amnlp

v0.1.1 suspicious
5.0
Medium Risk

Amharic NLP Library

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits potential typosquatting behavior targeting 'amqp' and has low maintainer activity, raising concerns about its legitimacy and long-term support. However, it does not present immediate security threats through network calls, shell execution, or obfuscation.

  • Potential typosquatting
  • Low maintainer activity
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no direct system command execution risks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some red flags including typosquatting potential and low maintainer activity, but lacks clear indicators of malicious intent.
  • ⚠ Typosquatting target: amqp

📦 Package Quality Overall: Low (2.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 2 test file(s) detected (e.g. test_pipeline.py)
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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 score 3.0

Possible typosquat of: amqp

  • "amnlp" is 2 edit(s) from "amqp"
✓ 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 "Eyosyas Yoseph" 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 amnlp 
Your task is to develop a mini-application called 'Amharic Text Analyzer' which leverages the 'amnlp' library to provide advanced natural language processing capabilities specifically for Amharic texts. This tool will serve as a versatile platform for researchers, educators, and enthusiasts interested in analyzing Amharic language data. Here’s a step-by-step guide on how to proceed with building this application:

1. **Project Setup**: Begin by setting up your development environment. Ensure you have Python installed, along with any necessary dependencies such as 'amnlp'. Install 'amnlp' using pip.
2. **Feature Implementation**:
   - **Text Tokenization**: Implement a feature that tokenizes input Amharic text into words and sentences. Utilize 'amnlp' to accurately tokenize the text based on Amharic grammar rules.
   - **Part-of-Speech Tagging**: Add functionality to tag each word in the text with its corresponding part of speech (noun, verb, adjective, etc.). Use 'amnlp' to perform this tagging, ensuring accuracy in identifying Amharic parts of speech.
   - **Named Entity Recognition**: Include a component that identifies and categorizes named entities within the text (e.g., person names, location names). Again, rely on 'amnlp' to recognize these entities according to Amharic linguistic norms.
   - **Sentiment Analysis**: Develop a sentiment analysis module that assesses the overall sentiment of the input text (positive, negative, neutral). For this, you might need to train a model using 'amnlp', taking into account the nuances of expressing sentiments in Amharic.
3. **User Interface**: Design a simple yet effective user interface where users can input their Amharic text and select which features they want to apply. Consider using web technologies like Flask or Django for backend services and HTML/CSS/JavaScript for frontend design.
4. **Testing and Validation**: Rigorously test all implemented features to ensure they work as expected. Validate the accuracy of your results against known datasets or manually curated examples.
5. **Documentation and Deployment**: Write comprehensive documentation detailing how to use your application, including setup instructions, API endpoints if applicable, and usage examples. Finally, deploy your application so others can access it online.

By following these steps, you'll create a powerful tool for anyone working with Amharic texts, showcasing the capabilities of 'amnlp' in real-world applications.

💬 Discussion Feed

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