Building a Full-Stack Web App from Scratch with DeepSeek Coder V2: A Tutorial

Building a modern full-stack web application from scratch can be daunting — you need to design the backend, connect a database, create APIs, build a frontend, and make sure everything integrates smoothly.

Now imagine doing all that in hours, not days, with AI that writes, explains, and optimizes your code in real time.

Welcome to DeepSeek Coder V2 — your full-stack AI co-developer.
In this tutorial, we’ll build a complete full-stack web app using DeepSeek Coder V2, from concept to deployment.

You’ll see how DeepSeek Coder helps you:

• Scaffold your backend (FastAPI + PostgreSQL)
• Build a responsive React frontend
• Connect both layers through a REST API
• Add authentication and environment variables
• Optimize performance and generate documentation

Let’s build. ⚙️

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🧩 1. Project Overview

We’ll build a simple yet scalable Task Manager App — allowing users to:

• Create, read, update, and delete tasks
• Authenticate with secure JWT tokens
• Store data in PostgreSQL
• Interact via a React-based UI

Tech Stack

• Backend: FastAPI (Python)
• Database: PostgreSQL
• Frontend: React (Vite + Tailwind CSS)
• AI Assistant: DeepSeek Coder V2

💡 Goal: Demonstrate how DeepSeek Coder accelerates every stage of full-stack development.

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⚙️ 2. Step 1 — Backend Setup with DeepSeek

Prompt:

“Generate a FastAPI app with a PostgreSQL database connection and a /tasks CRUD API.”

DeepSeek Output:

# main.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List
import psycopg2

app = FastAPI()

class Task(BaseModel):
    id: int | None = None
    title: str
    description: str
    completed: bool = False

conn = psycopg2.connect(
    dbname="tasksdb",
    user="postgres",
    password="password",
    host="localhost"
)

@app.get("/tasks", response_model=List[Task])
def get_tasks():
    cur = conn.cursor()
    cur.execute("SELECT id, title, description, completed FROM tasks;")
    rows = cur.fetchall()
    return [Task(id=r[0], title=r[1], description=r[2], completed=r[3]) for r in rows]

@app.post("/tasks", response_model=Task)
def create_task(task: Task):
    cur = conn.cursor()
    cur.execute(
        "INSERT INTO tasks (title, description, completed) VALUES (%s, %s, %s) RETURNING id;",
        (task.title, task.description, task.completed)
    )
    task.id = cur.fetchone()[0]
    conn.commit()
    return task

✅ Backend scaffolded.
✅ CRUD endpoints ready.
✅ Auto-generated Pydantic models.

💬 DeepSeek Coder even suggested adding connection pooling for scalability.

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🧮 3. Step 2 — Add Authentication

Prompt:

“Add JWT-based authentication to protect all /tasks endpoints.”

DeepSeek Output (excerpt):

from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from jose import jwt, JWTError
from datetime import datetime, timedelta

SECRET_KEY = "supersecret"
ALGORITHM = "HS256"
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")

@app.post("/token")
def login(form_data: OAuth2PasswordRequestForm = Depends()):
    if form_data.username == "admin" and form_data.password == "admin":
        token = jwt.encode({"sub": form_data.username, "exp": datetime.utcnow() + timedelta(hours=1)}, SECRET_KEY, algorithm=ALGORITHM)
        return {"access_token": token, "token_type": "bearer"}
    raise HTTPException(status_code=401, detail="Invalid credentials")

@app.get("/tasks")
def get_tasks(token: str = Depends(oauth2_scheme)):
    try:
        jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
    except JWTError:
        raise HTTPException(status_code=401, detail="Invalid token")
    # proceed with tasks query

✅ Authentication added.
✅ Protected routes.
✅ Tokens expire automatically.

💡 DeepSeek also provided notes on environment variable management using python-dotenv.

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🧱 4. Step 3 — Frontend with React + Tailwind

Prompt:

“Generate a React frontend with Tailwind CSS that connects to the /tasks API for CRUD operations.”

DeepSeek Output:

npm create vite@latest task-manager --template react
cd task-manager
npm install axios react-router-dom tailwindcss
npx tailwindcss init

App.jsx (Generated):

import { useState, useEffect } from "react";
import axios from "axios";

export default function App() {
  const [tasks, setTasks] = useState([]);
  const [title, setTitle] = useState("");

  useEffect(() => {
    axios.get("/tasks").then(res => setTasks(res.data));
  }, []);

  const addTask = async () => {
    const res = await axios.post("/tasks", { title, description: "", completed: false });
    setTasks([...tasks, res.data]);
    setTitle("");
  };

  return (
    <div className="max-w-xl mx-auto p-4">
      <h1 className="text-3xl font-bold mb-4">DeepSeek Task Manager</h1>
      <input
        className="border p-2 w-full"
        placeholder="Add new task"
        value={title}
        onChange={e => setTitle(e.target.value)}
      />
      <button onClick={addTask} className="bg-blue-600 text-white px-4 py-2 mt-2 rounded">
        Add
      </button>
      <ul className="mt-4">
        {tasks.map(t => (
          <li key={t.id} className="p-2 border-b">{t.title}</li>
        ))}
      </ul>
    </div>
  );
}

✅ Functional UI generated.
✅ API integration done.
✅ Uses Tailwind for instant styling.

💬 DeepSeek even recommended using React Query for caching later.

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🧠 5. Step 4 — Connect Frontend and Backend

Prompt:

“Show me how to proxy API requests from Vite frontend to FastAPI backend during local development.”

DeepSeek Output:

// vite.config.js
export default {
  server: {
    proxy: {
      '/tasks': 'http://127.0.0.1:8000'
    }
  }
};

💡 Explanation:

“This ensures all requests to /tasks are redirected to FastAPI backend while developing locally.”

✅ Cross-origin issues solved.
✅ Dev environment integrated.

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🔍 6. Step 5 — Add Unit Tests Automatically

Prompt:

“Generate pytest tests for the FastAPI endpoints and explain what each test checks.”

DeepSeek Output:

from fastapi.testclient import TestClient
from main import app

client = TestClient(app)

def test_create_task():
    res = client.post("/tasks", json={"title": "Test Task", "description": "Check"})
    assert res.status_code == 200
    assert res.json()["title"] == "Test Task"

def test_get_tasks():
    res = client.get("/tasks")
    assert res.status_code == 200
    assert isinstance(res.json(), list)

✅ Automated tests.
✅ Readable and reusable.
✅ Explanation of assertions provided automatically.

---

💬 7. Step 6 — Generate Documentation

Prompt:

“Generate a professional README.md for this full-stack app.”

DeepSeek Output:

# DeepSeek Task Manager

A full-stack web app built using FastAPI, PostgreSQL, and React — created with DeepSeek Coder V2.

## Features
- Secure JWT Authentication
- Full CRUD API
- Responsive React UI
- Unit Tests & Documentation
- Optimized for Scalability

## Setup
1. Run `uvicorn main:app --reload` for backend.
2. Run `npm run dev` for frontend.
3. Visit `http://localhost:5173`.

✅ Clean, structured, and professional documentation — instantly ready for GitHub.

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⚡ 8. Step 7 — Optimize for Production

Prompt:

“Optimize this full-stack project for deployment on Render or Vercel.”

DeepSeek Recommendations:

• Use .env for secrets and DB credentials
• Switch DB to managed PostgreSQL instance
• Add Gunicorn as FastAPI server
• Configure frontend build via Vite → /dist → static hosting
• Auto-generate a Dockerfile for container deployment

Generated Dockerfile:

FROM python:3.11-slim
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

✅ Deployment-ready.
✅ Cross-platform and scalable.

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📊 9. Time Savings and Developer Impact

Task	Manual Time	With DeepSeek Coder
Backend scaffolding	2–3 hours	6 minutes
Auth setup	1.5 hours	3 minutes
Frontend integration	3 hours	10 minutes
Testing & docs	2 hours	5 minutes
Deployment setup	1 hour	5 minutes

💡 Total saved: ~8 hours per developer — per project.
That’s a full workday regained.

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Conclusion

DeepSeek Coder V2 doesn’t just generate snippets — it builds entire systems with reasoning, structure, and best practices.

In this tutorial, you:

• Built a FastAPI + React full-stack app
• Added authentication, testing, and docs
• Integrated backend, frontend, and database seamlessly
• Optimized for deployment — all guided by AI

This is the new era of development:
AI-assisted full-stack engineering that’s explainable, scalable, and lightning fast.

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Next Steps

• 🧠 DeepSeek Coder vs GitHub Copilot: Which AI Coding Assistant Reigns Supreme?
• ⚙️ Optimizing Your Code for Performance with DeepSeek Coder V2
• 💬 A Guide to Writing Perfect Documentation with DeepSeek Coder

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