import streamlit as st def display_projects(): st.title('My Projects') # Define tab titles tab_titles = [ "Resume & CV Crafter", "Multi-Agent Job Search", "Resume Easz", "Job Easz", "Bitcoin Lightning Optimization", "National Infrastructure Monitoring", "Stock Market Analysis", "Twitter Trend Analysis", "Restaurant Recommendation", "ASL Translator", "Squat Easy" ] # Create tabs tabs = st.tabs(tab_titles) # Add content to each tab with tabs[0]: st.subheader("LLM-powered Resume & CV Crafter") st.markdown(""" - **Description**: Developed AI platform combining LLaMA-3 70B and Deepseek R1 with low-temperature settings for stable, tailored resume and CV generation - **Key Features**: • Smart Matching Algorithm analyzing profiles against job requirements • LaTeX-Powered Resumes with professional formatting • Automated 4-paragraph Cover Letter Generation • Performance Metrics evaluating match quality - **Technical Achievements**: • Implemented dual-agent architecture: LLaMA-3 8B for profile analysis and 70B for LaTeX generation • Engineered JSON schema validation system for error-free template integration • Achieved 5,000+ LinkedIn impressions with 80% reduction in creation time - **Technologies**: Streamlit, GROQ API (LLaMA-3 70B), LaTeX, JSON Schema - **Reference**: [Link to Project](https://huggingface.co/spaces/Niharmahesh/Resume_and_CV_crafter) """) with tabs[1]: st.subheader("Multi-Agent Job Search System") st.markdown(""" - **Description**: Built an AI-powered job search assistant using dual-LLaMA architecture for comprehensive job matching and analysis - **Key Features**: • Real-time scraping across LinkedIn, Glassdoor, Indeed, ZipRecruiter • Advanced resume parsing and job matching • Intelligent compatibility scoring system - **Technical Achievements**: • Developed batch processing pipeline handling 60+ positions/search • Reduced job search time by 80% through accurate matching • Implemented specialized agents for input processing, scraping, and analysis - **Technologies**: GROQ API, jobspy, Streamlit, Pandas, LLMOps - **Reference**: [Link to Project](https://huggingface.co/spaces/Niharmahesh/Multi_Agent_Job_search_and_match) """) with tabs[2]: st.subheader("Resume Easz") st.markdown(""" - **Description**: Created an AI-driven resume analysis and enhancement tool using LLaMA 3.3 model - **Key Features**: • Quick and in-depth resume analysis options • Comprehensive skill gap analysis • ATS compatibility optimization • Multiple output formats (DOCX, HTML, TXT) - **Technical Implementation**: • Integrated GROQ API for advanced language processing • Built visual diff system for resume changes • Developed custom prompt engineering pipeline - **Technologies**: GROQ API, Streamlit, Python, LLM - **Reference**: [Link to Project](https://resume-easz.streamlit.app/) """) with tabs[3]: st.subheader("Job Easz") st.markdown(""" - **Description**: Engineered comprehensive job aggregation platform for data roles with advanced analytics - **Technical Achievements**: • Designed Airflow pipeline with exponential backoff retry (120-480s intervals) • Optimized concurrent processing reducing runtime from 2h to 40min • Processes ~3000 daily job listings across various data roles - **Key Features**: • Daily updates with comprehensive job role coverage • Custom filtering by role and location • Interactive dashboard for market trends • Automated ETL pipeline - **Technologies**: Python, Airflow, ThreadPoolExecutor, Hugging Face Datasets - **Reference**: [Link to Project](https://huggingface.co/spaces/Niharmahesh/job_easz) """) with tabs[4]: st.subheader("Bitcoin Lightning Path Optimization") st.markdown(""" - **Description**: Advanced payment routing optimization system for Bitcoin Lightning Network - **Technical Achievements**: • Developed ML classifiers achieving 98.77-99.10% accuracy • Implemented tri-model consensus system for optimal routing • Engineered ensemble models with 0.98 F1-scores - **Implementation Details**: • Created simulation environment for multi-channel transactions • Optimized graph-based algorithms for payment routing • Integrated with Lightning payment interceptor - **Technologies**: XGBoost, Random Forest, AdaBoost, Graph Algorithms """) with tabs[5]: st.subheader("National Infrastructure Monitoring") st.markdown(""" - **Description**: Developed satellite imagery analysis system for infrastructure change detection - **Technical Achievements**: • Fine-tuned ViT+ResNet-101 ensemble on 40GB satellite dataset • Achieved 85% accuracy in change detection • Implemented 8 parallel GPU threads for enhanced performance - **Key Features**: • Temporal analysis with 1km resolution • Interactive map interface with bounding box selection • Automatic image chipping for 256x256 inputs • Contrast adjustment optimization - **Technologies**: Change ViT Model, Google Earth Engine, PyTorch, Computer Vision - **Reference**: [Link to Project](https://huggingface.co/spaces/Niharmahesh/Data298) """) with tabs[6]: st.subheader("Stock Market Analysis with OpenAI Integration") st.markdown(""" - **Description**: Created comprehensive stock market analysis system with multilingual capabilities - **Technical Achievements**: • Built Spark streaming pipeline with 30% efficiency improvement • Orchestrated Airflow Docker pipeline for Snowflake integration • Developed bilingual GPT-3.5 chatbot for SQL query generation - **Key Features**: • Real-time financial metric calculations • Custom indicator generation • Multilingual query support • Automated data warehousing - **Technologies**: PySpark, Apache Airflow, Snowflake, OpenAI GPT-3.5 """) with tabs[7]: st.subheader("Twitter Trend Analysis") st.markdown(""" - **Description**: Engineered comprehensive Twitter analytics platform using GCP services - **Technical Achievements**: • Developed GCP pipeline processing 40k tweets • Achieved 40% efficiency improvement through custom Airflow operators • Implemented real-time trend analysis algorithms - **Key Features**: • Automated ETL workflows • Interactive Tableau dashboards • Viral metrics tracking • Engagement rate calculations - **Technologies**: Google Cloud Platform, BigQuery, Apache Airflow, Tableau """) with tabs[8]: st.subheader("Restaurant Recommendation System") st.markdown(""" - **Description**: Built hybrid recommendation system combining multiple filtering approaches - **Technical Achievements**: • Created hybrid TF-IDF and SVD-based filtering system • Achieved 43% improvement in recommendation relevance • Reduced computation time by 65% - **Key Features**: • Location-based suggestions • Personalized recommendations • Interactive web interface • Efficient matrix factorization - **Technologies**: Collaborative Filtering, Content-Based Filtering, Flask, Folium """) with tabs[9]: st.subheader("ASL Translator") st.markdown(""" - **Description**: Developed real-time American Sign Language translation system - **Technical Achievements**: • Achieved 95% accuracy in real-time gesture interpretation • Implemented adaptive hand skeleton GIF generator • Optimized MediaPipe integration for point detection - **Key Features**: • Real-time hand tracking • Visual feedback system • Intuitive gesture recognition • Accessible interface - **Technologies**: MediaPipe Hand Detection, Random Forest, Hugging Face Platform - **Reference**: [Link to Project](https://huggingface.co/spaces/Niharmahesh/slr-easz) """) with tabs[10]: st.subheader("Squat Easy") st.markdown(""" - **Description**: Developed deep learning system for squat form analysis and error detection - **Technical Achievements**: • Engineered custom BiLSTM architecture in PyTorch • Achieved 81% training and 75% test accuracy • Implemented CUDA-based GPU acceleration - **Key Features**: • Real-time form analysis • Six-type error classification • Video processing pipeline • Performance optimization - **Technologies**: PyTorch, BiLSTM, CUDA, Object-Oriented Programming - **Reference**: [Link to Project](https://github.com/niharpalem/squateasy_DL) """)