AI Model Layer

The AI Model Layer handles the management and deployment of AI models. This layer provides functionalities for model uploading, sharing, and deployment, ensuring that users can access and utilize AI models efficiently. Key components include:

• Model Uploading: Allows users to upload their AI models to the platform.

• Model Repository: A secure storage for pre-defined and custom AI models, enabling easy access and sharing.

• Model Training: Facilitates the training of AI models using the allocated resources from the AI resource layer. This includes handling data preprocessing, training algorithms, and hyperparameter tuning.

• Model Inference: Manages the deployment and execution of trained AI models for real-time or batch processing tasks.

• Version Control: Keeps track of different versions of AI models, allowing for updates and improvements over time.

Code for Model Management (Python with Flask)

from flask import Flask, request, jsonify
app = Flask(__name__)

# Sample data
models = [
    {'id': 1, 'name': 'Model A', 'owner': '0xOwnerAddress1', 'price': 50},
    {'id': 2, 'name': 'Model B', 'owner': '0xOwnerAddress2', 'price': 30}
]

# Upload a new model
@app.route('/upload', methods=['POST'])
def upload_model():
    model_data = request.json
    models.append(model_data)
    return jsonify({'message': 'Model uploaded successfully'}), 201

# List all models
@app.route('/models', methods=['GET'])
def list_models():
    return jsonify(models)

# Deploy a model
@app.route('/deploy/<int:model_id>', methods=['POST'])
def deploy_model(model_id):
    model = next((m for m in models if m['id'] == model_id), None)
    if model:
        # Simulate model deployment
        return jsonify({'message': f'Model {model["name"]} deployed successfully'})
    else:
        return jsonify({'message': 'Model not found'}), 404

if __name__ == '__main__':
    app.run(debug=True)