Code Generation Examples

Explore our powerful code generation capabilities with these examples. Each example demonstrates a different aspect of our API.

Basic Code Generation

Generate a complete model with predefined methods and validation

Generate Model

curl -X POST "http://localhost:3001/api/generate-egg" \ -H "Content-Type: application/json" \ -H "X-Request-ID: test-model" \ -d '{ "type": "model", "parameters": { "name": "OrderModel", "methods": [ "validateBeforeSave", "calculateTotals", "applyDiscount", "generateInvoice", "processPayment", "updateInventory", "notifyCustomer" ] } }' | jq '.'

Advanced Usage

Create a fully-featured REST API controller with all CRUD operations

curl -X POST "http://localhost:3001/api/generate-egg" \ -H "Content-Type: application/json" \ -H "X-Request-ID: test-controller" \ -d '{ "type": "controller", "parameters": { "name": "ProductController", "routes": [ { "method": "GET", "path": "/products", "handler": "listProducts" }, { "method": "GET", "path": "/products/:id", "handler": "getProduct" }, { "method": "POST", "path": "/products", "handler": "createProduct" }, { "method": "PUT", "path": "/products/:id", "handler": "updateProduct" }, { "method": "DELETE", "path": "/products/:id", "handler": "deleteProduct" } ] } }' | jq '.'

AI Components

Neural Networks

Advanced machine learning capabilities

Natural Language Processing

Advanced language understanding with real-time processing capabilities.

Sentiment AnalysisText ClassificationNamed Entity Recognition

Computer Vision

State-of-the-art image recognition and processing algorithms.

Object DetectionFace RecognitionScene Understanding

Code Generation

AI-powered code synthesis

Service Integration

Generate a service integration with external APIs, including error handling and type safety:

curl -X POST "http://localhost:3001/api/generate-egg" \ -H "Content-Type: application/json" \ -H "X-Request-ID: test-service" \ -d '{ "type": "service", "parameters": { "name": "PaymentService", "provider": "stripe", "methods": [ "createCustomer", "createPaymentIntent", "processPayment", "refundPayment", "getPaymentStatus" ] } }' | jq '.'

XGBoost Model Training

Bleu.js includes a robust machine learning pipeline with XGBoost integration.

XGBoost Training Pipeline

Our new version includes a robust training pipeline with advanced features:

Data Scaling
Hyperparameter Optimization
Performance Monitoring
Model Deployment

Best Accuracy

0.9450

Best ROC-AUC

0.9869

Best F1 Score

0.9488

Best Precision

0.9444

How to Use

1. Model Loading

Activate the Virtual Environment and load models with ease:

source ~/Bleu.js/bleujs-env/bin/activate

from xgboost_model import predict input_features = [0.5, 0.3, 0.8, 1.2, 0.7] result = predict(input_features) print(result)

2. Training a New Model

To train a new XGBoost model with Optuna, run:

python train_xgboost.py