autorender

v0.2.1 suspicious
5.0
Medium Risk

The official Python library for the autorender API

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has some legitimate use cases but raises concerns due to its metadata issues and network interactions.

  • Suspicious non-HTTPS links and missing maintainer information
  • Uses httpx for network calls
Per-check LLM notes
  • Network: The package uses httpx to make network calls, which could indicate legitimate functionality but also potential risks if not properly managed.
  • Shell: No shell execution patterns were detected, indicating low risk in this area.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Suspicious non-HTTPS links and lack of maintainer information indicate potential risks.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 5 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (13207 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
  • 455 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • t should be used with httpx.Client(timeout=None) as http_client: client = Autorende
  • he httpx default with httpx.Client() as http_client: client = Autorender(
  • it being ignored with httpx.Client(timeout=HTTPX_DEFAULT_TIMEOUT) as http_client: c
  • arg"): async with httpx.AsyncClient() as http_client: Autorender(
  • True, http_client=httpx.Client(transport=MockTransport(handler=mock_handler)), ) as
  • , http_client=httpx.Client(), ), ], ids=["standard", "custo
✓ 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: autorender.io>

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://my.test.server.example.com:8083
  • Non-HTTPS external link: http://my.test.proxy.example.com
⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ 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 autorender 
Create a Python-based desktop application that leverages the 'autorender' package to generate personalized 3D renderings of custom furniture designs for interior design enthusiasts. This application will allow users to input basic dimensions and style preferences for various pieces of furniture, such as sofas, tables, and chairs, and then use the 'autorender' API to produce high-quality 3D visualizations. The app should include the following features:

1. A user-friendly GUI built with PyQt5, allowing users to easily input their furniture specifications.
2. An interface that supports uploading custom textures and color schemes to apply to the rendered models.
3. Real-time preview functionality so users can see changes in the 3D model as they adjust inputs.
4. Integration with the 'autorender' API to process the user’s input and generate the final 3D rendering.
5. Options for saving the generated 3D renders in popular file formats like .obj or .stl for further editing in professional software.
6. A feature to share the rendered images directly on social media platforms.

To utilize the 'autorender' package, you'll need to first install it via pip, import the necessary functions, and then set up API calls to handle the data input from the GUI and receive the rendered output. Ensure the app handles errors gracefully and provides clear feedback to the user at each stage of the rendering process.

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