Read the full article on DataCamp: DeepSeek-V3-0324 – A Guide With Demo Project

Learn how to use DeepSeek V3-0324 to build a UI component generator using Streamlit and Tailwind CSS.

Overview

DeepSeek V3-0324 is a fine-tuned upgrade of the DeepSeek-V3 model that significantly enhances frontend generation, reasoning, and tool-use capabilities.

In this tutorial, we’ll build a natural-language-to-UI generator using DeepSeek’s API. The tool will take a plain language prompt like “A red button with white Hello text” and return:

  • Tailwind-based HTML code
  • A live component preview in an iframe

Why Use DeepSeek-V3-0324?

  • 🚀 Better reasoning and multi-turn conversation
  • 🎨 Improved front-end generation with Tailwind CSS
  • 🧠 Accurate function calling and structured tool use
  • 🔓 Open-source (MIT licensed) with weights on Hugging Face

Project Overview

We’ll build a Streamlit app that uses DeepSeek-V3-0324 to:

  • Accept a prompt from the user
  • Generate corresponding Tailwind-based HTML code
  • Render it live in an iframe preview

Step-by-Step Implementation

Step 1: Prerequisites

python3 --version  # Python 3.8+
pip install requests streamlit
export DEEPSEEK_API_KEY="your_api_key"

Step 2: Building the UI Generator

frontend_generator.py

import requests
import json
import os

API_KEY = os.getenv("DEEPSEEK_API_KEY")
API_URL = "https://api.deepseek.com/chat/completions"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

System Prompt

SYSTEM_PROMPT = """
Generate clean HTML with Tailwind CSS classes. Focus on:
1. Use appropriate Tailwind utility classes
2. Ensure text is centered
3. Use clear, visible colors
4. Make content readable
"""

Component Code Function

def get_component_code(user_prompt: str) -> str:
    ...

This function sends the user prompt to DeepSeek and returns the generated Tailwind-based HTML.

Step 3: Building the UI with Streamlit

Imports

import streamlit as st
from frontend_generator import get_component_code
import html

Component Preview Function

def create_component_preview(raw_code: str) -> str:
    ...

This wraps the component inside a full HTML doc with TailwindCDN.

Streamlit App

st.set_page_config(page_title="UI Generator", layout="centered")
prompt = st.text_input("Describe your component", value="A red button with Hello text")
if st.button("⚡ Generate"):
    ...

The app shows:

  • Raw generated code with st.code()
  • Live preview in iframe using st.components.v1.html()

Step 4: Running the App

streamlit run streamlit_app.py

You’ll be able to type any UI prompt and instantly:

  • View the HTML output
  • Preview the rendered component

Demo Screenshots

  • ✅ Code generation using DeepSeek V3
  • 🌈 Tailwind component rendered live in iframe
  • 📦 Clean, isolated UI builder

Conclusion

In this project, we built a frontend UI generator using DeepSeek V3-0324, Streamlit, and Tailwind CSS.

We:

  • ✨ Transformed natural language prompts into HTML
  • 🖼 Rendered visual components instantly
  • 🧠 Explored DeepSeek’s reasoning + tool use capabilities

This setup is ideal for:

  • UI/UX prototyping
  • Frontend teaching tools
  • Prompt-to-code demos

Explore more AI tools in my recent blogs:

👉 Read the full tutorial