app.py

import streamlit as st
import base64

# Function to display resume contents
def display_resume():
    st.title("Ruthvik Kilaru")

    if 'section' not in st.session_state:
        st.session_state.section = 'welcome'

    if st.sidebar.button("Profile Summary"):
        st.session_state.section = 'profile_summary'
    if st.sidebar.button("Functional Skills"):
        st.session_state.section = 'functional_skills'
    if st.sidebar.button("Technical Skills"):
        st.session_state.section = 'technical_skills'
    if st.sidebar.button("Work Experience"):
        st.session_state.section = 'work_experience'
    if st.sidebar.button("Education"):
        st.session_state.section = 'education'
    if st.sidebar.button("Research and Publications"):
        st.session_state.section = 'research_publications'
    if st.sidebar.button("Certifications"):
        st.session_state.section = 'certifications'

    if st.session_state.section == 'welcome':
        st.header("Welcome")
        st.write("Click on the buttons in the sidebar to navigate through the resume sections.")
    elif st.session_state.section == 'profile_summary':
        st.header("Profile Summary")
        st.write("""
        - Detail-oriented professional with over 4 years of extensive experience in analyzing complex datasets to drive informed decision-making.
        - Proficient in ETL processes, data warehousing, big data technologies, Hadoop, Spark, and Kafka, Python, R, SQL, machine learning frameworks such as TensorFlow and scikit-learn, statistical analysis, data visualization, and business intelligence tools.
        - Experienced in developing and deploying data-driven solutions to solve complex business problems.
        - Skilled in data preprocessing, feature engineering, and model optimization, with a focus on achieving high accuracy and interpretability.
        - Experienced in natural language processing, computer vision, and time series analysis.
        - Expert in transforming raw data into actionable insights, enabling businesses to optimize strategies and improve operational efficiency.
        - Adept at communicating findings to stakeholders through clear, concise reports and dashboards. Passionate about leveraging data to solve problems and support strategic initiatives.
        """)
    elif st.session_state.section == 'functional_skills':
        st.header("Functional Skills")
        st.write("""
        - Data Analysis
        - Data Cleaning
        - Feature Engineering
        - Data Visualization
        - Data Mining
        - Data Preprocessing
        - SQL
        - Business Intelligence (BI)
        - Data Integration
        - Statistical Analysis
        - Data Reporting
        - Predictive Modeling
        - Data Preprocessing
        - Machine Learning
        - Model Evaluation
        """)
    elif st.session_state.section == 'technical_skills':
        st.header("Technical Skills")
        st.write("""
        - Programming Languages: Python, R, C, C++, HTML, CSS
        - Big Data & Machine Learning: PowerBI, Spark, Kafka, VectorDB, SQL(t-SQL, p-SQL)
        - Data Science and Miscellaneous Technologies: A/B testing, Jira, Shell scripting, ETL, Data science pipelines based on CI/CD, PowerBI(PQ, DAX, Slicers), Snowflake, NLP, GANs, LLMs, APIs(REST), Excel(Power Pivot, VBA, Macros), AWS(EC2, Kinesis streams, S3 buckets, Redshift), Google Analytics(BigQuery) & Ads, Langchain, Github, Docker & Kubernetes
        """)
    elif st.session_state.section == 'work_experience':
        st.header("Work Experience")
        st.subheader("Illinois Institute of Technology, Chicago, IL | Research & Teaching Assistant | Jan 2023 – Till Date")
        st.write("""
        - Directing joint research on how student behavior affects academic performance.
        - Utilizing advanced methods for person detection, emotion recognition, and posture tracking through algorithms such as Yolo, HaarCascade, PoseNet, Openpose, and AlphaPose.
        - Prototyping an Educational Advising Chatbot using RAG architecture with Langchain on AWS, showcasing integration of advanced AI frameworks and cloud technologies.
        - Organizing and preparing data for RAG-based chatbot by employing web scraping tools like BeautifulSoup and Unstructured.io, effectively gathering and structuring web data.
        - Improving chatbot’s performance by refining and prompt-tuning it with synthetic data generated through LLMs and CI workflows, applying Evol Instruct and Contrastive Learning methods to enhance metrics.
        - Conducting instructor-led recitations, grading assignments, and addressing over 250 student queries through 30+ interactive sessions, increasing engagement.
        - Developing a Python grading algorithm with Professor Yong Zheng, automating processes and saving 40 TA hours.
        """)
        st.subheader("Genesis Solutions, Hyderabad, India | Data Scientist | Jan 2021 – May 2022")
        st.write("""
        - Leveraged advanced regression techniques, including polynomial regression and ridge regression, to achieve a remarkably low Root Mean Squared Error (RMSE) of 5.27 in Stock Price Prediction Analysis.
        - Demonstrated proficiency in accurately forecasting stock prices, as evidenced by model's performance.
        - Achieved an R-squared value of 0.87, indicating model's robustness by explaining 87% of variability in stock prices.
        - Implemented a sophisticated text detection system using OpenCV for image processing and PyTesseract for Optical Character Recognition (OCR).
        - Attained impressive Precision (0.92), Recall (0.89), and F1-score (0.90) in text region identification within images.
        - Optimized algorithm for efficiency, enabling it to process images at a speed of 15 frames per second, making it suitable for real-time applications.
        """)
        st.subheader("Vedanta Resources Pvt Ltd, Orissa, India | Data Engineer | Jul 2019 – Nov 2021")
        st.write("""
        - Worked on real-time data in production department to make necessary technical improvements in that area.
        - Developed and deployed Kafka and Spark-based data pipeline(Debezium connectors, Apache hudi, STARGAZE, Hive for metadata), enhancing data throughput by 15% and reducing waste by 10% via real-time analytics of 1+ million daily data points.
        - Enhanced SQL database for supply chain efficiency, halving query times and cutting inventory costs by 5% through improved Forecasting.
        """)
    elif st.session_state.section == 'education':
        st.header("Education")
        st.write("""
        - Masters in Information Technology (Data Science) from Illinois Institute of Technology, College of Computing – 2024
        - Bachelor of Technology from National Institute of Technology, Jamshedpur, India – 2019
        """)
    elif st.session_state.section == 'research_publications':
        st.header("Research and Publications")
        st.write("""
        - International Conference on Recent Trends in Computer Science and Technology (ICRTCST) – IEEE 2021
        - Prediction of Maize Leaf Disease Using ML Models
        - Implemented Support Vector Machine (SVM) techniques that demonstrated superior accuracy in detecting maize diseases, achieving a high accuracy rate of 95.6%. This highlights ability to effectively apply machine learning algorithms to solve real-world agricultural problems.
        - Integrating Discrete Wavelet Transform (DWT) and YOLOv5 architecture for feature extraction and disease classification, my research contributed to a predictive model that increased precision of crop yield forecasts due to early disease detection, reflected in a sensitivity improvement of up to 92.3%.
        """)
    elif st.session_state.section == 'certifications':
        st.header("Certifications")
        st.write("""
        - Building Transformer-Based Natural Language Processing Applications from NVIDIA – Feb 2024
        - Generative AI with Diffusion Models from NVIDIA – Feb 2024
        - Building LLM-Powered Applications from W&B – Jan 2024
        - GCP - Google Cloud Professional Data Engineer from Udemy – Jan 2024
        - Large Language Models: Application through Production from Databricks – Dec 2023
        - Deep Learning- PadhAI from One Fourth Labs – Sep 2021
        - Machine Learning from Stanford Online – Aug 2021
        - Foundations in Data Science- PadhAI from One Fourth Labs – May 2021
        """)

# Add custom CSS to set the background image
st.markdown(
    f"""
    <style>
    .stApp {{
        background-image: url("data:image/jpg;base64,{base64.b64encode(open("pic.jpg", "rb").read()).decode()}");
        background-size: cover;
    }}
    </style>
    """,
    unsafe_allow_html=True
)

if __name__ == "__main__":
    display_resume()