Integrating Python backend with Unity3D C# frontend for behavior prediction

Frontend
1

I’m working on a project to predict human behavior. The backend uses deep learning in Python, while the frontend is a Unity3D simulation in C#. I’m stuck trying to connect these two parts.

I’ve looked online but couldn’t find a clear solution. Some sources mention using DLL files, but I’m not sure how to do this.

Has anyone successfully linked Python code to a Unity3D project? What’s the best way to make my Python backend work with the C# frontend?

I’m open to any suggestions or alternatives. The main goal is to get the behavior prediction working in the Unity3D simulation.

# Example backend code (Python)
def predict_behavior(input_data):
    model = load_trained_model()
    prediction = model.predict(input_data)
    return prediction

# How to connect this to Unity3D?

// Example frontend code (C# in Unity3D)
public class BehaviorSimulator : MonoBehaviour
{
    void UpdateBehavior()
    {
        // How to call Python prediction here?
        // var prediction = SomePythonInterface.PredictBehavior(inputData);
    }
}

Any help would be greatly appreciated!

2

hey, have u tried using python.net? its a library that lets u run python code directly in c#. could be perfect for ur project. just import ur python funcs into unity and call em like normal c# methods. might save u some headaches with apis n stuff. good luck with ur behavior prediction thing!

3

ooh, that sounds like a cool project! have you considered using zeromq for communication between python and unity? it’s pretty lightweight and fast. you could set up a simple pub/sub system where python publishes predictions and unity subscribes to them. might be worth exploring. what kind of behavior are you trying to predict, btw?

4

Having worked on a similar project, I can offer some insights. One effective approach is using a REST API to bridge your Python backend with Unity3D. Set up a Flask server in Python to expose your prediction function as an API endpoint. In Unity, use UnityWebRequest to make HTTP calls to this endpoint.

This method allows you to keep your Python and C# code separate, which is often cleaner than trying to integrate them directly. It’s also more flexible if you need to scale or modify either component later.

For implementation, host your Flask server locally during development. In production, you might consider deploying it to a cloud service. On the Unity side, you’ll need to serialize your input data, send it to the API, and then deserialize the response.

This approach has worked well in my experience, providing a robust and maintainable solution for integrating Python-based AI with Unity simulations.