NextFlick

A Flask-based web application for exploring movies and receiving personalized recommendations powered by a content-based recommendation system.

Overview

This project combines web development and machine learning to create a movie recommendation platform. Users can search for movies, view detailed information, and get AI-generated recommendations. The system uses TF-IDF vectorization and cosine similarity to suggest movies based on combined features like genre, cast, and plot.

Key Features

• Movie Search: Find movies by title; automatically adds new entries via scraper if not found.
• Detailed View: Display movie metadata (year, rating, director, cast, plot, runtime, poster).
• Smart Recommendations: Get 10 similar movies using ML-based content filtering.
• Dynamic Scraping: Integrates with scraper.py to fetch and add new movies on-demand.
• CSV Backend: Uses movies.csv as a lightweight database with 250+ entries.

Technical Architecture

Core Components

• Backend: Flask (Python) for routing and business logic.
• Data Processing: pandas for CSV handling and feature engineering.
• ML Pipeline:
  • TF-IDF Vectorization
  • Similarity Scoring
• Subprocess Management: Executes recommendation script as separate process.

Try it out here at movie-scraper-and-recommendation.onrender.com.

Render takes a little bit to load, it's worth the wait :)