autoencoders

v0.6.8 safe
3.0
Low Risk

A unified library for autoencoder-family models across deterministic, variational, and quantized latent spaces.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of risk with a focus on functionality rather than malicious intent. It uses urllib for network operations and exhibits some code obfuscation, but these do not point towards any clear malicious activity.

  • Use of urllib for network operations
  • Some code obfuscation practices
Per-check LLM notes
  • Network: The use of urllib to open URLs suggests the package may be fetching external resources, which is not inherently malicious but should be reviewed for legitimacy and necessity.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The observed patterns suggest some level of code obfuscation, possibly to hide the implementation details, but do not indicate clear malicious intent.
  • Credentials: No credentials or secrets harvesting patterns detected in the provided code snippet.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, but there are no other suspicious flags.

📦 Package Quality Overall: Low (3.6/10)

○ 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 (14393 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 5.0

Partial type annotation coverage

  • 195 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in Jyonn/autoencoders
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • try: with urllib.request.urlopen(url) as response, temp_path.open("wb") as handle:
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • del_name) self._model.eval() @staticmethod def _infer_device() -> str:
  • = self.training self.eval() with torch.no_grad(): outputs = self.f
✓ 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

Repository Jyonn/autoencoders appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "autoencoders 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 autoencoders 
Create a mini-application called 'ImageCompressor' using the Python package 'autoencoders'. This application will allow users to upload an image file, compress it using various types of autoencoders provided by the 'autoencoders' package, and then download the compressed image. The goal is to demonstrate the effectiveness of different autoencoder models in reducing the size of images while maintaining their visual quality.

Step 1: Set up the project environment.
- Install necessary packages including 'autoencoders', 'Pillow' for image processing, and 'Flask' for building a web interface.

Step 2: Design the user interface.
- Develop a simple web-based UI where users can select an image file from their device.

Step 3: Implement the autoencoder models.
- Use the 'autoencoders' package to instantiate and train different types of autoencoders such as Variational AutoEncoder (VAE), Deterministic AutoEncoder (DAE), and Quantized AutoEncoder (QAE).
- Each model should be trained on a dataset of images to learn efficient representations.

Step 4: Integrate the models into the application.
- Upon uploading an image, the application should present options for the user to choose which type of autoencoder they want to use for compression.
- The selected autoencoder will process the uploaded image and output a compressed version.

Step 5: Evaluate and display results.
- After compression, the application should display both the original and compressed images side-by-side.
- Include metrics such as file size reduction percentage and qualitative visual assessment.

Suggested Features:
- User-friendly interface for easy navigation and interaction.
- Multiple pre-trained models available for selection.
- Option to save and download the compressed image directly from the web interface.
- Detailed documentation explaining how each autoencoder works and its strengths in image compression.

This project aims to provide an interactive and educational experience on the capabilities of autoencoder models in handling image data compression tasks.

💬 Discussion Feed

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