AI Analysis
The package shows low risks across all checks with no network calls, shell executions, or credential harvesting attempts. The metadata risk is slightly elevated due to the package being new and lacking a GitHub repository, but there are no immediate red flags.
- No network calls detected
- Lacks a GitHub repository
- No shell execution or credential harvesting patterns detected
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package is new and lacks a GitHub repository, but there are no immediate red flags like suspicious links or multiple packages from the same author.
Package Quality Overall: Low (1.2/10)
No test suite detected
No test files or test-runner configuration detected
No documentation detected
No documentation URL, doc files, or meaningful description found
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Dr. Noor Fatima" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a Python-based mini-application that leverages the 'astralnet' package to analyze and predict stroke outcomes based on clinical radiomics data. This application will serve as a tool for medical researchers and clinicians to better understand patient-specific stroke risks and potential recovery paths. Step 1: Set up your development environment with Python and install the 'astralnet' package. Step 2: Develop a user-friendly interface where users can upload their clinical radiomics data in a supported format (e.g., DICOM). Step 3: Implement data preprocessing steps using 'astralnet' to clean and prepare the data for analysis. Step 4: Use 'astralnet' to extract relevant radiomic features from the preprocessed data. Step 5: Integrate a machine learning model provided by 'astralnet' to predict stroke outcomes based on the extracted features. Step 6: Visualize the prediction results alongside key radiomic features in an interactive manner. Step 7: Include documentation and examples to help new users understand how to use the application effectively. Suggested Features: - Support for multiple file formats commonly used in medical imaging. - Real-time feedback during data upload and processing stages. - Detailed explanations of the radiomic features and their significance. - Customizable machine learning models to cater to different research needs. - Export options to save the analysis results in various formats.
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