astro-swiper

v0.1.11 safe
4.0
Medium Risk

Web-based interactive FITS triplet classifier for astronomical image labelling

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with minimal risks. It has no network or shell execution activities, and while there is some obfuscation through base64 encoding, this is likely used for handling image data.

  • No network calls
  • No shell execution
  • Potential benign obfuscation through base64 encoding
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package that does not require external API interactions.
  • Shell: No shell execution detected, indicating the package does not execute system commands, which is typical and safe.
  • Obfuscation: The use of base64 encoding to write bytes might indicate an attempt to obfuscate code, but it could also be a legitimate use for handling image data.
  • Credentials: No suspicious patterns indicating credential harvesting were detected.
  • Metadata: The package shows some signs of low maintainer activity and effort, but there's no clear indication of malicious intent.

📦 Package Quality Overall: Low (2.0/10)

○ 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 (4647 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • out_path.write_bytes(base64.b64decode(clf._render(imgs))) except Exception as e:
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with astro-swiper 
Create a web-based application using the 'astro-swiper' Python package that enables astronomers and enthusiasts to classify astronomical images based on their characteristics. The app will serve as an interactive platform where users can swipe through triplets of FITS files (astronomical image formats) and classify them according to specific criteria such as star type, galaxy morphology, or other relevant attributes.

Step 1: Set up the development environment.
- Install necessary packages including 'astro-swiper', Flask for the web framework, and any required dependencies for handling FITS files.

Step 2: Design the user interface.
- Develop a clean, intuitive UI that allows users to easily navigate through the FITS file triplets and select their classification choices.

Step 3: Implement the backend logic.
- Utilize 'astro-swiper' to handle the core functionality of displaying and managing FITS files.
- Integrate a database to store user classifications and metadata about the images.

Step 4: Enhance the application.
- Add features like user authentication and profiles to track user contributions.
- Implement a leaderboard to showcase top contributors.
- Incorporate machine learning models to suggest initial classifications and improve over time based on user feedback.

Step 5: Test and deploy the application.
- Conduct thorough testing to ensure reliability and accuracy.
- Deploy the app on a cloud service provider such as AWS or Heroku for public access.

The application should provide value by enabling collaborative image classification efforts within the astronomy community, improving the efficiency and accuracy of data labeling processes.

💬 Discussion Feed

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