Nathabee Creations

Projects

  • 🐝 BeeLab — Dockerized Multi-Service Stack

    beelab Project Portal

    Welcome to BeeLab, my experimental platform for integrating multiple technologies into a single Dockerized environment.
    The project is open source: GitHub – nathabee/beelab

    Github Documentation : https://nathabee.github.io/beelab/index.html

    🔧 What’s inside BeeLab?

    BeeLab runs four main services, each in its own Docker container:

    • Django API (Python 3.12, Gunicorn)
      Core backend for data models and API endpoints.
      → Swagger API Explorer
    • WordPress (Dockerized)
      A separate WP instance to showcase custom plugins and theme integration.
      → BeeLab WordPress
    • Databases
      PostgreSQL for Django and MariaDB for WordPress.

    🔌 Custom WordPress Plugins

    BeeLab includes three original plugins that extend WP with features tied to the Django backend:

    1. BeeFont WP
      WordPress plugin to create your own font using SVG or PNG editor
    2. PomoloBee WP
      Connects to the PomoloBee module inside Django and displays farm/field data.
    3. Competence WP
      Adds competence-related content and interacts with Django data.

    🌍 Why Docker?

    • Each service is containerized and isolated.
    • Easy to run locally, or deploy to a VPS.
    • Clear port mapping for testing (Django 9001, Web 9080, WP 9082).
    • Can later be placed behind Apache/Nginx + HTTPS with subdomains.

    🚀 Try it out

    👉 This project is still work in progress, but the basic stack is up and running.
    Feedback and ideas are very welcome!

  • competence app projet

    Overview

    This project is a Django-based system designed to manage and evaluate student competencies through various assessments. It stores student data, tracks evaluation results, and analyzes progress over time. It integrates with a REST API, enabling access by an Android application.

    Project Demo

    A demo version is available on GitHub Pages.

    This demo showcases the frontend, compiled as static files and deployed to GitHub Pages, using mock data and simulated API calls:

    • Data: Demo data is static and may appear incoherent, as it’s not connected to a real database.
    • Backend: All API requests are mocked—no Django backend, MySQL database, or JWT authentication is used. Axios and other services are simulated.

    Note: This demo is for frontend display purposes only, with no real database interactions.

  • Plugins that I used regularly

    Fluent form

    Fluent SNMP

    Media Sync

  • PomoloBee

    “PomoloBee – Bee Smart Know Your Apple”
    PomoloBee is an AI-powered tool that helps farmers estimate apple yield using image or video analysis.
    This repository contains the mobile app, backend server, and ML microservice for end-to-end deployment.

    PomoloBee Logo

    Documentation

    ✅ For full documentation, see the GitHub Pages Portal

    Project Definition PomoloBee Bee Smart Know Your Apple

    Goal

    Develop an Android app (Kotlin + Android Studio) that allows farmers to estimate apple harvest yield using AI-based video or image analysis. The system will use a cloud-based backend (VPS) to process data and provide accurate results.

    PomoloBee App Android Fruit Detection App

    PomoloBee is an Android app for image-based fruit detection in orchards.
    It lets users capture or upload a photo, tag it with field and row location, and analyze it locally or remotely using a Django + ML backend.

    • Works offline with local model
    • Supports field/row selection via interactive SVG maps
    • Uses Jetpack DataStore, custom config sync (cloud or local), and stores photos in SAF-accessible folders.

    ️ App Screenshots

    Data Flow in PomoloBee

    The following diagram illustrates the interaction between the PomoloBee App, Django Backend, and ML Processing Service.

    Features Functionalities

    1 Mobile App Frontend Android

    📱 User Actions:
    ✅ Record or Upload Video – User walks through the orchard while capturing video.
    ✅ Take a Picture – Alternative to video for quick analysis.
    ✅ Mark Orchard Parameters – Farmer defines start and end of a tree row.
    ✅ Enter Field Data – Total orchard row length, tree count, sample apple size.
    ✅ Receive Harvest Estimate – Displays apple count and estimated yield.
    ✅ Local AI Estimation (NEW – Phase 2) – Farmers can analyze images offline using on-device AI.
    ✅ Manual Override of AI Results (NEW – Phase 2) – Farmers can manually adjust apple count & weight.
    ✅ Historical Tracking (NEW – Phase 3) – Compare past yield estimations.

    🔧 Tech Stack:

    • Language: Kotlin
    • Networking: Retrofit (API calls to VPS)
    • UI: Jetpack Compose
    • Local AI Processing: OpenCV + TensorFlow Lite (Phase 2)

    2 Cloud Backend VPS Django or Flask API

    🌐 Server Responsibilities:
    ✅ Receive video/image uploads from the app
    ✅ Extract key frames from video
    ✅ Apple Detection & Counting (AI Model)
    ✅ Calculate Total Yield Estimate
    ✅ Return Results to the App