Backend FitMeLook

Overview

This backend project is built with cutting-edge technologies to provide seamless and efficient services. This project leverages the power of NestJS, Node.js, Firebase and Cloud Firestore to create a robust and scalable backend solution for FitMeLook product app.

Technology Stack

• NestJS (Express): A progressive Node.js framework for building efficient and scalable server-side applications.
• Javascript (Node.js) & Typescript: The core programming languages used for building the application.
• Firebase & Cloud Firestore: For data storage and real-time database functionalities.

Installed Packages

• @nestjs/common
• @nestjs/config
• @nestjs/core
• @nestjs/platform-express
• @tensorflow/tfjs-node
• firebase-admin
• reflect-metadata
• rxjs

Prerequisites

Before running the project, ensure you have the following:

1. Seasonal Model: A machine learning model for seasonal analysis.
2. Face Shape Model: A machine learning model for face shape analysis.
3. MTCNN Model: A machine learning model for Multi-task Cascaded Convolutional Networks.
4. Google Cloud Service Account Key: Required for Firebase and Cloud Firestore integration.

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Setup Instructions

Step 1: Clone the Repository

git clone https://github.com/Bangkit-Project-FitMe/backend-cc.git

Step 2: Navigate to the Project Directory

cd FitMeLook-Backend

Step 3: Install Dependencies

npm install

Step 4: Configure Environment Variable

cat > .env << EOF
FIREBASE_SERVICE_ACCOUNT_PATH=<YOUR_FIREBASE_SERVICE_ACCOUNT_PATH>
MODEL_URL_SEASONAL=<YOUR_MODEL_URL>
MODEL_URL_FACE=<YOUR_MODEL_URL>
MODEL_URL_MTCNN=<YOUR_MODEL_URL>
EOF

Step 5: Run this project

npm run start

API Documentation

For detailed API Documentation, please visit the API_Documentation file.