Create projects.py

projects.py

@@ -0,0 +1,203 @@
+
+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)
+        """)