AetherOnePySocial

Group Analysis with AetherOnePy Integration

This API serves as a backend for extending the functionality of AetherOnePy, a powerful program for conducting individual analyses and broadcasts. While AetherOnePy excels in individual work, this project is designed to enable collaborative group analysis and broadcasting efforts by:

Combining Analysis Results:

When multiple users perform analyses on the same subject, this API collects and consolidates their results.

Group Broadcasting:

Facilitates group collaboration by merging intentions and ensuring coordinated broadcasts.

Team Collaboration:

The API is built to support distributed teams working together on shared projects, enhancing the efficiency of group efforts.

Getting started

install all packages

go to tha map AetherOnePySocial/py and run

pip install -e .

then in command line in the same map run

 uvicorn app.main:app --reload

If you want to run tests on PostMan

AetherOnePySocial_Postman_Collection.json

To run tests

pytest

To build frontend you could use openAPI

python ./generateOpenAPI.py

you will generate

openapi.json file

Introduction to the AetherOnePySocial Project

Overview

This project is a modular and scalable web application built using FastAPI, designed with a clear Model-View-Controller (MVC) architecture. It serves as a foundation for developing APIs with features like authentication, data management, and analysis. The application includes:

• User authentication (register and login).
• Management of machines, catalogs, rates, cases, sessions, analyses, and comparisons.
• Unit tests to ensure application reliability.
• Integration with an SQLite database (or any other database of your choice).

Key Components

1. Application Structure

The project follows an MVC structure:

• models/: Database models for each entity.
• schemas/: Pydantic schemas for request and response validation.
• routers/: FastAPI routers that define the API endpoints.
• tests/: Unit tests for all endpoints to validate functionality.

2. Database

The project uses SQLAlchemy for ORM and database management. It also includes an alembic integration for database migrations (not implemented here but can be added).

3. Endpoints

The application has endpoints for:

• Authentication (/api/auth/)
• Machines (/api/machines/)
• Catalogs (/api/catalog/)
• Rates (/api/rates/)
• Cases (/api/cases/)
• Sessions (/api/sessions/)
• Analyses (/api/analysis/)
• Merged Analyses (/api/merged-analysis/)
• Comparisons (/api/comparison/)

4. Unit Testing

All endpoints are tested using pytest. The tests/ directory includes test cases for creating, retrieving, and validating data for all entities.

How to Use

1. Clone the Repository

git clone <repository-url>
cd AetherOnePySocial

2. Set Up the Environment

Create a virtual environment and install dependencies:

python -m venv env
source env/bin/activate  # On Windows: env\Scripts\activate
pip install -e .

3. Run the Application

Start the FastAPI server:

uvicorn app.main:app --reload

Access the API documentation at http://127.0.0.1:8000/docs.

4. Run Unit Tests

Run the unit tests to verify functionality:

pytest

Features

• Automatic documentation: Interactive API documentation is available with Swagger UI.
• Validation: Input and output validation using Pydantic.
• Extensibility: Easily add new models, endpoints, and features.
• Testing: Comprehensive unit tests for all entities.

Example Usage

Register a User

Endpoint: POST /api/auth/register

{
  "username": "testuser",
  "email": "[email protected]",
  "password": "password123"
}

Response:

{
  "id": 1,
  "username": "testuser",
  "email": "[email protected]"
}

Create a Machine

Endpoint: POST /api/machines/

{
  "machine_name": "Machine1",
  "description": "Test machine",
  "api_key": "1234"
}

Response:

{
  "id": 1,
  "machine_name": "Machine1",
  "description": "Test machine",
  "api_key": "1234"
}

Contribution

Feel free to fork the repository and submit pull requests. For major changes, open an issue to discuss what you would like to change.

License

This project is licensed under the MIT License.

files schema

Directory Structure:

 project/
 ├── app/
 │   ├── __init__.py
 │   ├── main.py
 │   ├── config.py
 │   ├── database.py
 │   ├── models/
 │   │   ├── __init__.py
 │   │   ├── user.py
 │   │   ├── machine.py
 │   │   ├── catalog.py
 │   │   ├── rate.py
 │   │   ├── case.py
 │   │   ├── session.py
 │   │   ├── analysis.py
 │   │   ├── merged_analysis.py
 │   │   ├── comparison.py
 │   ├── routers/
 │   │   ├── __init__.py
 │   │   ├── auth.py
 │   │   ├── machines.py
 │   │   ├── catalog.py
 │   │   ├── rates.py
 │   │   ├── cases.py
 │   │   ├── sessions.py
 │   │   ├── analysis.py
 │   │   ├── merged_analysis.py
 │   │   ├── comparison.py
 │   ├── schemas/
 │   │   ├── __init__.py
 │   │   ├── user.py
 │   │   ├── machine.py
 │   │   ├── catalog.py
 │   │   ├── rate.py
 │   │   ├── case.py
 │   │   ├── session.py
 │   │   ├── analysis.py
 │   │   ├── merged_analysis.py
 │   │   ├── comparison.py
 ├── setup.py
 ├── tests/
 │   ├── __init__.py
 │   ├── test_auth.py
 │   ├── test_machines.py
 │   ├── test_catalog.py
 │   ├── test_rates.py
 │   ├── test_cases.py
 │   ├── test_sessions.py
 │   ├── test_analysis.py
 │   ├── test_merged_analysis.py
 │   ├── test_comparison.py
 ├── .gitignore