apunim

v1.0.2 suspicious
4.0
Medium Risk

Polarization attribution in annotation tasks

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a suspicious metadata profile with an anonymous author and low repository activity, raising concerns about its origin and legitimacy.

  • Anonymous author
  • Low repository activity
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communications.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious activities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some red flags including an anonymous author and low activity on the git repository, indicating potential risks.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

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

Some documentation present

  • 1 documentation file(s) (e.g. conf.py)
  • Brief PyPI description (795 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 19 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 31 commits in dimits-ts/apunim
  • Two distinct contributors found

🔬 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: aueb.gr>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 apunim 
Your task is to create a user-friendly web application that facilitates the analysis of polarization in annotated datasets using the Python package 'apunim'. This application will serve as a tool for researchers, data scientists, and educators to better understand the dynamics of polarization within their datasets. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Start by setting up a virtual environment for your project. Install necessary packages including Flask (for web framework), Pandas (for data manipulation), and 'apunim' (for polarization analysis).
2. **User Interface Design**: Develop a clean and intuitive UI where users can upload their annotated dataset files (CSV format). Include a feature for users to select specific columns from the dataset for analysis.
3. **Data Processing**: Utilize Pandas to read and preprocess the uploaded CSV file. Ensure that the data is cleaned and formatted correctly before feeding it into 'apunim'.
4. **Polarization Analysis**: Integrate 'apunim' to perform polarization attribution on the selected columns from the dataset. Use the package's functionalities to identify and quantify the degree of polarization present in the annotations.
5. **Visualization**: Implement visualization tools (using libraries like Matplotlib or Seaborn) to display the results of the polarization analysis. Provide interactive charts that allow users to explore different aspects of the analysis.
6. **Export Results**: Allow users to export the analyzed data and visualizations as downloadable files (PDF, CSV, etc.).
7. **Documentation & Help**: Include comprehensive documentation and help sections within the app to guide users through the process and explain key concepts related to polarization in annotation tasks.
8. **Testing & Feedback**: Conduct thorough testing of the application to ensure reliability and accuracy. Incorporate a feedback system where users can report bugs or suggest improvements.

Suggested Features:
- Support for multiple file uploads
- Real-time progress indicators during file processing
- Detailed explanations of the polarization metrics calculated by 'apunim'
- Customizable visualization options
- Integration with popular cloud storage services for direct file import

This application aims to provide a powerful yet accessible tool for analyzing polarization in annotated datasets, leveraging the unique capabilities of the 'apunim' package.

💬 Discussion Feed

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