Lovable Prompt Creator - AI Prompt Generator

## APPLICATION OVERVIEW
The application is a web-based dashboard designed to identify and surface emerging music artists on the verge of gaining significant traction. It leverages data from platforms like Instagram, Spotify, TikTok, and YouTube to provide insights for record label A&R teams, enabling them to discover talent before it becomes mainstream.

## CORE FEATURES
- **Audience Trend Analysis**: Offers live insights into listener and consumer trends, allowing users to track the rise of potential music artists in real-time.
- **Notable Creator Flagging**: Automatically flags songs that are being used by notable creators, providing immediate notifications to users about trending content.
- **Comments Clustering**: Analyzes social media comments using sentiment analysis to provide deeper insights into fan engagement and artist reception.
- **Pattern Detection**: Utilizes machine learning algorithms to detect viral data patterns among artists, helping to assess which musicians are gaining traction.
- **Artist Dashboard**: A comprehensive dashboard displaying a selection of artists, their virality scores, and a detailed breakdown of data across various platforms (e.g., Spotify streams, sound creation spikes, notable creator signals).

## DESIGN SPECIFICATIONS
- **Visual Style**: Minimalist, focusing on a clean and simple design with ample white space, ensuring that the content stands out and is easy to read.
- **Color Mode**: Light theme with dark text on light backgrounds to enhance readability and maintain a modern aesthetic.
- **Layout**: The main layout will feature a grid-based dashboard, with sections for trending artists, audience insights, and individual artist data. Each artist card will display their virality score and key metrics prominently.
- **Typography**: Use a sans-serif font like "Helvetica Neue" for headings (font-weight: bold, size: 24px) and a slightly lighter sans-serif font for body text (font-weight: regular, size: 16px). Ensure a clear hierarchy with larger sizes for headings and adequate spacing between elements.

## TECHNICAL REQUIREMENTS
- **Framework**: React with TypeScript for type safety and component reusability.
- **Styling**: Tailwind CSS to create a responsive, mobile-friendly design with utility-first CSS classes.
- **UI Components**: Utilize shadcn/ui for pre-built components that align with the minimalist design principles.
- **State Management**: Implement React Context or Zustand for state management to maintain and share application state across components.

## IMPLEMENTATION STEPS
1. **Set Up Project**: Initialize a new React project with TypeScript and install necessary dependencies (React, Tailwind CSS, shadcn/ui).
2. **Design Dashboard Layout**: Create the grid-based layout for the dashboard using Tailwind CSS classes, ensuring responsiveness on mobile devices.
3. **Build Core Components**: Develop reusable components for artist cards, trend analysis graphs, and comment clustering visualizations.
4. **Implement API Integration**: Set up API calls to gather data from Instagram, Spotify, TikTok, and YouTube, and integrate this data into the application state.
5. **Develop Machine Learning Features**: Incorporate machine learning models for pattern detection and sentiment analysis, ensuring seamless integration with the dashboard.
6. **Testing**: Conduct thorough testing for usability and performance, focusing on responsiveness and data accuracy.

## USER EXPERIENCE
Users will interact with the application primarily through the dashboard, where they can view and filter artists based on virality scores and trends. Key interactions will include:
- Clicking on an artist card to expand details and view specific metrics.
- Using filters to sort artists by platform data or engagement levels.
- Receiving notifications for notable creator flagging, enhancing the user’s ability to stay updated on emerging talents.
- Utilizing trend analysis graphs for a visual representation of audience engagement and sentiment over time, allowing for informed decision-making by A&R teams.