Foodinary Backend API

A backend service for the Foodinary application, focusing on Indonesian traditional cuisine recipe prediction and management.

Overview

This API provides the following features:

• User authentication (register, login)
• Password reset via email
• Food prediction using machine learning (via Hugging Face)
• Recipe information retrieval
• User history and favorites management

Tech Stack

• Framework: Hapi.js
• Database: PostgreSQL (via pg)
• Authentication: JWT (jsonwebtoken)
• Validation: Joi
• Image Storage: Google Cloud Storage
• ML Integration: Gradio client for Hugging Face
• Other: bcryptjs, nodemailer, axios

Setup

Prerequisites

• Node.js (v18+ recommended)
• PostgreSQL database
• Google Cloud Storage bucket (for profile pictures)
• Gmail account (for sending reset emails)
• Hugging Face space deployment

Installation

1. Clone the repository
2. Install dependencies:

   npm install

3. Create a .env file in the project root with the following variables:

   GMAIL_USER=your-gmail@gmail.com
   GMAIL_PASS=your-gmail-app-password
   HOST=localhost
   GCS_BUCKET_NAME=foodinary-profile-picture
   GOOGLE_APPLICATION_CREDENTIALS=path-to-your-credentials.json
   DATABASE_URL=postgresql://user:password@host:port/database
   JWT_SECRET=your-secret-key
   HUGGING_FACE_SPACE=your-hugging-face-space
   FRONTEND_URL=http://your-frontend-url
   

4. Start the server:

   # Development mode
   npm run start-dev
   # Production mode
   npm start

API Endpoints

Authentication

• POST /register

  • Register a new user
  • Body: { email, name, password, confirmPassword }
  • Password must contain at least one uppercase letter and one number

• POST /login

  • Authenticate a user
  • Body: { email, password }
  • Returns: JWT token and user info

• POST /reset-password

  • Request a password reset (send email with token)
  • Body: { email }
  • To reset: { email, token, newPassword }

Food Prediction

• POST /predict
  • Predict food from an image URL
  • Headers: Authorization: Bearer <token>
  • Body: { imageUrl }
  • Returns: Prediction and recipe information

Favorites & History

• GET /favorites — Get user's favorite recipes (JWT required)
• PUT /favorites — Add or update user's favorite recipes (JWT required)
• GET /history — Get user's prediction history (JWT required)
• DELETE /delete-history — Delete user's prediction history (JWT required)

Data Structure

The application uses a recipes.json file containing detailed information about Indonesian dishes, including:

• Recipe name
• Dominant taste
• Region of origin
• Description
• Tools and ingredients
• Preparation steps
• Image URL

Deployment

The application can be deployed using Docker. A Dockerfile is provided for containerization.

# Build Docker image
docker build -t foodinary-backend .
# Run Docker container
docker run -p 3000:3000 --env-file .env foodinary-backend

License

ISC