Aibrary: AI-Assisted Storybook Creation for Kids

Aibrary is a platform that combines children's creativity with AI assistance for creating personalized storybooks.

How It Works

1. Interactive Storytelling

   • Children create unique stories through a choice-based game powered by an LLM (e.g., GPT-4).
   • The AI guides the narrative by providing options at each step.

2. Background Generation

   • An image generation model analyzes the story and generates relevant background scenes.

3. Character Illustration

   • Children illustrate the characters on the AI-generated backgrounds, bringing their stories to life.

Features

• Interactive storytelling with AI guidance
• AI-generated contextual backgrounds
• Children's creative character illustrations
• Seamless AI integration

Benefits

• Fosters creativity and self-expression
• Engaging and fun learning experience
• Personalized storybooks
• Collaboration with AI technologies

Aibrary aims to provide a delightful and educational platform for children to develop their storytelling abilities, artistic skills, and familiarity with AI in an engaging way.

Usage

Starting the ML Server

To start the ML server, follow these steps:

1. Navigate to the ml_server directory:

   cd ml_server
   

2. Create a new Python virtual environment named aibrary with Python 3.8:

   conda create -n aibrary python=3.8
   

3. Activate the virtual environment:

   conda activate aibrary
   

4. Install the required Python packages:

   pip install -r requirements.txt
   

5. Run Server:

   flask run
   

The ML server should now be running and ready to serve AI models.

Starting the Frontend

To start the frontend, follow these steps:

1. Navigate to the frontend directory:

   cd frontend
   

2. Start the Nginx web server:

   sudo systemctl start nginx
   

Starting the Backend Server

To start the backend server, follow these steps:

1. Navigate to the backend directory:

   cd backend
   

2. Build the Gradle project:

   gradlew build
   

3. Run the JAR file:

   java -jar backend-0.0.1-SNAPSHOT.jar
   

The backend server should now be up and running, handling requests from the frontend and interacting with the ML server.

Manual