@@ -12,6 +12,21 @@ The Skincare Recommendation System is a web-based application designed to provid
- Efficient backend services powered by FastAPI for quick and accurate recommendations.
- Scalable database solution using AWS DynamoDB for real-time data syncing and storage.
## Folder structure
This repository contains all the files and folder intended to run the application. Following is an over of each of them:
- .xlsx files contain processed dataframes that are required to be loaded into memory upon back-end server startup.
- .gitignore files contains the files that were deemed to be unecessary for sharing over a git repo. This includes, but is not limited to unprocessed datasets, cache files, python and node modules.
- README.md is this markdown file.
- The **notebook.ipynb** is main the work file which contains all the code that was written and tested before being incorporated into the final Python server.
- RNN-Classifier files contain the Amazon SageMaker notebook used to create the RNN model, both incomplete error approach and the final process that was incorporated.
- .joblib files are the saved machine learning models and label encoders that get loaded into memory upon server startup.
- script.sql is the final sql script used to extract a meaningful dataset from the conjoined scrapped database. This was then processed and uploaded to DynamoDB.
-**fastapi_server.py** is the main Python FastAPI server which is required to be executed.
- requirements.txt is the list of Python virtual environment modules required to execute the server.
- The .json and .js files are for the front-end application these are for configuration of the React.js server.
- src/ and public/ folder contain the code files for the JavaScript front-end server.
- skincare-buddy-rnn-tf is the tensorflow model extracted from SageMaker for loading during server execution.
## Getting Started
To get a local copy up and running, follow these steps:
