Building a Face Emotion Detection App with Grok‑4 and Streamlit

Introduction

In this tutorial-style walkthrough, you'll learn how to build a web app that allows users to upload an image, processes it with Grok‑4 for emotion detection, and displays the results in real time using a sleek Streamlit interface.

Why This Project?

Emotion detection from images is compelling for:

• Enhancing user engagement in apps (e.g., mood-aware UI)

• Augmenting mental health monitoring tools

• Enabling interactive AI-driven experiences

Tech Stack

• Grok‑4: A vision model capable of interpreting facial expressions and classifying emotions such as joy, sadness, anger, surprise, etc.

• Streamlit: A Python framework for quickly assembling UIs with widgets like file uploaders and real-time output.

• Python: Connects Streamlit frontend to the Grok‑4 backend.

Demo Video

Github repo - https://github.com/vipulm124/llm-apps/