ajpegli

v1.0.0 safe
4.0
Medium Risk

Fast JPEG-to-NumPy image loading powered by Google jpegli

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SAFE

The ajpegli package appears to be safe with no detected network calls, shell executions, obfuscation, or credential harvesting. However, the metadata risk is elevated due to the maintainer's limited presence on GitHub.

  • No network calls
  • No shell executions
  • Single package maintainer
Per-check LLM notes
  • Network: No network calls suggest the package does not engage in external communications, which is normal unless expecting specific API interactions.
  • Shell: No shell executions indicate the package does not perform system commands, reducing the risk of unauthorized access or code execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious intent.
  • Metadata: The maintainer has only one package and no associated GitHub repository, which could indicate a lower level of trustworthiness.

📦 Package Quality Overall: Low (3.2/10)

○ 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 (6511 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
○ 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

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

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 2.0

1 maintainer concern(s) found

  • Author "ajpegli contributors" 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 ajpegli 
Create a Python-based image processing mini-app called 'JPEG Explorer' that leverages the 'ajpegli' package for fast JPEG image loading into NumPy arrays. This app will allow users to load JPEG images from their local file system or from URLs, display basic metadata about the images, and apply simple transformations such as resizing, rotating, and cropping. Additionally, users should be able to save these transformed images back to their local file system.

Steps to Build the Application:
1. Set up a virtual environment and install necessary packages including 'ajpegli', 'numpy', and 'matplotlib'.
2. Implement a function to load JPEG images using 'ajpegli' and convert them into NumPy arrays.
3. Develop a user interface that allows users to either upload a local image or input a URL to fetch an image online.
4. Add functionality to display the loaded image along with its metadata such as dimensions and color mode.
5. Integrate options for the user to apply transformations like resizing (with width and height inputs), rotating (with angle input), and cropping (with coordinates input).
6. Ensure the transformed images can be displayed alongside the original image for comparison.
7. Include a feature to save the transformed image to the local file system with a specified filename.
8. Test the application thoroughly with various images to ensure all functionalities work correctly.
9. Document the code properly and provide instructions on how to run the application.

Features:
- Fast image loading with 'ajpegli'
- User-friendly GUI for uploading images or entering URLs
- Display of image metadata
- Image transformation capabilities including resize, rotate, and crop
- Option to save transformed images locally
- Comprehensive error handling for file paths and URLs

Utilizing 'ajpegli':
- Use 'ajpegli' to efficiently load JPEG images into memory as NumPy arrays, which will then be manipulated and displayed using other libraries like 'numpy' and 'matplotlib'. This ensures quick processing times even for large images.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!