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LogoutM3 Moulin Moderne Customer Engagement Portal
This video demonstrates how to use the M3 Moulin Moderne Customer Engagement Portal template.
import logging
from gunicorn.app.base import BaseApplication
from app_init import create_initialized_flask_app
# Flask app creation should be done by create_initialized_flask_app to avoid circular dependency problems.
app = create_initialized_flask_app()
# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class StandaloneApplication(BaseApplication):
def __init__(self, app, options=None):
self.application = app
self.options = options or {}
super().__init__()
def load_config(self):
# Apply configuration to Gunicorn
for key, value in self.options.items():
if key in self.cfg.settings and value is not None:
self.cfg.set(key.lower(), value)
def load(self):Created: | Last Updated:
Introduction to the Template
This template is designed to create a web portal for M3 Moulin Moderne to enhance customer engagement and satisfaction. The portal features product listings, company information, and a contact form. The template includes both mobile and desktop headers, a home page with product and company information, and a contact form. It also includes the necessary backend setup using Flask.
Clicking Start with this Template
To get started with this template, click Start with this Template in the Lazy Builder interface.
Test
Press the Test button to begin the deployment of the app. The Lazy CLI will handle the deployment process and prompt you for any required user input.
Using the App
Once the app is deployed, you can use the following features:
Header
The header includes both mobile and desktop versions: - Mobile Header: A button to toggle the mobile menu. - Desktop Header: A navigation bar with links.
Home Page
The home page includes: - Welcome Section: A welcome message. - Products Section: A list of products with images and descriptions. - About Section: Information about the company. - Contact Form: A form to submit contact information.
Contact Form
The contact form allows users to submit their name, email, and message. The form data is sent to the backend, which processes the submission and returns a success message.
Integrating the App
Backend Setup
The backend is set up using Flask. The main components include: - main.py: Initializes and runs the Flask app using Gunicorn. - routes.py: Defines the routes for the home page and contact form submission. - app_init.py: Initializes the Flask app and registers routes.
External Integration Steps
- Email Sending Logic: Implement the email sending logic in the
submit_contactroute inroutes.py. You can use an email service like SendGrid or SMTP to send emails.
Example using SendGrid: ```python import sendgrid from sendgrid.helpers.mail import Mail
def send_email(name, email, message): sg = sendgrid.SendGridAPIClient(api_key='YOUR_SENDGRID_API_KEY') email_content = f"Name: {name}\nEmail: {email}\nMessage: {message}" mail = Mail( from_email='your-email@example.com', to_emails='recipient@example.com', subject='New Contact Form Submission', plain_text_content=email_content ) response = sg.send(mail) return response.status_code ```
-
Static Files: Ensure that the static files (CSS and JS) are correctly placed in the
staticfolder. -
Database Setup: If you need to store contact form submissions in a database, set up a database and configure it in
app_init.py.
Example using SQLAlchemy: ```python from flask_sqlalchemy import SQLAlchemy
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///your_database.db' db = SQLAlchemy(app)
class ContactFormSubmission(db.Model): id = db.Column(db.Integer, primary_key=True) name = db.Column(db.String(100)) email = db.Column(db.String(100)) message = db.Column(db.Text)
db.create_all() ```
- Environment Secrets: If the code requires any environment secrets (e.g., API keys), set them up in the Environment Secrets tab within the Lazy Builder.
Conclusion
This template provides a comprehensive solution for creating a customer engagement portal for M3 Moulin Moderne. By following the steps outlined above, you can deploy and customize the app to meet your specific needs. If you need to integrate additional features or services, refer to the provided code examples and documentation links.
Technologies
Apache Kafka with Lazy AI – Automate Data Streaming, Real-Time Processing, and Scalable Integrations
