ResPredAI

v1.9.2 suspicious
4.0
Medium Risk

Antimicrobial Resistance predictions via AI models

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate obfuscation practices that could be indicative of evasion techniques. However, there are no clear signs of malicious intent such as network calls, shell executions, or credential harvesting. The low metadata quality raises some suspicion about the maintainer's intentions.

  • Moderate obfuscation risk
  • Low quality maintainer metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution detected, indicating no direct system command execution from the package.
  • Obfuscation: The code pattern suggests an attempt to dynamically import modules, which could be used for evasion techniques but may also have legitimate uses.
  • Credentials: No obvious signs of credential harvesting detected.
  • Metadata: The maintainer has a new or inactive account and lacks detailed author information, raising some suspicion but not conclusive evidence of malintent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • try: mod = __import__(import_name) packages[pkg_name] = getattr(mod, "__version__"
✓ 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: unibo.it>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository EttoreRocchi/ResPredAI 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 ResPredAI 
Develop a comprehensive mini-application that leverages the ResPredAI package to predict antimicrobial resistance based on genetic sequences of bacteria. This application will serve as a valuable tool for medical researchers and healthcare professionals aiming to understand and combat antibiotic-resistant infections more effectively.

### Project Overview:
- **Name:** BactiResPredictor
- **Objective:** To create a user-friendly web application that predicts antimicrobial resistance from bacterial genetic sequences using machine learning models integrated within the ResPredAI package.
- **Target Audience:** Medical researchers, epidemiologists, and healthcare providers.

### Key Features:
1. **User Interface:** A simple, intuitive interface where users can input bacterial genetic sequences.
2. **Prediction Engine:** Utilize ResPredAI's prediction models to analyze the input sequences and predict antimicrobial resistance.
3. **Results Display:** Clearly present the prediction results with a summary of resistant and non-resistant antibiotics.
4. **Data Visualization:** Include charts or graphs to visually represent the prediction outcomes.
5. **Educational Resources:** Provide links or brief descriptions explaining the importance of antimicrobial resistance and how the application works.
6. **Feedback System:** Allow users to provide feedback on the accuracy of predictions and suggest improvements.

### Implementation Steps:
1. **Setup Environment:** Install necessary packages including ResPredAI, Flask (for web development), and any visualization libraries like Matplotlib or Seaborn.
2. **Data Input:** Develop a form in the frontend where users can upload their bacterial genetic sequence data.
3. **Integration with ResPredAI:** Use ResPredAI's prediction functions to process the uploaded sequences and generate resistance predictions.
4. **Result Presentation:** Design a section in the UI to display the prediction results clearly, highlighting key findings.
5. **Visualization:** Implement visual representations of the prediction results using graphs or charts.
6. **Documentation and Help:** Add sections explaining the significance of antimicrobial resistance and how the application aids in research.
7. **Testing and Feedback:** Conduct thorough testing and invite early users to provide feedback on usability and accuracy.
8. **Deployment:** Deploy the application on a platform like Heroku or AWS to make it accessible online.

### Expected Outcome:
A fully functional web-based application that allows users to upload bacterial genetic sequences, receive predictions about potential antimicrobial resistance, and visualize these predictions in an easy-to-understand format. The application should also facilitate continuous improvement through user feedback.