assignment_bp_roma_cherniak_2026

v0.1.1 suspicious
5.0
Medium Risk

make my own ASL clasifier on A , B , C , D , nothing latters using Pytorch and OpenCV

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits unusually high obfuscation levels without clear malicious activity, suggesting possible tampering or obfuscation for evasion purposes.

  • High obfuscation risk
  • Low effort in metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network functionality.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious shell command execution.
  • Obfuscation: The code shows signs of obfuscation with unusual formatting and potential code truncation, raising suspicion.
  • Credentials: No clear patterns indicating credential harvesting were detected.
  • Metadata: The package shows low effort in metadata and maintainer history, but there's no clear indication of malicious intent.

📦 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 (1970 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 score 4.0

Found 2 obfuscation pattern(s)

  • _location=device)) model.eval() return model def predict(model, image): devic
  • le() else "cpu") model.eval() cap = cv2.VideoCapture(0) while True:
✓ 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 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 assignment_bp_roma_cherniak_2026 
Create a real-time American Sign Language (ASL) classifier application using the 'assignment_bp_roma_cherniak_2026' package. This application will utilize PyTorch for deep learning model training and inference, and OpenCV for video processing. The primary goal of the app is to recognize five specific ASL signs: A, B, C, D, and a 'nothing' gesture, which indicates no sign is being made. Here’s a step-by-step guide on how to build the application:

1. **Setup Environment**: Ensure your development environment includes Python, PyTorch, and OpenCV. Install the 'assignment_bp_roma_cherniak_2026' package.
2. **Data Collection**: Collect or use pre-existing datasets containing images and videos of the five ASL signs. Ensure the dataset is well-labeled and diverse enough to train an accurate model.
3. **Model Training**: Use the 'assignment_bp_roma_cherniak_2026' package to train a neural network model capable of classifying the ASL signs. Adjust parameters as necessary to optimize performance.
4. **Real-Time Recognition**: Integrate OpenCV for capturing video input from a webcam. Apply preprocessing techniques to the frames to enhance recognition accuracy.
5. **Inference**: Implement a loop that continuously processes video frames, feeds them into the trained model, and outputs the recognized sign in real-time.
6. **User Interface**: Develop a simple GUI that displays the webcam feed and overlays the predicted ASL sign above it. Include options to start/stop the recognition process and display confidence scores if available.
7. **Testing & Evaluation**: Test the application thoroughly to ensure it accurately recognizes signs across various lighting conditions and backgrounds. Gather feedback and iterate on improvements.

Suggested Features:
- Support for multiple webcam sources.
- Adjustable sensitivity settings for different environments.
- Exporting recorded sessions for later analysis or sharing.
- Detailed documentation and user guides for ease of use.

💬 Discussion Feed

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