Lovable Prompt Creator - AI Prompt Generator

## APPLICATION OVERVIEW
Create a modern, production-style web application designed as a real-time industrial monitoring dashboard titled "Predictive Maintenance for Industrial Equipment." This application will monitor sensor data, predict equipment failures, and provide maintenance recommendations, utilizing clean design principles for optimal readability and user engagement.

## CORE FEATURES
1. **Dashboard Page**: The main control panel displaying real-time sensor data cards (Temperature, Vibration, Pressure, and RPM), line charts for live trends over the last 60 minutes, health status indicators, failure probability meters, and maintenance recommendations.
   
2. **Machine Detail Page**: Allows users to select a machine from a dropdown list, view historical performance charts, feature insights (including rolling averages and FFT/Vibration Spectrum), model confidence scores, and maintenance dates with recommendations.

3. **Alerts Page**: A list of triggered alerts with severity badges (Low, Medium, High), timestamps, machine IDs, and action-required messages for quick reference.

4. **System Architecture Page**: A visual diagram illustrating the data flow from sensors to the dashboard, including a simple explanation of the pipeline, aimed at providing clarity for judges.

5. **Demo Mode**: A toggle feature that simulates real-time streaming sensor data, enhancing the demo experience during presentations.

## DESIGN SPECIFICATIONS
- **Visual Style**: Minimalist - Clean and simple design with a focus on readability and data visualization, ensuring an industrial feel while maintaining professionalism.
  
- **Color Mode**: Light theme with dark text on light backgrounds, ensuring high contrast for readability and a clean aesthetic.
  
- **Layout**: A responsive layout featuring a sidebar navigation for easy access to core pages, with a grid structure for data display on the dashboard and detailed views for individual machines. Use ample white space to prevent clutter and enhance focus on data.

- **Typography**: Utilize a modern sans-serif font for headings and body text (e.g., 'Roboto' or 'Open Sans'), ensuring a clear hierarchy with larger sizes for headings and smaller sizes for body text, maintaining a clean look.

## TECHNICAL REQUIREMENTS
- **Framework**: React with TypeScript for building the frontend application.
  
- **Styling**: Tailwind CSS for responsive design and utility-first styling.
  
- **UI Components**: Integrate shadcn/ui for pre-built components that match the design specifications.
  
- **State Management**: Utilize React's Context API or Redux for managing global state if required.

## IMPLEMENTATION STEPS
1. Set up the React project with TypeScript and configure Tailwind CSS for styling.
2. Create the core components for the dashboard, machine detail, alerts, and system architecture pages.
3. Implement the sidebar navigation for seamless transitions between pages.
4. Develop the real-time sensor data cards and line charts, ensuring they update dynamically.
5. Build the machine detail page with dropdown selections and historical performance charts.
6. Set up the alerts page to display triggered alerts with appropriate severity badges.
7. Create the demo mode toggle, simulating real-time data for a smooth presentation experience.
8. Implement mock API endpoints for GET /sensors, POST /predict, and GET /alerts to support frontend functionality.
9. Ensure responsiveness across all devices and refine user interactions for clarity and ease of use.

## USER EXPERIENCE
Users will interact with a streamlined dashboard where they can quickly view real-time data and trends. Selecting a machine from the dropdown on the Machine Detail Page will display historical performance and insights, while the Alerts Page provides a quick overview of any critical issues. The demo mode will allow users to experience the application in real-time, enhancing engagement and understanding during demonstrations. The overall user flow is designed to be intuitive, with clear navigational elements and a focus on data accessibility.