Update app.py

app.py

@@ -3,7 +3,8 @@ 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
+from streamlit_option_menu import option_menu
+from projects import display_projects
 
 #setting layout to wide
 st.set_page_config(layout="wide")
@@ -69,208 +70,6 @@ def display_work_experience():
     - 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.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)
-        """)
 def display_skills():
     st.title('Skills')