azureml-acft-contrib-hf-diffusion

v0.0.91 suspicious
4.0
Medium Risk

Contains the acft nlp-hf-contrib package used in script to build azureml components.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risks in terms of network, shell execution, obfuscation, and credential handling. However, the metadata risk due to a new or inactive author account and lack of a linked GitHub repository raises some concerns, making the package suspicious.

  • Metadata risk due to new/inactive author account
  • No linked GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is normal for packages not requiring external API interactions.
  • Shell: No shell execution patterns detected, indicating no immediate risk of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has a new or inactive account and no linked GitHub repository, which raises some suspicion but does not strongly indicate malice.

📦 Package Quality Overall: Low (2.0/10)

○ 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 (269 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ 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

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 2.0

1 maintainer concern(s) found

  • Author "Microsoft Corp" 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 azureml-acft-contrib-hf-diffusion 
Create a text-to-image generation web application using Azure ML and the 'azureml-acft-contrib-hf-diffusion' package. This application will allow users to input a text prompt, and the app will generate an image based on that text using a diffusion model from Hugging Face. Here’s a step-by-step guide to building this application:

1. **Setup Environment**: Ensure you have Python installed and create a virtual environment. Install Azure ML SDK, Flask for the web framework, and the 'azureml-acft-contrib-hf-diffusion' package.
2. **Model Setup**: Use the 'azureml-acft-contrib-hf-diffusion' package to load a pre-trained diffusion model from Hugging Face. This package simplifies the process of integrating these models into your Azure ML pipeline.
3. **API Development**: Develop a REST API using Flask that accepts POST requests with a JSON payload containing the user's text input. The API should route this request to a function that processes the text through the loaded diffusion model and generates an image.
4. **Image Generation**: Implement the logic within the API function to use the 'azureml-acft-contrib-hf-diffusion' package to convert the input text into an image. This involves preprocessing the text, running it through the diffusion model, and saving the output as an image file.
5. **Web Interface**: Create a simple HTML form for users to submit their text prompts. Upon submission, the form should send a request to the Flask API, which processes the request and returns the generated image.
6. **Deployment**: Deploy both the Flask API and the front-end web interface using Azure services such as Azure App Service and Blob Storage for storing generated images.
7. **Testing**: Test the application thoroughly to ensure that text inputs are correctly converted to images and that the images are displayed properly on the web interface.
8. **Documentation**: Write comprehensive documentation for setting up and using the application, including details on how the 'azureml-acft-contrib-hf-diffusion' package is integrated and utilized.

Suggested Features:
- User authentication for tracking image generation history.
- A gallery of previously generated images for inspiration.
- Integration with social media sharing options.
- Customizable model settings to adjust image style or quality.

💬 Discussion Feed

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