Welcome to the Movie Recommendation Project:

Introduction:

Welcome to the movie recommendation system built by Ashish Vinodkumar and Leon Zhang. We built a clean and user friendly web interface that provides users with movie suggestions based on movies that they pick. The website is built using Flask application with NLP algorithms supporting the backend computations. The application also utilizes cloud services and placed great emphasis on continuous integration and deployment (CI/CD) allowing us to update and make changes effectively. For instructions on running, using the website and setting up continuous deployment on GCP, please look at the instruction sections below. At last, feel free to visit our presentation and demo videos listed below.

Live Website link: https://app-4yyborwtmq-uc.a.run.app/home

Note: The website could be slow due to the low-end configurations we setup to save cost and also the large amount to backend computations our recommendation engine uses

Youtube Links:

Project Overview and Cloud Architecture: https://youtu.be/TfDCWe--SGo

Live Demo and code walkthrough: https://youtu.be/ko5kjdIpnro

Running the web application locally:

Step 1: Clone github repository:

git clone https://github.com/leonz12345/Movie_Recommendation_Engine.git

Step 2:

cd Movie_Recommendation_Engine/

Step 3: Install all package dependencies.

make install

Step 4: Run Flask app locally: (Copy link displayed on console on your preffered browser)

python3 main.py

Website Navigation Instructions:

Step 1: Enter a movie title (or the keyword of that movie) that you like in the search bar and hit submit.

Step 2: This brings you to a new webpage of all the search results. If the search is not found, please click “Try again” to retry. If the search is successful, find the movie that you want and copy and paste it's IMDBid (on the right side of the movie title) to the search bar and hit submit to begin search. You could also check the movie summary by clicking the movie title. This will bring you to the IMDB website of that movie.

Step 3: After the computation is finished. You will be redirected to the recommendation result web page. The five movies shown on the page are the movies we recommended based on the movie you gave us. For more info, you could click on the movie title. This will bring you to the IMDB website of that movie.

Step 4: To recommend different movies, hit the “Try a different movie” button.

Set up Google Cloud Project:

Step 1: Create new gcp project:

Step 2: Check to see if your console is pointing to the correct project:

gcloud projects describe $GOOGLE_CLOUD_PROJECT

Step 3: Set working project if not correct:

gcloud config set project $GOOGLE_CLOUD_PROJECT

Step 4: Follow steps 1-4 under "Running the Web Application locally" to set up github repo and test Flask application:

Step 5: In the root project of the folder, replace PROJECT-ID below with your GCP project-id, and build the google cloud containerized flask application:

gcloud builds submit --tag gcr.io/<PROJECT-ID>/app

Step 6: In the root folder of the project, replace PROJECT-ID below with your GCP project-id, and run the flask application:

gcloud run deploy --image gcr.io/<PROJECT-ID>/app --platform managed

Step 7: Paste the URL link provided on the console, in your preferred browser to run the application!

Set up Continuous Integration/Continuous Deployment:

Step 1: Navigate to Github Actions and create a new workflow. Add a main.yaml file with the below specifications:

name: Movie Recommendation Engine

on: [push]

jobs:
  build:

    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v2
    - name: Set up Python 3.8
      uses: actions/setup-python@v1
      with:
        python-version: 3.8
    - name: Install dependencies
      run: |
        make install
    - name: Lint with pylint
      run: |
        make lint

Step 2: Open Cloud Run on GCP console:

Update Memory specification to 512 MiB.

Update Timeout specification to 500 seconds.

Save and Re-Deploy Cloud Run application.

Step 3: Open Cloud Build on GCP console:

Create a new trigger.

Specify github repository.

Triggered on Master branch.

Deployment specifications already available in: cloudbuild.yaml file.

Step 4: Test CI/CD: Update github repo by merging feature branch into master branch. This will automatically trigger Github actions to lint and test the code, along with triggering Cloud Build to deploy the code updates into the production flask container.

Cloud Architecture

The above diagram is the cloud architecture of our movie recommendation engine. Inside the cloud diagram we have our google cloud services listed: Storage Bucket, Compute Instance, Cloud Run, and Cloud Build. The storage bucket stores the movie dataset and the docker image of our website. The compute instances are where code files lie within the cloud platform. Cloud run sets up continuous deployment which allows us to deploy our website smoothly every time we make changes. Outside of cloud, our github repository set up github actions to allow us to continuously integrate our pushes into the repository.

Technologies Used:

Backend:

1. GCP Storage Bucket

2. GCP Compute Instance

3. GCP Cloud Run

4. GCP Cloud Build

5. NLP methods

Frontend:

1. Flask

2. HTML

3. CSS

4. JavaScript