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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 pursuing a Master's degree in Data Analytics at 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). | |
| """) | |
| import streamlit as st | |
| def display_projects(): | |
| st.title('My Projects') | |
| # Define tab titles | |
| tab_titles = [ | |
| "Squat Easy", | |
| "ASL Translator", | |
| "Face Recognition", | |
| "Stock Market Chatbot", | |
| "Twitter Trend Analysis", | |
| "Restaurant Recommendation System", | |
| "Bitcoin Lightning Path Optimization", | |
| "National Infrastructure Monitoring" | |
| ] | |
| # Create tabs | |
| tabs = st.tabs(tab_titles) | |
| # Add content to each tab | |
| with tabs[0]: | |
| st.header("Squat Easy") | |
| st.markdown(""" | |
| - **Description**: Engineered a custom BiLSTM architecture in PyTorch with extensive hyperparameter tuning, achieving 81% training and 75% test accuracy in classifying six types of squatting errors from video data. Optimized through data augmentation and CUDA-based GPU acceleration. | |
| - **Technologies Used**: PyTorch, Object-Oriented Programming (OOP), GitHub | |
| - **Reference**: [Link to Project](https://github.com/niharpalem/squateasy_DL) | |
| """) | |
| with tabs[1]: | |
| st.header("ASL Translator") | |
| st.markdown(""" | |
| - **Description**: Crafted a ASL translation system using MediaPipe for point detection and trained a Random Forest Model , achieving 95% accuracy in real-time gesture interpretation. Implemented an adaptive hand skeleton GIF generator for intuitive visual representation. | |
| - **Technologies Used**: MediaPipe Hand Detection, Hugging Face Platform | |
| - **Reference**: [Link to Project](https://huggingface.co/spaces/Niharmahesh/slr-easz) | |
| """) | |
| with tabs[2]: | |
| st.header("Face Recognition") | |
| st.markdown(""" | |
| - **Description**: Integrated fiducial point features, CNN-extracted image features, and Siamese networks in TensorFlow, attaining 85% test accuracy for facial recognition. Optimized for real-world security applications by balancing computational efficiency with accuracy. | |
| - **Technologies Used**: TensorFlow, Siamese Networks | |
| - **Reference**: [Link to Project](#) | |
| """) | |
| with tabs[3]: | |
| st.header("Stock Market Chatbot") | |
| st.markdown(""" | |
| - **Description**: Architected a multilingual stock analysis system using Apache Spark and a custom LLM, boosting query performance by 25% over traditional SQL approaches. Interfaced with Snowflake for efficient financial data retrieval and real-time insights in English and Chinese. | |
| - **Technologies Used**: PySpark for Querying, Data Warehousing with Redshift and Snowflake | |
| - **Reference**: [Link to Project](#) | |
| """) | |
| with tabs[4]: | |
| st.header("Twitter Trend Analysis") | |
| st.markdown(""" | |
| - **Description**: Engineered an ELT pipeline using GCP's BigQuery for Twitter data processing and sentiment analysis. Integrated Tableau for live, interactive dashboards, showcasing advanced cloud data engineering skills and cost-effective data storage solutions. | |
| - **Technologies Used**: Apache Airflow for Automation, Docker, Tableau for Dashboards | |
| - **Reference**: [Link to Project](#) | |
| """) | |
| with tabs[5]: | |
| st.header("Restaurant Recommendation System") | |
| st.markdown(""" | |
| - **Description**: Engineered a recommendation engine using collaborative and content-based filtering, achieving a 15% accuracy increase. Constructed a Flask web app with Folium integration for an interactive, location-based restaurant suggestion interface. | |
| - **Technologies Used**: Collaborative Filtering, Content-Based Filtering | |
| - **Reference**: [Link to Project](#) | |
| """) | |
| with tabs[6]: | |
| st.header("Bitcoin Lightning Path Optimization") | |
| st.markdown(""" | |
| - **Description**: Implemented a graph-based algorithm to optimize payment routing in the Bitcoin Lightning Network. Created a simulation environment to validate improved efficiency in multi-channel transactions under various network conditions. | |
| - **Technologies Used**: Graph-Based Algorithms, Simulation Environments | |
| - **Reference**: [Link to Project](#) | |
| """) | |
| with tabs[7]: | |
| st.header("National Infrastructure Monitoring") | |
| st.markdown(""" | |
| - **Description**: Utilized satellite imagery to detect changes between different time frames by fine-tuning a Change ViT model. Developed a UI where users can draw bounding boxes on a Python map library; these coordinates are used in Google Earth Engine (GEE) to extract Sentinel-2 imagery. Users can select the resolution of images for caching. The processing function includes contrast adjustments and automatic image chipping as the model requires 256x256 inputs, generating change masks effectively. | |
| - **Technologies Used**: Satellite Imagery Analysis, Change ViT Model, Google Earth Engine (GEE), Python Map Libraries | |
| - **Reference**: [Link to Project](https://huggingface.co/spaces/Niharmahesh/Data298) | |
| """) | |
| def display_skills(): | |
| st.markdown('## Skills') | |
| st.write(""" | |
| - **Programming Languages**: Python, SQL | |
| - **Data Processing/Wrangling**: pandas, NumPy | |
| - **Data Visualization**: Matplotlib, Seaborn, Plotly, Tableau, Power BI | |
| - **Machine Learning/Deep Learning**: scikit-learn, TensorFlow, Keras | |
| - **Model Deployment**: Streamlit, Flask | |
| - **Cloud Platforms**: AWS, Google Cloud Platform (GCP), Azure | |
| - **Big Data Technologies**: Apache Spark, Hadoop | |
| - **Databases**: MySQL, PostgreSQL, MongoDB | |
| - **Version Control**: Git, GitHub | |
| - **Collaboration Tools**: JIRA, Notion | |
| """) | |
| def display_apps(): | |
| st.markdown('## Apps') | |
| st.markdown(""" | |
| - [CNN arch](https://cnn-arch.streamlit.app/) | |
| """) | |
| def display_certifications(): | |
| st.markdown('## Certifications') | |
| certifications = [ | |
| {"path": 'mlds-pyt.png', "title": "Python for Data Science and Machine Learning Bootcamp"}, | |
| {"path": 'sql_basic certificate (1).png', "title": "HackerRank SQL (Basic)"}, | |
| {"path": 'aws.png', "title": "AWS Cloud Practitioner Udemy Course by Stephane Maarek"}, | |
| {"path": 'AWS Certified Cloud Practitioner certificate.png', "title": "AWS Certified Cloud Practitioner"} | |
| ] | |
| for certification in certifications: | |
| st.image(certification["path"], caption=certification["title"], width=150) | |
| 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() | |