Lovable Prompt Creator - AI Prompt Generator

## APPLICATION OVERVIEW
This project is a web application designed for predicting house prices using a machine learning model. The application will allow users to input property features and receive predictions based on a trained model. It will feature a user-friendly interface to facilitate interaction with the machine learning predictions while providing insights into the model's workings.

## CORE FEATURES
1. **User Input Form**: A clean and straightforward form where users can enter property details such as area, number of bedrooms, and location.
2. **Price Prediction**: A button that, when clicked, uses the trained machine learning model to predict the house price based on the provided inputs.
3. **Model Training Dashboard**: A section where users can view the training status and performance metrics of the machine learning model.
4. **Data Visualization**: Interactive charts and graphs to visualize historical data trends and model accuracy.
5. **User Authentication**: Secure login and registration for users to save their input data and access previous predictions.
6. **Contact & Support**: A contact form for user inquiries and support requests.

## DESIGN SPECIFICATIONS
- Visual Style: minimalist - Clean, simple design with plenty of white space, minimal color palette, and focus on typography
- Color Mode: Light theme with dark text on light backgrounds
- Primary Color: #1978E5 (accent for buttons, links, highlights)
- Typography: Use Inter from Google Fonts for headings, Inter for body text and UI elements
- Border Radius: 8px (moderately rounded) for buttons, cards, and inputs
- Layout: The main layout will consist of a header with navigation links, a hero section for the price prediction form, followed by sections for features, a dashboard, and a contact form, all designed to maintain visual clarity and focus.

## TECHNICAL REQUIREMENTS
- Framework: React with TypeScript
- Styling: Tailwind CSS
- UI Components: shadcn/ui
- State Management: React Context API for managing user authentication and model data

## IMPLEMENTATION STEPS
1. **Set up the React project**: Initialize a new React project using Create React App with TypeScript.
2. **Install necessary dependencies**: Install Tailwind CSS, shadcn/ui, and any other required libraries for data visualization (e.g., Chart.js).
3. **Create the folder structure**: Organize components, pages, and styles in a clear directory structure.
4. **Implement User Authentication**: Create login and registration forms using the Context API for state management.
5. **Build the Price Prediction Form**: Develop the user input form with controlled components for each input field.
6. **Integrate Machine Learning Model**: Use a backend service (such as Flask or FastAPI) to handle predictions and connect it with the frontend.
7. **Develop Data Visualization**: Implement charts using Chart.js to display historical data trends and model performance metrics.
8. **Finalize UI/UX**: Ensure the design is responsive and user-friendly, with attention to the minimalist aesthetic.

## USER EXPERIENCE
Users will start on the landing page, where they can see a brief introduction to the application and a call-to-action button to start predicting house prices. After entering the required property details in the input form, users will click the prediction button to see the estimated house price. The dashboard will provide insights into the model's performance, allowing users to understand how predictions are made. Finally, users can access support via the contact form for any inquiries.