AI Analysis
Final verdict: SAFE
The EMReport package presents a low risk profile with no indications of malicious activity, network risks, shell risks, obfuscation, or credential harvesting. The metadata risk is slightly elevated due to the maintainer's limited package history, but this alone does not suggest a supply-chain attack.
- No network or shell execution detected.
- Low risk of code obfuscation or credential harvesting.
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 detected, which is normal and suggests no direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other suspicious activities are flagged.
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: gmail.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository Alkiviadisss/EMReport appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "alkiviadis" 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 EMReport
Create a mini-application called 'ModelEvaluator' using the Python package 'EMReport'. This application will serve as a user-friendly tool for data scientists and machine learning enthusiasts to evaluate their models quickly and comprehensively. The primary goal of ModelEvaluator is to provide detailed insights into model performance across different types of machine learning tasks such as regression, classification, and clustering. Here’s how you can structure the application: 1. **User Interface**: Develop a simple command-line interface (CLI) where users can input details about their dataset and model predictions. 2. **Input Data Handling**: Allow users to upload datasets and prediction results either via CSV files or direct input. 3. **Task Selection**: Provide options for users to select the type of task (regression, classification, clustering). 4. **Evaluation Metrics**: Use EMReport to generate comprehensive reports including accuracy, precision, recall, F1-score for classification; R^2 score, MSE, RMSE for regression; and silhouette score for clustering. 5. **Visualization**: Implement basic visualizations such as confusion matrices, ROC curves, and cluster plots to complement the textual reports. 6. **Output Reports**: Generate and display detailed reports summarizing the evaluation metrics and visualizations. 7. **Customization Options**: Allow users to customize certain aspects of the report generation process, like choosing which metrics to include or excluding specific visualizations. 8. **Integration with Popular ML Libraries**: Ensure that ModelEvaluator works seamlessly with popular machine learning libraries like scikit-learn, TensorFlow, and PyTorch. 9. **Documentation and Help**: Include comprehensive documentation within the application to guide users on how to use each feature effectively. Utilize EMReport's core functionalities to streamline the evaluation process and ensure that all outputs are accurate and insightful. Make sure to test your application thoroughly with various datasets and models to validate its effectiveness.