aigcqa

v0.1.0 suspicious
5.0
Medium Risk

Production-grade quality gate for AIGC videos — not a benchmark

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its novelty and limited maintainer history. While the network calls appear benign, the lack of an associated repository raises concerns about the package's provenance.

  • Limited maintainer history
  • No associated repository
Per-check LLM notes
  • Network: The observed network call is likely for downloading a model file, which is common in AI-related packages.
  • Shell: No shell execution patterns were detected.
  • Metadata: The package is new with limited maintainer history and no associated repository.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • 5 test file(s) detected (e.g. test_explainer.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4071 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

  • 30 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 1.5

Found 1 network call pattern(s)

  • model to {dest} ...") urllib.request.urlretrieve(_MODEL_URL, dest) return True except
⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • self._clip_model = model.eval() self._clip_preprocess = preprocess
  • brisque = BRISQUE(channels=3).eval() self._use_brisque = True excep
  • self._clipiqa.eval() self._use_clipiqa = True excep
  • False) self._lpips_fn.eval() if self.backend in ("light", "full"):
  • ) model.eval() self._clip_model = model s
✓ 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 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

  • Only one version has ever been released — brand new package
  • Author "AIGCQA 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 aigcqa 
Your task is to develop a web-based application using Python and Flask framework, which will serve as a content moderation tool for AIGC (AI-generated Content) videos. This tool will leverage the 'aigcqa' package to ensure the quality and appropriateness of the uploaded videos before they are made public on your platform. The application should include the following features:

1. User Authentication: Implement user registration and login functionalities to ensure only authorized users can upload and moderate videos.
2. Video Upload Interface: Provide a simple interface where users can upload their AIGC videos. The application should handle various video formats and sizes efficiently.
3. Quality Check Using 'aigcqa': After uploading, each video should undergo a quality check using the 'aigcqa' package. The tool should analyze the video content to ensure it meets predefined quality standards and does not contain inappropriate material.
4. Moderation Dashboard: Create a dashboard for administrators where they can view all pending videos for review, along with the results of the 'aigcqa' analysis. Admins should be able to approve or reject videos based on these results.
5. Notifications: Implement a notification system that alerts users when their videos have been reviewed and informs them of the decision.
6. Reporting: Allow users to report inappropriate videos directly from the platform. These reports should be flagged for immediate review by the admin team.
7. Analytics: Include basic analytics to track the number of videos uploaded, approved, rejected, and reported over time.

To utilize the 'aigcqa' package effectively, integrate its API calls within your application’s backend to perform real-time quality checks during the upload process. Ensure that the application provides meaningful feedback to users about why a video may have failed the quality check, promoting better content creation practices.

💬 Discussion Feed

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