Movie Recomendation System with Sentiment Analysis

About

Epic Movie Zone is an AI based web application in which you can search for any Hollywood Movie. This application will provide all the information related to that movie, does sentiment analysis on the movie reviews and the most interesting part, this application will provide you the top 15 movie recommendations based on your search. Also it fetches the data of Popular/Up-coming/Top-rated Movies from The Movie-Database through API. (As we are fetching the latest data so it may/may not bo present in the dataset we used, in case of absence it will pop-up as "This data is not in our database")
Also it tracks the activiity of user in browser, it adds the movies watched by user into the Local Storage so that next time when user visits the site, if fetches the data from Local storage & shows to User and provided movie-suggessions based on previously watched movie.
Also It provides User-Authentication through Google & Facebook, Every time user logs into the site it adds the data to firebase Database.

ReactJS is used for frontend which is deployed using Firebase and a Flask APIis deployed using Docker container on Heroku to serve the machine learning models to the Frontend.

This application uses Content Based Movie Recommendation (Through Cosine Similiarity Matrix) to recommend movies to the user.TMDB API is used to retrieve all the information related to the movie and its cast. Web Scraping was done on IMDB website to get the reviews related to the searched movie. Sentiments analysis is done using a machine learning model trained on a sample of IMDB Dataset.

Deployed Web Application Link: https://movie-recommender-system-3408e.web.app/
Deployed Flask API Link: https://epic-mrs-api.herokuapp.com/
Youtube Demo Link: https://www.youtube.com/watch?v=O6cX2WNxVBc

Demo

Architecture

How to generate TMDB API Key?

1. Login to you your tmdb account: https://www.themoviedb.org/ or create one if you dont have.
2. Then open https://www.themoviedb.org/settings/api link and create your api key by filing all the necessary information.
3. IMPORTANT: After generating the TMDB API KEY, replace "ENTER YOUR TMDB_API_KEY" with your generated key in the server directory (app.py File) and client (src->utils.js) code.

TMDB API End Points

1. BASE URL: https://api.themoviedb.org/3
2. FOR MOVIE DATA: https://api.themoviedb.org/3/movie/{tmdb_movie_id}?api_key={TMDB_API_KEY}
3. FOR MOVIE CAST DATA: https://api.themoviedb.org/3/movie/{tmdb_movie_id}/credits?api_key={TMDB_API_KEY} NOTE: Please do refer the documentation at the BASE URL for better understanding.
4. For Trending DATA: https://api.themoviedb.org/3/movie/popular?api_key=${TMDB_API_KEY}
5. For Top-Rated DATA: https://api.themoviedb.org/3/movie/top_rated?api_key=${TMDB_API_KEY}
6. For Up-coming DATA: https://api.themoviedb.org/3/movie/upcoming?api_key=${TMDB_API_KEY}

Flask API end points

1. To get recommendations: https://epic-mrs-api.herokuapp.com/recommend_movie

Data to be sent in POST request:
{
    movie_name:"The Avengers",
    number_of_recommendations:"15"
}

Data Returned by the API in JSON format:
{
    input_movie:{
        movie_id:TMDB_MOVIE_ID
    },
    recommendations:[
        {
            rank:1,
            movie_id:TMDB_MOVIE_ID
        },
        {
            rank:2,
            movie_id:TMDB_MOVIE_ID
        },
        .
        .
        .
    ]
}

2. To get Movie Reviews with Sentiments: https://epic-mrs-api.herokuapp.com/movie_reviews_sentiment

Data to be sent in POST request:
{
    movie_imdb_id:"MOVIE_IMDB_ID"
}

Data Returned by the API in JSON format:
[
    {
        id: 1,
        content: "THE REVIEW",
        sentiment: "SENTIMENT FOR THE REVIEW"
    },
    {
        id: 2,
        content: "THE REVIEW",
        sentiment: "SENTIMENT FOR THE REVIEW"
    },
    .
    .
    .
    10
]

NOTE: The error messages are returned in the following format:

{
    error:"Content of ERROR Message"
}

Steps to run the React Project

1. Clone or download the repository in your local machine.
2. Open command prompt in the following folder client
3. Install all the npm packages

npm install

4. Since the Flask API is already deployed on Heroku no need to run the Flask API in your local machine to start the React frontend. You can start the react application using the following command:

npm start

Steps to run the Flask API

1. Clone or download the repository and open command prompt in server folder.

2. Install all the dependencies

pip install -r requirements.txt

3. Run the app.py file

python app.py

The API will be running at http://127.0.0.1:5000/

NOTE: You can run the Flask API and the React Frontend in parallel and can use for development by replacing the baseURL, present in client/src/api/recommenderapi.js, with the Flask API running link.

Steps to run the Jupiter Notebooks

NOTE: you need to download the following datasets from the given links and need to add them recommendation-engine-notebooks/data directory to be accessible for notebooks NOTE: python 3.8 or earlier (i used python 3.7.3 to get tmdbv3 api package)

1. movies_metadata_2016
2. credits_2017
3. movies_metadata_2017
4. Clean and Lemmatized Dataset
5. IMDB 50k Movie reviews to create clean& lemmatized dataset
6. Remaining Datasets are generated using MovieRecommendationDatasetPreparation.ipynb in MovieRecommendationCodes folder

Steps to Dockerize and Deploy the Flask API on Heroku

1. Clone or download the repository and open command prompt in server folder.
2. Create your docker account at https://hub.docker.com
3. Download the docker desktop based on you windows version from the official website of Dockers and login to the docker desktop.
4. Start the Docker desktop in you machine.
5. The Dockerfile for dockerinzing this Flask API is already present in the Server folder.
6. Open command prompt in Server folder and run the below mentioned commands:
7. Building the Image:

docker build -t ENTER_YOUR_OWN_TAG_NAME .

It will take some time for the execution of the above command. After execution of the above command you can see the docker image details using the following command:

docker images

8. Install Heroku CLI in your local machine.
9. Login to your account using follwing command:

heroku login

10. Run the following commands for deplyment. Logging into heroku container:

heroku container:login

11. Create a app in heroku:

heroku create YOUR_APP_NAME

11. Pushing the docker image into heroku:

heroku container:push web --app YOUR_APP_NAME

12. Releasing the web app:

heroku container:release web --app YOUR_APP_NAME

That's it, you can see your API running at https://YOUR_APP_NAME.herokuapp.com/

Steps to Deploy the React-App on FireBase

1. open the command-prompt in client

2. Run the following command, this will make a build folder in client directory, use this as root folder later for deployment

npm run build

3. Install the firebase CLI

npm install -g firebase-tools

4. Initialize the project

firebase login
firebase init

5. Deploy the project

firebase deploy

Tech Stack Used

Referred Article Links

For Movie Recommendation System

1. Article 1
2. Article 2

For Deployment Using Dockers

1. Article 1
2. Article 2
3. Article 3
4. Article 4
5. Article 5

Please do ⭐ this repo if you liked.