almo-eda

v0.2.0 safe
3.0
Low Risk

Machine learning models for ALMO-EDA energy prediction

🤖 AI Analysis

Final verdict: SAFE

The package almo-eda v0.2.0 presents minimal risks with no network calls, shell executions, or credential harvesting activities observed. Although there are some signs of low activity and potential obfuscation, these do not indicate malicious behavior.

  • Low network and shell execution risk
  • No evidence of credential harvesting
  • Some signs of low activity and minor obfuscation
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 patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: The provided code snippet appears to be part of a normal machine learning evaluation process and does not show signs of malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were found in the given code snippet.
  • Metadata: The package shows signs of low activity and metadata quality, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

  • 4 test file(s) detected (e.g. test_dataset.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2116 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 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in htahmasbi/ALMO_EDA
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • dation logic... model.eval() valid_loss = 0.0 with torch.no_grad():
✓ 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 score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://dx.doi.org/10.1063/5.0303825},
⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Hossein Tahmasbi" 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 almo-eda 
Create a Python-based web application that predicts energy consumption using the 'almo-eda' package. Your application should allow users to input various parameters such as weather conditions, time of day, and occupancy levels to predict energy usage. The app will utilize machine learning models from 'almo-eda' to perform real-time predictions.

Steps to Build the Application:
1. Install the necessary packages including 'almo-eda', Flask for the web framework, and any other required libraries.
2. Preprocess data to ensure it's compatible with 'almo-eda'. This might include normalizing inputs like temperature, humidity, and occupancy levels.
3. Use 'almo-eda' to train your model if needed or load a pre-trained one.
4. Develop a simple yet effective user interface using HTML/CSS/JavaScript where users can enter their specific details.
5. Implement backend logic in Python to handle form submissions, pass the data to 'almo-eda' for processing, and return the predicted energy consumption.
6. Ensure the application can handle errors gracefully and provide meaningful feedback to the user.
7. Test the application thoroughly to ensure accuracy and usability.
8. Deploy the application to a platform like Heroku or AWS so it can be accessed online.

Suggested Features:
- Real-time prediction based on live weather data integration.
- Historical data visualization showing past predictions vs actual usage.
- Recommendations for energy-saving measures based on predicted consumption.
- User authentication allowing personalized predictions based on previous inputs.

💬 Discussion Feed

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