Movie Recommendation System

This project presents a comprehensive movie recommendation and information retrieval system built using Flask, DeepFace, and the TMDB API. The goal is to create an interactive platform that offers movie enthusiasts a personalized and immersive movie-watching experience. The system allows users to explore trending movies and TV series, search for movies by name, and receive genre-based recommendations. These features are powered by the TMDB API, which provides access to detailed movie information such as cast, director, screenplay, reviews, and trailers. One of the key highlights of this project is its integration with DeepFace, a facial emotion recognition tool. By analyzing the user's facial expression, the system provides emotion-based movie recommendations, matching specific emotions with genres. This personalization allows for a more engaging user experience, as users receive suggestions tailored to their current mood. In addition to emotion-based recommendations, the system supports scene-based movie searches. Users can search for films by iconic scenes, making it easier to find specific movies based on memorable moments. This feature further enhances the platform's ability to cater to different user preferences, whether they are looking for a specific genre, mood, or scene. The user interface is designed with Flask and Bootstrap, ensuring a responsive and aesthetically pleasing experience across devices. With features such as movie search, trending lists, and detailed movie information, the system offers an all-in-one solution for movie discovery and enjoyment. By combining real-time emotion recognition with data-driven recommendations, this project aims to redefine the way users engage with movie content, making the process more intuitive and enjoyable.