by neurhona
Desperdicio 0
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):
Frequently Asked Questions
How can Desperdicio 0 help reduce food waste in restaurants?
Desperdicio 0 is designed to help restaurants minimize food waste through several key features. The dashboard allows restaurant managers to track inventory levels, monitor expiration dates, and analyze consumption patterns. By providing real-time insights, Desperdicio 0 enables better purchasing decisions and menu planning, reducing the likelihood of overstocking perishable items. Additionally, the AI chat feature can suggest creative ways to repurpose ingredients that are nearing expiration, further minimizing waste and potentially creating new menu items.
What are the potential cost savings for a business implementing Desperdicio 0?
Implementing Desperdicio 0 can lead to significant cost savings for restaurants. By reducing food waste, businesses can lower their food costs, which typically account for 28-35% of restaurant expenses. Desperdicio 0's analytics can help identify inefficiencies in purchasing and usage, potentially cutting food waste by 2-6% of sales. For a restaurant with $1 million in annual revenue, this could translate to $20,000-$60,000 in savings per year. Moreover, Desperdicio 0's inventory management features can help optimize stock levels, reducing carrying costs and freeing up capital.
How can I customize the AI chat instructions in Desperdicio 0 for my specific restaurant needs?
Desperdicio 0 allows you to customize the AI chat instructions through the settings page. You can modify the moderation_prompt
field to tailor the AI's responses to your restaurant's specific needs. Here's an example of how you might set custom instructions:
```python @app.route("/api/update_settings", methods=["POST"]) def update_settings(): try: settings = ChatbotSettings.query.first() if not settings: settings = ChatbotSettings() db.session.add(settings)
settings.moderation_prompt = request.form.get("moderation_prompt", "")
settings.moderation_prompt += "\n\nPlease provide suggestions for reducing food waste in a fine dining Italian restaurant."
db.session.commit()
return jsonify({"status": "success", "message": "Settings updated successfully"})
except Exception as e:
db.session.rollback()
return jsonify({"status": "error", "message": str(e)}), 500
```
This code snippet shows how you can update the AI chat instructions to focus on food waste reduction strategies specific to your restaurant type.
Can Desperdicio 0 integrate with my existing point-of-sale (POS) system?
While the current template of Desperdicio 0 doesn't include built-in POS integration, it's designed with extensibility in mind. The modular structure of Desperdicio 0 allows for the addition of integration modules. You could create a new route and corresponding function to handle data import from your POS system. This would enable Desperdicio 0 to use real-time sales data for more accurate inventory management and waste reduction recommendations.
How can I add a new feature to track the environmental impact of waste reduction in Desperdicio 0?
To add an environmental impact tracking feature to Desperdicio 0, you would need to create a new model, route, and update the frontend. Here's a basic example of how you might start:
```python # In models.py class EnvironmentalImpact(db.Model): id = db.Column(db.Integer, primary_key=True) date = db.Column(db.Date, nullable=False) waste_reduced_kg = db.Column(db.Float, nullable=False) co2_saved_kg = db.Column(db.Float, nullable=False)
# In routes.py @app.route("/api/environmental_impact", methods=["POST"]) def add_environmental_impact(): data = request.json impact = EnvironmentalImpact( date=datetime.strptime(data['date'], '%Y-%m-%d').date(), waste_reduced_kg=float(data['waste_reduced']), co2_saved_kg=float(data['waste_reduced']) * 2.5 # Assuming 2.5 kg CO2 saved per kg of food waste reduced ) db.session.add(impact) db.session.commit() return jsonify({"status": "success", "message": "Environmental impact recorded"}) ```
This code creates a new model to store environmental impact data and a route to add new entries. You would then need to create a new page in the Desperdicio 0 dashboard to display this data, perhaps using charts to visualize the environmental savings over time.
Created: | Last Updated:
Here's a step-by-step guide for using this template as a starting point:
Introduction to the Template
This template provides a basic structure for a Flask-based web application with user authentication, an AI chat interface, and admin management features. It's designed to be a starting point for building a more complex AI-powered web application.
Getting Started
To begin using this template:
- Click the "Start with this Template" button in the Lazy Builder interface.
Test the Application
After starting with the template:
- Click the "Test" button in the Lazy Builder interface.
- The Lazy CLI will initiate the deployment process.
Using the Application
Once the application is deployed, you can access its features:
Home Page
The home page (home.html
) allows you to configure chatbot settings:
- Customize the chatbot instructions
- Choose an AI model
- Adjust the AI response temperature
AI Chat Simulator
The AI Chat Simulator (ai_chat.html
) lets you test your chatbot configuration:
- Send messages to the AI
- View AI responses
- Clear chat history
Team Management
The Company Admins page (company_admins.html
) provides tools for managing access:
- Add or remove individual admin accounts
- Manage domain-wide access by adding or removing email endings
- Block or unblock admin accounts
Integrating the Application
This template creates a standalone web application. To integrate it with other services or expand its functionality:
- Use the server link provided by the Lazy CLI after deployment to access your application.
- If you need to add API endpoints, you can extend the
routes.py
file with new Flask routes.
For example, to add a new API endpoint:
python
@app.route("/api/new_endpoint", methods=["POST"])
def new_endpoint():
# Your endpoint logic here
return jsonify({"status": "success", "message": "New endpoint response"})
- If you want to integrate this application with external services, you can use the server link as the base URL for API requests.
Remember that all deployment and execution are handled by the Lazy platform, so you don't need to worry about server setup or environment configuration.
Here are 5 key business benefits of this template:
Template Benefits
-
Customizable AI Chat Interface: Provides an interactive AI chat simulator that allows businesses to test and refine their chatbot settings and responses before deploying to customers.
-
Flexible Admin Management: Offers robust tools for managing admin access, including the ability to add individual admins or allow entire email domains, as well as block/unblock functionality.
-
Configurable AI Model Settings: Allows easy customization of AI model selection, temperature settings, and custom instructions to tailor the chatbot's behavior to specific business needs.
-
Secure Authentication System: Implements a comprehensive authentication system with domain and user-level access controls to ensure only authorized personnel can access the admin dashboard.
-
Responsive Design: Features a mobile-friendly, responsive layout that adapts to different screen sizes, enabling administrators to manage the system on various devices.