az-cortex-sdk

v0.2.3 suspicious
4.0
Medium Risk

Python SDK for Azure Cortex ML API

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate metadata risk due to potential new or inactive maintainers and a missing repository, which raises concerns about its legitimacy.

  • Moderate metadata risk
  • Missing repository link
Per-check LLM notes
  • Network: Network calls are common for SDKs, especially if they interact with cloud services like Azure.
  • Shell: No shell execution patterns detected, which is normal and expected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of potential new or inactive maintainer and a missing repository, raising concerns about its legitimacy.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: pyproject.toml
  • 7 test file(s) detected (e.g. test_data_manager.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://az-cortex-sdk.readthedocs.io
  • Detailed PyPI description (18241 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

  • 256 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • None self._session = requests.Session() self._session.verify = self.config.verify_ssl
  • ders() response = requests.get( url, headers=headers,
  • session.""" session = requests.Session() session.verify = self.config.verify_ssl #
✓ 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: nearlyhuman.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 az-cortex-sdk 
Develop a real-time sentiment analysis web application using the 'az-cortex-sdk' package for Azure Cortex ML API. This application will allow users to input any text and receive a sentiment score indicating whether the text is positive, negative, or neutral. The app should also provide a simple visualization of the sentiment score over time if multiple analyses are performed.

### Steps to Develop the Application:
1. **Set Up Your Development Environment:** Ensure you have Python installed along with Flask or Django for web development. Install the 'az-cortex-sdk' package and any other necessary dependencies such as Matplotlib for plotting graphs.
2. **Configure Azure Cortex ML API Access:** Obtain your API key from Azure and configure it within your application so that it can communicate with the Azure Cortex ML API.
3. **Create the Frontend Interface:** Design a simple user interface where users can enter their text and submit it for sentiment analysis. Include a form field for text input and a button to trigger the analysis.
4. **Implement Backend Logic:** Use the 'az-cortex-sdk' package to call the Azure Cortex ML API with the user-provided text. Handle the response to extract the sentiment score.
5. **Display Sentiment Analysis Results:** On the frontend, display the sentiment score alongside the analyzed text. Optionally, include a color-coded indicator (green for positive, red for negative, and yellow for neutral).
6. **Add Time-Series Visualization:** If the user performs multiple analyses, store each result with a timestamp. Use Matplotlib to plot these results on a graph, showing how the sentiment scores change over time.
7. **Enhance User Experience:** Consider adding features like saving sentiment analysis results to a database, allowing users to view past analyses, or integrating a word cloud generator to show common words associated with each sentiment type.
8. **Testing and Deployment:** Thoroughly test the application to ensure it works correctly with various types of input texts. Deploy the application to a hosting service like Heroku or AWS so others can use it.

### Utilization of 'az-cortex-sdk':
- Initialize the SDK with your API credentials.
- Use the SDK's methods to send text data to the Azure Cortex ML API for processing.
- Parse the returned JSON data to retrieve the sentiment score.
- Implement error handling for cases where the API might not return valid data.

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