Movie Recommendation App - Project Documentation

Project Overview

The Movie Recommendation App is a web application built with Next.js that allows users to browse trending movies, receive personalized recommendations, and save their favorite movies. The app integrates with external movie APIs to fetch real-time data and provides an engaging user experience.

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Table of Contents

1. Technical Stack
2. Running the App Locally
3. Architecture Overview
4. Feature Breakdown
5. Development Timeline
6. API Integration Guide
7. Component Structure
8. Testing Strategy
9. Deployment Plan

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Technical Stack

Core Technologies

• Next.js: Server-side rendering and dynamic routing
• TypeScript: Type safety and maintainability
• Styled Components: Styling and theming

Additional Libraries

• React Query: API data fetching, caching, and state management
• Framer Motion: Animations and transitions
• Jest & React Testing Library: Unit and integration testing
• ESLint & Prettier: Code quality and formatting

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Running the App Locally

Prerequisites

• Node.js (v18 or higher)
• npm or yarn

Steps

1. Clone the repository:

    git clone https://github.com/Donald-Edinam/sankofa-flix.git
   cd sankofa-flix

2. Install dependencies:

   npm install

3. Set up the environment variables:

   • Create a .env file in the root directory of the project.

   • Add the following environment variable:

     NEXT_PUBLIC_API_BASE_URL=http://localhost:8000/api

4. Run the development server:

   npm run dev

5. Open the app in your browser:

   • Navigate to http://localhost:3000

6. Build for production (optional):

   npm run build
   npm start

Architecture Overview

The application follows a modular architecture with clear separation of concerns:

/app
  /api            # API route handlers
  /components     # Reusable UI components
    /ui           # Basic UI elements
    /layout       # Layout components
    /movie        # Movie-specific components
  /contexts       # React contexts for state management
  /hooks          # Custom hooks
  /lib            # Utility functions and API clients
  /pages          # Page components and routes
  /public         # Static assets
  /styles         # Global styles and theme configurations
  /types          # TypeScript type definitions

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Feature Breakdown

1. Movie Dashboard (Homepage)

• Components: MovieCard, TrendingSection, RecommendationSection
• Features: Fetch trending movies, infinite scrolling for recommendations, skeleton loaders

2. Dynamic Movie Details Pages

• Components: MovieHeader, MovieDetails, RelatedMovies
• Features: Dynamic routing, server-side rendering, SEO optimization

3. Favorites Management

• Components: FavoriteButton, FavoritesPage
• Features: Save/remove favorites, local storage persistence, backend API integration

4. Search Functionality

• Components: SearchBar, SearchResults
• Features: Debounced search, API handler, filtering by genre, release year

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Development Timeline

Week 1: Foundation and Core Features

• Day 1-2: Setup Next.js, API integration, folder structure
• Day 3-4: Implement Movie Dashboard UI, trending movies section
• Day 5-6: Create dynamic movie details pages
• Day 7: Code review, refactor, document API integration

Week 2: Enhanced Features and Finalization

• Day 8-9: Implement favorites management
• Day 10-11: Build search functionality, animations
• Day 12-13: Testing and optimization
• Day 14: Documentation and deployment

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API Integration Guide

Authentication

• Implement auth context for user sessions
• Create interceptors for API requests

Endpoints

• /api/movies/trending - Fetch trending movies
• /api/movies/recommended - Get recommendations
• /api/movies/{id} - Fetch movie details
• /api/user/favorites - Manage user favorites
• /api/search - Search functionality

Error Handling

• Global error utility
• Retry logic for failed requests
• User-friendly error messages

Third-Party API

• Use The Movie Database (TMDB) API
• Service layer for API calls
• Implement caching to reduce API calls

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Component Structure

Core Components

<MovieCard />

interface MovieCardProps {
  movie: Movie;
  variant: 'poster' | 'horizontal' | 'detailed';
  onFavoriteToggle: (id: string) => void;
  isFavorite: boolean;
}

<MovieGrid />

interface MovieGridProps {
  movies: Movie[];
  loading: boolean;
  error?: string;
  onLoadMore?: () => void;
  hasMore?: boolean;
}

<FavoriteButton />

interface FavoriteButtonProps {
  movieId: string;
  isFavorite: boolean;
  onToggle: (id: string) => void;
}

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Testing Strategy

Unit Testing

• Test components in isolation
• Mock API responses
• Verify behavior with different props

Integration Testing

• Test interactions between components
• Verify context and API usage

End-to-End Testing

• Simulate user journeys
• Ensure core functionality and responsiveness

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Deployment Plan

Pre-Deployment Checklist

• Run final tests
• Verify API endpoints
• Optimize assets for production

Deployment Steps

• Build for production
• Deploy to Vercel/Netlify
• Configure environment variables

Post-Deployment

• Monitor errors and performance
• Validate all features in production

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Page Components Breakdown

1. Homepage (Dashboard)

Sections: Header, Hero Banner, Trending Movies, Recommendations, Categories, Footer Key Components: <Navbar />, <SearchBar />, <HeroBanner />, <MovieCarousel />, <MovieGrid />

2. Movie Details Page

Sections: Movie Hero, Overview, Cast, Media, Reviews, Similar Movies Key Components: <MovieBackdrop />, <MovieInfo />, <RatingDisplay />, <CastCarousel />

3. Favorites Page

Sections: Favorites Hero, Empty State, Favorites Grid Key Components: <FavoritesGrid />, <FavoriteMovieCard />, <SortDropdown />

4. Search Results Page

Sections: Search Input, Search Results Key Components: <SearchBar />, <SearchResults />, <GenreFilter />

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Conclusion

The Movie Recommendation App is designed for an engaging and seamless user experience, utilizing Next.js for performance optimization, React Query for state management, and TMDB API for real-time data. The structured development approach ensures maintainability and scalability.

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Future Enhancements

• Implement user authentication for personalized recommendations
• Add watchlist feature for users
• Improve AI-based recommendations using machine learning
• Implement progressive web app (PWA) features for offline access