ai-edge-eval

v0.0.1 safe
3.0
Low Risk

Evaluation framework and CLI runner for LiteRT LM and native models.

🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risk with no network calls, shell executions, or credential risks. The metadata risk slightly elevates the score due to sparse author details.

  • No network calls detected.
  • No shell execution patterns detected.
  • Sparse author details increase metadata risk.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no direct system command execution risk.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's details are sparse, indicating potential lack of transparency.

📦 Package Quality Overall: Low (3.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (10988 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 23 commits in google-ai-edge/eval
  • Active community — 5 or more distinct contributors

🔬 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: google.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository google-ai-edge/eval appears legitimate

⚠ 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 ai-edge-eval 
Develop a mini-application that evaluates the performance of different machine learning models on edge devices using the 'ai-edge-eval' Python package. This application will serve as a tool for developers and researchers to easily assess the efficiency and accuracy of their models when deployed on resource-constrained environments such as smartphones or IoT devices.

### Project Overview:
- **Application Name:** EdgeModelEvaluator
- **Core Functionality:** Evaluate various machine learning models using 'ai-edge-eval' for metrics like inference time, memory usage, and accuracy.
- **Target Audience:** Machine Learning Engineers, Researchers, and Data Scientists working with edge computing.

### Features:
1. **Model Selection:** Allow users to select from a pre-defined list of popular machine learning models (e.g., LiteRT LM, ResNet, MobileNet).
2. **Evaluation Metrics:** Provide comprehensive evaluation metrics including inference time, memory footprint, and prediction accuracy.
3. **Visualization:** Offer graphical representations of the evaluation results to facilitate quick comparisons between models.
4. **Custom Model Support:** Enable users to upload their own custom models for evaluation.
5. **CLI Interface:** Implement a command-line interface for running evaluations without needing a graphical user interface.
6. **Report Generation:** Automatically generate detailed reports summarizing the evaluation outcomes.

### Utilization of 'ai-edge-eval':
- Use 'ai-edge-eval' to run the actual evaluations on selected models. Ensure you leverage its CLI capabilities for automated testing and reporting.
- Integrate 'ai-edge-eval' into your application's backend to handle the heavy lifting of model evaluation while your frontend provides a user-friendly interface for interaction.
- Explore advanced features of 'ai-edge-eval' such as benchmarking against native models and LiteRT LM to understand the trade-offs between performance and resource consumption.

### Development Steps:
1. Set up a development environment with Python and install necessary packages including 'ai-edge-eval'.
2. Design and implement the backend logic for model selection and evaluation using 'ai-edge-eval'.
3. Develop a simple GUI (optional) or focus entirely on building a robust CLI for user interaction.
4. Implement visualization tools within the application to display evaluation results effectively.
5. Add functionality for custom model uploads and ensure these models are evaluated correctly using 'ai-edge-eval'.
6. Create a reporting module that generates summaries of the evaluation process and results.
7. Test the application thoroughly across multiple scenarios and edge device simulators to ensure reliability.
8. Document the application's features, usage, and limitations comprehensively.
9. Release the application under an open-source license to contribute back to the community.