AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks for obfuscation and credential harvesting, but the metadata suggests potential concerns due to the author's lack of a GitHub repository and having only one package.
- Low obfuscation risk
- Low credential risk
- Metadata indicates a less established or potentially suspicious entity
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious intent.
- Metadata: The author has only one package and lacks a GitHub repository, which may indicate a less established or potentially suspicious entity.
Package Quality Overall: Low (2.4/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Brief PyPI description (234 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ 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: tensorflow.org
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Google AI Edge Authors" 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-litert
Develop a real-time object detection mini-app using the 'ai-edge-litert' package, designed specifically for mobile and embedded devices. This app will leverage the lightweight inference capabilities of LiteRT to perform on-device object detection without the need for cloud-based processing, ensuring faster response times and better privacy for users. **Step-by-Step Development Guide:** 1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with the necessary libraries such as 'ai-edge-litert'. Additionally, include any other dependencies required for image capture and processing. 2. **Model Selection**: Choose a pre-trained model from the LiteRT library that suits your object detection needs. This could be a YOLO variant optimized for edge devices. 3. **Image Capture**: Integrate the ability to capture images either from a camera feed or from a file system. Use this captured data as input for your object detection algorithm. 4. **Object Detection**: Implement the core functionality of detecting objects within the captured images using the selected LiteRT model. Ensure that the detection process is optimized for speed and accuracy on edge devices. 5. **Display Results**: Once objects are detected, display the results back to the user in real-time. This could involve overlaying bounding boxes on the original image feed and labeling each detected object. 6. **User Interface**: Develop a simple but effective user interface that allows users to interact with the app easily. Include options to start/stop the camera feed, switch between different models, and view detection statistics. 7. **Optimization and Testing**: Optimize the app for performance on various edge devices. Test the app extensively to ensure it works as expected under different conditions and with varying hardware configurations. **Suggested Features**: - Support for multiple models (e.g., YOLOv3-tiny, SSD-Lite) - Real-time video stream processing - Ability to save detected images or video clips - User-friendly UI for easy interaction - Detailed logs and analytics for debugging and improvement This project aims to showcase the power of LiteRT for real-world applications, particularly where low latency and high efficiency are crucial.