Update app.py

app.py

@@ -32,7 +32,7 @@ def display_header():
 
 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.""")    
+    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 a Graduate with Master's degree in Data Analytics (Applied Data Science) from 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')
@@ -86,7 +86,8 @@ def display_projects():
         "Bitcoin Lightning Path Optimization",
         "National Infrastructure Monitoring",
         "Job Easz",
-        "Prompt Easz"
+        "Prompt Easz",
+        "Resume_Easz"
     ]
 
     # Create tabs
@@ -187,6 +188,50 @@ def display_projects():
         - Allows users to customize prompt strength and style
     - **Reference**: [Link to Project](https://huggingface.co/spaces/Niharmahesh/PromptEasz)
     """)
+    with tabs[10]:
+        st.header("Resume Easz")
+        st.markdown("""
+    # Resume Easz: An LLM-powered application for resume analysis and enhancement
+
+    Resume Easz is an innovative AI-driven tool that analyzes and enhances resumes based on job descriptions. Leveraging the power of prompt engineering and the GROQ API, it utilizes the versatile Llama 3.3 model to provide comprehensive resume analysis and improvement.
+
+    **Key Features**:
+    - Upload DOCX resumes for analysis
+    - Input job descriptions for targeted optimization
+    - Quick or in-depth analysis options
+    - AI-enhanced resume generation
+    - Multiple output formats (DOCX, HTML, TXT)
+
+    **Analysis Types**:
+    1. **Quick Analysis**:
+        - Skills Match: Top 5 required skills with proficiency ratings
+        - Experience Alignment: Comparison of required vs. demonstrated experience
+        - Pros and Cons: Top 3 strengths and areas for improvement
+        - Match Percentage: Overall compatibility score with explanation
+    2. **In-Depth Analysis**:
+        - Comprehensive Skill Gap Analysis
+        - Detailed Experience and Impact Analysis
+        - Content Enhancement Recommendations
+        - Strategic Recommendations for Competitive Edge
+        - Application Strategy Suggestions
+    3. **Resume Enhancement**:
+        - Optimizes content based on analysis
+        - Improves formatting and structure
+        - Highlights key achievements and skills
+        - Ensures ATS compatibility
+
+    **User Experience**:
+    - Intuitive Streamlit interface
+    - Visual diff to highlight resume changes
+    - Multiple download options (DOCX, HTML, TXT)
+
+    **Limitations**:
+    - GROQ API token limit (100,000 tokens per model)
+    - Potential wait times for API rate limits
+
+    This powerful tool streamlines the job application process by providing tailored resume optimization, increasing candidates' chances of success in their job search.
+     - **Reference**: [Link to Project](https://resume-easz.streamlit.app/)
+""")
 
 
 def display_skills():
@@ -195,9 +240,9 @@ def display_skills():
 - **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
+- **Machine Learning/Deep Learning**: scikit-learn, TensorFlow, Keras, LLM, Prompt Engineering
+- **Model Deployment**: Streamlit
+- **Cloud Platforms**: AWS, Google Cloud Platform (GCP)
 - **Big Data Technologies**: Apache Spark, Hadoop
 - **Databases**: MySQL, PostgreSQL, MongoDB
 - **Version Control**: Git, GitHub