🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with low risks associated with network communication and no evidence of malicious activity such as shell execution or credential harvesting.

Network calls to an external service for predictions
No signs of obfuscation or malicious intent

Per-check LLM notes

Network: The presence of network calls suggests the package might be communicating with an external service, which is not inherently suspicious but requires further investigation to ensure it's legitimate and secure.
Shell: No shell execution patterns were detected, indicating a low risk of direct system command execution from the package.
Obfuscation: The observed pattern is likely for reading the README file and does not indicate malicious obfuscation.
Credentials: No suspicious patterns indicating credential harvesting were detected.
Metadata: The package is likely new and the maintainer may be inexperienced, but there are no clear signs of malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

) resp = requests.post( API_URL, headers=he

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

= open("README.md").read() if __import__("os").path.exists("README.md") else DESCRIPTION AUTHOR = "Carlos"

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Carlos" 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 Orange3-TabH2O

Create a data analysis and visualization tool using the 'Orange3-TabH2O' package. This tool will allow users to upload datasets, apply machine learning models from H2O.ai's TabH2O foundation model, and visualize the results interactively. Here's a detailed breakdown of the steps and features:

1. **User Interface Setup**: Design a simple yet intuitive user interface where users can upload their datasets in various formats (CSV, Excel, etc.).
2. **Data Preprocessing**: Implement basic data preprocessing functionalities such as handling missing values, scaling, and encoding categorical variables.
3. **Model Selection and Training**: Utilize 'Orange3-TabH2O' to integrate H2O.ai's TabH2O models. Provide options for users to select different types of models (e.g., regression, classification) and train these models on their dataset.
4. **Visualization of Results**: After training, display the performance metrics of the models. Use plots and charts to visualize predictions, feature importance, and other key insights.
5. **Export Options**: Allow users to export the trained models and visualizations in various formats (JSON, CSV, PNG).

The 'Orange3-TabH2O' package will be crucial in facilitating the connection between Orange's data mining framework and H2O.ai's powerful machine learning algorithms, enabling seamless integration and analysis.