AI Analysis
Final verdict: SAFE
The package shows minimal risk indicators with no network calls, shell executions, or obfuscations detected. The metadata risk, while slightly elevated due to a non-HTTPS link and a new maintainer account, does not strongly suggest malicious intent.
- No network calls detected
- No shell execution detected
- Metadata risk due to non-HTTPS link and new maintainer account
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution detected, indicating no immediate risk of unauthorized command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The presence of a non-HTTPS link and a new maintainer account raise some concerns, but there are no clear signs of typosquatting or malicious intent.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
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://pinebioml.icmol.ncu.edu.tw/
Git Repository History
Repository ICMOL/PineBioML appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "ICMOL" 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 PineBioML
Create a mini-application that predicts plant health conditions using the PineBioML toolkit. Your application should allow users to input various parameters such as leaf color, soil moisture levels, and temperature, and then predict whether the plant is healthy or suffering from a common disease like rust or blight. Step 1: Set up your development environment by installing Python and the PineBioML package. Ensure you have the necessary libraries installed alongside PineBioML. Step 2: Collect or simulate a dataset that includes various plant health conditions along with the corresponding input parameters (leaf color, soil moisture, temperature). This dataset will be used to train your model. Step 3: Preprocess your data using PineBioML's data preprocessing utilities. Normalize the input parameters and encode categorical variables if necessary. Step 4: Split your dataset into training and testing sets. Use PineBioML's built-in functions for splitting datasets efficiently. Step 5: Train a machine learning model using PineBioML. Experiment with different algorithms available within the toolkit to find the best one for your dataset. Step 6: Evaluate your model's performance on the test set. Use PineBioML's evaluation metrics to assess accuracy, precision, recall, and F1 score. Step 7: Develop a simple user interface where users can input their plant's condition parameters. Utilize PineBioML's prediction API to make real-time predictions based on the user inputs. Suggested Features: - A clean and intuitive UI for inputting plant parameters. - Real-time feedback on the predicted plant health condition. - An option to visualize the model's decision-making process. - Integration of PineBioML's model tuning capabilities to optimize the prediction model. This project will not only demonstrate the power of PineBioML but also provide a practical tool for monitoring plant health.