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| import streamlit as st | |
| st.markdown( | |
| """ | |
| <style> | |
| body { | |
| background-color: #1e1e1e; | |
| color: #f0f0f0; | |
| font-family: sans-serif; | |
| } | |
| h1, h2, h3, h4, h5, h6 { | |
| color: #f0f0f0; | |
| } | |
| a { | |
| color: #89b4fa; | |
| text-decoration: none; | |
| } | |
| a:hover { | |
| text-decoration: underline; | |
| } | |
| .social-link-button { | |
| display: inline-block; | |
| padding: 8px 16px; | |
| margin: 4px; | |
| background-color: #333; | |
| color: #fff; | |
| border-radius: 5px; | |
| text-decoration: none; | |
| } | |
| .social-link-button:hover { | |
| background-color: #555; | |
| } | |
| .project-card { | |
| background-color: #282828; | |
| border: 1px solid #444; | |
| } | |
| .skills, .experience, .projects, .volunteering, .achievements, .certifications, .education { | |
| background-color: #282828; | |
| border: 1px solid #444; | |
| } | |
| .status.deployed { | |
| color: #a3be8c; | |
| } | |
| .status.ongoing { | |
| color: #f9bb6d; | |
| } | |
| footer { | |
| background-color: #333; | |
| color: #f0f0f0; | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| st.html( | |
| """<!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Debopam Chowdhury (Param)</title> | |
| </head> | |
| <body> | |
| <header style="text-align: center; padding: 2rem 0; border-bottom: 1px solid #444;"> | |
| <img src="https://tinyurl.com/ysa6yekw" alt="Debopam Chowdhury (Param)" class="profile-image" style="width: 150px; height: 150px; border-radius: 50%; object-fit: cover; margin-bottom: 1rem;"> | |
| <h1>Debopam Chowdhury <span class="nickname" style="font-weight: normal; color: #999;">(Param)</span></h1> | |
| <p class="tagline" style="color: #ccc; margin-bottom: 1rem;">Creator of <a href="http://aigymbuddy.in" target="_blank" style="color: #89b4fa;">AiGymBuddy.in</a> | Machine Learning | Deep Learning | Flutter | Math | MLops | TensorFlow | FastAPI</p> | |
| <p class="pronouns" style="font-size: 0.9rem; color: #999; margin-bottom: 1rem;">(He/Him)</p> | |
| <div class="social-links"> | |
| <a href="https://www.linkedin.com/in/debopam-chowdhury-param-600619229/" target="_blank" class="social-link-button">LinkedIn</a> | |
| <a href="https://www.youtube.com/@DCparam/featured" target="_blank" class="social-link-button">YouTube</a> | |
| <a href="https://github.com/DebopamParam" target="_blank" class="social-link-button">GitHub</a> | |
| <a href="https://www.instagram.com/debopam_param.ai/" target="_blank" class="social-link-button">Instagram</a> | |
| </div> | |
| </header> | |
| <section class="about-me" style="padding: 2rem; margin-bottom: 1.5rem; border-radius: 8px;"> | |
| <h2>About Me</h2> | |
| <p style="line-height: 1.7;">Passionate about Machine Learning, Computer Science and Mathematics. Has a strong grip over Machine & Deep Learning Fundamentals, Computer Networks, OS and DSA. Solved 100+ problems on leetcode. Have a keen interest in learning Mathematics, Deep Learning and Statistics. I like to understand things in a deeper way.</p> | |
| </section> | |
| <section class="skills" style="padding: 2rem; margin-bottom: 1.5rem; border-radius: 8px;"> | |
| <h2>Skills</h2> | |
| <div class="skills-grid" style="display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 1.5rem;"> | |
| <div class="skill-category"> | |
| <h3>Technical</h3> | |
| <ul style="padding-left: 0; list-style: none;"> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;"><strong>Programming:</strong> Python, Java, Dart, VS Code, Git, GitHub, Jupyter Notebooks, CI/CD</li> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;"><strong>Machine Learning & Deep Learning:</strong> TensorFlow, PyTorch, Scikit-learn, Keras, Supervised Learning, Unsupervised Learning, Neural Networks, Sequence Modeling, Convolution, Attention Mechanisms, Transformer, GPT, BERT, Hyperparameter Optimization</li> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;"><strong>Data Handling:</strong> Pandas, NumPy, Data Manipulation, Data Preparation, SQL, Pyspark, NoSQL</li> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;"><strong>Cloud & DevOps:</strong> Docker, AWS, Cloud-AI, FastAPI</li> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;"><strong>Mathematics:</strong> Linear Algebra, Probability, Statistics, Boosting Methods</li> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;"><strong>Frameworks & Tools:</strong> Flutter, Firebase, Deep Learning Frameworks, Model Training & Optimization, Version Control, LLM FineTuning</li> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;"><strong>Generative AI:</strong> Vector Embeddings, Indexing, Chunking, RAG pipelines, LlamaIndex, LangChain, Colpali, Byaldi, Chroma DB</li> | |
| </ul> | |
| </div> | |
| </div> | |
| </section> | |
| <section class="experience" style="padding: 2rem; margin-bottom: 1.5rem; border-radius: 8px;"> | |
| <h2>Experience</h2> | |
| <div class="experience-item" style="margin-bottom: 1.5rem;"> | |
| <h3>GENERATIVE AI ENGINEER (Contract - Remote)</h3> | |
| <p class="company" style="font-weight: bold; color: #bbb; margin-bottom: 0.25rem;">Private Client | Sydney, Australia</p> | |
| <p class="duration" style="font-size: 0.9rem; color: #999; margin-bottom: 0.75rem;">October - November 2024</p> | |
| <ul style="margin-top: 0.5rem; padding-left: 20px;"> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;">Developed secure, on-premise solutions for private Complex data (Complex PDF with Images and Charts) Q&A and knowledge retrieval.</li> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;">Built data ingestion pipeline (100-500 documents daily) with vision embeddings and task scheduler updates.</li> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;">Built multimodal RAG pipelines (Byaldi, Colqwen2, Pixtral 12B) optimized for diverse document types (70% accuracy improvement).</li> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;">Containerized the application with Docker for deployment flexibility.</li> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;"><strong>Open Source Contribution - Byaldi - 575✩:</strong> while working on It</li> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;"><strong>Technologies:</strong> NLP, Vision Embeddings, Local Multimodal RAG, LangChain, Pixtral 12B, Col-Qwen2, Byaldi</li> | |
| </ul> | |
| </div> | |
| </section> | |
| <section class="projects" style="padding: 2rem; margin-bottom: 1.5rem; border-radius: 8px;"> | |
| <h2>Projects</h2> | |
| <div class="project-list" style="display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 1.5rem;"> | |
| <div class="project-card" style="border: 1px solid #444; padding: 1.5rem; border-radius: 8px;"> | |
| <h3 style="margin-top: 0; margin-bottom: 0.75rem;">LLM-finetuning and SQL Agent with Auto_Execution with DuckDB, Schema Retriever from CSVs, manual SQL executer.</h3> | |
| <p class="status ongoing" style="font-size: 0.85rem; font-weight: bold; margin-top: 0.5rem; color: #f9bb6d;">Current WebAPP which you are using</p> | |
| <p class="status deployed" style="font-size: 0.85rem; font-weight: bold; margin-top: 0.5rem; color: #a3be8c;">Deployed <i class="fas fa-check-circle"></i></p> | |
| </div> | |
| <div class="project-card" style="border: 1px solid #444; padding: 1.5rem; border-radius: 8px;"> | |
| <h3 style="margin-top: 0; margin-bottom: 0.75rem;">Deep Learning Based Recommendation System</h3> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;">Initially, I aimed to build a recommendation system from scratch using TensorFlow Recommenders (TFRS) on the MovieLens 1M dataset. This involved creating user and movie embeddings with a candidate generation and ranking model.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;">However, this TFRS approach proved too resource-intensive and time-consuming for effective training and testing. Crucially, the initial results weren't satisfactory for deployment.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;">Therefore, for the deployed web application, I switched to pre-trained models (BGE embeddings and re-ranking). This offered:</p> | |
| <ul style="padding-left: 20px;"> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;"><b>Better Performance:</b> More relevant recommendations.</li> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;"><b>Reduced Resources/Time:</b> Faster training and deployment.</li> | |
| </ul> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;">While the TFRS code is also included in the web app, the pre-trained model approach was chosen for its superior results and efficiency in a deployment setting. A future improvement could be fine-tuning the pre-trained models for even better performance.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Technologies used:</strong> TensorFlow Recommenders, Scann, Vector DB, Distributed GPU Training, Langchain, Streamlit, BAAI BGE Models</p> | |
| <div class="project-links"> | |
| <a href="https://debopam-movie-recommendation-system.streamlit.app/" target="_blank" style="display: inline-block; margin-right: 1rem; font-size: 0.9rem;">Try it out APP Live</a> | |
| </div> | |
| <p class="status deployed" style="font-size: 0.85rem; font-weight: bold; margin-top: 0.5rem; color: #a3be8c;">Deployed <i class="fas fa-check-circle"></i></p> | |
| </div> | |
| <div class="project-card" style="border: 1px solid #444; padding: 1.5rem; border-radius: 8px;"> | |
| <h3 style="margin-top: 0; margin-bottom: 0.75rem;">IBM EMPLOYEE ATTRITION PREDICTOR (End to End with Deployment to AWS, FastAPI with Proxy Server)</h3> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Objective:</strong> Predicted employee attrition with 85% AUC to improve employee retention and business performance.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Model Development:</strong> Hyperparameter optimized Multi-Layer Perceptron, XGBoost, Logistic Regression with Inference as well as Training Pipeline</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Backend:</strong> Developed a FastAPI backend for real-time predictions, using Pydantic for schema validation for incoming and outgoing requests</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Deployment:</strong> Containerized with Docker, deployed on AWS EC2, managed via AWS ECR.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>CI/CD:</strong> Set up an automated CI/CD pipeline using GitHub Actions for seamless updates.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Web Application:</strong> Built a user-friendly interface using Flutter Web for real-time interaction.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Security:</strong> Handled HTTPS requests using Caddy as a reverse proxy server</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Technologies used:</strong> TensorFlow, AWS, Docker, FastAPI, CI/CD Pipeline, Multi-Layer Perceptron, Neural Network, XGBoost, Logistic Regression, Hyperparameter Tuned Models, GitHub Actions, Pydantic, Flutter Web, Reverse-Proxy-Server: Caddy</p> | |
| <div class="project-links"> | |
| <a href="https://www.linkedin.com/posts/debopam-chowdhury-param-600619229_machinelearning-deeplearning-aws-activity-7244476917884608512-DfbD/?utm_source=share&utm_medium=member_desktop" target="_blank" style="display: inline-block; margin-right: 1rem; font-size: 0.9rem;">Explanation & Live Demo</a> | |
| <a href="http://www.debopamchowdhury.works" target="_blank" style="display: inline-block; margin-right: 1rem; font-size: 0.9rem;">Click to try out Live</a> | |
| </div> | |
| <p class="status deployed" style="font-size: 0.85rem; font-weight: bold; margin-top: 0.5rem; color: #a3be8c;">Deployed <i class="fas fa-check-circle"></i></p> | |
| </div> | |
| <div class="project-card" style="border: 1px solid #444; padding: 1.5rem; border-radius: 8px;"> | |
| <h3 style="margin-top: 0; margin-bottom: 0.75rem;">AI GYM BUDDY (Langchain | Flutter | Riverpod | Gemini)</h3> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;">Personalized AI-Driven Workouts with Smart Equipment Detection and Progress Tracking</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Features:</strong> Al Instrument Detection (Camera or Gallery), Exercises based on Available Equipments, Time, Preffered Muscle Groups & Custom requests, Dynamic Video Tutorial Finder for each exercise, Super personalized Al generated routine, Workout History Tracker, Easy SignUp/Login with Google Oauth</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Technologies used:</strong> Dart, flutter, firebase, gemini 1.5 flash, riverpod, langchain, fastapi, google oauth</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Licenses:</strong> This code of this app/website is written from scratch and I hold all the rights over distribution</p> | |
| <div class="project-links"> | |
| <a href="https://www.youtube.com/shorts/0ZR0IWiZJQE" target="_blank" style="display: inline-block; margin-right: 1rem; font-size: 0.9rem;">1 Minute Video Demo</a> | |
| <a href="https://play.google.com/store/apps/details?id=com.aigymbuddy.me&hl=en" target="_blank" style="display: inline-block; margin-right: 1rem; font-size: 0.9rem;"> | |
| <img src="https://static-00.iconduck.com/assets.00/google-play-icon-2048x2048-487quz63.png" alt="Google Play Store" class="playstore-icon" style="width: 16px; height: 16px; vertical-align: middle; margin-right: 0.25rem;"> Google Play Store (Android) | |
| </a> | |
| <a href="http://www.aigymbuddy.in" target="_blank" style="display: inline-block; margin-right: 1rem; font-size: 0.9rem;">Prototype for WEB www.aigymbuddy.in</a> | |
| </div> | |
| <p class="status deployed" style="font-size: 0.85rem; font-weight: bold; margin-top: 0.5rem; color: #a3be8c;">Deployed <i class="fas fa-check-circle"></i></p> | |
| </div> | |
| <div class="project-card" style="border: 1px solid #444; padding: 1.5rem; border-radius: 8px;"> | |
| <h3 style="margin-top: 0; margin-bottom: 0.75rem;">Non-Sequential Breast Cancer Classification System</h3> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Multi-Modal Cancer Detection:</strong> Developed a novel multi-output deep learning model for breast cancer detection, predicting cancer presence, invasiveness, and difficult-negative case status. The model incorporates both mammogram images and tabular clinical data, leveraging a non-sequential architecture to process distinct data modalities.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Fine-Tuned Image Feature Extraction:</strong> Utilized a pre-trained EfficientNetV2B3 model for image feature extraction, fine-tuning layers from block 6 onwards to enhance its applicability to the specific task, thus improving the quality of learned representations and potentially making the model more robust and accurate.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Distributed Training:</strong> Accelerated model training through distributed training using TensorFlow's MirroredStrategy on 2xT4 GPUs for 9 hours on Kaggle, demonstrating proficiency in optimizing model training with limited computational resources.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Technologies used:</strong> TensorFlow, Transfer Learning, EfficientNetV2, Fused MB-CNN</p> | |
| <div class="project-links"> | |
| <a href="https://debopamparam-bcd-inference-vvyb1v.streamlit.app/" target="_blank" style="display: inline-block; margin-right: 1rem; font-size: 0.9rem;">Live Webapp + Architecture + Training Code + Evaluation Metrics</a> | |
| </div> | |
| <p class="status deployed" style="font-size: 0.85rem; font-weight: bold; margin-top: 0.5rem; color: #a3be8c;">Deployed <i class="fas fa-check-circle"></i></p> | |
| </div> | |
| <div class="project-card" style="border: 1px solid #444; padding: 1.5rem; border-radius: 8px;"> | |
| <h3 style="margin-top: 0; margin-bottom: 0.75rem;">Image Entity Extraction with Qwen2 VL: Large-Scale Inference</h3> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Problem Statement:</strong> E-commerce and healthcare industries struggle to efficiently extract product details (weight, volume, dimensions) from images at scale.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Action:</strong> Developed a large-scale image-to-text inference pipeline using Qwen2 VL: 2B, incorporating image preprocessing, Regex, and parallel processing. Processed 84,000 of 131,000 test images.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Result:</strong> Successfully extracted product values from a significant portion of the dataset. Our team of four ranked 172nd out of ~75,000 in the Amazon ML Challenge with Fl-Score=0.47, demonstrating the solution's potential for automated product information extraction.</p> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Technologies used:</strong> Qwen2 VL, Python, Regex, Parallel Processing</p> | |
| <div class="project-links"> | |
| <a href="https://colab.research.google.com/drive/1V5F1XMlYNHzv-hA9xmIJ-Jx5vuD0fKAR?usp=sharing" target="_blank" style="display: inline-block; margin-right: 1rem; font-size: 0.9rem;">Click Here to see the code</a> | |
| </div> | |
| </div> | |
| <div class="project-card" style="border: 1px solid #444; padding: 1.5rem; border-radius: 8px;"> | |
| <h3 style="margin-top: 0; margin-bottom: 0.75rem;">LLM based ATS System using VertexAI Embedding</h3> | |
| <p style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>Technologies used:</strong> Langchain, VertexAI Embedding, StreamLit, PostGresVector</p> | |
| <p class="status ongoing" style="font-size: 0.85rem; font-weight: bold; margin-top: 0.5rem; color: #f9bb6d;">Ongoing</p> | |
| </div> | |
| </div> | |
| </section> | |
| <section class="volunteering" style="padding: 2rem; margin-bottom: 1.5rem; border-radius: 8px;"> | |
| <h2>Volunteering</h2> | |
| <div class="volunteering-item" style="margin-bottom: 1rem;"> | |
| <h3>Git/GitHub Instructor (Volunteer)</h3> | |
| <p style="color: #ccc;">Carried out sessions to teach juniors the fundamentals of Git and GitHub, covering version control, collaboration, and best practices.</p> | |
| </div> | |
| <div class="volunteering-item" style="margin-bottom: 1rem;"> | |
| <h3>Event Coordinator</h3> | |
| <p style="color: #ccc;">Acharya Technical Club - Steigen</p> | |
| </div> | |
| </section> | |
| <section class="achievements" style="padding: 2rem; margin-bottom: 1.5rem; border-radius: 8px;"> | |
| <h2>Achievements</h2> | |
| <ul style="padding-left: 20px;"> | |
| <li style="margin-bottom: 0.75rem; font-size: 0.95rem;">Secured rank 172 out of 75000 participants in AMAZON ML Challenge Hackathon 2024.</li> | |
| <li style="margin-bottom: 0.75rem; font-size: 0.95rem;">2nd place out of 60 in the TechnioD Hackathon.</li> | |
| <li style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong><a href="https://github.com/AnswerDotAI/byaldi/pull/50" target="_blank" style="color: #89b4fa;">Open - Source Contribution - Byaldi - 575☆</a></strong> | |
| <ul style="padding-left: 20px;"> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;">Fix langchain integration not present in pypi tar & whl-- pyproject.toml</li> | |
| </ul> | |
| </li> | |
| <li style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>NON TECHNICAL :</strong> | |
| <ul style="padding-left: 20px;"> | |
| <li style="margin-bottom: 0.5rem; font-size: 0.95rem;">Performed in IIT BOMBAY'S Mood Indigo Bengaluru Event - Finalist.</li> | |
| </ul> | |
| </li> | |
| </ul> | |
| </section> | |
| <section class="certifications" style="padding: 2rem; margin-bottom: 1.5rem; border-radius: 8px;"> | |
| <h2>Certifications</h2> | |
| <ul style="padding-left: 20px;"> | |
| <li style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>COURSERA:</strong> Advanced Learning Algorithms | <a href="https://coursera.org/share/d540318c8cb4a7e802d8c4964a471d34" target="_blank" style="color: #89b4fa;">View Certificate</a></li> | |
| <li style="margin-bottom: 0.75rem; font-size: 0.95rem;"><strong>COURSERA:</strong> Supervised Machine Learning: Regression and Classification | <a href="https://coursera.org/share/d540318c8cb4a7e802d8c4964a471d34" target="_blank" style="color: #89b4fa;">View Certificate</a></li> | |
| </ul> | |
| </section> | |
| <section class="education" style="padding: 2rem; margin-bottom: 1.5rem; border-radius: 8px;"> | |
| <h2>Education</h2> | |
| <div class="education-item" style="margin-bottom: 0.75rem; font-size: 0.95rem;"> | |
| <h3>BE in Information Science</h3> | |
| <p class="institution" style="color: #ccc;">Acharya Institute of Technology, Bangalore</p> | |
| <p class="duration" style="font-size: 0.9rem; color: #999;">2021-2025</p> | |
| <p>CGPA-8.12</p> | |
| </div> | |
| <div class="education-item" style="margin-bottom: 0.75rem; font-size: 0.95rem;"> | |
| <h3>Higher Secondary Education</h3> | |
| <p class="institution" style="color: #ccc;">Kalyani Public School, Barasat, Kolkata</p> | |
| <p class="duration" style="font-size: 0.9rem; color: #999;">2021</p> | |
| <p>77% (Auto Pass Covid Batch)</p> | |
| </div> | |
| <div class="education-item" style="margin-bottom: 0.75rem; font-size: 0.95rem;"> | |
| <h3>Secondary Education</h3> | |
| <p class="institution" style="color: #ccc;">Sacred Heart Day High School, Kolkata</p> | |
| <p class="duration" style="font-size: 0.9rem; color: #999;">2019</p> | |
| <p>90%</p> | |
| </div> | |
| </section> | |
| <footer style="text-align: center; padding: 1rem 0; font-size: 0.9rem;"> | |
| <p>© 2024 Debopam Chowdhury (Param)</p> | |
| </footer> | |
| </body> | |
| </html>""" | |
| ) | |