autointent

v0.3.0 safe
3.0
Low Risk

A tool for automatically configuring a text classification pipeline for intent prediction.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential harvesting attempts. The metadata risk is slightly elevated due to the maintainer having only one package, but this alone is insufficient to conclude malicious activity.

  • No network calls detected
  • Single package by maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious shell command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, indicating a new or less active account which may warrant further investigation but does not strongly suggest malicious intent.

📦 Package Quality Overall: Medium (7.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
  • Classifier: Framework :: Pytest
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://deeppavlov.github.io/AutoIntent/
  • Detailed PyPI description (2030 chars)
◈ Medium Contributing Guide 7.0

Some contribution signals present

  • Separate author ("Alexeev Ilya, Kuznetsov Denis") and maintainer ("Alexeev Ilya, Solomatin Roman") listed
  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 307 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 10 unique contributor(s) across 100 commits in deeppavlov/AutoIntent
  • Active community — 5 or more distinct contributors

🔬 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository deeppavlov/AutoIntent appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Alexeev Ilya, Kuznetsov Denis" 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 autointent 
Create a mini-application called 'IntentBot' using the Python package 'autointent'. This application will serve as a simple chatbot that can predict user intents from their input messages. Here are the steps and features you need to implement:

1. **Setup Environment**: Ensure you have Python installed on your system. Install the necessary packages including 'autointent', 'flask' for the web server, and 'requests' for API handling.
2. **Data Preparation**: Prepare a dataset of sample conversations where each message is labeled with its corresponding intent (e.g., 'greeting', 'ordering', 'complaint', etc.).
3. **Model Training**: Use 'autointent' to automatically configure and train a text classification model based on the prepared dataset. The goal is to create a model that can accurately predict the intent behind any given message.
4. **Integration into Flask App**: Integrate the trained model into a Flask web application. This app should have an endpoint that accepts POST requests containing user messages and returns the predicted intent.
5. **User Interface**: Develop a simple HTML form where users can input their messages. Upon submission, the form should send the message to the Flask app's endpoint and display the predicted intent back to the user.
6. **Testing**: Test the application thoroughly to ensure it correctly predicts intents for various types of messages.
7. **Documentation**: Write clear documentation explaining how to run the application, how to interpret the output, and any limitations of the current setup.

Suggested Features:
- Include a feature to log all interactions for later analysis.
- Allow users to provide feedback on the accuracy of the predicted intents.
- Implement a mechanism to retrain the model with new data if provided by users.

How 'autointent' is Utilized:
- 'autointent' simplifies the process of setting up a text classification pipeline. It handles tasks like preprocessing text data, selecting appropriate algorithms, and tuning parameters to optimize performance. In this project, 'autointent' will be used to streamline the creation of a machine learning model capable of understanding user intents from text inputs.

💬 Discussion Feed

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