🤖 AI Analysis

Final verdict: SUSPICIOUS

The package uses significant obfuscation techniques which raises concerns about hidden functionality, despite showing low risks in other areas like network calls and credential harvesting.

High obfuscation risk
Incomplete maintainer profile

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external communication.
Shell: No shell execution detected, indicating no immediate risk of command execution or privilege escalation.
Obfuscation: The code appears to be using obfuscation techniques which could indicate an attempt to hide the functionality of the code, raising suspicion.
Credentials: No clear patterns indicative of credential harvesting were found.
Metadata: The maintainer has an incomplete profile and appears to be new or inactive, which may indicate potential risk.

📦 Package Quality Overall: Low (4.6/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

Test runner config found: pyproject.toml
10 test file(s) detected (e.g. test_app_logging.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (4621 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

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 54 commits in kouya-marino/AlchemyDetect
Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

l = predictor.model model.eval() task = "instance_segmentation" if cfg.MODEL.MASK_ON e
, sample_inputs) adapter.eval() log_fn("Running a trace pass to determine output layo

✓ 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

Repository kouya-marino/AlchemyDetect appears legitimate

⚠ 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 alchemydetect

Create a desktop GUI application named 'AlchemyVision' using the Python package 'alchemydetect'. This application will serve as a user-friendly interface for training and running inference with Detectron2 models, focusing on object detection tasks. Here's a detailed breakdown of the application's functionalities and steps to implement them:

1. **Setup**: Begin by installing the necessary packages including 'alchemydetect', 'tkinter' for the GUI, and any other dependencies required by Detectron2.

2. **User Interface Design**: Design a simple yet intuitive GUI with the following components:
- A file explorer button to upload images or video files for inference.
- A dropdown menu to select pre-trained models from Detectron2.
- An option to upload custom datasets for model training.
- Buttons for 'Train Model' and 'Run Inference'.
- A display area to show the results of inference, such as bounding boxes and labels overlaid on images or videos.

3. **Model Training**: Implement functionality that allows users to upload their own dataset for training a new model. Ensure the application guides users through the process of preparing data, configuring training parameters, and starting the training process.

4. **Inference Mode**: After training or selecting a pre-trained model, enable users to run inference on uploaded images or videos. Display the results in real-time within the application, highlighting detected objects with bounding boxes and labels.

5. **Save Results**: Provide options for users to save the results of their inference runs either as annotated images or videos, or as a report summarizing the findings.

6. **Documentation and Help**: Include a help section within the application that explains how to use each feature, provides tips on optimizing model performance, and offers troubleshooting advice.

7. **Testing and Validation**: Before releasing the application, thoroughly test it with various datasets and models to ensure stability and accuracy.

Throughout the development process, utilize the 'alchemydetect' package to streamline the integration of Detectron2 functionalities into your application. Focus on making the application accessible to users with varying levels of expertise in machine learning.