AI Analysis
The package audian v2.5 appears mostly benign with no detected network, shell, or obfuscation risks. However, the metadata risk due to the maintainer's new or inactive account and lack of detailed information raises some suspicion.
- No network or shell execution risks detected
- Maintainer has a new or inactive account with limited details
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access to function properly.
- Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has a new or inactive account and lacks detailed author information, raising some suspicion but not conclusive evidence of malice.
Package Quality Overall: Low (4.2/10)
Partial test coverage signals detected
Test runner config found: pyproject.toml
Some documentation present
Detailed PyPI description (9640 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Limited contributor diversity
1 unique contributor(s) across 100 commits in bendalab/audianSingle author but highly active (100 commits)
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: uni-tuebingen.de>
All external links appear legitimate
Repository bendalab/audian appears legitimate
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a mini-application using the 'audian' package that allows users to analyze animal vocalization recordings. Your application should include the following features: 1. **File Importation**: Allow users to import audio files containing animal vocalizations. Support common formats like WAV, MP3, and FLAC. 2. **Spectral Analysis**: Implement spectral analysis tools provided by 'audian' to visualize the frequency spectrum of the imported audio files. Users should be able to adjust parameters such as window size and overlap to customize their analysis. 3. **Waveform Visualization**: Display the waveform of the audio file alongside the spectrogram for a comprehensive view of the sound data. 4. **Annotation Tool**: Include an annotation tool where users can mark specific segments of the audio file for closer inspection. These annotations should be saved with the audio file metadata. 5. **Playback Controls**: Provide basic playback controls (play, pause, stop, fast forward, rewind) to navigate through the audio file easily. 6. **Export Functionality**: Enable users to export both the annotated audio file and the analysis results in a user-friendly format such as CSV or JSON. 7. **User Interface**: Design an intuitive and clean user interface that makes it easy for users to interact with the application without needing extensive technical knowledge. Your task is to outline the steps required to develop this application, including how to integrate 'audian', handle file operations, implement the UI, and ensure all functionalities work seamlessly together. Consider how you would structure your code to make it modular and maintainable.
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