🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network and shell activities, but the low package activity and the maintainer's limited history with PyPI raise some concerns about potential supply-chain risks.

Low package activity
Limited maintainer history with PyPI

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell executions detected, indicating the package does not execute system commands which reduces the risk of malicious activities.
Metadata: The package shows low activity and the maintainer has limited history with PyPI, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

4 test file(s) detected (e.g. test_artemis_collator.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (6675 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

11 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 8 commits in Schneewolf-Labs/Artemis
Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

odel=Qwen3VLVisionModel(vcfg).eval(), image_token_id=IMG_PAD, torch_dtype=torch.bfloat16, )
torch_dtype=torch.bfloat16, ).eval() dev = model.language_model.device tok = AutoTokenizer.from
odel=Qwen3VLVisionModel(vcfg).eval(), image_token_id=22, torch_dtype=torch.bfloat16, ).eval

✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Schneewolf Labs" 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 artemis-vlm

Create a Python-based mini-application called 'ImageCaptioner' that leverages the 'artemis-vlm' package to generate descriptive captions for images. This application will serve as a user-friendly tool where users can upload an image, and the app will return a caption describing the content of the image. The core functionality of 'artemis-vlm' will be used to process the visual information from the uploaded image and generate a natural language description based on its contents.

Steps to Develop:
1. Set up a basic Flask web application framework to handle HTTP requests and responses.
2. Integrate the 'artemis-vlm' package into your application for handling the vision-language tasks.
3. Design a simple yet effective UI using HTML/CSS for uploading images and displaying the generated captions.
4. Implement a function within the Flask app that processes the uploaded image through the 'artemis-vlm' model, generating a caption based on the image's visual content.
5. Ensure that the application can handle various image formats and sizes gracefully.
6. Add error handling for scenarios such as image upload failures or processing errors.
7. Test the application thoroughly to ensure it works as expected across different browsers and devices.
8. Deploy the application to a cloud service like Heroku or AWS for public access.

Suggested Features:
- Image preview before submission for user confirmation.
- Ability to see a history of previously submitted images and their corresponding captions.
- Option to save favorite image-caption pairs.
- Provide feedback mechanism to improve model accuracy over time through user input.

How 'artemis-vlm' is Utilized:
- Use the 'artemis-vlm' package to initialize the vision-language model that will analyze the uploaded images.
- After an image is uploaded, pass it through the model using the appropriate API calls provided by the 'artemis-vlm' package.
- Retrieve the generated caption from the model's output and display it back to the user along with the original image.