assignment-bp-roma-cherniak-2026

v0.1.1 suspicious
6.0
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 shows moderate risk due to obfuscated code and low maintainer activity, which raises concerns about its authenticity and safety.

  • Obfuscation risk 7/10
  • Metadata risk 5/10
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access to function properly.
  • Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
  • Obfuscation: The code snippet shows signs of obfuscation which may hinder readability and analysis, indicating potential malicious intent.
  • Credentials: No clear evidence of credential harvesting patterns detected.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate a potential risk.

📦 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 Python-based application that leverages the 'assignment-bp-roma-cherniak-2026' package to develop a real-time American Sign Language (ASL) classifier capable of recognizing the letters A, B, C, D, and the 'nothing' gesture. This application will utilize PyTorch for deep learning model training and inference, as well as OpenCV for video capture and processing. Here’s a step-by-step guide to building the app:

1. **Setup Environment**: Ensure you have Python installed along with necessary libraries like PyTorch, OpenCV, and 'assignment-bp-roma-cherniak-2026'. Install these via pip.

2. **Data Collection & Preprocessing**: Collect or use pre-existing datasets of ASL gestures for the letters A, B, C, D, and 'nothing'. Preprocess this data to fit the requirements of the 'assignment-bp-roma-cherniak-2026' package, including resizing images and normalizing pixel values.

3. **Model Training**: Use the 'assignment-bp-roma-cherniak-2026' package to train a custom deep learning model on your dataset. This involves defining the architecture, compiling the model, and fitting it to your training data.

4. **Real-Time Inference**: Implement a feature within your application that captures video from a webcam in real-time, processes each frame through OpenCV, and feeds the processed frames into your trained model for prediction.

5. **User Interface**: Develop a simple user interface that displays the live feed from the webcam and overlays the predicted ASL letter or 'nothing' gesture on the screen.

Suggested Features:
- Accuracy metrics display for each recognized gesture.
- Ability to switch between different models or parameters for comparison.
- Logging of predictions for later analysis.
- Support for saving and loading trained models.

Utilize the 'assignment-bp-roma-cherniak-2026' package to streamline the model development process, leveraging its capabilities for efficient ASL classification.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!