🤖 AI Analysis

Final verdict: SAFE

The package appears to have legitimate functionality without significant indicators of malicious activity. The primary concern is its use of network calls, but this is likely necessary for its intended genomics evaluation purposes.

Moderate network risk due to external service interactions.
Low risk in other categories including shell execution, obfuscation, and credential handling.

Per-check LLM notes

Network: The presence of network calls suggests the package may interact with external services, which could be legitimate but also raises concerns about potential data exfiltration.
Shell: No shell execution patterns were detected, indicating a lower risk of immediate system compromise.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 11 test file(s) found

Test runner config found: conftest.py
11 test file(s) detected (e.g. conftest.py)

◈ Medium Documentation 5.0

Some documentation present

Brief PyPI description (348 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

157 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 34 commits in Huang-lab/AiGCT
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

itory.root_dir response = requests.get(url, stream=True) try: header = response.headers

✓ 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: yahoo.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Huang-lab/AiGCT 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 aigct

Develop a web-based application using Flask that integrates with the 'aigct' Python package to enable researchers to evaluate the performance of different machine learning models on genomic variant data. This application will serve as a user-friendly interface for uploading genomic datasets, selecting ML models from a list of pre-configured options within AIGCT, running evaluations, and visualizing the results.

Steps to develop the application:
1. Set up a Flask environment and install necessary packages including 'aigct'.
2. Design a simple yet effective UI/UX for file uploads, model selection, and result visualization.
3. Implement backend logic to handle file processing, interaction with the 'aigct' package for model evaluation, and storage of results.
4. Integrate visualization libraries such as Plotly or Matplotlib to display evaluation metrics like accuracy, precision, recall, and F1-score.
5. Ensure the application supports multiple datasets and models simultaneously, allowing users to compare outcomes easily.
6. Add documentation and examples for new users to understand how to use the app effectively.

Features:
- User authentication and authorization for secure access.
- Support for various file formats commonly used in genomics research.
- Detailed reports of model performance including ROC curves and confusion matrices.
- Option to save and share results via unique URLs or download as PDFs.
- Real-time progress tracking during model evaluations.

How 'aigct' is utilized:
- The 'aigct' package provides the core functionality for evaluating the selected ML models against the uploaded genomic datasets. Users will choose from a list of models available in AIGCT, which the application will then run through the AIGCT pipeline. The package also offers methods for preprocessing data, configuring model parameters, and generating evaluation metrics, all of which will be integrated into the application's workflow.