OverDose Server - Django Processing Pipeline

A Django + Django REST Framework backend that powers a multi-step processing pipeline for scans, risk assessment, and recommendations.

Overview

This project was developed as part of coursework at Esprit School of Engineering. It provides a structured API for managing users and products, running scans, assessing risks, and returning recommendations through a clear pipeline.

Features

• User management with custom user and allergy mapping
• Product catalog with categories and extraction methods
• Scan API that orchestrates the pipeline and stores scan data
• Risk assessment API with persisted results
• Recommendation API tied to scans and risks
• Simple dashboard view and summary endpoint

Tech Stack

• Python 3.13
• Django 6
• Django REST Framework
• SQLite

GitHub Repository Metadata (Suggested)

Description: Backend Django API for scan, risk, and recommendation pipeline built for a university project.

Topics: python, django, django-rest-framework, api, backend, sqlite, web-development

Directory Structure

App_Django/
  config/                  # Project config (settings, urls)
  users/                   # Custom user model + allergies
  products/                # Product catalog model
  scan/                    # Scan model + scan API endpoint
  risk/                    # Risk API endpoint + persisted risk results
  recommendation/          # Recommendation API endpoint + persisted recommendations
  dashboard/               # Simple UI + dashboard API summary
  templates/dashboard/     # Dashboard HTML template
  requirements.txt
  manage.py

Apps and Responsibilities

users

• User custom model (AUTH_USER_MODEL)
• Allergy list model
• UserAllergy mapping table (many-to-many through model)
• API endpoints to list/create users, allergies, and user-allergy mappings

products

• Product model with category (food, cosmetic)
• Extraction method (lens, barcode)
• Ingredients as JSON
• API endpoint to list/create products

scan

• Scan model with optional user and image
• POST /api/scan/ endpoint:
  • creates scan
  • builds ingredient list
  • triggers risk assessment
  • triggers recommendation generation
  • returns a combined response

risk

• POST /api/risk/ endpoint
• Input: scan_id, ingredients
• Output: risk list with levels (low, medium, high)
• Persisted models: RiskAssessment, RiskItem

recommendation

• POST /api/recommend/ endpoint
• Input: scan_id, risks
• Output: recommendations (product, reason)
• Persisted models: RecommendationBatch, RecommendationItem

dashboard

• Browser dashboard at /
• Summary API at /api/dashboard/summary/

Database Schema (High-Level)

• users.User 1--* scan.Scan
• users.User -- users.Allergy through users.UserAllergy
• scan.Scan 1--1 risk.RiskAssessment
• risk.RiskAssessment 1--* risk.RiskItem
• scan.Scan 1--1 recommendation.RecommendationBatch
• recommendation.RecommendationBatch 1--* recommendation.RecommendationItem

Getting Started

1. Install dependencies:

python -m pip install -r requirements.txt

1. Create and apply migrations:

python manage.py makemigrations
python manage.py migrate

1. Optional: create admin user:

python manage.py createsuperuser

1. Start server:

python manage.py runserver

1. Open:

• Dashboard: http://127.0.0.1:8000/
• Admin: http://127.0.0.1:8000/admin/

API Endpoints

• GET/POST /api/users/
• GET/POST /api/users/allergies/
• GET/POST /api/users/user-allergies/
• GET/POST /api/products/
• POST /api/scan/
• POST /api/risk/
• POST /api/recommend/
• GET /api/dashboard/summary/

Postman Collection

• Collection file: postman_collection.json
• Includes all API endpoints with ready-to-run sample payloads

Import steps:

1. Open Postman and click Import.
2. Select postman_collection.json.
3. Ensure baseUrl is set to http://127.0.0.1:8000.
4. Run requests in this order for quick demo: create user -> create allergy -> create user allergy -> scan pipeline.

Example: POST /api/scan/

Request body (optional fields):

{
  "user": 1
}

Response:

{
  "scan_id": 1,
  "ingredients": ["ingredient_1", "ingredient_2", "ingredient_3"],
  "risks": [
    {"ingredient": "ingredient_1", "level": "low"},
    {"ingredient": "ingredient_2", "level": "medium"},
    {"ingredient": "ingredient_3", "level": "high"}
  ],
  "recommendations": [
    {
      "product": "Alternative for ingredient_1",
      "reason": "Recommendation based on the assessed risk level."
    }
  ]
}

Acknowledgments

Completed under academic guidance for coursework at Esprit School of Engineering.