This repository contains a machine learning application for recommending Netflix shows based on user preferences. The application is built using Python and Flet for the frontend.
- Python 3.8 or higher
- Poetry for dependency management
- Git (optional, for cloning the repository)
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Clone the Repository:
git clone https://github.com/your-repo/mlops-netflix.git cd mlops-netflix -
Install Dependencies with Poetry:
poetry install
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Activate the Virtual Environment:
poetry shell
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Run the Application:
flet --web -p 8080 ui.py
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Clone the Repository:
git clone https://github.com/your-repo/mlops-netflix.git cd mlops-netflix -
Create a Virtual Environment:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts�ctivate`
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Install Required Python Packages:
pip install -r requirements.txt
If there is no
requirements.txt, manually install the packages:pip install pandas flet scikit-learn faker cryptography
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Run the Application:
flet --web -p 8080 ui.py
env.sh: Shell script for setting up environment variables.gallery.py: Handles the gallery view of movie recommendations.gallery-tests.py: Contains tests for the gallery module.like.py: Manages the like/dislike functionality for movie recommendations.login.py: Manages user authentication via OAuth (e.g., AWS Cognito).machine_learning_utils.py: Contains machine learning utilities, including data preprocessing, model training, and evaluation.movie_db_utils.py: Provides utility functions for interacting with the movie database.poetry.lock: Generated by Poetry, locks the project to specific versions of dependencies.pyproject.toml: Contains project metadata and dependencies configuration for Poetry.ui.py: The main entry point of the application, where the Flet app is initialized and run.
To run tests, use the following command:
pytestIf you encounter issues with missing dependencies or modules not being found, you may need to install them manually using pip, as described in the manual installation section.