In this tutorial, you'll learn how to build an interactive Streamlit Python-based UI that connects seamlessly with your vLLM-powered multimodal backend. You'll write a simple yet flexible frontend that lets users upload images, enter text prompts, and receive smart, vision-aware responses from the LLaVA model - served via vLLM's OpenAI-compatible interface. By the end, you'll have a clean multimodal chat interface that can be deployed locally or in the cloud - ready to power real-world apps in healthcare, education, document understanding, and beyond.
In this tutorial, you'll learn how to set up the vLLM inference engine to serve powerful open-source multimodal models (e.g., LLaVA) - all without needing to clone any repositories. We'll install vLLM, configure your environment, and demonstrate two core workflows: offline inference and OpenAI-compatible API testing. By the end of this lesson, you'll have a blazing-fast, production-ready backend that can easily integrate with frontend tools such as Streamlit or your custom applications.