alibabacloud-eas20210701

v7.0.0 safe
3.0
Low Risk

Alibaba Cloud eas (20210701) SDK Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk across all categories except for obfuscation, which is slightly elevated due to uncommon techniques. However, there is no concrete evidence of malicious intent.

  • Low network and shell risks
  • Potential use of uncommon obfuscation techniques
Per-check LLM notes
  • Network: No network calls detected, which is normal for packages that do not require internet access.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands.
  • Obfuscation: The obfuscation technique used is not common and could be used to hide version information, but it's also possible for legitimate reasons like dynamic imports.
  • Credentials: No clear evidence of credential harvesting patterns detected.
  • Metadata: The author has only one package, which might indicate a new or less active account, but no other suspicious activities are observed.

📦 Package Quality Overall: Low (4.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1171 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in aliyun/alibabacloud-python-sdk
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • bacloud-python-sdk" VERSION = __import__(PACKAGE).__version__ REQUIRES = [ "alibabacloud_tea_util>=0.3.13
✓ 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: alibabacloud.com

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository aliyun/alibabacloud-python-sdk appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Alibaba Cloud SDK" 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 alibabacloud-eas20210701 
Create a simple web application using Flask that allows users to deploy machine learning models to Alibaba Cloud EAS (Elastic AI Service). The application should utilize the 'alibabacloud-eas20210701' Python package to interact with the EAS service. Here are the key steps and features of your project:

1. **Setup**: Install Flask and the 'alibabacloud-eas20210701' package. Set up an Alibaba Cloud account and get your AccessKey ID and AccessKey Secret.
2. **Authentication**: Implement user authentication within the Flask app so that only authenticated users can access the deployment functionality.
3. **Model Upload**: Allow users to upload their machine learning models (in formats supported by EAS, such as TensorFlow, PyTorch, etc.) through the web interface.
4. **Deployment Interface**: Provide an interface where users can configure the deployment settings including instance type, number of instances, and resource limits.
5. **Deployment Execution**: Use the 'alibabacloud-eas20210701' package to call the EAS API to deploy the uploaded model according to the user-defined settings.
6. **Status Monitoring**: Display real-time status updates about the deployment process and provide links to monitor the deployed model on Alibaba Cloud.
7. **User Feedback**: After successful deployment, send an email notification to the user with details about the deployed model and its URL for accessing the inference endpoint.
8. **Security Measures**: Ensure all data transmitted between the client and server is encrypted and that sensitive information like AccessKeys are stored securely.

Your application should demonstrate proficiency in integrating cloud services with web applications while providing a user-friendly experience for deploying machine learning models.