🤖 AI Analysis

Final verdict: SAFE

The package appears to serve a legitimate purpose with minimal risk indicators. Network and obfuscation risks are present but low.

Network calls require further clarification but seem legitimate.
No evidence of malicious activities or credentials harvesting.

Per-check LLM notes

Network: The package makes network calls which could be legitimate for fetching resources or updates, but further investigation is needed to confirm the purpose and destination.
Shell: No shell execution patterns were detected, indicating a lower risk of immediate command execution vulnerabilities.
Obfuscation: The provided patterns do not clearly indicate malicious obfuscation; they appear to be part of normal code execution and model evaluation routines.
Credentials: No suspicious patterns for credential harvesting were detected in the provided code snippet.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other suspicious flags were raised.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

xist_ok=True) response = requests.get(url, stream=True) response.raise_for_status() total

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

model.to(device) model.eval() return model def ensure_large_image_ok() -> None:

✓ 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: ing.uc3m.es

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository irortiza/ContrAI appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Irene Ortiz" 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 Contrai

Create a web-based application named 'ContrailAnalyzer' using Python and the Contrai package. This application will allow users to upload images of clouds, and it will use Contrai's deep learning models to detect and analyze contrails within those images. The application should provide a user-friendly interface where users can upload their cloud images, view real-time detection results, and receive detailed information about any detected contrails.

Core Features:
1. Image Upload: Users should be able to upload their own cloud images through a simple file input form.
2. Real-Time Detection: Utilize Contrai's detection capabilities to identify contrails in uploaded images instantly after they are uploaded.
3. Detailed Analysis: For each detected contrail, display key details such as its length, width, altitude (if available), and other relevant attributes.
4. Visualization: Present the detected contrails on the original image with bounding boxes or overlays for easy identification.
5. Information Panel: Include an information panel next to the image which summarizes the detected contrails' characteristics and provides additional context.
6. Save Results: Allow users to save the annotated images with detection overlays and the analysis summary to their local machine.
7. Documentation: Provide comprehensive documentation on how to use the application, including setup instructions for running the app locally.

Utilizing Contrai Package:
- Use Contrai's pre-trained models for contrail detection and analysis.
- Integrate Contrai's API or functions into your Flask/Django backend to handle image processing and analysis tasks.
- Ensure that the Contrai package is properly installed and configured in your development environment before starting the project.