🤖 AI Analysis

Final verdict: SUSPICIOUS

The package appears to be low-risk based on the absence of network calls, shell execution, obfuscation, and credential harvesting. However, the metadata quality is poor with missing maintainer history and author details, raising suspicion about its authenticity.

Low metadata quality
Missing maintainer history
Missing author information

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low effort and could be suspicious due to its lack of maintainer history and missing author information.

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with Dmytro-Shapovalov-brach-assignment-2026

Create a simple yet powerful image classification app using the 'Dmytro-Shapovalov-brach-assignment-2026' Python package, which provides a PyTorch wrapper around a pretrained ResNet18 model. Your goal is to develop an application that allows users to upload an image, and then returns the predicted class of the image based on the ResNet18 model's classification capabilities. Here’s a detailed breakdown of what your app should include:

1. **User Interface**: Design a clean and user-friendly interface where users can upload images directly from their device or via a URL. Ensure the UI is responsive and works well on both desktop and mobile devices.

2. **Image Upload Handling**: Implement functionality to handle file uploads securely and efficiently. The application should accept various image formats such as JPEG, PNG, etc., and validate them before processing.

3. **Image Classification**: Utilize the 'Dmytro-Shapovalov-brach-assignment-2026' package to perform real-time image classification. The package should be initialized with the pretrained ResNet18 model, and you will need to preprocess the uploaded images according to the model's requirements.

4. **Prediction Display**: After processing, display the top predictions along with confidence scores. For example, if the model predicts the image is of a cat with 95% confidence, the result should reflect this clearly.

5. **Error Handling and Feedback**: Implement robust error handling to manage cases where the image might not be suitable for classification (e.g., non-image files, corrupted images). Provide meaningful feedback to users in such scenarios.

6. **Optional Features**:
   - Allow users to view a gallery of previously classified images.
   - Include a feature to save classified images and their predictions to a personal gallery.
   - Implement a search function to find images based on keywords from their predictions.

7. **Deployment Considerations**: Plan how the application will be deployed, considering factors like scalability, cost-effectiveness, and ease of maintenance. Think about cloud platforms like AWS, Google Cloud, or Azure for deployment.

Your task is to design and implement this application from scratch, ensuring it leverages the capabilities of the 'Dmytro-Shapovalov-brach-assignment-2026' package effectively while providing a seamless user experience.