Tutorial: Generate Code from User Stories using Mistral AI
Comprehensive Full-Stack Development with Mistral.ai and VSCode
This tutorial demonstrates how to leverage Mistral.ai's capabilities within Visual Studio Code for full-stack development of a feature-rich todo list application. We'll work through detailed user stories
Prerequisites and Setup
3. For Linux: Open Terminal and run:
4. For Windows: Visit https://ollama.ai/ Download and run the Windows installer from the Ollama website
5. Set up Ollama with Mistral model:
6. Configure CodeGPT extension:
Project Overview: Enhanced Todo List Application
Our application will be a sophisticated todo list manager with user authentication
User Stories and Requirements
User Authentication
Todo Management
Analytics
Collaboration
Mobile Responsiveness
Performance Requirements
Development Tasks
Let's use Mistral.ai to help us develop this enhanced todo list application.
Front-End Development
Task 1: Create a Registration Form Component
Create a new file called RegistrationForm.js and use the following prompt
Create a React component for user registration with the following fields:
- Email (with validation)
- Password (with strength meter)
- Confirm Password
Use Material-UI components and implement client-side validation as per the user story requirements.
Include a terms of service checkbox.
Output from AI:
import React, { useState } from 'react';
import { TextField, Button, Checkbox, FormControlLabel } from '@material-ui/core';
const RegistrationForm = () => {
const [formData, setFormData] = useState({
email: '',
password: '',
confirmPassword: '',
agreeToTerms: false
});
const handleChange = (e) => {
const { name, value, checked } = e.target;
setFormData(prev => ({
...prev,
[name]: name === 'agreeToTerms' ? checked : value
}));
};
const handleSubmit = (e) => {
e.preventDefault();
// Implement form submission logic here
};
return (
<form onSubmit={handleSubmit}>
<TextField
name="email"
label="Email"
type="email"
value={formData.email}
onChange={handleChange}
required
fullWidth
/>
<TextField
name="password"
label="Password"
type="password"
value={formData.password}
onChange={handleChange}
required
fullWidth
/>
<TextField
name="confirmPassword"
label="Confirm Password"
type="password"
value={formData.confirmPassword}
onChange={handleChange}
required
fullWidth
/>
<FormControlLabel
control={
<Checkbox
name="agreeToTerms"
checked={formData.agreeToTerms}
onChange={handleChange}
required
/>
}
label="I agree to the terms of service"
/>
<Button type="submit" variant="contained" color="primary">
Register
</Button>
</form>
);
};
export default RegistrationForm;
Task 2: Create a Todo List Component
Create a file called TodoList.js and use this prompt:
Create a React component for displaying a list of todos with the following features:
- Pagination (20 items per page)
- Filtering by completion status, priority, and due date
- Each todo item should display title, due date, priority, and completion status
- Include options to edit, delete, and toggle completion status for each todo
Use Material-UI components and implement the filtering logic.
Output from AI:
import React, { useState, useEffect } from 'react';
import { List, ListItem, ListItemText, ListItemSecondaryAction, IconButton, Button } from '@material-ui/core';
import { Delete, Edit, CheckCircle } from '@material-ui/icons';
const TodoList = () => {
const [todos, setTodos] = useState([]);
const [page, setPage] = useState(1);
useEffect(() => {
// Fetch todos from API
fetchTodos(page);
}, [page]);
const fetchTodos = async (page) => {
// Implement API call to fetch todos
};
const handleComplete = (id) => {
// Implement todo completion logic
};
const handleDelete = (id) => {
// Implement todo deletion logic
};
const handleEdit = (id) => {
// Implement todo edit logic
};
return (
<div>
<List>
{todos.map((todo) => (
<ListItem key={todo.id}>
<ListItemText
primary={todo.title}
secondary={`Due: ${todo.dueDate} | Priority: ${todo.priority}`}
/>
<ListItemSecondaryAction>
<IconButton edge="end" aria-label="complete" onClick={() => handleComplete(todo.id)}>
<CheckCircle />
</IconButton>
<IconButton edge="end" aria-label="edit" onClick={() => handleEdit(todo.id)}>
<Edit />
</IconButton>
<IconButton edge="end" aria-label="delete" onClick={() => handleDelete(todo.id)}>
<Delete />
</IconButton>
</ListItemSecondaryAction>
</ListItem>
))}
</List>
<Button onClick={() => setPage(prev => prev - 1)} disabled={page === 1}>Previous</Button>
<Button onClick={() => setPage(prev => prev + 1)}>Next</Button>
</div>
);
};
export default TodoList;
Task 3: Implement Collaboration UI
Create a file called CollaborationModal.js and use this prompt:
Create a React component for a modal that allows users to manage collaborators for a todo list. Include the following features:
- Input field to add collaborators by email
- List of current collaborators with their permissions
- Ability to change collaborator permissions (view-only, edit)
- Option to remove collaborators
Use Material-UI components and implement proper state management. Ensure the UI is user-friendly and provides clear feedback on actions.
Recommended by LinkedIn
Task 4: Develop Mobile-Responsive Layout
Create a file called ResponsiveLayout.js and use this prompt:
Create a responsive layout component using React and Material-UI that adapts to different screen sizes. The layout should include:
- A collapsible sidebar for navigation on larger screens
- A bottom navigation bar for mobile screens
- A responsive grid system for content layout
- Touch-friendly UI elements for mobile users
Implement media queries and flex layouts to ensure smooth transitions between desktop and mobile views. Test the layout on various screen sizes from 320px to 1920px wide.
Back-End Development
Task 5: Implement User Authentication API
Create a file called auth.py and use this prompt:
Create Flask routes for user authentication including:
- User registration with email verification
- Login with JWT token generation
- Logout (token invalidation)
- Password reset
Implement proper error handling and use SQLAlchemy for database operations. Ensure password hashing and JWT token generation with a 24-hour expiration.
Output from AI:
from flask import Blueprint, request, jsonify
from werkzeug.security import generate_password_hash, check_password_hash
from flask_jwt_extended import create_access_token, jwt_required, get_jwt_identity
from models import User, db
auth = Blueprint('auth', __name__)
@auth.route('/register', methods=['POST'])
def register():
data = request.json
hashed_password = generate_password_hash(data['password'])
new_user = User(email=data['email'], password=hashed_password)
db.session.add(new_user)
db.session.commit()
return jsonify({"message": "User created successfully"}), 201
@auth.route('/login', methods=['POST'])
def login():
data = request.json
user = User.query.filter_by(email=data['email']).first()
if user and check_password_hash(user.password, data['password']):
access_token = create_access_token(identity=user.id)
return jsonify(access_token=access_token), 200
return jsonify({"message": "Invalid credentials"}), 401
@auth.route('/logout', methods=['POST'])
@jwt_required()
def logout():
# Implement token invalidation logic here
return jsonify({"message": "Logged out successfully"}), 200
Task 6: Implement Todo CRUD Operations
Create a file called todos.py and use this prompt:
Create Flask routes for todo CRUD operations including:
- Create a new todo (with validation for due date and priority)
- Retrieve todos with filtering and pagination
- Update an existing todo
- Delete a todo
- Mark a todo as complete/incomplete
Use SQLAlchemy for database operations and implement proper error handling. Ensure that each operation is tied to the authenticated user.
Output from AI:
from flask import Blueprint, request, jsonify
from flask_jwt_extended import jwt_required, get_jwt_identity
from models import Todo, db
todos = Blueprint('todos', __name__)
@todos.route('/todos', methods=['POST'])
@jwt_required()
def create_todo():
data = request.json
new_todo = Todo(
title=data['title'],
description=data['description'],
due_date=data['due_date'],
priority=data['priority'],
user_id=get_jwt_identity()
)
db.session.add(new_todo)
db.session.commit()
return jsonify(new_todo.to_dict()), 201
@todos.route('/todos', methods=['GET'])
@jwt_required()
def get_todos():
page = request.args.get('page', 1, type=int)
todos = Todo.query.filter_by(user_id=get_jwt_identity()).paginate(page=page, per_page=20)
return jsonify([todo.to_dict() for todo in todos.items]), 200
# Implement update, delete, and complete todo routes here
Task 7: Implement Analytics API
Create a file called analytics.py and use this prompt:
Create Flask routes for todo analytics including:
- Completion rate for the last 30 days
- Number of overdue tasks
- Distribution of todos by priority
Implement efficient database queries using SQLAlchemy to handle large datasets. Ensure the analytics are specific to the authenticated user.
Task 8: Develop Collaboration API
Create a file called collaboration.py and use this prompt:
Create Flask routes for managing todo list collaboration, including:
- Invite collaborators by email
- Update collaborator permissions
- Remove collaborators from a todo list
- Retrieve a list of collaborators for a given todo list
Implement proper authentication and authorization checks. Use SQLAlchemy for database operations and ensure efficient querying for large lists. Handle edge cases such as inviting non-existent users or duplicate invitations.
Task 9: Implement Real-time Updates
Create a file called realtime.py and use this prompt:
Implement a real-time update system using Flask-SocketIO for the todo list application. Create the following features:
- Real-time notifications for task assignments and updates
- Live updates when collaborators modify shared todo lists
- Presence indicators for online collaborators
Ensure the implementation is scalable and can handle thousands of concurrent connections. Implement proper error handling and reconnection logic. Consider using Redis for pub/sub functionality to manage distributed socketio instances.
Infrastructure Development
Task 10: AWS Infrastructure with Terraform
Create a file called main.tf and use this prompt:
Create a Terraform configuration for AWS that sets up the following:
- VPC with public and private subnets across two availability zones
- ECS cluster for running containerized applications
- RDS instance for PostgreSQL database
- Elasticache Redis cluster for caching
- Application Load Balancer for distributing traffic
- S3 bucket for static asset storage
- CloudFront distribution for content delivery
- ECS service for the todo list application with auto-scaling
- ElastiCache cluster for storing real-time socket connections
- CloudWatch alarms for monitoring key metrics
Ensure proper security groups and IAM roles are set up. The infrastructure should be scalable to handle up to 10,000 concurrent users and support real-time features. Include tagging for cost allocation and environment separation.
Output from AI:
provider "aws" {
region = "us-west-2"
}
resource "aws_vpc" "main" {
cidr_block = "10.0.0.0/16"
tags = {
Name = "todo-app-vpc"
}
}
resource "aws_subnet" "public" {
count = 2
vpc_id = aws_vpc.main.id
cidr_block = "10.0.${count.index}.0/24"
availability_zone = data.aws_availability_zones.available.names[count.index]
tags = {
Name = "todo-app-public-subnet-${count.index + 1}"
}
}
resource "aws_ecs_cluster" "main" {
name = "todo-app-cluster"
}
resource "aws_db_instance" "main" {
identifier = "todo-app-db"
engine = "postgres"
engine_version = "13.7"
instance_class = "db.t3.micro"
allocated_storage = 20
name = "tododb"
username = var.db_username
password = var.db_password
vpc_security_group_ids = [aws_security_group.db.id]
db_subnet_group_name = aws_db_subnet_group.main.name
tags = {
Name = "todo-app-db"
}
}
# Add more resources (ALB, ECS service, S3, CloudFront, etc.) here
Testing and Optimization
For each component and API endpoint, create unit tests and integration tests. Use the following prompt to generate test cases:
Create pytest test cases for the [component/API endpoint name]. Include tests for both successful operations and error handling. Ensure coverage of edge cases and performance requirements.
Output from AI:
import pytest
from app import create_app
from models import db, Todo
@pytest.fixture
def client():
app = create_app('testing')
with app.test_client() as client:
with app.app_context():
db.create_all()
yield client
db.session.remove()
db.drop_all()
def test_create_todo(client, auth_token):
response = client.post('/todos', json={
'title': 'Test Todo',
'description': 'This is a test',
'due_date': '2023-12-31',
'priority': 'High'
}, headers={'Authorization': f'Bearer {auth_token}'})
assert response.status_code == 201
assert 'id' in response.json
# Add more test cases for other API endpoints
Generate additional test cases for new components:
Create unit and integration tests for the CollaborationModal component, focusing on user interactions and state management. Include tests for error handling and edge cases in collaborator management.
Generate performance tests for the real-time update system, simulating high concurrency and verifying message delivery under load.
Deployment
Create a Dockerfile and docker-compose.yml for containerizing the application
Create a Dockerfile for the Flask application and a docker-compose.yml file that includes services for the web application, PostgreSQL database, and Redis cache. Ensure proper environment variable handling and volume mounting for persistence.
version: '3.8'
services:
web:
build: .
ports:
- "5000:5000"
environment:
- DATABASE_URL=postgresql://user:password@db:5432/tododb
- REDIS_URL=redis://redis:6379
depends_on:
- db
- redis
db:
image: postgres:13
environment:
- POSTGRES_DB=tododb
- POSTGRES_USER=user
- POSTGRES_PASSWORD=password
volumes:
- postgres_data:/var/lib/postgresql/data
redis:
image: redis:6
volumes:
postgres_data:
Performance Optimization
Implement performance optimization techniques
Implement performance optimization techniques for the todo list application, including:
- Code splitting and lazy loading for React components
- Server-side rendering for initial page load
- Implement efficient caching strategies for API responses
- Optimize database queries for analytics and collaboration features
- Set up a CDN for static assets delivery
Provide code examples and configuration settings for each optimization technique.
Output from AI:
import React, { lazy, Suspense } from 'react';
import { BrowserRouter as Router, Route, Switch } from 'react-router-dom';
const TodoList = lazy(() => import('./components/TodoList'));
const RegistrationForm = lazy(() => import('./components/RegistrationForm'));
function App() {
return (
<Router>
<Suspense fallback={<div>Loading...</div>}>
<Switch>
<Route path="/todos" component={TodoList} />
<Route path="/register" component={RegistrationForm} />
</Switch>
</Suspense>
</Router>
);
}
export default App;
Conclusion
This tutorial has guided you through the process of developing a sophisticated, full-stack todo list application using Mistral.ai's capabilities in combination with VS Code. We've covered various aspects of modern web development, including:
Remember that while Mistral.ai provides an excellent starting point, it's crucial to review, test, and refine the generated code to ensure it meets your specific requirements and follows best practices.
As you continue to develop your application, regularly revisit your user stories and performance requirements to ensure your implementation aligns with the project goals. Continuous testing and iterative improvements will help you create a high-quality, production-ready application.
CIO | CTO and Technology Mentor for Startups in the Americas.
10moops... after the completion of this tutorial, just saw codestral that has more caveats for codig generation: https://mistral.ai/news/codestral/
passion for innovation, agility and transformation
10moInteresting!