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rayshaun-code / repository
A Django-based movie recommender that uses a trained ML model with a Jupyter notebook for customization. Features an interactive web UI with auto-suggestions and supports both local setup and cloud deployment.
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This repository contains all the project files and necessary details about applications required to run the project on your local machine as well as host it as a Django Application on your Server/Domain.
| Title | Description | Link |
|---|---|---|
| Demo :movie_camera: | Sample Demo of MRS Hosted on free cloud PaaS | :point_down: Refer |
| Requirements :heavy_check_mark: | Requirements and essential links to get started with the project locally | :point_down: Refer |
| Model Training :small_red_triangle_down: | How the MRS was trained for Demo as well as on Large Movie Dataset from Kaggle | :point_down: Refer |
| Project Versatility :page_with_curl: | Reference documentation of how to plug in any general recommendation model into this project and host it on servers | :point_down:Refer |
| Troubleshooting Issues :muscle: | Guide to resolve errors faced during reproducibility | To be Updated |
Do you like it? :heart: Follow me on Twitter, GitHub, & LinkedIn to say Hi :wave:
In this section, we try to understand through video demo to play around the project and what all can be achieved through it.
Sample Screenshots
Home Screen
Navigation Screen
Search with Auto Suggestion
Recommended Movies
Please be slightly patient while I create and upload the demo video. Follow and star this project to get latest notifications and update. :raised_hands:
To build this project without any errors/issues, the following requirements needs to be satisfied
Create a Virtual Environment using python>=3.8 (Tested on 3.9.16)
Install the dependencies from the requirements text file from the repository.
For complete guide to training your model and inference using the trained model, refer to "Movie Recommendation System Python Notebook".
Here is a detailed blog explaining about complete approach and directory structure essential to understand Django integration.
Here is a detailed blog explaining about complete approach and essential details to deploy not just this application but also any other web-application you like to built.
I am assuming you have completed section 2 in the above reference for creating your environment. Let's start by activating it.
/path/to/env/bin/activate
Once done, you should go to project root directory and run the following command
python manage.py runserver
It will take a moment and then show the following output on the terminal.
You can now open your browser and hit the server IP http://localhost:8000 provided to run the demo on your local system.
By default, this project will run on Demo model. If you wish to change model, you can train and download the model of your choice using the python notebook to get better or faster recommendations. Once trained, you can integrate by modifying these 2 lines of code inside recommender/views.py
Line 5 : movies_data = pd.read_parquet("static/<dataset_name>.parquet")
Line 73: model = pa.parquet.read_table('static/<model_name>.parquet').to_pandas()
Note that you have to place dataset and model into the static directory.
This code implements a movie recommendation system based on user input. The system provides a simple web interface built on HTML, CSS, and JavaScript libraries.
Inputs: The user can search for movies by providing a partial or complete movie name.
Outputs: The system provides movie recommendations based on user input.
Dependencies:
static/recommender/ -- contains the following CSS files: cursor.css, page.css, and navbar.cssstatic/logo.png -- the logo of the applicationstatic/production ID_4779866.mp4 -- a background video for the web page@tabler/icons@latest/iconfont/tabler-icons.min.cssnormalize/5.0.0/normalize.min.cssjquery-ui.cssfont-awesome.min.cssbootstrap.min.cssjquery.min.jsjquery-ui.jsUsage:
Note: Only the top 2.5K movies based on IMBD are present in this system's database.
Working on version 2 of movie recommendation system on new repository which can process 1 million movies within similar memory footprints, better recommendations, and diverse selections, with added features like recommendation bucket and mutual sharing. Stay tuned and do not forget to start the repository to reach out to open-source community.