movie_recommendation

A full-stack, multiplatform, User ID-based movie recommendation system, powered by MovieLens retrieval and ranking models, and deployed via TensorFlow Serving in a Docker container. Backend technology utilizes Python's Flask package, while the multiplatform frontend is powered by the Google's Flutter Dart framework. This follows a Google Codelab tutorial.

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Building

Ensure you have the prerequisite packages:

cd ./frontend
flutter pub get

cd ../backend
pip install Flask flask-cors requests numpy
# or python -m pip install Flask flask-cors requests numpy

To run the Docker container on mapped ports 5000:5000, 5001:5001, run the following command (if on Windows, be sure to run the Docker Engine first!):

docker run -t --rm -p 8501:8501 -p 8500:8500 -v "$(pwd)/:/models/" tensorflow/serving --model_config_file=/models/models.config

Where $(pwd) is your current, to-be-expanded, working directory. For the development purposes on non-UNIX/non-LINUX (Windows) systems, you may replace this with your absolute path.

To run the Flask development server, execute the following command:

# Windows (via CMD/PS)
cd ./backend
flask --app recommender.py run

# Linux
export FLASK_APP=recommender.py
export FLASK_ENV=development
flask run

Running

To run the Flutter app, you could use any of the existing running configurations (in VS Code or IntelliJ files), or, via CLI:

cd ./frontend
flutter run --disable-analytics --verbose