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dc780a3ec4 ... 7f6c9c2680

Author SHA1 Message Date
Murtadha
7f6c9c2680 Add more media that are relevant to the projects 2024-07-28 14:22:28 -04:00
Murtadha
9ccb1d5a1d v0.12.4 Update Project card to filter with array categories, and polish project card presentation 2024-07-28 14:22:02 -04:00
Murtadha
7875d92b4c Update pipeline script to match best practices 2024-07-28 00:47:40 -04:00
Murtadha
b13e368702 v0.12.4 Update resume download name 2024-07-28 00:45:22 -04:00
Murtadha
4fcab54183 v0.12.3 Update Favicon 2024-07-28 00:44:46 -04:00
Murtadha
594d08f84d v0.12.2 Move Data fetching to its own script for more modular and clearer code base 2024-07-28 00:32:47 -04:00
Murtadha
50d9a2207b Downscale image to load faster when fetching 2024-07-28 00:31:23 -04:00
Murtadha
b94482de3d Remove resume from dist directory 2024-07-27 21:41:17 -04:00
Murtadha
b005424aa9 Refactor pipeline script 2024-07-27 21:20:21 -04:00
Murtadha
6bb221cbce Refactor pipeline script 2024-07-27 21:18:10 -04:00
20 changed files with 385 additions and 183 deletions
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45
.woodpecker.yml
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@ -37,6 +37,7 @@ steps:
- dist/ - dist/
- nginx.conf - nginx.conf
- version.txt - version.txt
deploy: deploy:
image: appleboy/drone-ssh image: appleboy/drone-ssh
settings: settings:
@ -78,22 +79,18 @@ steps:
from_secret: ssh_key from_secret: ssh_key
port: 2332 port: 2332
script: script:
- cd /home/mnisyif/docker-containers/mnisyif/frontend - echo "Verifying deployment..."
- VERSION=$(cat version.txt) # Verify the container is running
- echo "Confirming deployment for version: $VERSION" - docker ps | grep frontend || { echo "Container failed to start"; exit 1; }
- docker ps -a # Display container logs
- if ! docker ps | grep -q frontend-$VERSION; then - docker logs frontend
echo "Container failed to start"; # Test Nginx configuration
docker logs frontend-$VERSION; - docker exec frontend nginx -t
exit 1; # Check Nginx process
fi - docker exec frontend ps aux | grep nginx
- echo "Container is running, checking Nginx configuration..." # Check contents of /usr/share/nginx/html in the container
- docker exec frontend-$VERSION nginx -t || { echo "Nginx configuration test failed"; exit 1; } - docker exec frontend ls -la /usr/share/nginx/html
- echo "Listing contents of /usr/share/nginx/html" # Perform a simple HTTP request to check if the server is responding
- docker exec frontend-$VERSION ls -la /usr/share/nginx/html
- echo "Listing contents of /usr/share/nginx/html/resumes"
- docker exec frontend-$VERSION ls -la /usr/share/nginx/html/resumes || echo "Resumes directory not found"
- echo "Checking HTTP response..."
- curl -I http://localhost:5173 || { echo "HTTP request failed"; exit 1; } - curl -I http://localhost:5173 || { echo "HTTP request failed"; exit 1; }
- echo "Deployment confirmed successfully" - echo "Deployment confirmed successfully"
@ -112,12 +109,16 @@ steps:
- docker system prune -f --volumes - docker system prune -f --volumes
- > - >
for img in $(docker images frontend --format "{{.Tag}}" | grep -v $(cat /home/mnisyif/docker-containers/mnisyif/frontend/version.txt)); do for img in $(docker images frontend --format "{{.Tag}}" | grep -v $(cat /home/mnisyif/docker-containers/mnisyif/frontend/version.txt)); do
docker rmi frontend:$img || true; docker rmi frontend:$img || true
done done
- echo "Cleanup completed" - echo "Cleanup completed"
trigger: # trigger:
branch: # branch:
- master # - master
event: # event:
- push # - push
when:
- branch: master
event: push

2
index.html
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@ -2,7 +2,7 @@
<html lang="en"> <html lang="en">
<head> <head>
<meta charset="UTF-8" /> <meta charset="UTF-8" />
<link rel="icon" type="image/svg+xml" href="/logos/logo.png" /> <link rel="icon" type="image/svg+xml" href="/logos/favicon.svg" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Murtadha Nisyif | Portfolio</title> <title>Murtadha Nisyif | Portfolio</title>
</head> </head>

125
public/assets/data/projectsData.json
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@ -2,52 +2,147 @@
{ {
"id": 1, "id": 1,
"title": "PaperKeypad", "title": "PaperKeypad",
"category": "Misc", "category": ["Misc"],
"images": ["/assets/projects/keypad0.jpg"], "images": ["/assets/projects/keypad0.jpg"],
"description": "Ever need to use a keyboard, but you got only your phone and a printer, PaperKeypad is a keypad that is made of paper.", "description": "Ever need to use a keyboard, but you got only your phone and a printer, PaperKeypad is a keypad that is made of paper.",
"technologies": ["Java", "JavaFX", "Android Studio"], "technologies": ["Java", "JavaFX", "Android Studio"],
"features": ["Mobile sensor manipulation", "Responsive design"], "features": ["Mobile sensor manipulation", "Responsive design"],
"githubLink": "https://github.com/betato/PaperKeypad" "githubLink": "https://github.com/betato/PaperKeypad",
"date": 2019
}, },
{ {
"id": 2, "id": 2,
"title": "StonkBot", "title": "StonkBot",
"category": "Misc", "category": ["Misc"],
"images": ["/assets/projects/stonkbot0.jpg"], "images": ["/assets/projects/stonkbot0.jpg"],
"description": "The fear of losing money is common among first-time and seasoned investors alike. This inspired the creation of Stonk Bot, a fantasy trading platform that can be implemented in Discord.", "description": "The fear of losing money is common among first-time and seasoned investors alike. This inspired the creation of Stonk Bot, a fantasy trading platform that can be implemented in Discord.",
"technologies": ["Python", "VS Code", "Matplotlib", "Financial Modeling Prep API", "Discord API"], "technologies": ["Python", "VS Code", "Matplotlib", "Financial Modeling Prep API", "Discord API"],
"features": ["Buy shares", "Sell shares", "View stock information", "View personal portfolio", "View leaderboard"], "features": ["Buy shares", "Sell shares", "View stock information", "View personal portfolio", "View leaderboard"],
"githubLink": "https://github.com/aidanbruneel/stonkbot", "githubLink": "https://github.com/aidanbruneel/stonkbot",
"liveLink": "https://discord.com/invite/tQNkk7v7R8" "liveLink": "https://discord.com/invite/tQNkk7v7R8",
"date": 2022
}, },
{ {
"id": 3, "id": 3,
"title": "Car Model Classification", "title": "Car Model Classification",
"category": "Machine Learning", "category": ["Machine Learning"],
"images": ["/assets/projects/carmodelclass0.png"], "images": ["/assets/projects/carmodelclass0.png"],
"description": "Developing a computer vision application to identify a vehicle model from a given image is an interesting and challenging problem to solve. Challenge of this problem is that different vehicle models can appear very similar and the same vehicle can look different and hard to identify depending on lighting conditions, angle and many other factors. In this project, I decided to train a Convolutional Neural Network(CNN) to generate a model that can identify a given vehicle model.", "description": "Developing a computer vision application to identify a vehicle model from a given image is an interesting and challenging problem to solve. Challenge of this problem is that different vehicle models can appear very similar and the same vehicle can look different and hard to identify depending on lighting conditions, angle and many other factors. In this project, I decided to train a Convolutional Neural Network(CNN) to generate a model that can identify a given vehicle model.",
"technologies": ["Python", "Tensorflow", "CNN", "Deep learning", "ResNet", "EfficientNet", "Stanford Cars Dataset"], "technologies": ["Python", "Tensorflow", "CNN", "Deep learning", "ResNet", "EfficientNet", "Stanford Cars Dataset"],
"features": ["Buy shares", "Sell shares", "View stock information", "View personal portfolio", "View leaderboard"], "features": ["Buy shares", "Sell shares", "View stock information", "View personal portfolio", "View leaderboard"],
"githubLink": "https://github.com/mnisyif/carClassificationModel" "githubLink": "https://github.com/mnisyif/carClassificationModel",
"date": 2022
}, },
{ {
"id": 4, "id": 4,
"title": "Memory Allocation Simulations", "title": "Memory Allocation Simulations",
"category": "Misc", "category": ["Misc"],
"images": ["/assets/projects/memallc0.png"], "images": ["/assets/projects/memallc0.png"],
"description": "This implementation uses doubly linked list to simulate memory allocation given 4 different memory management algorithms", "description": "This implementation uses doubly linked list to simulate memory allocation given 4 different memory management algorithms",
"technologies": ["C", "CMake", "Data structures"], "technologies": ["C", "CMake", "Data structures"],
"features": ["First fit", "Best fit", "Next fit", "Worst fit"], "features": ["First fit", "Best fit", "Next fit", "Worst fit"],
"githubLink": "https://github.com/mnisyif/MemoryAllocationAlgorithm/tree/main" "githubLink": "https://github.com/mnisyif/MemoryAllocationAlgorithm/tree/main",
"date": 2022
}, },
{ {
"id": 5, "id": 5,
"title": "Portfolio Website", "title": "Transformer-based Semantic Transcoding",
"category": "Web Development", "category": ["Machine Learning"],
"images": ["/assets/projects/memallc0.png"], "images": ["/assets/projects/semantic01.png"],
"description": "This implementation uses doubly linked list to simulate memory allocation given 4 different memory management algorithms", "description": "Developed PyTorch models for E2E semantic transcoding, deployed on Xilinx SoC boards using Vitis AI™",
"technologies": ["C", "CMake", "Data structures"], "technologies": ["PyTorch", "Vitis AI", "Xilinx SoC", "Machine Learning", "C++"],
"features": ["First fit", "Best fit", "Next fit", "Worst fit"], "features": ["E2E semantic transcoding", "Hardware acceleration", "SoC deployment", "C++ deployment"],
"githubLink": "https://github.com/mnisyif/MemoryAllocationAlgorithm/tree/main" "githubLink": "https://github.com/mnisyif/masters-research",
"date": 2024
},
{
"id": 6,
"title": "Clean Architecture C# Backend",
"category": ["Web Development"],
"images": ["/assets/projects/clean_architecture_backend.png"],
"description": "Engineered a scalable portfolio website backend using C#, adhering to Clean Architecture principles and implementing CI/CD pipeline for efficient deployment",
"technologies": ["C#", "Clean Architecture", "CI/CD", "REST Api"],
"features": ["Scalable backend", "Clean Architecture implementation", "Automated deployment", "RESTful"],
"githubLink": "https://github.com/mnisyif/portfolio-backend",
"date": 2024
},
{
"id": 7,
"title": "DevOps Homelab Maestro",
"category": ["DevOps"],
"images": ["/assets/projects/homelab_maestro.png"],
"description": "Orchestrating a robust homelab environment with Docker containers, Kubernetes clusters, Ceph distributed storage, and CI/CD pipelines for seamless application deployment",
"technologies": ["Docker", "Kubernetes", "Ceph", "CI/CD"],
"features": ["Containerized applications", "Orchestration", "Distributed storage", "Automated deployment"],
"date": 2023
},
{
"id": 8,
"title": "RL Dynamic Noise Cancelling",
"category": ["Machine Learning"],
"images": ["/assets/projects/noise_cancelling.png"],
"description": "Implemented real-time Automatic Noise Filtering using Reinforcement Learning and Dynamic Sparse Training in PyTorch",
"technologies": ["PyTorch", "Reinforcement Learning", "Dynamic Sparse Training", "Jupyter Notebooks"],
"features": ["Real-time filtering", "Automatic noise cancellation", "Sparse training", "Interactive development"],
"githubLink": "https://github.com/mnisyif/rl-noise-cancelling",
"date": 2023
},
{
"id": 9,
"title": "Real-Time Text-to-Braille",
"category": ["Embedded Systems"],
"images": ["/assets/projects/braille01.jpg","/assets/projects/braille02.jpg"],
"description": "Built a Raspberry Pi device for real-time image-to-Braille conversion, enhancing accessibility for the deaf-blind community",
"technologies": ["Raspberry Pi", "Image Processing", "OCR", "Python"],
"features": ["Real-time conversion", "Low-cost OCR algorithm", "Lookup table for Braille conversion", "Accessibility enhancement"],
"date": 2023
},
{
"id": 10,
"title": "ZAMAZ UTI Diagnosis",
"category": ["Embedded Systems"],
"images": ["/assets/projects/zamaz01.jpg","/assets/projects/zamaz02.jpg"],
"description": "Developed a Raspberry Pi-based system for automated urine test analysis, achieving 16x faster results than standard methods",
"technologies": ["Raspberry Pi", "Python", "Image Processing", "Healthcare Technology"],
"features": ["Automated analysis", "Pixel-based concentration calculation", "E. Coli and Staph bacteria detection", "Rapid results"],
"date": 2022
},
{
"id": 11,
"title": "HAM10K Image Classification with Deep Networks",
"category": ["Machine Learning"],
"images": ["/assets/projects/ham10k_classification.png"],
"description": "Developed and compared three deep learning models (MLP+PCA, DCNN, RegNetY-320) for skin cancer classification using the HAM10000 dataset, achieving 96.89% accuracy with RegNetY-320",
"technologies": ["PyTorch", "Deep Learning", "CNN", "RegNet", "PCA", "Python"],
"features": ["Multi-model comparison", "Data balancing and augmentation", "High accuracy classification", "Medical image analysis"],
"githubLink": "https://github.com/nithinprasad94/ENGG6600_DL_Final_Project",
"liveLink":"https://youtu.be/zHbRmIn7gPo",
"date": 2023
},
{
"id": 12,
"title": "Heart Disease Prediction Web App",
"category": ["Machine Learning", "Web Development"],
"images": ["/assets/projects/heartdis01.png"],
"description": "Developed a Flask-based web application that predicts the likelihood of heart disease using machine learning models. The app processes user input, applies feature encoding and scaling, and provides instant predictions.",
"technologies": [
"Python",
"Flask",
"NumPy",
"Scikit-learn",
"Pickle",
"HTML",
"Machine Learning"
],
"features": [
"User-friendly web interface for input",
"Real-time prediction using pre-trained model",
"Feature encoding and scaling",
"Integration of multiple ML preprocessing steps",
"Handling of both categorical and numerical inputs"
],
"githubLink": "https://github.com/zeyadghulam/engg6600-assignment3",
"date":2023
} }
] ]

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public/assets/logos/favicon.svg Normal file
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@ -0,0 +1,30 @@
<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 20010904//EN"
"http://www.w3.org/TR/2001/REC-SVG-20010904/DTD/svg10.dtd">
<svg version="1.0" xmlns="http://www.w3.org/2000/svg"
width="257.000000pt" height="257.000000pt" viewBox="0 0 257.000000 257.000000"
preserveAspectRatio="xMidYMid meet">
<g transform="translate(0.000000,257.000000) scale(0.100000,-0.100000)"
fill="#000000" stroke="none">
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42
src/App.jsx
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@ -13,6 +13,8 @@ import InfoSection from "./shared/components/info/InfoSection";
import styles from "./App.module.css"; import styles from "./App.module.css";
import { fetchEducationData, fetchExperienceData, fetchPersonalData, fetchProjectsData } from "./utils/dataFetcher";
function App() { function App() {
const [educationData, setEducationData] = useState([]); const [educationData, setEducationData] = useState([]);
const [experienceData, setExperienceData] = useState([]); const [experienceData, setExperienceData] = useState([]);
@ -20,35 +22,21 @@ function App() {
const [personalData, setPersonalData] = useState([]); const [personalData, setPersonalData] = useState([]);
useEffect(() => { useEffect(() => {
const fetchEducationData = async () => { const fetchData = async () => {
const response = await fetch("/assets/data/educationData.json"); const education = await fetchEducationData();
const data = await response.json(); setEducationData(education);
setEducationData(data);
const experience = await fetchExperienceData();
setExperienceData(experience);
const projects = await fetchProjectsData();
setProjectsData(projects);
const personal = await fetchPersonalData();
setPersonalData(personal);
}; };
const fetchExperienceData = async () => { fetchData();
const response = await fetch("/assets/data/experienceData.json");
const data = await response.json();
setExperienceData(data);
};
const fetchProjectsData = async () => {
const response = await fetch("/assets/data/projectsData.json");
const data = await response.json();
setProjectsData(data);
};
const fetchPersonalData = async () => {
const response = await fetch("/assets/data/personalData.json");
const data = await response.json();
setPersonalData(data);
// console.log(data)
};
fetchEducationData();
fetchExperienceData();
fetchProjectsData();
fetchPersonalData();
}, []); }, []);
return ( return (

26
src/components/projects/Projects.jsx
View file

@ -17,13 +17,16 @@ function Projects({ title, data }) {
}, []); }, []);
const categories = useMemo(() => { const categories = useMemo(() => {
const cats = new Set(data.map((project) => project.category)); const cats = new Set(data.flatMap((project) => project.category));
return ["All", ...Array.from(cats)]; return ["All", ...Array.from(cats)];
}, [data]); }, [data]);
const filteredProjects = useMemo(() => { const sortedAndFilteredProjects = useMemo(() => {
if (activeFilter === "All") return data; let filteredProjects = activeFilter === "All"
return data.filter((project) => project.category === activeFilter); ? data
: data.filter((project) => project.category.includes(activeFilter));
return filteredProjects.sort((a, b) => b.date - a.date);
}, [data, activeFilter]); }, [data, activeFilter]);
const handleFilterClick = (category) => { const handleFilterClick = (category) => {
@ -41,14 +44,23 @@ function Projects({ title, data }) {
<h2 className={styles.sectionTitle}>{title}</h2> <h2 className={styles.sectionTitle}>{title}</h2>
<div className={styles.filterContainer}> <div className={styles.filterContainer}>
{categories.map((category) => ( {categories.map((category) => (
<button key={category} className={`${styles.filterButton} ${activeFilter === category ? styles.active : ""}`} onClick={() => handleFilterClick(category)}> <button
key={category}
className={`${styles.filterButton} ${activeFilter === category ? styles.active : ""}`}
onClick={() => handleFilterClick(category)}
>
{category} {category}
</button> </button>
))} ))}
</div> </div>
<div className={styles.projectGrid}> <div className={styles.projectGrid}>
{filteredProjects.map((project) => ( {sortedAndFilteredProjects.map((project) => (
<ProjectCard key={project.id} project={project} onClick={openModal} className={animatingOut ? styles.fadeOut : styles.fadeIn} /> <ProjectCard
key={project.id}
project={project}
onClick={openModal}
className={animatingOut ? styles.fadeOut : styles.fadeIn}
/>
))} ))}
</div> </div>
{selectedProject && <ProjectModal project={selectedProject} onClose={closeModal} />} {selectedProject && <ProjectModal project={selectedProject} onClose={closeModal} />}

49
src/components/projects/projectCard/ProjectCard.jsx
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@ -1,7 +1,44 @@
import React from "react"; import React, { useRef, useEffect, useState } from "react";
import styles from "./ProjectCard.module.css"; import styles from "./ProjectCard.module.css";
function ProjectCard({ project, onClick, className }) { function ProjectCard({ project, onClick, className }) {
const [truncatedDescription, setTruncatedDescription] = useState(project.description);
const descriptionRef = useRef(null);
const formatList = (items) => {
return items.map((item, index, arr) => (
<React.Fragment key={index}>
{item}
{index < arr.length - 1 && <span className={styles.separator}>, </span>}
</React.Fragment>
));
};
useEffect(() => {
const truncateDescription = () => {
const element = descriptionRef.current;
if (!element) return;
const maxHeight = parseInt(window.getComputedStyle(element).lineHeight) * 4; // 4 lines
let text = project.description;
element.textContent = text;
while (element.scrollHeight > maxHeight && text.length > 0) {
text = text.slice(0, -1);
element.textContent = text + '...';
}
setTruncatedDescription(element.textContent);
};
truncateDescription();
window.addEventListener('resize', truncateDescription);
return () => {
window.removeEventListener('resize', truncateDescription);
};
}, [project.description]);
return ( return (
<div className={`${styles.card} ${className}`} onClick={() => onClick(project)}> <div className={`${styles.card} ${className}`} onClick={() => onClick(project)}>
<div className={styles.imageSlider}> <div className={styles.imageSlider}>
@ -9,8 +46,14 @@ function ProjectCard({ project, onClick, className }) {
</div> </div>
<div className={styles.content}> <div className={styles.content}>
<h3 className={styles.title}>{project.title}</h3> <h3 className={styles.title}>{project.title}</h3>
<p className={styles.category}>{project.category}</p> <div className={styles.categories}>
<p className={styles.description}>{project.description}</p> {formatList(project.category)}
</div>
<p ref={descriptionRef} className={styles.description}>{truncatedDescription}</p>
<div className={styles.technologies}>
{formatList(project.technologies)}
</div>
{/* <p className={styles.date}>{project.date}</p> */}
</div> </div>
</div> </div>
); );

30
src/components/projects/projectCard/ProjectCard.module.css
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@ -5,9 +5,10 @@
overflow: hidden; overflow: hidden;
transition: transform 0.3s ease; transition: transform 0.3s ease;
cursor: pointer; cursor: pointer;
height: 450px; /* Fixed height for the card */
display: flex; display: flex;
flex-direction: column; flex-direction: column;
height: auto;
min-height: 450px;
} }
.card:hover { .card:hover {
@ -32,7 +33,6 @@
display: flex; display: flex;
flex-direction: column; flex-direction: column;
padding: 1rem; padding: 1rem;
overflow: hidden;
} }
.title { .title {
@ -42,7 +42,7 @@
color: #333; color: #333;
} }
.category { .categories {
font-size: 0.8rem; font-size: 0.8rem;
color: #666; color: #666;
margin-bottom: 0.5rem; margin-bottom: 0.5rem;
@ -51,10 +51,24 @@
.description { .description {
font-size: 0.9rem; font-size: 0.9rem;
color: #666; color: #666;
flex-grow: 1; margin-bottom: 0.5rem;
overflow: hidden; overflow: hidden;
display: -webkit-box; line-height: 1.4;
-webkit-line-clamp: 4; /* Adjust this number to show more or fewer lines */ max-height: calc(1.4em * 4); /* 4 lines of text */
-webkit-box-orient: vertical; }
text-overflow: ellipsis;
.technologies {
font-size: 0.8rem;
color: #0066cc;
margin-bottom: 0.5rem;
}
.date {
font-size: 0.8rem;
color: #999;
margin-top: auto;
}
.separator {
margin: 0 2px;
} }

2
src/shared/components/resumeDownloader/ResumeDownloader.jsx
View file

@ -8,7 +8,7 @@ const ResumeDownloader = ({ resumeLink }) => {
const link = document.createElement("a"); const link = document.createElement("a");
link.href = resumeLink; link.href = resumeLink;
console.log(link.href); console.log(link.href);
link.download = "Murtadha.pdf"; link.download = "Murtadha_Nisyif_Resume.pdf";
link.target = "_blank"; link.target = "_blank";
link.rel = "noopener noreferrer"; link.rel = "noopener noreferrer";

19
src/utils/dataFetcher.js Normal file
View file

@ -0,0 +1,19 @@
export const fetchEducationData = async () => {
const response = await fetch("/assets/data/educationData.json");
return await response.json();
};
export const fetchExperienceData = async () => {
const response = await fetch("/assets/data/experienceData.json");
return await response.json();
};
export const fetchProjectsData = async () => {
const response = await fetch("/assets/data/projectsData.json");
return await response.json();
};
export const fetchPersonalData = async () => {
const response = await fetch("/assets/data/personalData.json");
return await response.json();
};

2
version.txt
View file

@ -1 +1 @@
0.12.1 0.12.4