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| import streamlit as st | |
| from PIL import Image | |
| from streamlit_lottie import st_lottie | |
| import json | |
| from streamlit_option_menu import option_menu # Import the option_menu | |
| #setting layout to wide | |
| st.set_page_config(layout="wide") | |
| # Load CSS for styling with a minimalist grey background | |
| with open("style.css") as f: | |
| css_content = f.read() | |
| css_content += ''' | |
| body { | |
| background-color: #f0f2f6; | |
| } | |
| ''' | |
| st.markdown('<style>{}</style>'.format(css_content), unsafe_allow_html=True) | |
| def load_lottiefile(filepath: str): | |
| with open(filepath, "r") as file: | |
| return json.load(file) | |
| def display_header(): | |
| st.write(''' | |
| # Nihar Palem | |
| ##### | |
| ''') | |
| # Assuming you have a Lottie animation to display | |
| lottie_animation = load_lottiefile("bio.json") | |
| st_lottie(lottie_animation, height=300, key="header_animation") | |
| def display_summary(): | |
| #st.markdown('## Summary', unsafe_allow_html=True) | |
| st.markdown("""Hello! This is Nihar Palem. I'm originally from Hyderabad and currently residing in the Silicon Valley Bay Area, San Jose. I'm a Graduate with Master's degree in Data Analytics (Applied Data Science) from San Jose State University. In this portfolio, you can explore my academic background, work experience, and projects in the data science field. You'll also find links to my skills, other hobbies, and certifications.""") | |
| def display_education(): | |
| st.markdown('## Education') | |
| st.write(""" | |
| - **Masters In Data Analytics**, *San Jose State University*, USA (2023-2024) | |
| - Courses: Data Mining, Deep Learning, Big Data Technologies, Data Visualization, Machine Learning, Database Management Systems | |
| - Achievements: | |
| - A Grade in Deep Learning | |
| - Instructional Student Assistant (ISA) | |
| - Mentored 80+ students on technical aspects of data modeling projects, guiding them through comprehensive project report writing and adhering to proper grading formats. | |
| - Reviewed and provided feedback on data pipeline demonstrations, ensuring quality and best practices, while offering expert advice on resolving complex technical issues related to data analysis and machine learning models. | |
| - **Bachelor of Technology (B.Tech) in Electrical and Electronics Engineering (EEE)**, *Sreenidhi Institute of Science and Technology (SNIST)*, Hyderabad (2015-2019) | |
| - Activities: | |
| - Memeber of the Robotics Club:, built line follower and theft-alert detection bots. | |
| - Member of the college cricket team; won the Hyderabad zone-level tournament | |
| """) | |
| def display_work_experience(): | |
| st.markdown('## Work Experience') | |
| st.write(""" | |
| **Bharat Electronics Limited, Hyderabad** | |
| February 2021 - March 2022 | |
| - **Data Analyst** | |
| - Optimized complex SQL queries for multi-million row datasets, boosting performance by 40% and accelerating reporting capabilities. | |
| - Engineered robust Python scripts with Pandas for large-scale data extraction and analysis from SQL Server. | |
| - Designed and implemented interactive dashboards using Matplotlib and Seaborn, delivering real-time insights into key business indicators and financial metrics, enhancing strategic decision-making and streamlining centralized government reporting. | |
| - Automated monthly processes and improved data quality by implementing SQL stored procedures and triggers, resulting in a 30% reduction in data entry errors and a 40% decrease in routine reporting time. | |
| **Technical Writer** | |
| 2023-Present | |
| - Embarked on a new journey in 2023 as a technical writer, sharing insights and developments in data science and data engineering with a growing audience. | |
| - Authored numerous articles that explore complex topics in an accessible and informative manner, focusing on data science, machine learning, bioinformatics, and data engineering. | |
| - This new habit aims to educate and inspire, bridging the gap between technical expertise and practical application in the modern data landscape. | |
| - Find my work on [Medium](https://medium.com/@nihar-palem) and [Substack](https://niharpalem.substack.com/publish/posts). | |
| """) | |
| 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.header("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.header("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.header("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.header("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.header("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.header("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.header("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.header("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.header("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.header("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.header("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) | |
| """) | |
| def display_skills(): | |
| st.title('Skills') | |
| # Define tab titles | |
| tab_titles = [ | |
| "Programming & Core", | |
| "AI & ML", | |
| "Data Engineering", | |
| "Data Architecture", | |
| "Visualization", | |
| "Specialized Systems" | |
| ] | |
| # Create tabs | |
| tabs = st.tabs(tab_titles) | |
| # Programming & Core Technologies | |
| with tabs[0]: | |
| st.header("Programming & Core Technologies") | |
| st.markdown(""" | |
| - **Programming Languages**: | |
| • Python (Advanced) | |
| • SQL (Advanced) | |
| • Shell Scripting | |
| - **Database Systems**: | |
| • Relational: MySQL, PostgreSQL | |
| • NoSQL: MongoDB | |
| • Data Warehouses: Snowflake, Redshift | |
| - **Development Tools**: | |
| • Version Control: Git, GitHub | |
| • Containerization: Docker | |
| • IDE: VS Code, PyCharm | |
| • Microsoft Office Suite | |
| """) | |
| # AI & Machine Learning | |
| with tabs[1]: | |
| st.header("AI & Machine Learning") | |
| st.markdown(""" | |
| - **Machine Learning Frameworks**: | |
| • PyTorch | |
| • TensorFlow | |
| • Scikit-Learn | |
| • XGBoost, Random Forest, AdaBoost | |
| - **Deep Learning**: | |
| • Vision Transformers | |
| • ResNet Architectures | |
| • Neural Networks | |
| • BiLSTM | |
| - **LLM & NLP**: | |
| • LLaMA-3 (70B/8B) | |
| • GPT-3.5 | |
| • Sentence Transformers | |
| • Advanced Prompt Engineering | |
| • Multi-agent Architectures | |
| - **Computer Vision**: | |
| • MediaPipe | |
| • OpenCV | |
| • Image Processing Pipelines | |
| """) | |
| # Data Engineering & Cloud | |
| with tabs[2]: | |
| st.header("Data Engineering & Cloud") | |
| st.markdown(""" | |
| - **Cloud Platforms**: | |
| • AWS (Certified) | |
| • Google Cloud Platform (GCP) | |
| • Cloud Architecture Design | |
| - **Big Data Technologies**: | |
| • Apache Spark (PySpark) | |
| • Apache Airflow | |
| • BigQuery | |
| • Hadoop Ecosystem | |
| - **Data Pipeline Tools**: | |
| • ETL/ELT Pipeline Design | |
| • Workflow Orchestration | |
| • Concurrent Processing | |
| • Real-time Data Streaming | |
| - **Infrastructure**: | |
| • CI/CD Pipelines | |
| • Infrastructure as Code | |
| • Kubernetes Basics | |
| """) | |
| # Data Architecture & Analytics | |
| with tabs[3]: | |
| st.header("Data Architecture & Analytics") | |
| st.markdown(""" | |
| - **Data Modeling**: | |
| • OLAP/OLTP Systems | |
| • Star/Snowflake Schema | |
| • Data Normalization | |
| • Database Optimization | |
| - **Analytics Techniques**: | |
| • Streaming Analytics | |
| • Batch Processing | |
| • Time Series Analysis | |
| • Statistical Analysis | |
| - **Data Processing**: | |
| • Pandas, NumPy | |
| • Data Wrangling | |
| • Feature Engineering | |
| • Data Quality Assurance | |
| - **Performance Optimization**: | |
| • Query Optimization | |
| • Indexing Strategies | |
| • Caching Mechanisms | |
| """) | |
| # Visualization & Deployment | |
| with tabs[4]: | |
| st.header("Visualization & Tools") | |
| st.markdown(""" | |
| - **Business Intelligence**: | |
| • Tableau | |
| • Power BI | |
| • Dashboard Design | |
| • KPI Monitoring | |
| - **Technical Visualization**: | |
| • Plotly | |
| • Seaborn | |
| • Matplotlib | |
| • Interactive Charts | |
| - **Deployment & Interface**: | |
| • Streamlit | |
| • Flask | |
| • Web Development | |
| • API Design | |
| - **Collaboration Tools**: | |
| • JIRA | |
| • Notion | |
| • Git Workflow | |
| • Agile Methodologies | |
| """) | |
| # Specialized Systems | |
| with tabs[5]: | |
| st.header("Specialized Systems") | |
| st.markdown(""" | |
| - **Recommender Systems**: | |
| • Hybrid Filtering Techniques | |
| • Content-Based Filtering | |
| • Collaborative Filtering | |
| • Matrix Factorization (SVD) | |
| - **Ensemble Methods**: | |
| • Multi-model Consensus Systems | |
| • Classifier Combinations | |
| • Voting Systems | |
| • Stacking Implementations | |
| - **Performance Optimization**: | |
| • CUDA Acceleration | |
| • Parallel Processing | |
| • Resource Management | |
| • Scalability Design | |
| - **Custom Solutions**: | |
| • Natural Language Processing | |
| • Computer Vision Systems | |
| • Time Series Forecasting | |
| • Anomaly Detection | |
| """) | |
| def display_apps(): | |
| st.markdown('## Apps') | |
| st.markdown(""" | |
| - [CNN arch](https://cnn-arch.streamlit.app/) | |
| """) | |
| st.markdown(""" | |
| - [TuNNe](https://huggingface.co/spaces/Niharmahesh/TuNNe) | |
| """) | |
| def display_certifications(): | |
| st.markdown('## Certifications') | |
| certifications = [ | |
| {"title": "Python for Data Science and Machine Learning Bootcamp", "issuer": "Udemy", "date": "2023", "skills": "Python, Data Science, Machine Learning"}, | |
| {"title": "HackerRank SQL (Basic)", "issuer": "HackerRank", "date": "2023", "skills": "SQL, Database Management"}, | |
| {"title": "AWS Cloud Practitioner", "issuer": "Udemy", "date": "2023", "skills": "Cloud Computing, AWS Services"}, | |
| {"title": "AWS Certified Cloud Practitioner", "issuer": "Amazon Web Services", "date": "2023", "skills": "Cloud Architecture, AWS Best Practices"} | |
| ] | |
| for cert in certifications: | |
| with st.expander(cert["title"]): | |
| st.write(f"**Issuer:** {cert['issuer']}") | |
| st.write(f"**Date:** {cert['date']}") | |
| st.write(f"**Skills:** {cert['skills']}") | |
| def display_social_media(): | |
| st.markdown('## Social Media') | |
| st.markdown(""" | |
| - [LinkedIn](https://www.linkedin.com/in/sai-nihar-1b955a183/) | |
| - [GitHub](https://github.com/niharpalem) | |
| - [Medium](https://medium.com/@nihar-palem) | |
| - [Twitter](https://twitter.com/niharpalem_2497) | |
| - [Email](mailto:[email protected]) | |
| """) | |
| menu_items_with_icons = { | |
| "🎓": display_education, | |
| "💼": display_work_experience, | |
| "📁": display_projects, | |
| "🛠️": display_skills, | |
| "🌐": display_social_media, | |
| "🏆": display_certifications, | |
| "📱": display_apps | |
| } | |
| def main(): | |
| # Initialize session state for selected function | |
| if 'selected_function' not in st.session_state: | |
| st.session_state.selected_function = None # Default to None to not display any section initially | |
| # Display the header with your name and Lottie animation first | |
| display_header() | |
| # Display the summary section immediately after the header | |
| display_summary() | |
| # Create a row of buttons for each icon in the menu | |
| cols = st.columns(len(menu_items_with_icons)) | |
| for col, (icon, func) in zip(cols, menu_items_with_icons.items()): | |
| if col.button(icon): | |
| # Update the session state to the selected function | |
| st.session_state.selected_function = func | |
| # If a function has been selected, call it | |
| if st.session_state.selected_function is not None: | |
| st.session_state.selected_function() | |
| if __name__ == "__main__": | |
| main() | |