In this article, we will explore how to create, build and deploy every component behind this Bikes recommendation website: 🔗 demo 🎯 Goal High-level The idea behind this project is to test the opportunity to build a recommendation system using , (ML) models, and only resources. public data unsupervised machine learning free To achieve this we will: Leverage to gather the data Reddit as ML framework Spacy transformers to run the ML model, to host the back-end, to host the front-end Google Colab Heroku GitHub pages Implementation is a mountain bike ( ) recommendation system. In other words, you could choose a bike brand or model that you like and then the system will suggest 3 bikes considered interesting and related to your chosen bike. Garrascobike MTB when people talk about some on the same subreddit thread those bikes . So we could extract the names and/or brands from one thread’s comments and intersect that information with other Reddit threads with similar bikes. The idea behind the recommendation system is: Bikes should be related in some way Bike’s 📢 Foreword The goal of this guide is to chase all the aspects involved in the creation of a Web App that serves a simple recommendation system, . trying to keep the complexity level lower as possible So the technical level of this experiment won’t be too deep and we don’t follow industrial-level best practices, nevertheless, this is the guide I would like to have one year ago before starting a simple project: create a WebApp with an ML model at is core. 🗺️ Roadmap What we will do could be summarized in the following steps: Download text comments from Reddit 🐍 Extract interesting entities from the comments 🐍🤖 Create a simple recommendation system model 🐍🤖 Deploy the model on a back-end 🐍 Create a front-end that expose the model predictions 🌐 🐍 = blocks that use Python 🤖 = blocks with Machine Learning topics involved 🌐 = blocks that use HTML, CSS & Javascript 🚵‍♀️ Garrascobike The project is structured in five major sections: scraping, entities extraction, ML train, back-end, front-end (and they coincide with the five chapters of this post). On the above image, the first four sections (front-end is excluded) are reported with their dependencies and where necessary the platform where the code should be executed. Let’s dive deep into each component. 1. Download text comments from Reddit 🐍 Intro First of all: we need the data and Reddit is an amazing social network where people talk about topic. Moreover, expose some API that python packages like could use to scrape the data. any Reddit praw Prerequisites We need to choose some that talk about the topic on which we would create the recommendation system subreddits In we want to build a Mountain Bike recommendation system, so we need subreddits that talk about those bikes. Garrascobike The subreddits selected for this PoC project are: Whichbike, MTB, Mountainbiking, Bicycling How to 👨‍💻Code to use hosted on Github: subreddit-comments-dl Follow this guide to download Reddit comments: https://towardsdatascience.com/how-download-subreddit-comments-f79557c99170 More information is also on my blog: https://www.pistocop.dev/posts/subreddit_downloader/ Outcome Two CSV files with all the information about the subreddit comments. We will create two CSV files, if you are not familiar with Reddit could be useful to read this little glossary comments.csv Contain all the comments with related information, e.g. the comment’s text, the creation date, the and thread where the comment was written subreddit submissions.csv Contain all the information about the : a submission is a post that appears in each subreddit submissions 2. Extract interesting entities from the comments 🐍🤖 Intro Entities are words or sections of a text that belong to some pre-defined “family” e.g. from a text like “ ” we would like to extract something like "2019” is a and "Enduro” is a I have a 2019 Enduro DATE PRODUCT The entities extraction is related to an ML task called Named Entity Recognition (NER), for more examples see the official Spacy webpage So this block get the Reddit comments and will extract the Bikes names contained within them ( ) extraction.parquet That information will be uploaded to an instance Elasticsearch Prerequisites The comments extracted from the desired subreddit How many comments? More comments are directly proportional to better performances For this project, we have downloaded comments from 800.000 103.159 submissions How to 👨‍💻Code to use hosted on Github: garrascobike-core Extract the entities Steps Clone the repository https://github.com/pistocop/garrascobike-core Copy the scraped data under /data/01_subreddit_extractions/ Run the extraction with the script /garrascobike/01_entities_extraction.py Under the hood the script will use system with a ML model Spacy NER spacy-transformers 💡Tip: use this to run the above tasks Colab Notebook Because the process will use a machine-learning model it’s suggested to execute the code on a machine with a GPU. If you aren’t rich like me there is a big chance that you haven’t a powerful GPU for ML tasks, so use the above notebook and leverage the Google GPU’s for free 💘 Load on Elasticsearch What and why is a documental NoSQL database that could store data even if we don’t know the data structure Elasticsearch Why we should use Elasticsearch (ES)? It wasn’t mandatory to use this technology if we remain in the zone, and a python script could be enough to do the same job required for this project. POC However, use ES pledge us with the opportunity to be ready if we go out of the POC and/or we want to reuse the data in different ways for other projects. ES was used during the first data scouting phase, when wasn’t clear which data the ML model has extracted and if it’s possible to shape that information to our requirements Using ES on this scouting phase was very helpful to data discovery and express queries like “ ”, “ ” or “ ” how many threads exist with at least 1 product entity get all the entities typer list all the PRODUCT entities Run the Elasticsearch service locally Install Docker Following to run a mini-cluster locally, the cluster is suggested this guide 01_single-node Load the data Run the script script providing the parquet file and the ES endpoints /garrascobike/02_es_uploader.py # folder garrascobike-core
$ python garrascobike/02_es_uploader.py --es_index my_index \
                                        --es_host http://localhost \
										--es_port 9200 \
                                        --input_file ./data/02_entities_extractions/extraction.parquet 3. Create a simple recommendation system model 🐍🤖 Intro The parsed comments are now associated with text snippets named from the entities extraction process product After a look at those snippets, we could see that the entities here are the Bikes names and brands that users on subreddits talk about product 🦂 Of course there is also noise labelled as e.g. “canyon/giant” is a snippet that contains two brands and should not be included and/or split into two entities “canyon” and “giant”. Moreover, “12speed” isn’t a brand but the number of gears of the bike. product, For this toy project, we don’t care about the noise, so the predictions may result in suggesting bikes that don’t exist. In a real-world scenario, we should apply some revision to those products, e.g. search each brand on an images search engine and see if a bike picture is returned. Nevertheless, some brands cleaning and filtering is actually performed - code link The recommendation system will be built around the intersection of bikes brands and the comments threads to which the comments belong We will create a semifinished product: (new code version store a file named this file contain the entities found for each subreddit correlations.npz presences.csv), product thread In the last step, will train ad store an AI model based on the KNN algorithm able to make the bikes recommendations process 04_recommendation_trainer.py More about this process could be found in those good articles: https://towardsdatascience.com/how-did-we-build-book-recommender-systems-in-an-hour-part-2-k-nearest-neighbors-and-matrix-c04b3c2ef55c https://www.analyticsvidhya.com/blog/2020/11/create-your-own-movie-movie-recommendation-system/ Finally, we will upload by hand (no automatic script provided) the files create by 04_recommendation_trainer.py Use is suggested and supported by the next chapter section, it provides 10 GB of storage for free backblaze.com Prerequisites An ES instance running with the entities extracted products An account on backblaze.com How to 👨‍💻Code to use hosted on Github: https://github.com/pistocop/garrascobike-core Run the script, parameters: 03_correlation_extraction.py the Elasticsearch instance address es_host: the Elasticsearch instance port es_port: the Elasticsearch indexes names. They could be more than one because it’s possible to join more entities extraction. es_index_list: However, for this guide, we could use only one parameter: my_index. $ python garrascobike/03_correlation_extraction.py --es_host localhost \
                                                   --es_port 9200 \
                                                   --es_index_list my_index Run the script, parameters: 04_recommendation_trainer.py : the path to the file created by presence_data_path 03_correlation_extraction.py the model to use to build the recommendation system, only is currently available ml_model: knn : where to save the recommendation system model files, those files should be then uploaded on output_path backblaze $ python garrascobike/04_recommendation_trainer.py --presence_data_path ./data/03_correlation_data/presence_dataset/20210331124802/presences.csv \
                                                   --output_path ./data/04_recommendation_models/knn/ Outcome The recommendation system model trained to predict bikes suggestion from one bike name provided All the recommendation model files should be uploaded on Backblaze 4. Deploy the model on a back-end 🐍 Intro To make the recommendation we need a machine that runs the prediction model We will wrap the model inside a back-end that: Is in Python and use framework FastAPI Run on free Dyno as the hardware provider heroku Run for 30m and then go to sleep mode If it’s in the sleep node, need ~1m to wake up and be ready to use The back-end will expose those API: that take a bike name and return 3 bikes suggested /recommender/{bike_name} that return the list of supported bike names /brands/available that returns a timestamp and will be used to check if the back-end is up and in running state /health Prerequisites We need the list of bikes that the recommendation system model can manage: brands.csv The recommendation model that the back-end will use to make the predictions How to 👨‍💻Code to use hosted on Github: https://github.com/pistocop/garrascobike-be Set the correct credentials and path according to your backblaze account: To set the Backblaze bucket name and path modify the file at: /garrascobike_be/ml_model/hosted-model-info.json To set the Backblaze connection credentials modify and rename to the file: .env /garrascobike_be/.env_example Create an account on and publish the back-end: follow this guide for more detailed information: heroku 🔗 https://towardsdatascience.com/how-to-deploy-your-fastapi-app-on-heroku-for-free-8d4271a4ab9 Outcome A back-end up and running on Heroku, you could get the API URL from the “open app” button inside the Heroku webpage The back-end openAPI specification could be found under and will look like this: https://<heroku-app-url>/docs Note: you may need to wait ~1m before seeing this OpenAPI specification 5. Create a front-end that expose the model predictions 🌐 Intro We need a website that the user could use to choose the desired bike and receive the suggestions The website must use the API exposed by the back-end However, for this reason, the FE need to show some kind of “wait disclaimer” while Heroku wake up the App The FE should also provide succinctly the list of bikes that the user could choose from We will build an autocomplete text field using the amazing framework autoComplete.js We will create a static website with HTML, CSS & JS The website is then hosted for free on GitHub Pages Prerequisites The Garrascobike back-end up and running online The URL of the back-end program to run the website locally - live-server npm How to 👨‍💻Code to use hosted on Github: https://github.com/pistocop/garrascobike-fe Steps to set up a new front-end Fork the project Replace with your back-end URL this line Commit the changes Follow to enable the GitHub pages service on your repository (note: the repo must be public!) this guide Follow the link generated by the GitHub interface Enjoy your new front-end! Outcome Demo of the website: https://pistocop.github.io/garrascobike-fe/ The website you will see should resemble like this: 🎌 Other languages All the project is built and work for English Subreddits Other languages could be supported, the only section that requires a change is the spacy model in the entities extraction task https://github.com/pistocop/garrascobike-core Spacy transformers models of different languages could be found here: language support 💭 Final Thoughts We have just seen how to build a recommendation system from the scraping process to a WebApp. All the data are scraped from Reddit and processed with unsupervised ML models (Spacy), using the Google Colab platform. This has saved us a lot of work and supplied a straightforward path that could be automatized also. Finally, a back-end free hosted on Heruko and a front-end free hosted on GitHub pages complete the project thanks to a web app page. From here, a lot of work could be done to refine each of the components, e.g. if we want to move from a POC project like this to production and more serious system we should: Filter and remove all the entities extracted that aren’t bikes Scrape and process more data Build and test different parameters or families for the recommendation system As final, I want to say that it was nice to build this project, I haven’t experience with the front-end world and the simple website is made after following free course. theodinproject.com 🍺 And now, after completing every component and the is up and running, I must admit that I feel satisfied, hope you’ve enjoyed the journey! website **📧 Found an error or have a question? let’s [connect](https://www.pistocop.dev/)** This article was first published here: https://www.pistocop.dev/posts/garrascobike/

How a recommendation system Web App was Built

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