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https://github.com/jabercrombia/video-game-data

This project integrates FastAPI as the backend and Next.js as the frontend to create a full-stack web application. It processes and displays vides game sales data, enabling seamless API communication while maintaining a scalable and efficient architecture.
https://github.com/jabercrombia/video-game-data

data-analysis nextjs nintendo playstation python typescript video-game

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This project integrates FastAPI as the backend and Next.js as the frontend to create a full-stack web application. It processes and displays vides game sales data, enabling seamless API communication while maintaining a scalable and efficient architecture.

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README

          

# FastAPI & Next.js Project
![dashboard](public/thumbnail.png)

## Overview
This project is a **Next.js** dashboard that visualizes sales data by ingesting data from an external FastAPI service: [Video Game Sales API](https://github.com/jabercrombia/video-game-sales-api).

You'll need to run the above repo and this repo to get it working on your local machine.

## Project Structure
```
myproject/
│-- pages/ # Next.js pages
│-- components/ # Reusable UI components
│-- public/ # Static assets
│-- package.json # JavaScript dependencies
```

## Setup & Installation
### Frontend (Next.js)
1. Install dependencies:
```sh
npm install
```
2. Run the Next.js development server:
```sh
npm run dev
```

## API Integration
- The dashboard fetches sales data from the external FastAPI service: [Video Game Sales API](https://github.com/jabercrombia/video-game-sales-api).
- The API repository is available at: [GitHub - Video Game Sales API](https://github.com/jabercrombia/video-game-sales-api).
- Uses `fetch` or Axios to retrieve and display real-time sales insights.
##Technologies Used
-ShadCN: Provides pre-styled and accessible UI components for seamless integration.
-Tailwind CSS: Enables rapid styling and responsive design for the dashboard.

## Future Improvements
- Enhance UI with interactive charts and filters
- Implement caching for optimized performance
- Deploy to production using Vercel

Feel free to contribute and enhance the project!