aic-sdk

v2.3.0 suspicious
4.0
Medium Risk

Python bindings for ai-coustics SDK

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network and shell execution but has metadata issues such as missing author information and a potentially inactive maintainer, raising concerns about its legitimacy.

  • Missing author name
  • New or inactive maintainer account
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution detected, indicating no immediate risk of executing system commands.
  • Metadata: The package has some red flags including an absent author name and a new or inactive maintainer account, but there are no clear signs of typosquatting or other malicious activity.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

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

Some documentation present

  • Detailed PyPI description (8270 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

  • 13 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: ai-coustics.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 aic-sdk 
Create a voice analysis mini-app using the 'aic-sdk' Python package. This app will serve as a tool for analyzing audio files to extract various acoustic features such as pitch, loudness, and formants. Additionally, it will provide insights into the emotional state of the speaker based on their vocal patterns. Here’s a step-by-step guide to building this application:

1. **Setup Project Environment**: Initialize a new Python environment and install the 'aic-sdk' package along with other necessary libraries such as NumPy and Pandas for data manipulation.
2. **Audio File Input**: Allow users to upload an audio file (WAV format recommended). Ensure the app can handle different types of audio inputs and validate the file format.
3. **Feature Extraction**: Use 'aic-sdk' to extract key acoustic features from the uploaded audio file. Implement functions to calculate and display features like pitch, loudness, and formant frequencies.
4. **Emotion Analysis**: Integrate emotion recognition capabilities within the 'aic-sdk'. Develop a model or use pre-trained models provided by the SDK to analyze the emotional state of the speaker based on the extracted acoustic features.
5. **Visualization**: Create visual representations of the extracted features and emotional analysis results. Use Matplotlib or Seaborn for plotting graphs and charts.
6. **User Interface**: Design a simple yet intuitive user interface using Flask or Django for web-based deployment. The UI should allow users to upload files, view results, and interact with the app easily.
7. **Testing & Documentation**: Thoroughly test the app with various audio samples and document your code and setup instructions clearly.

Suggested Features:
- Real-time audio input analysis
- Comparative analysis between multiple audio files
- Exporting results in CSV format
- Integration with cloud storage services for file uploads

This project aims to demonstrate the capabilities of the 'aic-sdk' package while providing a practical tool for analyzing vocal patterns and emotions from audio recordings.