I’m working on a project that predicts human behavior using machine learning models. My backend is built in Python with neural networks and deep learning algorithms. For the frontend, I created a Unity3D application in C# that shows visual simulations of human actions.
The problem I’m facing is linking these two parts together. I need my Unity game to communicate with the Python ML models and get predictions back. I’ve heard about using DLL files to bridge Python and C#, but I’m not sure how to implement this approach.
I’ve done some research online but couldn’t find clear step-by-step instructions for this specific setup. Some sources mention creating Python DLLs, while others suggest different methods like REST APIs or named pipes.
What would be the most reliable way to integrate my Python machine learning code with my Unity C# application? Are there any specific libraries or tools that work well for this kind of integration?
Flask REST API works great for Unity-Python ML projects like this. I’ve used it myself with good results. Just set up a Flask server that exposes your neural network models as HTTP endpoints, then use Unity’s UnityWebRequest to send requests and get JSON responses back. Way more reliable than messing with DLL complications. Here’s the basic setup: create a Flask route that takes your input data, runs it through your ML models, and spits out predictions as JSON. From Unity, you fire off HTTP POST requests with your simulation data and handle responses asynchronously. Plus you can scale your backend separately and debugging is much easier than DLL binding.
i’d go for named pipes - they’re faster than REST and no network setup needed. used em for my unity ml project last year, worked great! just set up a named pipe server in python, listen for unity requests, send your data, and get predictions back. way simpler than DLLs!
interesting project! have you thought about using websockets for real-time communication? what latency are you looking at - how quick do you need those predictions? also, which neural network architecture are you using? that info could help figure out the best way to connect.