Cognimates Training Platform

A platform where kids can train their own custom AI models for text and image classification using Google Cloud Platform's AutoML capabilities.

Features

• Text classification model training and inference
• Image classification model training using labeled images
• User-friendly API for integrating with other applications
• Modern error handling and validation

Architecture

The application follows a modular architecture:

• Services: Encapsulate all GCP API interactions
• Controllers: Handle HTTP requests and responses
• Routes: Define API endpoints and validation
• Middleware: Provide cross-cutting concerns (error handling, validation)

Prerequisites

• Node.js 18.x or later
• Google Cloud Platform account with the following enabled:
  • Vertex AI API
  • Cloud Storage
• GCP Project with permissions to create:
  • Datasets
  • Training pipelines
  • Models and endpoints

Environment Variables

Create a .env file in the root directory with the following variables:

# Required GCP Configuration
GCP_PROJECT_ID=your-gcp-project-id
GCP_REGION=us-central1
GCS_BUCKET_NAME=your-gcs-bucket-name
GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/credentials.json

# Server Configuration (Optional)
PORT=2634  # Default if not specified
SERVER_PORT=2634  # Alternative to PORT

# SSL Configuration (Optional)
SSL_KEY_PATH=/path/to/ssl/key.pem
SSL_CERT_PATH=/path/to/ssl/cert.pem

Setup and Installation

1. Clone the repository

git clone https://github.com/your-username/cognimates-training.git
cd cognimates-training

2. Install dependencies

npm install

3. Set up Google Cloud credentials

   • Create a service account with appropriate permissions in the Google Cloud Console
   • Download the JSON credentials file
   • Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of this file

4. Create a Cloud Storage bucket to store training data

   • Set the GCS_BUCKET_NAME environment variable to this bucket name

5. Start the server

npm start

Development

• npm run dev - Start the server with Nodemon for automatic reloading
• npm run sass-build - Build the SCSS files
• npm run lint - Run ESLint on the source code
• npm test - Run the test suite

API Usage

Text Classification

Create a Classifier

POST /classify/text/create
Content-Type: application/json

{
  "classifier_name": "my-text-classifier"
}

Train a Classifier

POST /classify/text/{classifier_name}/train
Content-Type: application/json

{
  "training_data": {
    "positive": ["Great product", "Excellent service", "Highly recommend"],
    "negative": ["Poor quality", "Not satisfied", "Would not recommend"]
  }
}

Classify Text

POST /classify/text/{classifier_name}
Content-Type: application/json

{
  "phrase": "This is an amazing product"
}

Image Classification

Train an Image Classifier

POST /classify/image/{classifier_name}/train
Content-Type: multipart/form-data

Form field: "images" (ZIP file with folders named by label)

The ZIP file structure should be:

training_images.zip
├── label1/
│   ├── image1.jpg
│   └── image2.png
└── label2/
    ├── image3.jpeg
    └── image4.jpg

Check Operation Status

GET /classify/text/operations/{operation_name}

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.