ai-parrot-pipelines

v0.1.59 safe
1.0
Low Risk

Pipelines for AI-Parrot planogram and vision workflows

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity, with very low risks across all categories including network, shell, obfuscation, and credential handling.

  • No network calls detected.
  • No shell execution patterns found.
  • No obfuscation techniques observed.
  • No credential harvesting attempts.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API access or communication.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 2 test file(s) detected (e.g. test_endcap_no_shelves_promotional.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/phenobarbital/ai-parrot/
  • Detailed PyPI description (1686 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
  • 153 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-pipelines 
Create a mini-app called 'RetailVision' that leverages the 'ai-parrot-pipelines' package to streamline retail shelf management and optimize product placement. RetailVision will allow users to upload images of store shelves and receive actionable insights on how to improve their product layout based on AI analysis. Here’s a detailed breakdown of the app's functionality and features:

1. **Image Upload**: Users can upload images of store shelves. This feature will require integrating an image upload component, possibly using Flask or Django for backend development.
2. **AI Analysis**: Utilize 'ai-parrot-pipelines' to process these images through its vision pipelines. This includes analyzing shelf space utilization, product visibility, and compliance with planograms.
3. **Insight Generation**: Based on the analysis, generate detailed reports and recommendations for improving shelf layouts. These could include suggestions for moving products to more visible locations, optimizing product grouping, or addressing any discrepancies with the planogram.
4. **User Dashboard**: Develop a simple dashboard where users can view their uploaded images, analysis results, and generated insights. This dashboard should be user-friendly and visually appealing.
5. **Integration with Planograms**: Allow users to input or select existing planograms from a database or file upload. RetailVision will then compare the actual shelf layout against the planogram to identify discrepancies and suggest adjustments.
6. **Real-time Feedback**: Implement real-time feedback mechanisms, such as notifications or alerts, when significant discrepancies between the actual shelf layout and the planogram are detected.
7. **Security and Privacy**: Ensure all data is handled securely, with proper encryption for stored images and planograms. Respect user privacy and adhere to GDPR or similar regulations if applicable.

To utilize 'ai-parrot-pipelines', you'll need to install it via pip and familiarize yourself with its API documentation. Specifically, focus on how to integrate its vision processing capabilities into your application flow. This might involve preprocessing images, invoking specific pipeline stages for analysis, and interpreting the output data to generate meaningful insights for the user.