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TeacherStudentParentAdminAi Documentation

This document provides a detailed overview of the TeacherStudentParentAdminAi system, a comprehensive web application designed to facilitate seamless communication and interaction between teachers, students, parents, administrators, and an integrated AI module. This document also includes updates on the latest features, refactors, and tests added to the system.

Table of Contents

System Overview

The TeacherStudentParentAdminAi system is designed to enhance educational processes through features like auto-grading, chat, quizzes, tests, and personalized learning. The system has different user roles including Teacher, Student, Parent, Administrator, and AI Module, each with their unique functionalities.

System Architecture

The system is built using a combination of Python/Flask for server-side logic, SQLAlchemy for database management, and a RESTful API for frontend communication. The frontend is built using React/TypeScript for a dynamic and responsive user interface, Redux for state management, and Axios for handling API requests. The AI features are implemented using Scikit-learn, a machine learning library for Python.

AI Module

The AI module, defined in ai_module.py and ai_features.py, uses Hugging Face Transformers for natural language processing tasks such as lesson planning, personalized learning recommendations, and assessment feedback. Additionally, it uses Scikit-learn for data analysis and predictive modeling. The DataAnalyzer class in ai_features.py provides methods for analyzing data, training a linear regression model, and making predictions.

API Endpoints

The system provides several API endpoints for managing users, courses, assignments, tests, and chats. These endpoints are defined in the routes.py file. All routes are protected with JWT authentication, except for the user creation route.

AI Module

Latest Updates

This section provides updates on the latest features, refactors, and tests added to the system. For a detailed changelog, please refer to the CHANGELOG.md file.

The AI module, defined in ai_module.py, uses Hugging Face Transformers for natural language processing tasks such as lesson planning, personalized learning recommendations, and assessment feedback.

Testing

The system includes unit tests for server-side components and integration tests for API endpoints. These tests are defined in the test_backend.py file. The frontend tests include unit tests for React components and integration tests for frontend services, defined in the test_frontend.ts file.

Deployment

The system is packaged using Docker for containerization and Kubernetes for orchestration. The Dockerfile and Kubernetes configuration are included in the project files.

Security

The system uses JWT for secure user authentication and HTTPS for secure data transmission. These features are implemented in the auth.py and encryption.py files respectively.

Latest Tests

This section provides updates on the latest tests added to the system. For a detailed test log, please refer to the TESTLOG.md file.

Database Models

The database models are defined in the models.py file. The models include User, Course, UserCourse (a relationship table), Assignment, Test, and Chat.

Future Enhancements

Future enhancements include advanced AI features for predictive analytics and the development of mobile applications for Android and iOS.

For more information, please refer to the ROADMAP.md file.

Using the AI Features

To use the AI features, you need to create an instance of the DataAnalyzer class and call its methods. Here is an example:

from ai_features import DataAnalyzer
## Latest Features

This section provides updates on the latest features added to the system. For a detailed feature log, please refer to the `FEATURELOG.md` file.
import pandas as pd

# Create a DataFrame
data = pd.DataFrame({
    'A': [1, 2, 3, 4, 5],
    'B': [2, 3, 4, 5, 6]
})

# Create a DataAnalyzer instance
analyzer = DataAnalyzer()

# Analyze the data
print(analyzer.analyze_data(data))

# Train a model
# Analyze the data
print(analyzer.analyze_data(data, 'A', 'B'))

# Train a model
X = data[['A']]
y = data['B']
print(analyzer.train_model(X, y))

# Make predictions
print(analyzer.predict_data(X))

Train a model

X = data[['A']] y = data['B'] print(analyzer.train_model(X, y, 'linear_regression'))

Make predictions

print(analyzer.predict_data(X, 'linear_regression'))