ai-parrot-visualizations

v0.1.7 safe
4.0
Medium Risk

Visualization renderers for AI-Parrot outputs

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with network calls, shell executions, and obfuscation. The metadata risk is slightly elevated due to incomplete author information, but this alone does not indicate malicious intent.

  • Low risk scores across all categories except metadata.
  • Incomplete author information raises minor concern.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a visualization package unless it requires external data sources.
  • Shell: No shell executions detected, reducing the risk of unauthorized system access or command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author information is incomplete, which could indicate a lack of transparency or intent to remain anonymous, but there are no other red flags.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2630 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 184 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in phenobarbital/ai-parrot
  • Small but multi-author team (3–4 contributors)

🔬 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: phenobarbital.info>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository phenobarbital/ai-parrot 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 ai-parrot-visualizations 
Create a Python-based mini-application called 'AI Art Explorer' that leverages the 'ai-parrot-visualizations' package to visualize and explore different types of AI-generated art. This application will serve as a tool for artists, designers, and enthusiasts to understand and appreciate the nuances of AI-generated visuals. Here’s a detailed breakdown of what your application should achieve:

1. **Project Setup**: Begin by setting up a new Python virtual environment and installing the necessary packages, including 'ai-parrot-visualizations'. Ensure that you also install other dependencies such as Flask for web serving.
2. **Data Source Integration**: Integrate a source of AI-generated art data. This could be a local dataset or a remote API endpoint that provides AI-generated images. The data should include metadata like creation method, style, and any other relevant information.
3. **Visualization Engine**: Utilize the 'ai-parrot-visualizations' package to create interactive visualizations of the AI-generated art. Implement features such as zooming, panning, and the ability to switch between different visualization modes (e.g., grid view, full-screen view).
4. **Interactive Features**: Allow users to interact with the visualizations through a user-friendly interface. Users should be able to filter artworks based on specific criteria (e.g., style, artist, date of creation). Additionally, implement tooltips that provide detailed information about each artwork when hovered over.
5. **Customization Options**: Enable customization options for the visualizations. For example, allow users to choose color schemes, layout styles, and other aesthetic preferences to tailor their viewing experience.
6. **Export Functionality**: Provide functionality for users to export visualizations as high-quality images or PDFs. This feature should be accessible via a button within the application.
7. **Documentation & Deployment**: Write comprehensive documentation explaining how to use the application and deploy it to a server or cloud platform. Include setup instructions, configuration details, and best practices for maintaining the application.

By following these steps, you'll create a robust and engaging mini-application that showcases the capabilities of 'ai-parrot-visualizations' while providing valuable insights into the world of AI-generated art.