🤖 AI Analysis

Final verdict: SAFE

The package adnipy v1.0.1 exhibits minimal risks across all assessed categories, with only a slight concern noted in metadata. There are no indications of malicious activity or supply-chain attacks.

No network calls or shell executions detected.
Minimal obfuscation and credential risks.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or unauthorized system access.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets.
Metadata: The package has some minor red flags but no clear indicators of malicious intent.

🔬 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: smail.uni-koeln.de>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://adni.loni.usc.edu/

✓ Git Repository History

Repository mcsitter/adnipy appears legitimate

⚠ 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 adnipy

Your task is to develop a mini-application named 'ADNIPortal' that leverages the Python package 'adnipy' to process and analyze data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. This application will serve as a user-friendly tool for researchers and clinicians to gain insights into the progression of Alzheimer's disease based on neuroimaging data. Here are the steps and features your application should include:

1. **Data Import**: The application should allow users to import ADNI study data files (e.g., MRI scans, clinical data). Use the `adnipy.load_data()` function from the 'adnipy' package to facilitate this process.
2. **Preprocessing**: Implement a preprocessing module using `adnipy.preprocess()` to clean and normalize the imported data. This could involve removing noise, aligning images, and adjusting contrast.
3. **Feature Extraction**: Develop a feature extraction module that utilizes `adnipy.extract_features()` to identify key characteristics of the neuroimaging data, such as brain volume measurements, cortical thickness, and white matter integrity.
4. **Analysis Dashboard**: Create a dashboard where users can visualize the extracted features alongside clinical data. This dashboard should use interactive plots and graphs, possibly leveraging libraries like Plotly or Bokeh.
5. **Prediction Model**: Incorporate a machine learning model using `adnipy.train_model()` to predict the likelihood of Alzheimer's disease progression based on the processed data. Allow users to input new patient data and receive predictions.
6. **Report Generation**: Enable users to generate comprehensive reports summarizing the analysis results, including visualizations and predictive outcomes. The report should be exportable in PDF format.
7. **User Interface**: Design a clean, intuitive user interface using a web framework like Flask or Django to make the application accessible via a web browser.
8. **Documentation**: Provide detailed documentation explaining how to install and use the application, along with examples and tutorials.

By following these steps and implementing these features, you will create a powerful yet easy-to-use tool for analyzing ADNI study data.