AI Analysis
Final verdict: SAFE
The package shows no signs of obfuscation or credential harvesting, and the risk factors identified are minimal, suggesting a low likelihood of malicious intent.
- Low obfuscation risk
- No credential harvesting detected
- Metadata quality is poor but does not indicate malicious activity
Per-check LLM notes
- 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 PyPI account and lacks PyPI classifiers, indicating low effort or poor metadata quality.
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
Detailed PyPI description (6050 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 4.0
2 maintainer concern(s) found
Author "Aspose" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with aspose-ocr-python-net
Create a desktop application named 'DocumentReader' using Python and the 'aspose-ocr-python-net' package that allows users to extract text from various image and document formats. This application should serve as a versatile tool for professionals who frequently deal with scanned documents, photos, or PDFs where text needs to be extracted for further processing or analysis. Step 1: Design the User Interface - Create a simple and intuitive UI using a Python GUI library like PyQt5 or Tkinter. - Include options for users to upload files (images or PDFs). - Provide a button to start the OCR process. - Display the extracted text in a readable format within the app. Step 2: Implement File Handling - Allow users to select multiple file types including JPEG, PNG, BMP, TIFF, and PDF. - Ensure the application supports both single-file and batch processing. Step 3: Integrate 'aspose-ocr-python-net' - Use 'aspose-ocr-python-net' to perform OCR on uploaded files. - Optimize settings for different file types to improve accuracy. - Handle errors gracefully, such as when a file cannot be read or processed. Step 4: Enhance Functionality - Add a feature to save the extracted text to a new file (TXT or DOCX). - Include an option to copy the extracted text directly to the clipboard. - Offer adjustable OCR settings, such as language detection and character recognition modes. Step 5: Testing and Optimization - Test the application with a variety of input files to ensure reliability. - Optimize performance, especially when dealing with large files or batches. - Gather user feedback to identify areas for improvement. The goal is to create a robust, user-friendly tool that simplifies the process of converting images and documents into editable text.
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