AI Movie Recommendation System

A full-stack, AI-powered movie recommendation system designed to provide personalized movie suggestions. It leverages modern frontend technologies and a robust Python backend with access to TMDB APIs to fetch rich data (movie posters, cast photos, real-time ratings, etc.) and utilizes ML/AI recommendation engines for high-quality suggestions.

Features

• Rich Data Extraction: Integrated with TMDB API to fetch large datasets including primary movie details, comprehensive cast information, trailers, high-resolution backdrops, and exact "Suzume" style aesthetics (developed based on conversation histories).
• Advanced Recommendations: Supports a massive repository of over 1000+ movies per request, ensuring vast pools of relevant selections.
• Modern User Interface: A highly polished, responsive front-end experience built in React (with a custom "Suzume" aesthetic), featuring optimized components (e.g., MovieCard.js) and stylish transitions.
• Robust Backend: Python Flask-based backend, capable of multiple recommendation strategies (Content-based, Collaborative filtering, and Deep Learning mechanisms).

Tech Stack

Frontend

• Framework: React 19 / create-react-app
• Styling: Custom CSS (app.css) with premium design aesthetics.
• Icons & Requests: lucide-react for smooth iconography and axios for HTTP requests.

Backend

• Framework: Flask (Python) with flask-cors
• Data handling: pandas
• Machine Learning: scikit-learn
• External Data Source: TMDB API via Python's requests library.

Prerequisites

Make sure you have the following installed on your machine:

• Node.js (v14 or higher)
• NPM or Yarn
• Python 3.8+
• TMDB API Key (You'll need this to fetch the movie data)

Installation

1. Clone the repository:

   git clone <your-repository-url>
   cd Ai-movie-recommendation-system

2. Setup the Backend: Navigate to the backend directory, create a virtual environment, and install the dependencies.

   cd ai-movie-recommender/backend
   python -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts\activate
   pip install -r requirements.txt

   Note: Ensure to add your TMDB API Key in the backend environment/configuration if required by tmdb.py.

3. Setup the Frontend: Navigate to the frontend directory and install dependencies.

   cd ../../frontend
   npm install

Usage

1. Start the Backend Server:

   cd ai-movie-recommender/backend
   source venv/bin/activate  # On Windows use: venv\Scripts\activate
   python app.py

   The Flask server will start generally on http://localhost:5000 or http://0.0.0.0:5000.

2. Start the Frontend Application:

   cd frontend
   npm start

   This will spin up the React development server on http://localhost:3000.

3. Explore the App: Open your browser to the local frontend address, search for a movie, and discover rich, AI-generated recommendations.

Project Structure

Ai-movie-recommendation-system/
├── ai-movie-recommender/
│   └── backend/
│       ├── app.py                # Main Flask application entry point
│       ├── recommender.py        # Core recommendation logic
│       ├── collaborative.py      # Collaborative filtering models
│       ├── deep_learning.py      # Deep learning extensions
│       ├── tmdb.py               # TMDB API Integration & rich data fetching
│       ├── tmdb_5000_credits.csv # Local primary dataset
│       └── requirements.txt      # Python dependencies
└── frontend/
    ├── package.json              # React project metadata
    ├── public/                   # Static files
    └── src/
        ├── components/           # UI Components (e.g., MovieCard.js)
        ├── app.css               # Main styling rules
        └── index.js              # React entry point

Future Scope

• Expand Deep Learning Models: Moving from scikit-learn base prototypes to advanced TensorFlow or PyTorch recommender nodes.
• User Authentication: Save user profiles and past searches for continuous long-term personalized recommendations.
• Production Deployment: Containerize everything via Docker and deploy on scalable platforms like AWS or Vercel/Render.

License

This project is licensed under the ISC License (as noted in the frontend setup).