Movie Recommendation System

This project implements a movie recommendation system using data from The Movie Database (TMDb) API. The system allows users to search for movies, view movie details, get similar movie recommendations, and explore cast details.

Features

• Search Movies: Users can search for movies by title.
• Autocompletion: Auto suggest films title based on user input.
• View Movie Details: Detailed information about each movie, including posters, overview, ratings, genres, release date, and runtime, is displayed.
• Similar Movie Recommendations: Based on the selected movie, users can get recommendations for similar movies.
• Explore Cast Details: Users can explore details about the cast of a movie, including their birthdays, biographies, and place of birth.

Architecture

Technologies Used

• Frontend: HTML, CSS, JavaScript, jQuery, Bootstrap
• Backend: Python, Flask framework
• API: The Movie Database (TMDb) API

Setup Instructions

1. Clone the repository: git clone https://github.com/KumaarBalbir/FilmFlow.git
2. Create a virtual environment using Conda: conda create --name filmflow-venv python=3.8
3. Activate the virtual environment: conda activate filmflow-venv
4. Install the required dependencies: pip install -r requirements.txt
5. Run the Flask application: python main.py
6. Open the browser and navigate to http://localhost:5000 to access the application.

Project Structure

• main.py: Main Flask application file containing route definitions and API integrations.
• static/: Contains static files such as CSS stylesheets and JavaScript scripts (recommend.js: JavaScript file for frontend functionality such as AJAX requests and event handling and autocomplete.js is for autosuggestion while user enters title name).
• templates/: Contains HTML templates for pages of the application.
• artifact: transform.pkl contains a serialized version of the TF-IDF vectorizer or text transformer used for text preprocessing and sentiment-model.pkl is serialized trained model for sentiment analysis, specifically a Multinomial Naive Bayes classifier.
• preprocess: Contains python scripts for data extraction and preprocessing of the movies details used in this project.
• sentiment-model: Contains script for training multinomial naive bayes model used for viewers sentiments.
• assets: Some project related resource.
• requirements.txt: List of Python dependencies required for the project.

Usage

1. Enter the title of a movie in the search box and click on search icon.
2. Select a movie from the search results to view its details.
3. Explore similar movie recommendations and cast details.
4. Enjoy exploring and discovering new movies!

🙂 Feel free to contribute, provide feedback, or suggest improvements to the project!