audio-case-grade

v1.0.0 suspicious
4.0
Medium Risk

Library to score audio medical case studies

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package appears generally benign with low risks across multiple dimensions but lacks a linked GitHub repository and incomplete maintainer information, raising some concerns about its provenance and support.

  • No network calls or shell executions detected.
  • Incomplete maintainer information and lack of associated GitHub repository.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of credential theft.
  • Metadata: The package has no associated GitHub repository and the maintainer's information is incomplete, which raises some suspicion.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

  • 8 test file(s) detected (e.g. test_clean.py)
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (493 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

  • 33 type-annotated function signatures detected in source
○ 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

Email domain looks legitimate: infernored.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 audio-case-grade 
Create a web-based application using Python and Flask that allows medical professionals to upload audio recordings of patient case studies and receive automated grading based on predefined criteria. The application should utilize the 'audio-case-grade' library to analyze the uploaded audio files and generate a score indicating the quality and adherence to medical standards of the case study presentation. Here’s a detailed breakdown of the project requirements:

1. **Setup Environment**: Set up a virtual environment for your project and install necessary libraries including Flask for the web framework, and 'audio-case-grade' for grading the audio files.

2. **User Interface**: Design a simple yet intuitive user interface where users can log in (basic authentication), view their previous submissions, and upload new audio files. Ensure that only authorized users can access the grading functionality.

3. **File Upload**: Implement a file upload feature that allows users to select and upload .wav or .mp3 audio files. Validate the file type and size before processing.

4. **Audio Analysis**: Use the 'audio-case-grade' package to process the uploaded audio files. This includes extracting key metrics relevant to medical case studies such as clarity, adherence to clinical guidelines, and overall presentation quality.

5. **Grading System**: Develop a grading system that converts the extracted metrics into a final score. Provide a detailed report alongside the score that highlights strengths and areas for improvement in the case study presentation.

6. **Feedback Mechanism**: Allow users to download their grading report as a PDF document which includes the score, comments from the grading system, and suggestions for improvement.

7. **Testing and Validation**: Rigorously test the application to ensure it handles various edge cases, such as different file formats, sizes, and content types. Validate the grading accuracy by comparing the automated scores with manually assigned grades.

8. **Deployment**: Deploy the application on a cloud platform like Heroku or AWS so that it can be accessed by medical professionals worldwide.

This project aims to streamline the evaluation process for medical case studies, providing valuable feedback to healthcare professionals and enhancing the educational experience in medical training programs.

💬 Discussion Feed

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