app.py

import streamlit as st
import PyPDF2
import base64

# Function to display PDF
def display_pdf(file_path):
    try:
        with open(file_path, 'rb') as file:
            base64_pdf = base64.b64encode(file.read()).decode('utf-8')
        pdf_display = f'<embed src="data:application/pdf;base64,{base64_pdf}" width="700" height="1000" type="application/pdf">'
        st.markdown(pdf_display, unsafe_allow_html=True)
    except Exception as e:
        st.error(f"Error reading PDF file: {e}")

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

    st.sidebar.header("Profile Summary")
    st.sidebar.header("Functional Skills")
    st.sidebar.header("Technical Skills")
    st.sidebar.header("Work Experience")
    st.sidebar.header("Education")
    st.sidebar.header("Research and Publications")
    st.sidebar.header("Certifications")

    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.
    """)

    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
    """)

    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
    """)

    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.
    """)

    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
    """)

    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%.
    """)

    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
    """)

    st.sidebar.download_button(
        label="Download Resume",
        data=open("path/to/your/resume.pdf", "rb").read(),
        file_name="Ruthvik_Kilaru_Resume.pdf",
        mime="application/pdf",
    )

if __name__ == "__main__":
    display_resume()