Skip to content

🚗 API Setup Guide

This project provides a complete REST API solution for traffic signal control using trained RL models.

Project Structure

TUS-26-ETP2-Python-Data-Science-and-ML-Pipeline/
├── java-api-gateway/                       # Java Spring Boot gateway
│   ├── src/
│   │   ├── main/java/com/example/gateway/
│   │   │   ├── GatewayApplication.java     # Spring Boot app
│   │   │   ├── controller/
│   │   │   │   └── TrafficController.java  # REST endpoints
│   │   │   └── service/
│   │   │       └── RlInferenceClient.java  # RL service client
│   │   └── main/resources/
│   │       └── application.properties      # Spring config
│   ├── pom.xml                             # Maven configuration
│   └── Dockerfile                          # Java service Docker image
├── rl-inference-service/                   # Python FastAPI service
│   ├── app/
│   │   ├── main.py                         # FastAPI application
│   │   └── models/                         # Directory for trained models
│   ├── Dockerfile                          # Python service Docker image
│   ├── requirements.txt                    # Python dependencies
│   └── .env.example                        # Environment variables template
├── SUMO/                                   # 
│   ├── results/
│   │   ├── Base/
│   ├── Simulations/
│   │   ├── Base/
├── docker-compose.yml                      # Docker Compose orchestration
└── CHANGELOG.md                            # Change log
└── FILE_MAINFEST.md                        # 
└── QUICKSTART.md                           # Quick start guide
└── README.md                               # This file
└── SETUP_COMPLETE.md                       # 
└── SYSTEM_ARCHITECTURE.md                  #

Components

1. Python FastAPI Service (RL Inference Service)

  • Port: 8000
  • Purpose: Loads and serves trained PPO models for action prediction
  • Key Endpoints:
  • GET /health - Health check
  • POST /predict_action - Action prediction endpoint
  • GET /model_info - Model information

2. Java Spring Boot API Gateway

  • Port: 8080
  • Purpose: REST API gateway that communicates with the Python service
  • Key Endpoints:
  • GET /api/traffic/health - Health check
  • GET /api/traffic/action - Get traffic action (generates dummy observations)
  • POST /api/traffic/action - Predict action with custom observations

3. Docker Compose

  • Orchestrates both services
  • Manages networking and dependencies
  • Provides health checks and monitoring

The Java Gateway receives raw vehicle counts, transforms them into an observation vector, and forwards them to the Python service for a decision.

Setup Instructions

Prerequisites

  • Docker & Docker Compose
  • Or locally:
  • Python 3.9+
  • Java 17+
  • Maven 3.9+

Steps to Deploy

  1. Copy your trained model: 

    # Copy trained model to the models directory
# Example: Copy from C:\Users\gemer\Sumo\my-network\Results\sweeps\<sweep_dir>\<seed>\<variant>\model.zip
mkdir -p rl-inference-service/app/trained_models
copy "C:\Users\gemer\Sumo\my-network\Results\sweeps\pressure\seed_42\A\model.zip" "rl-inference-service\app\trained_models\model.zip"

  2. Set environment variables (optional): 

    # Create .env file for RL Inference Service
echo MODEL_PATH=/app/trained_models/model.zip > rl-inference-service/.env
echo OBSERVATION_SHAPE_DIM=10 >> rl-inference-service/.env
echo NUM_AGENTS=1 >> rl-inference-service/.env

  3. Build and start services: 

    docker-compose up --build

  4. Test the API: 

    # Get traffic action (auto-generated observations)
curl http://localhost:8080/api/traffic/action

# Predict action with custom observations
curl -X POST http://localhost:8080/api/traffic/action \
  -H "Content-Type: application/json" \
  -d '{"observations": [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]}'

# Check health
curl http://localhost:8080/api/traffic/health

Option 2: Local Development

Python Service: 

cd rl-inference-service

# Create virtual environment
python -m venv venv
venv\Scripts\activate  # Windows
source venv/bin/activate  # Linux/Mac

# Install dependencies
pip install -r requirements.txt

# Set environment variables
set MODEL_PATH=path\to\your\model.zip  # Windows
export MODEL_PATH=path/to/your/model.zip  # Linux/Mac

# Run service
python -m uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

Java Gateway: 

cd java-api-gateway

# Set environment variable
set RL_INFERENCE_SERVICE_URL=http://localhost:8000/predict_action  # Windows
export RL_INFERENCE_SERVICE_URL=http://localhost:8000/predict_action  # Linux/Mac

# Build and run
mvn clean install
mvn spring-boot:run

Includes Swagger/OpenAPI documentation for all traffic control endpoints.