Hamed Essam is a highly skilled Full Stack Developer with 6+ years of experience in web and mobile development, specializing in Laravel, Flutter, Vue.js, and PHP. Proficient in building Android & iOS applications, cloud solutions with AWS, and scalable system architecture. Explore my portfolio, projects, and services.
Project Overview
Eco Up was developed as a graduation project for the Department of Computer Science at Northern Border University. The primary goal was to tackle the accumulation of recyclable waste on campus by shifting student behavior from passive awareness to active participation .
Unlike traditional recycling methods, Eco Up integrates Google ML Kit to automatically scan and classify waste (plastic, paper, etc.) via the phone’s camera. It features a complete ecosystem where students earn “Green Points” for every item recycled, which can be exchanged for real-world rewards like cafeteria discounts.
My Role & Contributions
As a Mobile Application Developer on this project, I was responsible for:
Full-Cycle Development: Participating in the analysis, design, and implementation phases using the Agile Scrum methodology.
- UI/UX Implementation: Building a responsive and user-friendly interface using Flutter (Dart), ensuring a seamless experience across Android devices.
- AI Integration: Implementing Google ML Kit (Vision API) to enable real-time object detection and automatic waste classification.
- Backend Integration: Connecting the app to Firebase for secure user authentication, real-time database management (Firestore), and handling the points/rewards logic.
- System Optimization: Ensuring high performance with low memory usage (peak 78MB) and battery efficiency.
Technologies & Tools Used
Framework: Flutter SDK 3.10.0+Β
Language: Dart 3.0.0+
Backend (BaaS): Google Firebase (Auth, Firestore)
Artificial Intelligence: Google ML Kit (Image Labeling)
IDE: Android Studio
- Version Control: Git
Key Features
π± Smart Scanner: Instant waste recognition using the camera to verify recyclable items.
π Gamification Engine: An algorithm that calculates and awards points based on waste type and quantity.
π Rewards Marketplace: A digital store where users can redeem points for coupons and discounts.
- π Impact Dashboard: Visual statistics showing personal contribution and environmental impact.
Project Outcomes
75% Increase in recycling participation rates during testing.
40% Reduction in waste sent to landfills.
- Successfully deployed a scalable architecture capable of handling high user loads with no memory leaks.
Wasaq Platform β GIS-based Endowment Management System
Overview
Wasaq is an advanced GIS-based Endowment Management System designed to optimize the planning, development, and analysis of endowment lands. This intelligent web platform offers real-time data visualization, AI-driven insights, and secure investor interactionβall in one place.
Built with scalability, usability, and security in mind, Wasaq empowers endowment institutions and stakeholders to make data-driven decisions with confidence.
Key Features
Interactive GIS Map: Visualize all endowment lands with real-time status updates using dynamic layers and filters.
Advanced Analytics & Reporting: Analyze land use, performance trends, and development potential using big data and visual dashboards.
AI-Powered Recommendations: Get smart suggestions for land development opportunities using machine learning models.
Investor Support Chatbot: Engage potential investors with an AI-driven assistant built on NLP frameworks.
Smart Filtering & Search: Seamlessly find properties and projects by location, status, category, or development stage.
Economic Insights: Leverage big data analytics for economic forecasting and decision support.
Technology Stack
Backend: Python, Flask
Frontend: HTML5, CSS3, JavaScript, Bootstrap
Database: PostgreSQL with PostGIS (for spatial data)
GIS Tools: GeoPandas, Folium
AI/ML: TensorFlow, scikit-learn, Transformers
Chatbot: ChatterBot, NLTK, spaCy
Data Visualization: Plotly, Matplotlib
Flutter Python Bridge β Run Python Natively in Flutter Apps
Flutter Python Bridge is a powerful Flutter plugin that enables seamless integration between Flutter (Dart) and Python. Whether you’re building apps that rely on machine learning, data analysis, or image processing, this package lets you run Python code directly within your Flutter applicationβopening up endless possibilities by bridging two powerful ecosystems.
π Why Flutter Python Bridge?
As Flutter excels in UI/UX and cross-platform development, Python dominates the world of data science and automation. This library brings them togetherβallowing you to:
Run Python code and script files natively
Integrate ML models using Scikit-learn or TensorFlow
Perform data analysis using pandas, numpy, and matplotlib
Process images using OpenCV and Pillow
Use Python packages dynamically (on desktop, partially on Android)
Build rich, data-driven apps with real-time visualization
π½οΈ Demo
Behind the Scenes
Flutter Python Bridge combines:
Chaquopy for Android integration
System Python for desktop platforms
Dart platform channels to communicate across native layers
Cross-platform normalization for path and environment handling
Its Dart-first API is designed for seamless developer experience in Flutter, while offering detailed results via structured output and error objects.
Platform Limitations
Due to security and sandbox restrictions:
iOS cannot execute or install external code like Python
Web apps are limited to JavaScript and WebAssembly; direct Python execution is not allowed
Alternatives
Use a backend API powered by Python and connect via HTTP
Explore Pyodide/WebAssembly for limited use cases in the browser
Use Cases
ML-driven mobile apps
Educational data visualization tools
Scientific mobile utilities
On-device Python automation
Cross-platform data dashboards
Final Thoughts
Flutter Python Bridge bridges the gap between two incredibly powerful ecosystemsβDart and Python. Whether you’re a data scientist wanting to build a mobile front-end or a Flutter developer needing Pythonβs muscle, this library empowers you to build hybrid solutions with ease.
GradBridge β Graduation Project Management App
GradBridge is a mobile application I developed to support final-year university students in managing their graduation projects more effectively. The app provides an all-in-one platform where students can track their tasks, schedule meetings, record grades, and communicate seamlessly with their team members and supervisors.
Google Play: GradBridge
Technologies Used
Flutter: For cross-platform mobile development (Android & iOS) with a clean and responsive UI.
Firebase:
Authentication for secure login/sign-up.
Firestore for real-time database and data syncing.
Firebase Storage for uploading and storing files.
GetX: For state management, navigation, and reactive UI updates, ensuring a smooth and responsive user experience.
Main Features
Task Management
Users can create, edit, and track tasks with due dates, completion status, and assigned team members. Tasks are organized by project milestones to help students stay on track.
Meeting Scheduler
Students and supervisors can schedule and manage project-related meetings. Notifications and reminders help prevent missed appointments.
Degree Tracking
Supervisors can assign and update grades for different project phases (proposal, midterm, final, etc.), giving students a clear overview of their performance.
Real-time Chat
A built-in chat system allows real-time messaging between students and supervisors, supporting group and individual conversations to enhance communication.
Team Collaboration
Team members can view shared tasks, collaborate on updates, and track project progress in a shared dashboard.
Problem Solved
Many students struggle to stay organized during the final year of university, especially when working in teams on a graduation project. Between coordinating meetings, tracking tasks, and communicating with supervisors, important information can get lost.
GradBridge solves this by bringing everything into one streamlined platform that helps students stay focused, organized, and connected β all in real time.
GradBridge is a mobile application I developed to support final-year university students in managing their graduation projects more effectively. The app provides an all-in-one platform where students can track their tasks, schedule meetings, record grades, and communicate seamlessly with their team members and supervisors.
Conclusion
GradBridge is more than just a task manager β itβs a full-fledged academic collaboration tool that helps students take control of their final-year project. With clean UX, real-time communication, and smart organization, it offers real-world value to students and supervisors alike.
ErrorTrace Pro β Advanced Python Exception Handler with Visual Tracebacks and Cloud Logging
GitHub: View Repository
PyPI: View Package
Project Overview
ErrorTrace Pro is a professional-grade Python library I developed and published on PyPI and GitHub.
It enhances Python exception handling by transforming cryptic error tracebacks into beautiful, informative, and actionable outputs. ErrorTrace Pro not only suggests solutions for common exceptions but also offers cloud logging support across multiple platforms like AWS, GCP, and Azure β all with minimal setup.
Itβs designed for developers who want better debugging experiences and faster troubleshooting, whether they are building scripts, applications, or large-scale systems.
Key Features
Visual Traceback Mapping: Colorful, clear traceback outputs that highlight exactly where and why an error occurred.
AI-Powered Solution Suggestions: Intelligent recommendations to resolve exceptions, powered by a custom solutions database.
Multi-Cloud Logging Support: Log errors seamlessly to Google Cloud, AWS CloudWatch, Azure Application Insights, or any custom HTTP endpoint.
Simple Integration: Install as a drop-in replacement for Pythonβs default exception handler in just one line.
Powerful CLI: Execute scripts with enhanced error reporting and cloud logging directly from the command line.
Customizable: Easily customize settings like verbosity, cloud provider credentials, and error display formats.
Extensible Solutions Database: Define your own solutions for custom or business-specific exceptions.
Technologies Used
Python (3.7+)
Rich (for colorful terminal output)
Click (for CLI tools)
Cloud SDKs (GCP, AWS, Azure integrations)
HTTP APIs (for custom cloud logging)
Why I Built It
Traditional Python tracebacks can often be cryptic and unhelpful, especially for newer developers or in production environments. I wanted to reimagine the debugging process β making errors more readable, actionable, and easier to log and track across platforms.
ErrorTrace Pro empowers developers to catch, understand, and fix errors faster, while also helping teams track issues across cloud systems without heavy lifting.
Cloud Logging Integration
Supports cloud logging with:
Google Cloud Logging
AWS CloudWatch
Azure Application Insights
Generic HTTP endpoints (for custom backends)
Simply set environment variables or configure through code to start capturing errors in real-time cloud dashboards.
Albashmoparmeg.com – Teaching Programming to Thousands Through Web and Mobile
Albashmoparmeg.com is an educational platform dedicated to helping people master programming for both web and mobile applications. Over several years of continuous work and development, I built this platform to deliver a structured, accessible, and high-quality learning experience for anyone passionate about coding.
Technologies Used
Frontend: HTML, CSS, JavaScript, Bootstrap.
Backend: PHP (WordPress), MySQL
Mobile App: Flutter (for both iOS and Android)
Key Features
Extensive Programming Courses: Covering web development, app development, and other tech fields.
High-Quality Articles: I authored and optimized hundreds of articles, many of which achieved Rank #1 on Google for competitive keywords.
Mobile Application: I developed a dedicated app using Flutter, ensuring fast performance and a native-like experience on both iOS and Android devices.
Database Management: A robust and scalable MySQL database to manage thousands of users, articles, and courses.
Custom WordPress Development: I customized WordPress beyond traditional themes and plugins to fit the platform’s unique educational needs.
User-Centered Design: Built with a focus on ease of use, fast loading times, and a smooth learning journey.
Challenges Overcome
Building a large-scale educational platform that stays lightweight and fast.
Maintaining SEO dominance across a wide range of highly competitive keywords.
Developing a cross-platform mobile app with a seamless experience.
Managing content at scale while maintaining consistent quality and SEO performance.
Results and Achievements
Hundreds of articles ranking on Google’s first page, with many articles consistently holding the #1 position.
A fast-growing community of learners using the website and mobile app.
High retention rates thanks to the engaging structure and easy-to-follow programming tutorials.
A proven track record of helping thousands of users advance their programming skills.
Conclusion
Albashmoparmeg.com stands as a testament to my dedication to quality education, full-stack development, SEO expertise, and mobile app development. It showcases my ability to take a project from idea to reality, building both the technical foundations and the content ecosystem needed for long-term success.
π©Έ DonareBlood β A Modern Blood Donation App Built with Flutter & Firebase
DonareBlood is a powerful, user-friendly mobile application designed to connect blood donors with recipients in need. Developed using Flutter for cross-platform compatibility and Firebase for real-time backend support, the app simplifies and accelerates the process of finding and donating blood, potentially saving countless lives.
π Google Play: DonareBlood
π± Platform: Android & iOS
π Built With: Flutter, Firebase (Firestore, Authentication, Cloud Functions, Realtime Database), Google Maps API
Key Features
Smart Blood Requests
Users can request blood by selecting blood type, location, urgency, and additional medical details. The app notifies nearby eligible donors instantly.
Donor Registration & Profile Management
Donors can register, verify eligibility, and manage their donation history. Each user has a detailed profile with:
Blood type
Donation history
Location & availability
Last donation date tracking
Integrated Google Maps
Visualize donor and hospital locations on a live map. This enhances navigation, donor reachability, and emergency response.
Push Notifications
Custom notifications are sent for:
New nearby requests
Donation reminders
Blood request approval or success stories
Donation History & Tracking
Both donors and recipients can track past donations, successful matches, and feedback.
Emergency Mode
In urgent cases, users can activate Emergency Mode, which:
Prioritizes their request
Notifies all donors in a wider radius
Shares location with nearby hospitals
Technologies Used
Flutter: Cross-platform UI toolkit for native performance
Firebase Firestore: Real-time cloud database for user and request data
Firebase Auth: Secure user login system
Firebase Cloud Functions: Background triggers for notifications and request management
Firebase Storage: For uploading documents or donor ID verification
Google Maps API: Live maps, navigation, and donor proximity detection
Why DonareBlood?
There are countless patients in need of urgent blood transfusions, but the process of connecting donors and recipients is often delayed by outdated systems.Β DonareBlood bridges this gap with technology, enabling instant connections, data-driven decisions, and life-saving responses.
Whether youβre a volunteer donor, a hospital, or a person in need,Β DonareBlood ensures you’re just a tap away from help.
Medication Classification System β AI-Powered Drug Categorization Platform
Overview
The Medication Classification System is a powerful machine learning platform developed to automatically classify medications as either prescription or over-the-counter (OTC). Leveraging advanced Natural Language Processing (NLP) and transformer-based deep learning models, this tool assists medical professionals, researchers, and developers in understanding drug classifications quickly and accurately.
This project showcases a combination of AI, Django web development, and automated PDF processing, delivering a complete end-to-end solution for analyzing medical texts.
How It Works
1. Input
Users can either:
Upload PDF documents containing medication leaflets or packaging information.
Enter the medication name directly into the platform.
2. Text Extraction (For PDF)
Our system uses PyPDF2 to:
Extract raw content from the uploaded file
Identify critical drug details such as:
Medication Name
Active Ingredients
Dosage Form & Strength
3. Preprocessing & Analysis
The extracted or entered data undergoes:
Text normalization (lowercasing, cleaning)
Stopword removal
Lemmatization
Tokenization with RoBERTa tokenizer
4. AI-Powered Classification
Once processed, the information is passed into fine-tuned NLP models:
RoBERTa(primary model)BERTandDistilBERT(alternative and comparative models)
The system returns:
Classification: Prescription or OTC
A confidence score for each prediction
Technical Stack
- Language: Python
- Backend Framework: Django
- AI Models: RoBERTa, BERT, DistilBERT
- NLP Libraries: HuggingFace Transformers, NLTK
- PDF Processing: PyPDF2
- Frontend: Django Templates
Use Cases
Healthcare Systems β Automate drug classification workflows
Medical Research β Analyze large datasets of drug literature
Pharma HR & Legal β Ensure regulatory compliance on medication labeling
HealthTech Apps β Integrate medication classification in mobile/web platforms
ChatVerse: A Powerful & Customizable Flutter Chat Library with Firebase Integration
GitHub: ChatVerse on GitHub
Pub.dev: ChatVerse on pub.dev
Overview
ChatVerse is a feature-rich, beautifully designed Flutter chat library that simplifies building modern real-time messaging apps with Firebase. Whether you’re developing a simple messaging interface or a full-featured group chat application, ChatVerse is designed to be the ultimate plug-and-play solution for chat functionality in Flutter apps.
With built-in support for Firebase Authentication, Cloud Firestore, and Firebase Storage, it empowers developers to focus on user experience instead of infrastructure, all while offering full customization for themes, colors, and layouts.
Key Features
Firebase Integration
Authentication β Seamlessly integrate with Firebase Auth for login and user management.
Cloud Firestore β Real-time database support for storing and syncing messages.
Firebase Storage β Effortlessly upload and retrieve media files (images, documents, etc.).
Real-Time Messaging
Instant message sending and receiving with zero-lag communication.
Typing indicators, message seen status, and online/offline detection.
Show last seen timestamps for individual or group members.
Advanced Group Chat Support
Create, manage, and delete groups.
Add/remove group members, assign admins, and manage roles.
Support for group avatars and personalized settings.
Modern UI/UX
Smooth and minimalistic chat layout with beautiful message bubbles.
Automatic date separators, scrollable message history, and intuitive UX.
Custom animations and responsive design across Android and iOS.
Rich Media Support
Send and receive images and files with previews.
Integrates media pickers and image viewer functionality out of the box.
Customizable Themes
Easily switch between Light and Dark modes.
Fully configurable color schemes, text styles, and layout settings.
High Performance
Optimized for performance even with large message histories.
Smooth scrolling and lazy loading for long conversations.
Efficient handling of media and attachments.
Cross-Platform Support
Fully functional on Android and iOS with platform-specific optimizations.
Why I Built ChatVerse
In many of my Flutter projects, I found myself rebuilding the same messaging functionality repeatedly. I needed a reusable, modular, and developer-friendly chat component that worked seamlessly with Firebase. That’s why I built ChatVerse β to save time and empower others to integrate beautiful chat interfaces with minimal effort and maximum control.
Whether you’re working on a social networking app, a customer support platform, or a team collaboration tool, ChatVerse gives you the flexibility and scalability you need.
Use Cases
Social Media Apps
Customer Support Chats
Educational Platforms
E-Commerce Messaging
In-app Team Collaboration Tools
Conclusion
ChatVerse is a complete solution for modern chat features in Flutter applications. With its sleek design, Firebase integration, and advanced capabilities, it’s perfect for developers looking to build reliable, fast, and scalable messaging apps.
Whether you’re a hobbyist or a professional Flutter developer, ChatVerse is ready to supercharge your app’s communication experience.
π Explore ChatVerse on GitHub
π Install ChatVerse from pub.dev
Flutter AI Kit β Bring Powerful AI to Your Flutter Apps
Flutter AI Kit is a feature-rich, all-in-one Flutter library I built to simplify the integration of advanced AI models into mobile applications. Whether you’re working on a chatbot, AI-generated images, or text analysis tools, this package provides everything you need with minimal setup and a clean, modern UI out of the box.
About the Project
With the rising popularity of AI-powered apps, developers often face complex integrations, inconsistent APIs, and UI customization challenges. Flutter AI Kit solves these problems by offering:
Unified integration with OpenAI, Google Gemini, and HuggingFace
Beautiful, pre-built chat and image generation UI
Easy customization and theming to match any app design
This package is ideal for building AI chatbots, virtual assistants, content creation tools, educational apps, or any application that benefits from AI-powered functionality.
Key Features
AI Chat Interfaces
Pre-configured chat UI widgets for OpenAI and Google Gemini
Supports conversational state management, user/AI message formatting
Clean and responsive UI design
AI Image Generation
DALLΒ·E Integration for AI-generated images
Includes loading states, error handling, and image previews
Text Processing with HuggingFace
Text classification, summarization, translation, and more
Easy integration of any HuggingFace-supported model with REST API support
Modern Flutter Design
Fully customizable components using Flutterβs built-in theming
Dark/light mode support
Rounded shapes, shadows, and responsive design elements
Developer-Friendly
Simple API key setup for OpenAI, Gemini, and HuggingFace
Full null safety and type safety
Modular architecture for easy feature scaling
π Project Links
π» GitHub: github.com/Hamed233/flutter_ai_kit
π¦ Pub.dev: pub.dev/packages/flutter_ai_kit
