azure-di-reconstruct

v0.1.1 safe
4.0
Medium Risk

Reconstruct Azure Document Intelligence JSON output into readable spatial text layouts

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity such as network calls, shell executions, or obfuscation techniques. However, the metadata risk due to the maintainer's new and inactive status warrants cautious monitoring.

  • No network calls detected
  • No shell execution patterns
  • Maintainer's new and inactive status
Per-check LLM notes
  • Network: No network calls detected, which is typical for many packages and suggests no immediate risk from data exfiltration or C2 communication.
  • Shell: No shell execution patterns detected, indicating the package does not appear to execute commands that could be used for malicious purposes.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository's lack of engagement and the maintainer's new/inactive status raise concerns but do not conclusively indicate malicious intent.

📦 Package Quality Overall: Medium (5.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 2 test file(s) detected (e.g. test_edge_cases.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (8178 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 12 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 14 commits in Gopi-Pitchai/azure-di-reconstruct
  • Two distinct contributors found

🔬 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

Email domain looks legitimate: gmail.com>

✓ 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 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 azure-di-reconstruct 
Create a Python-based desktop application named 'DocLayoutVisualizer' that leverages the Azure Document Intelligence service and the 'azure-di-reconstruct' package to transform complex JSON outputs from Azure's AI document analysis into visually understandable layouts. This tool will help users easily interpret and visualize the spatial structure of extracted text from scanned documents or images.

Step-by-Step Project Outline:
1. Set up the environment: Ensure Python 3.8+ is installed, along with necessary libraries such as azure-di-reconstruct, PyQT5 for the GUI, and requests for HTTP requests.
2. Design the UI: Use PyQt5 to create a simple yet intuitive interface where users can upload a PDF file or image containing text. The UI should have options to select the type of document (e.g., invoice, receipt, resume).
3. Integrate Azure DI Service: Implement functionality to call Azure's Document Intelligence API, passing the uploaded document data. Store the JSON response received from the API.
4. Utilize azure-di-reconstruct: Parse the JSON output using azure-di-reconstruct to reconstruct the spatial layout of the text within the document. This step is crucial for visualizing where on the page each piece of text was located.
5. Visualize Layout: Display the reconstructed layout back to the user through the GUI. Highlight different sections of the document based on their semantic meaning (e.g., total amount, date, name) to make it more readable.
6. Export Option: Allow users to export the visualized layout as an image or a PDF file, which could serve as a reference or for further processing.

Suggested Features:
- Support for multiple languages to cater to a broader audience.
- An option to manually adjust the layout if the automatic reconstruction doesn't meet expectations.
- Integration with other Azure services for additional document processing capabilities.
- A feature to compare different versions of the same document to track changes over time.

How 'azure-di-reconstruct' is Utilized:
- After receiving the JSON output from the Azure DI service, use azure-di-reconstruct to parse and reconstruct the spatial layout of the text elements. This involves identifying coordinates, bounding boxes, and text content to accurately represent the original document's structure. The reconstructed layout is then used to guide the visualization process in the GUI.

💬 Discussion Feed

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