report or another preferred format.

To achieve these goals, you'll need to leverage SleePyPhases' capabilities for data preprocessing, model training, and prediction. Ensure that the application is designed with both novice and advanced users in mind, providing clear instructions and options for customization.