Movie Recommendation System

A content-based movie recommendation system with a modern web interface. The backend is built with Python and Flask, using a pre-processed movie dataset and cosine similarity to recommend movies. The frontend is built with React, TypeScript, and Tailwind CSS. Movie posters and details are fetched live from the TMDB API.

Key Highlights

• 🎯 Content-based movie recommendations (CountVectorizer + cosine similarity)
• 🎬 Movie posters, trailers, cast, and similar movies via the TMDB API
• 🎨 Modern, responsive UI with a dark theme
• 🚀 Fast search and real-time results
• 📱 Mobile-friendly design

How It Works

1. The backend loads backend/data/processed_movies.csv, which contains pre-processed movie data with a combined_features column (genres, keywords, cast, etc.).
2. A CountVectorizer converts the combined features into a feature matrix and computes pairwise cosine similarity across all movies.
3. When a movie title is submitted, the backend returns the 5 most similar movies based on cosine similarity.
4. For each recommended movie, the backend fetches the poster image from the TMDB API.
5. The frontend (React + TypeScript) calls the Flask API at http://localhost:5000/recommend and displays the results. Clicking a movie card fetches full details (overview, cast, trailer, similar movies) via the /api/movie/<id> endpoint.

Project Structure

.
├── backend/
│   ├── data/
│   │   └── processed_movies.csv      # Pre-processed movie dataset
│   └── movie_recommender_api.py      # Flask API server
├── frontend/
│   ├── src/
│   │   ├── components/               # React UI components
│   │   ├── hooks/                    # Custom hooks (recommendations, movie details)
│   │   ├── types/                    # TypeScript type definitions
│   │   └── App.tsx                   # Root component
│   ├── package.json
│   └── vite.config.ts
├── notebooks/
│   └── Project Movie Recommender(Rule-Based).ipynb  # Jupyter notebook for exploration
└── requirements.txt                  # Python dependencies

Technologies Used

Frontend

• React 18
• TypeScript
• Tailwind CSS
• Vite
• lucide-react (icons)

Backend

• Python 3.8+
• Flask
• Flask-CORS
• Pandas
• scikit-learn (CountVectorizer, cosine similarity)
• NumPy
• Requests (TMDB API calls)

Prerequisites

• Python 3.8+
• Node.js and npm
• Git

Installation and Setup

1. Clone the Repository

git clone <repository-url>
cd Movie-Recommendation-System

2. Backend Setup

Create and activate a virtual environment:

# Using venv
python -m venv venv

# On Windows
.\venv\Scripts\activate

# On macOS/Linux
source venv/bin/activate

Install Python dependencies:

pip install -r requirements.txt

Start the Flask server:

cd backend
python movie_recommender_api.py

The backend will start at: http://localhost:5000

3. Frontend Setup

Open a new terminal, then:

cd frontend
npm install
npm run dev

The frontend will start at: http://localhost:5173

Usage

1. Open your browser and go to http://localhost:5173
2. Type a movie title in the search bar and press Get Recommendations
3. The system returns 5 similar movies with posters fetched from TMDB
4. Click the info button on any movie card to see overview, cast, trailer, and similar movies

API Endpoints

Method	Endpoint	Description
POST	/recommend	Returns 5 recommended movies + poster URLs for a given movie_title
GET	/api/movie/<id>	Returns full movie details (overview, cast, genres, trailer, similar movies) from TMDB

Example request to /recommend:

POST http://localhost:5000/recommend
Content-Type: application/json

{ "movie_title": "The Dark Knight" }

Troubleshooting

• Port conflicts: Make sure ports 5000 (Flask) and 5173 (Vite) are free
• Movie not found: The movie title must exactly match a title in processed_movies.csv
• Missing packages: Re-run pip install -r requirements.txt (backend) or npm install (frontend)
• Python version: Python 3.8 or higher is required

Development Notes

• The Flask backend runs in debug mode by default (app.run(debug=True))
• The Vite dev server supports hot module replacement
• Use Ctrl+C to stop either server

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

License

MIT License

Copyright (c) 2025 Adnan

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Note

This is a development setup. For production deployment, additional configuration and security measures are required (e.g., move the TMDB API key to an environment variable, disable Flask debug mode, add HTTPS).