analyze-audio

Key Features

1. Audio Analysis and Prompt Generation

• The system is designed to handle audio input, break it down into prompts, and store those prompts in a PostgreSQL database.
• It supports integrations with various APIs or local models for processing audio, allowing flexibility in how audio is transcribed into text prompts.
• Prompts are stored alongside task metadata, including the audio file reference, task name, and user ownership.

2. Task Management (CRUD)

• GET: Retrieve task details based on the task ID.
• POST: Create a new task, including the name and prompts.
• PUT: Update an existing task’s name or prompts using batch updates.
• DELETE: Remove a task by its ID, supporting batch deletion for efficiency.
• Each task is linked to a user, ensuring multi-tenancy and data isolation per user.

3. User Authentication

• Implements a secure authentication mechanism using JWT tokens.
• Endpoints include user registration, login, and logout.
• Protected routes for task management are accessible only to authenticated users, ensuring data security.

4. Modern, Scalable Architecture

• Built with FastAPI, providing fast, asynchronous support for high-performance needs.
• Uses PostgreSQL for relational data management, ensuring reliable storage.
• Designed with modularity and extensibility in mind, enabling future expansion without sacrificing performance or maintainability.

5. Dockerized for Portability

• Complete Docker support, including a docker-compose.yml configuration to quickly spin up the application and its dependencies.
• Application and database run as separate services, ensuring clean separation of concerns and making the project easy to scale and deploy.

Getting Started

Prerequisites

• Docker: Ensure that Docker and Docker Compose are installed on your machine.
• Python 3.9+: If running locally without Docker.
• PostgreSQL: If running locally without Docker, ensure that PostgreSQL is installed and running.

Installation and Setup

1. Clone the repository:

   git clone https://github.com/yourusername/yourrepository.git
   cd yourrepository

2. Environment Configuration:

   • The database connection URL and other environment variables can be adjusted in the docker-compose.yml file and the application’s database.py configuration.

3. Build and run the application with Docker:

   docker-compose up --build

   This will spin up both the FastAPI application and the PostgreSQL database. The API will be available at http://localhost:8000.

4. Access API Documentation:

   You can access the auto-generated API documentation provided by FastAPI via:

   • Swagger UI: http://localhost:8000/docs
   • ReDoc: http://localhost:8000/redoc

Running Locally Without Docker

1. Install Dependencies:

   pip install -r requirements.txt

2. Set Up the Database:

   Ensure that PostgreSQL is installed and running, and create a database for the application. Update the connection string in app/database.py.

3. Run the Application:

   uvicorn app.main:app --reload

Usage

Authentication Endpoints

• POST /register: Register a new user by providing a username, password, and email.
• POST /login: Authenticate the user and receive a JWT token for subsequent requests.
• GET /user: Retrieve the authenticated user's profile.
• POST /logout: Log out the user (handled on the client side by discarding the token).

Task Endpoints

• POST /tasks: Create a new task with prompts.
• GET /tasks/{task_id}: Retrieve task details by ID.
• PUT /tasks/{task_id}: Update a task’s name or prompts.
• DELETE /tasks/{task_id}: Delete a task by ID.

Database Structure

The application uses PostgreSQL to store:

• Users: Authenticated users with secure hashed passwords.
• Tasks: Tasks linked to users, containing audio links and prompt data.

Project Structure

├── app/
│   ├── main.py            # Main FastAPI application
│   ├── models.py          # SQLAlchemy models for users and tasks
│   ├── schemas.py         # Pydantic schemas for request/response validation
│   ├── crud.py            # Database operations for tasks and users
│   ├── auth.py            # Authentication-related logic (JWT, password hashing)
│   ├── database.py        # Database connection setup
├── Dockerfile             # Dockerfile for FastAPI app
├── docker-compose.yml     # Docker Compose configuration
├── requirements.txt       # Python dependencies
├── README.md              # Project documentation

Testing

• Unit and integration tests should be added for all major components, including the authentication system and task management.
• FastAPI's built-in testing framework (based on pytest) can be used for writing and running tests.

Future Improvements

1. Audio Processing Enhancements:

   • Implement more sophisticated models for audio transcription.
   • Add support for various audio formats and asynchronous processing for larger files.

2. Task Management:

   • Implement tagging and categorization for tasks.
   • Add support for task history and version control.

3. Authentication & Security:

   • Implement multi-factor authentication (MFA).
   • Add role-based access control (RBAC) for more granular permission management.

4. Deployment:

   • Provide a CI/CD pipeline for automated deployment to cloud environments.