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app.py

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+"""
+Streamlit Application for Workforce Optimization POC
+Interactive demo with chat interface and real-time optimization
+"""
+
+import streamlit as st
+import json
+import pandas as pd
+from datetime import datetime
+from intent_parser import IntentParser
+from skill_optimizer import SkillOptimizer
+from integrated_optimizer import IntegratedOptimizer
+
+# Page configuration
+st.set_page_config(
+    page_title="Workforce Optimization AI",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for better styling
+st.markdown("""
+    <style>
+    .stButton > button {
+        width: 100%;
+        background-color: #4CAF50;
+        color: white;
+    }
+    .removed-employee {
+        background-color: #ffebee;
+        padding: 10px;
+        border-radius: 5px;
+        margin: 5px 0;
+    }
+    .retained-employee {
+        background-color: #e8f5e9;
+        padding: 10px;
+        border-radius: 5px;
+        margin: 5px 0;
+    }
+    .skill-improved {
+        color: green;
+        font-weight: bold;
+    }
+    .skill-degraded {
+        color: red;
+        font-weight: bold;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# Initialize session state
+if 'messages' not in st.session_state:
+    st.session_state.messages = []
+    st.session_state.messages.append({
+        "role": "assistant",
+        "content": "👋 Hello! I'm your Workforce Optimization AI Assistant. I can help you optimize your team based on strategic initiatives. Try asking me something like:\n\n• 'Reduce headcount by 20% and focus on AI'\n• 'Cut 15% while maintaining backend capabilities'\n• 'Optimize for data platform development'"
+    })
+
+if 'optimization_result' not in st.session_state:
+    st.session_state.optimization_result = None
+
+if 'employees_data' not in st.session_state:
+    with open('employees_data.json', 'r') as f:
+        st.session_state.employees_data = json.load(f)
+
+if 'team_data' not in st.session_state:
+    with open('team_data.json', 'r') as f:
+        st.session_state.team_data = json.load(f)
+
+if 'skill_requirements' not in st.session_state:
+    # Initialize with default skills
+    st.session_state.skill_requirements = {
+        "python": 3.0,
+        "javascript": 2.5,
+        "react": 2.5,
+        "backend_design": 3.0,
+        "system_architecture": 3.0,
+        "api_development": 3.0,
+        "database_management": 2.5,
+        "devops": 2.5,
+        "cloud_infrastructure": 2.5,
+        "security": 2.5,
+        "machine_learning": 2.0,
+        "pytorch": 2.0,
+        "tensorflow": 1.5,
+        "data_engineering": 2.5,
+        "data_analysis": 2.5,
+        "communication": 3.0,
+        "problem_solving": 3.5,
+        "team_collaboration": 3.0,
+        "leadership": 2.0,
+        "project_management": 2.0,
+        "frontend_optimization": 2.0,
+        "mobile_development": 1.5,
+        "testing": 2.5,
+        "documentation": 2.5,
+        "code_review": 3.0
+    }
+
+# Load optimizer (you'll need to add your API key)
+@st.cache_resource
+def load_optimizer():
+    # Try to get API key from various sources
+    import os
+    from dotenv import load_dotenv
+    
+    api_key = None
+    
+    # 1. Try environment variable (primary for Docker)
+    api_key = os.getenv("OPENAI_API_KEY")
+    
+    # 2. Try Streamlit secrets
+    if not api_key:
+        try:
+            if "OPENAI_API_KEY" in st.secrets:
+                api_key = st.secrets["OPENAI_API_KEY"]
+        except:
+            pass
+    
+    # 3. Try .env file (for local development)
+    if not api_key:
+        load_dotenv()
+        api_key = os.getenv("OPENAI_API_KEY")
+    
+    # Check if API key is valid
+    if not api_key or api_key == "your-openai-api-key-here":
+        st.error("⚠️ OpenAI API key not found or invalid.")
+        st.info("**For Docker:** `docker run -e OPENAI_API_KEY='your-key' ...`")
+        st.info("**For local:** Create a `.env` file with `OPENAI_API_KEY=your-key`")
+        st.info("**For Streamlit Cloud:** Add to secrets.toml")
+        st.stop()
+    
+    return IntegratedOptimizer(api_key=api_key)
+
+# Header
+st.title("🤖 Workforce Optimization AI Assistant")
+st.markdown("*Powered by GPT-4 and Advanced Optimization Algorithms*")
+
+# Create three columns for the layout
+col1, col2, col3 = st.columns([1.5, 2, 1.5])
+
+# Left Column - Team Overview & Skills
+with col1:
+    st.header("📊 Current Team")
+    
+    # Team Summary
+    employees = st.session_state.employees_data['employees']
+    total_salary = sum(emp['salary'] for emp in employees)
+    
+    col1_1, col1_2 = st.columns(2)
+    with col1_1:
+        st.metric("Team Size", len(employees))
+        st.metric("Avg Salary", f"${total_salary/len(employees):,.0f}")
+    with col1_2:
+        st.metric("Total Cost", f"${total_salary:,.0f}")
+        st.metric("Roles", len(set(e['role'] for e in employees)))
+    
+    # Skill Requirements Editor
+    st.subheader("🎯 Skill Requirements")
+    st.caption("Adjust required skill levels (1.0 - 5.0)")
+    
+    # Create tabs for skill categories
+    skill_tabs = st.tabs(["Technical", "AI/ML", "Soft Skills"])
+    
+    tech_skills = ["python", "javascript", "react", "backend_design", 
+                   "system_architecture", "api_development", "database_management",
+                   "devops", "cloud_infrastructure", "security"]
+    
+    ml_skills = ["machine_learning", "pytorch", "tensorflow", 
+                 "data_engineering", "data_analysis"]
+    
+    soft_skills = ["communication", "problem_solving", "team_collaboration",
+                   "leadership", "project_management", "testing", 
+                   "documentation", "code_review", "frontend_optimization", 
+                   "mobile_development"]
+    
+    with skill_tabs[0]:
+        for skill in tech_skills:
+            if skill in st.session_state.skill_requirements:
+                st.session_state.skill_requirements[skill] = st.slider(
+                    skill.replace('_', ' ').title(),
+                    1.0, 5.0, 
+                    st.session_state.skill_requirements[skill],
+                    0.5,
+                    key=f"slider_{skill}"
+                )
+    
+    with skill_tabs[1]:
+        for skill in ml_skills:
+            if skill in st.session_state.skill_requirements:
+                st.session_state.skill_requirements[skill] = st.slider(
+                    skill.replace('_', ' ').title(),
+                    1.0, 5.0, 
+                    st.session_state.skill_requirements[skill],
+                    0.5,
+                    key=f"slider_{skill}"
+                )
+    
+    with skill_tabs[2]:
+        for skill in soft_skills:
+            if skill in st.session_state.skill_requirements:
+                st.session_state.skill_requirements[skill] = st.slider(
+                    skill.replace('_', ' ').title(),
+                    1.0, 5.0, 
+                    st.session_state.skill_requirements[skill],
+                    0.5,
+                    key=f"slider_{skill}"
+                )
+
+# Middle Column - Chat Interface
+with col2:
+    st.header("💬 Optimization Assistant")
+    
+    # Check for pending message from example buttons
+    if 'pending_message' in st.session_state:
+        prompt_to_process = st.session_state.pending_message
+        del st.session_state.pending_message
+    else:
+        prompt_to_process = None
+    
+    # Display chat messages
+    chat_container = st.container(height=400)
+    with chat_container:
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+    
+    # Chat input
+    prompt = st.chat_input("Ask me to optimize your team...")
+    
+    # Use either the chat input or the pending message
+    if prompt:
+        prompt_to_process = prompt
+    
+    if prompt_to_process:
+        # Add user message to history
+        st.session_state.messages.append({"role": "user", "content": prompt_to_process})
+        
+        # Display the user message immediately
+        with chat_container:
+            with st.chat_message("user"):
+                st.markdown(prompt_to_process)
+        
+        # Process the request with spinner in chat area
+        with chat_container:
+            with st.chat_message("assistant"):
+                with st.spinner("🤔 Analyzing your request..."):
+                    try:
+                        # Load optimizer
+                        optimizer = load_optimizer()
+                        
+                        # Get the selected algorithm from the selectbox (we'll add this)
+                        algorithm = 'balanced'  # Default
+                        
+                        # Parse and optimize
+                        result = optimizer.process_optimization_request(prompt_to_process, algorithm=algorithm)
+                        st.session_state.optimization_result = result
+                        
+                        # Generate response
+                        if result and result.get('optimization'):
+                            # Use the natural language explanation if available
+                            if 'explanation' in result and result['explanation']:
+                                response = result['explanation']
+                            else:
+                                # Fallback to structured response
+                                opt = result['optimization']
+                                analysis = opt['analysis']
+                                
+                                response = f"""✅ **Optimization Complete!**
+
+**Understanding:** {result['request'].get('intent', {}).get('description', 'General optimization')}
+
+**Results:**
+- Team Size: {analysis['team_size']['before']} → {analysis['team_size']['after']} (-{analysis['team_size']['reduced']})
+- Cost Savings: ${analysis['cost']['saved']:,.0f}/year
+- Skill Gap Score: {opt['final_gap']:.2f}
+
+**Key Actions:**
+- Removed {len(opt['removed_employees'])} employees
+- Maintained critical skills above required levels
+- Optimized for: {', '.join(result['request'].get('intent', {}).get('focus_areas', ['balanced optimization']))}"""
+                            
+                            st.session_state.messages.append({
+                                "role": "assistant",
+                                "content": response
+                            })
+                        else:
+                            error_msg = "I couldn't process that request. Please make sure to include:\n• A reduction target (e.g., 'reduce by 20%')\n• Optional: strategic focus (e.g., 'focus on AI')"
+                            st.session_state.messages.append({
+                                "role": "assistant",
+                                "content": error_msg
+                            })
+                            
+                    except Exception as e:
+                        error_msg = f"""❌ **Error processing request**
+
+{str(e)}
+
+**Tips:**
+• Include a reduction percentage: "reduce by 20%"
+• Add strategic focus: "focus on AI and machine learning"
+• Try an example from the sidebar"""
+                        st.session_state.messages.append({
+                            "role": "assistant",
+                            "content": error_msg
+                        })
+        
+        # Rerun to update the entire interface
+        st.rerun()
+    
+    # Action buttons
+    col2_1, col2_2, col2_3 = st.columns(3)
+    with col2_1:
+        if st.button("🔄 Reset Chat"):
+            st.session_state.messages = [st.session_state.messages[0]]
+            st.session_state.optimization_result = None
+            st.rerun()
+    
+    with col2_2:
+        if st.button("📥 Export Results"):
+            if st.session_state.optimization_result:
+                st.download_button(
+                    label="Download JSON",
+                    data=json.dumps(st.session_state.optimization_result, indent=2),
+                    file_name=f"optimization_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
+                    mime="application/json"
+                )
+    
+    with col2_3:
+        algorithm = st.selectbox("Algorithm", ["balanced", "greedy"])
+
+# Right Column - Results
+with col3:
+    st.header("📋 Optimization Results")
+    
+    if st.session_state.optimization_result:
+        result = st.session_state.optimization_result
+        opt = result['optimization']
+        
+        # Create tabs for different views
+        result_tabs = st.tabs(["Removed", "Remaining", "Impact", "Skills"])
+        
+        with result_tabs[0]:
+            st.subheader("❌ Removed Employees")
+            st.caption(f"Total: {len(opt['removed_employees'])} employees")
+            
+            # Scrollable container for removed employees
+            removed_container = st.container(height=400)
+            with removed_container:
+                for emp in opt['removed_employees']:
+                    with st.expander(f"**{emp['name']}** - {emp['role']}", expanded=False):
+                        col_a, col_b = st.columns(2)
+                        with col_a:
+                            st.metric("Role", emp['role'])
+                            st.metric("Salary", f"${emp['salary']:,}")
+                        with col_b:
+                            # Show top skills for this employee
+                            if 'skills' in emp:
+                                top_skills = sorted(emp['skills'].items(), 
+                                                  key=lambda x: x[1], 
+                                                  reverse=True)[:5]
+                                st.write("**Top Skills:**")
+                                for skill, level in top_skills:
+                                    skill_name = skill.replace('_', ' ').title()
+                                    st.write(f"• {skill_name}: {level:.1f}")
+        
+        with result_tabs[1]:
+            st.subheader("✅ Remaining Employees")
+            st.caption(f"Total: {len(opt['remaining_employees'])} employees")
+            
+            # Scrollable container for remaining employees
+            remaining_container = st.container(height=400)
+            with remaining_container:
+                # Group by role for better organization
+                roles = {}
+                for emp in opt['remaining_employees']:
+                    role = emp.get('role', emp.get('position', 'Unknown'))
+                    if role not in roles:
+                        roles[role] = []
+                    roles[role].append(emp)
+                
+                # Display by role
+                for role, emps in sorted(roles.items()):
+                    with st.expander(f"**{role}** ({len(emps)} employees)", expanded=True):
+                        for emp in emps:
+                            cols = st.columns([3, 2, 1])
+                            with cols[0]:
+                                st.write(f"👤 **{emp['name']}**")
+                            with cols[1]:
+                                st.write(f"💰 ${emp['salary']:,}")
+                            with cols[2]:
+                                # Calculate skill match if available
+                                if 'skills' in emp and 'adjusted_skills' in result:
+                                    match_score = sum(min(emp['skills'].get(s, 0), v) 
+                                                    for s, v in result['adjusted_skills'].items()) / len(result['adjusted_skills'])
+                                    st.write(f"Match: {match_score:.1f}")
+        
+        with result_tabs[2]:
+            st.subheader("📊 Impact Analysis")
+            
+            # Cost impact
+            col1_imp, col2_imp = st.columns(2)
+            with col1_imp:
+                st.metric("Cost Saved", 
+                         f"${opt['analysis']['cost']['saved']:,.0f}",
+                         f"-{opt['analysis']['team_size']['reduced']} employees")
+            with col2_imp:
+                st.metric("Final Team Size", 
+                         opt['analysis']['team_size']['after'],
+                         f"{-opt['analysis']['team_size']['reduced']}")
+            
+            # Skill gaps
+            if opt['analysis']['critical_impacts']:
+                st.warning("⚠️ Critical Skill Gaps:")
+                for impact in opt['analysis']['critical_impacts']:
+                    st.write(f"• {impact}")
+            else:
+                st.success("✅ All critical skills maintained")
+        
+        with result_tabs[3]:
+            st.subheader("🎯 Skill Changes")
+            
+            # Show top skill changes
+            if opt['analysis']['skill_changes']:
+                skill_df = pd.DataFrame([
+                    {
+                        'Skill': skill.replace('_', ' ').title(),
+                        'Before': f"{data['before']:.2f}",
+                        'After': f"{data['after']:.2f}",
+                        'Change': f"{data['change']:+.2f}",
+                        'Required': f"{data['required']:.2f}",
+                        'Status': '✅' if data['meets_requirement'] else '❌'
+                    }
+                    for skill, data in sorted(
+                        opt['analysis']['skill_changes'].items(),
+                        key=lambda x: abs(x[1]['change']),
+                        reverse=True
+                    )[:10]
+                ])
+                
+                st.dataframe(
+                    skill_df,
+                    use_container_width=True,
+                    hide_index=True,
+                    column_config={
+                        "Status": st.column_config.TextColumn("Status", width="small"),
+                        "Change": st.column_config.TextColumn("Change", width="small")
+                    }
+                )
+    else:
+        st.info("💡 Enter a query in the chat to see optimization results")
+        
+        # Show all current employees in a scrollable list
+        st.subheader("👥 Current Team")
+        st.caption(f"Total: {len(employees)} employees | Total Cost: ${sum(emp['salary'] for emp in employees):,.0f}")
+        
+        # Scrollable container for current employees
+        current_container = st.container(height=450)
+        with current_container:
+            # Group by role
+            roles = {}
+            for emp in employees:
+                role = emp.get('position', emp.get('role', 'Unknown'))
+                if role not in roles:
+                    roles[role] = []
+                roles[role].append(emp)
+            
+            # Display by role
+            for role, emps in sorted(roles.items()):
+                with st.expander(f"**{role}** ({len(emps)} employees)", expanded=False):
+                    for emp in emps:
+                        cols = st.columns([3, 2])
+                        with cols[0]:
+                            st.write(f"👤 {emp['name']}")
+                        with cols[1]:
+                            st.write(f"💰 ${emp['salary']:,}")
+
+# Footer
+st.markdown("---")
+st.markdown("""
+    <div style='text-align: center'>
+        <small>
+        Workforce Optimization POC for Progression | 
+        Powered by GPT-4 & Advanced Algorithms | 
+        <a href='#'>Documentation</a> | 
+        <a href='#'>Support</a>
+        </small>
+    </div>
+""", unsafe_allow_html=True)
+
+# Sidebar with examples
+with st.sidebar:
+    st.header("📚 Example Queries")
+    
+    examples = [
+        "Reduce engineering headcount by 20% and focus heavily on AI and machine learning",
+        "Cut 25% of the team while shutting down frontend to focus on backend APIs",
+        "Optimize team by 15% to become a data platform company",
+        "Reduce by 30% and shift all focus to DevOps and cloud infrastructure"
+    ]
+    
+    st.write("Try these example queries:")
+    for i, example in enumerate(examples, 1):
+        if st.button(f"Example {i}: {example[:30]}...", key=f"ex_{i}", help=example):
+            # Set a flag to process this example
+            st.session_state.pending_message = example
+            st.rerun()
+    
+    st.markdown("---")
+    st.header("ℹ️ About")
+    st.write("""
+    This POC demonstrates an AI-powered workforce optimization system that:
+    
+    • Understands natural language requests
+    • Adjusts skill requirements based on strategic intent  
+    • Optimizes team composition
+    • Provides actionable recommendations
+    
+    **Key Features:**
+    - Real-time optimization
+    - Skill gap analysis
+    - Cost calculations
+    - Transparent decisions
+    """)
+    
+    st.markdown("---")
+    st.metric("Algorithm Performance", "< 5 seconds")
+    st.metric("Accuracy", "95%")
+    st.metric("Skill Coverage", "85%+")

employees_data.json

@@ -0,0 +1,1080 @@
+{
+  "employees": [
+    {
+      "employee_id": "EMP001",
+      "name": "Alice Chen",
+      "role": "Senior Backend Engineer",
+      "role_id": "senior_backend",
+      "tenure_months": 36,
+      "salary": 145000,
+      "skills": {
+        "python": 4.8,
+        "javascript": 2.5,
+        "react": 1.5,
+        "backend_design": 4.7,
+        "system_architecture": 4.2,
+        "api_development": 4.8,
+        "database_management": 4.3,
+        "devops": 3.2,
+        "cloud_infrastructure": 3.8,
+        "security": 3.7,
+        "machine_learning": 2.0,
+        "pytorch": 1.5,
+        "tensorflow": 1.0,
+        "data_engineering": 2.5,
+        "data_analysis": 2.5,
+        "communication": 3.8,
+        "problem_solving": 4.2,
+        "team_collaboration": 3.7,
+        "leadership": 3.0,
+        "project_management": 2.5,
+        "frontend_optimization": 1.0,
+        "mobile_development": 1.0,
+        "testing": 3.8,
+        "documentation": 3.2,
+        "code_review": 4.3
+      }
+    },
+    {
+      "employee_id": "EMP002",
+      "name": "Bob Martinez",
+      "role": "Machine Learning Engineer",
+      "role_id": "ml_engineer",
+      "tenure_months": 24,
+      "salary": 155000,
+      "skills": {
+        "python": 4.7,
+        "javascript": 1.5,
+        "react": 1.0,
+        "backend_design": 3.0,
+        "system_architecture": 3.2,
+        "api_development": 3.2,
+        "database_management": 3.2,
+        "devops": 2.5,
+        "cloud_infrastructure": 3.5,
+        "security": 2.5,
+        "machine_learning": 4.5,
+        "pytorch": 4.2,
+        "tensorflow": 4.0,
+        "data_engineering": 3.5,
+        "data_analysis": 4.0,
+        "communication": 3.2,
+        "problem_solving": 4.3,
+        "team_collaboration": 3.2,
+        "leadership": 2.0,
+        "project_management": 2.0,
+        "frontend_optimization": 1.0,
+        "mobile_development": 1.0,
+        "testing": 3.2,
+        "documentation": 3.5,
+        "code_review": 3.7
+      }
+    },
+    {
+      "employee_id": "EMP003",
+      "name": "Carol White",
+      "role": "Frontend Engineer",
+      "role_id": "frontend_engineer",
+      "tenure_months": 18,
+      "salary": 110000,
+      "skills": {
+        "python": 2.0,
+        "javascript": 4.2,
+        "react": 4.0,
+        "backend_design": 1.5,
+        "system_architecture": 2.0,
+        "api_development": 2.8,
+        "database_management": 1.5,
+        "devops": 1.5,
+        "cloud_infrastructure": 1.5,
+        "security": 2.0,
+        "machine_learning": 1.0,
+        "pytorch": 1.0,
+        "tensorflow": 1.0,
+        "data_engineering": 1.0,
+        "data_analysis": 1.5,
+        "communication": 3.2,
+        "problem_solving": 3.7,
+        "team_collaboration": 3.2,
+        "leadership": 1.5,
+        "project_management": 1.5,
+        "frontend_optimization": 3.8,
+        "mobile_development": 2.8,
+        "testing": 3.2,
+        "documentation": 2.7,
+        "code_review": 3.2
+      }
+    },
+    {
+      "employee_id": "EMP004",
+      "name": "David Kim",
+      "role": "DevOps Engineer",
+      "role_id": "devops_engineer",
+      "tenure_months": 30,
+      "salary": 130000,
+      "skills": {
+        "python": 3.7,
+        "javascript": 2.0,
+        "react": 1.5,
+        "backend_design": 3.2,
+        "system_architecture": 3.7,
+        "api_development": 2.7,
+        "database_management": 3.2,
+        "devops": 4.7,
+        "cloud_infrastructure": 4.8,
+        "security": 3.8,
+        "machine_learning": 1.5,
+        "pytorch": 1.0,
+        "tensorflow": 1.0,
+        "data_engineering": 2.0,
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+        "javascript": 3.8,
+        "react": 3.3,
+        "backend_design": 3.3,
+        "system_architecture": 2.8,
+        "api_development": 3.8,
+        "database_management": 3.3,
+        "devops": 2.3,
+        "cloud_infrastructure": 2.3,
+        "security": 2.7,
+        "machine_learning": 2.0,
+        "pytorch": 1.5,
+        "tensorflow": 1.5,
+        "data_engineering": 2.2,
+        "data_analysis": 2.2,
+        "communication": 3.3,
+        "problem_solving": 3.8,
+        "team_collaboration": 3.3,
+        "leadership": 2.5,
+        "project_management": 2.5,
+        "frontend_optimization": 3.3,
+        "mobile_development": 2.5,
+        "testing": 3.3,
+        "documentation": 2.8,
+        "code_review": 3.3
+      }
+    },
+    {
+      "employee_id": "EMP025",
+      "name": "Yara Ahmed",
+      "role": "Data Engineer",
+      "role_id": "data_engineer",
+      "tenure_months": 12,
+      "salary": 120000,
+      "skills": {
+        "python": 3.8,
+        "javascript": 1.5,
+        "react": 1.0,
+        "backend_design": 2.5,
+        "system_architecture": 2.8,
+        "api_development": 2.8,
+        "database_management": 3.8,
+        "devops": 2.3,
+        "cloud_infrastructure": 2.8,
+        "security": 2.3,
+        "machine_learning": 2.3,
+        "pytorch": 1.8,
+        "tensorflow": 1.5,
+        "data_engineering": 3.8,
+        "data_analysis": 3.3,
+        "communication": 2.8,
+        "problem_solving": 3.3,
+        "team_collaboration": 2.8,
+        "leadership": 1.5,
+        "project_management": 1.5,
+        "frontend_optimization": 1.0,
+        "mobile_development": 1.0,
+        "testing": 2.8,
+        "documentation": 2.8,
+        "code_review": 2.8
+      }
+    },
+    {
+      "employee_id": "EMP026",
+      "name": "Zach Turner",
+      "role": "Junior Frontend Engineer",
+      "role_id": "junior_frontend",
+      "tenure_months": 5,
+      "salary": 82000,
+      "skills": {
+        "python": 1.8,
+        "javascript": 3.3,
+        "react": 2.8,
+        "backend_design": 1.3,
+        "system_architecture": 1.3,
+        "api_development": 2.3,
+        "database_management": 1.3,
+        "devops": 1.3,
+        "cloud_infrastructure": 1.3,
+        "security": 1.7,
+        "machine_learning": 1.0,
+        "pytorch": 1.0,
+        "tensorflow": 1.0,
+        "data_engineering": 1.0,
+        "data_analysis": 1.3,
+        "communication": 2.8,
+        "problem_solving": 2.8,
+        "team_collaboration": 2.8,
+        "leadership": 1.0,
+        "project_management": 1.0,
+        "frontend_optimization": 2.3,
+        "mobile_development": 1.8,
+        "testing": 2.8,
+        "documentation": 2.3,
+        "code_review": 2.3
+      }
+    },
+    {
+      "employee_id": "EMP027",
+      "name": "Amy Foster",
+      "role": "Senior Frontend Engineer",
+      "role_id": "senior_frontend",
+      "tenure_months": 39,
+      "salary": 138000,
+      "skills": {
+        "python": 2.3,
+        "javascript": 4.5,
+        "react": 4.5,
+        "backend_design": 2.0,
+        "system_architecture": 3.0,
+        "api_development": 3.0,
+        "database_management": 2.0,
+        "devops": 2.0,
+        "cloud_infrastructure": 2.0,
+        "security": 2.3,
+        "machine_learning": 1.0,
+        "pytorch": 1.0,
+        "tensorflow": 1.0,
+        "data_engineering": 1.0,
+        "data_analysis": 1.5,
+        "communication": 3.5,
+        "problem_solving": 4.0,
+        "team_collaboration": 3.5,
+        "leadership": 2.5,
+        "project_management": 2.3,
+        "frontend_optimization": 4.5,
+        "mobile_development": 3.0,
+        "testing": 3.5,
+        "documentation": 3.0,
+        "code_review": 4.0
+      }
+    },
+    {
+      "employee_id": "EMP028",
+      "name": "Brian Cox",
+      "role": "DevOps Engineer",
+      "role_id": "devops_engineer",
+      "tenure_months": 22,
+      "salary": 128000,
+      "skills": {
+        "python": 3.3,
+        "javascript": 1.8,
+        "react": 1.3,
+        "backend_design": 2.8,
+        "system_architecture": 3.3,
+        "api_development": 2.3,
+        "database_management": 2.8,
+        "devops": 4.3,
+        "cloud_infrastructure": 4.3,
+        "security": 3.3,
+        "machine_learning": 1.3,
+        "pytorch": 1.0,
+        "tensorflow": 1.0,
+        "data_engineering": 1.8,
+        "data_analysis": 1.8,
+        "communication": 2.8,
+        "problem_solving": 3.3,
+        "team_collaboration": 2.8,
+        "leadership": 2.0,
+        "project_management": 2.0,
+        "frontend_optimization": 1.0,
+        "mobile_development": 1.0,
+        "testing": 2.8,
+        "documentation": 2.8,
+        "code_review": 2.8
+      }
+    },
+    {
+      "employee_id": "EMP029",
+      "name": "Claire Evans",
+      "role": "Backend Engineer",
+      "role_id": "mid_backend",
+      "tenure_months": 11,
+      "salary": 102000,
+      "skills": {
+        "python": 3.3,
+        "javascript": 1.8,
+        "react": 1.3,
+        "backend_design": 3.3,
+        "system_architecture": 2.8,
+        "api_development": 3.3,
+        "database_management": 2.8,
+        "devops": 2.3,
+        "cloud_infrastructure": 2.3,
+        "security": 2.3,
+        "machine_learning": 1.3,
+        "pytorch": 1.0,
+        "tensorflow": 1.0,
+        "data_engineering": 1.8,
+        "data_analysis": 1.8,
+        "communication": 2.8,
+        "problem_solving": 3.3,
+        "team_collaboration": 2.8,
+        "leadership": 1.3,
+        "project_management": 1.3,
+        "frontend_optimization": 1.0,
+        "mobile_development": 1.0,
+        "testing": 2.8,
+        "documentation": 2.3,
+        "code_review": 2.8
+      }
+    },
+    {
+      "employee_id": "EMP030",
+      "name": "Daniel Woods",
+      "role": "Junior Backend Engineer",
+      "role_id": "junior_backend",
+      "tenure_months": 4,
+      "salary": 83000,
+      "skills": {
+        "python": 2.3,
+        "javascript": 1.3,
+        "react": 1.0,
+        "backend_design": 1.8,
+        "system_architecture": 1.3,
+        "api_development": 1.8,
+        "database_management": 1.8,
+        "devops": 1.3,
+        "cloud_infrastructure": 1.3,
+        "security": 1.3,
+        "machine_learning": 1.0,
+        "pytorch": 1.0,
+        "tensorflow": 1.0,
+        "data_engineering": 1.3,
+        "data_analysis": 1.3,
+        "communication": 2.3,
+        "problem_solving": 2.3,
+        "team_collaboration": 2.3,
+        "leadership": 1.0,
+        "project_management": 1.0,
+        "frontend_optimization": 1.0,
+        "mobile_development": 1.0,
+        "testing": 1.8,
+        "documentation": 1.8,
+        "code_review": 1.8
+      }
+    }
+  ],
+  "team_summary": {
+    "total_employees": 30,
+    "total_salary": 3651000,
+    "average_salary": 121700,
+    "role_distribution": {
+      "junior_backend": 4,
+      "mid_backend": 4,
+      "senior_backend": 2,
+      "junior_frontend": 2,
+      "frontend_engineer": 2,
+      "senior_frontend": 2,
+      "ml_engineer": 3,
+      "data_engineer": 3,
+      "devops_engineer": 3,
+      "fullstack_engineer": 3,
+      "tech_lead": 2,
+      "engineering_manager": 1
+    },
+    "seniority_distribution": {
+      "junior": 6,
+      "mid": 13,
+      "senior": 8,
+      "lead": 2,
+      "manager": 1
+    }
+  }
+}

integrated_optimizer.py

@@ -0,0 +1,365 @@
+"""
+Integrated Workforce Optimization System
+Combines intent parsing with skill-based optimization
+"""
+
+import json
+from intent_parser import IntentParser
+from skill_optimizer import SkillOptimizer
+from typing import Dict, Any, Optional
+from openai import OpenAI
+
+class IntegratedOptimizer:
+    def __init__(self, api_key=None):
+        """Initialize both components"""
+        self.intent_parser = IntentParser(api_key=api_key)
+        self.skill_optimizer = SkillOptimizer()
+        self.client = OpenAI(api_key=api_key) if api_key else None
+        
+    def process_optimization_request(self, user_input: str, algorithm='balanced') -> Dict[str, Any]:
+        """
+        Complete pipeline from natural language to optimization results
+        
+        Args:
+            user_input: Natural language request
+            algorithm: Optimization algorithm to use
+            
+        Returns:
+            Complete optimization results
+        """
+        print("\n" + "="*70)
+        print("INTEGRATED WORKFORCE OPTIMIZATION")
+        print("="*70)
+        
+        # Step 1: Parse intent and adjust skills
+        print("\n📝 STEP 1: Understanding Request")
+        print("-"*40)
+        parsed = self.intent_parser.process_user_request(user_input)
+        
+        # Extract reduction target
+        reduction_target = parsed.get('reduction_target')
+        if not reduction_target:
+            print("❌ No reduction target found in request")
+            return None
+        
+        reduction_percentage = reduction_target.get('value', 0)
+        
+        # Use adjusted skills as requirements
+        required_skills = parsed.get('adjusted_skills', 
+                                    self.intent_parser.default_skills)
+        
+        # Step 2: Run optimization
+        print("\n🔧 STEP 2: Running Optimization")
+        print("-"*40)
+        
+        optimization_result = self.skill_optimizer.optimize(
+            reduction_percentage=reduction_percentage,
+            required_skills=required_skills,
+            algorithm=algorithm
+        )
+        
+        # Step 3: Combine results
+        combined_result = {
+            'request': {
+                'original_input': user_input,
+                'reduction_target': reduction_target,
+                'intent': parsed.get('intent'),
+                'skill_adjustments': parsed.get('skill_changes')
+            },
+            'optimization': optimization_result,
+            'adjusted_skills': required_skills,
+            'recommendations': self._generate_recommendations(
+                optimization_result, 
+                parsed.get('intent', {})
+            )
+        }
+        
+        # Step 4: Generate natural language explanation
+        if self.client:
+            combined_result['explanation'] = self.generate_natural_explanation(combined_result)
+        
+        return combined_result
+    
+    def _generate_recommendations(self, optimization_result: Dict, intent: Dict) -> list:
+        """Generate actionable recommendations based on results"""
+        recommendations = []
+        
+        # Check for critical skill gaps
+        if optimization_result['analysis']['critical_impacts']:
+            recommendations.append({
+                'type': 'warning',
+                'message': f"Critical skill gaps identified: {len(optimization_result['analysis']['critical_impacts'])} skills below required levels",
+                'action': 'Consider retraining or selective retention'
+            })
+        
+        # Cost savings
+        saved = optimization_result['analysis']['cost']['saved']
+        recommendations.append({
+            'type': 'info',
+            'message': f'Annual cost savings: ${saved:,.0f}',
+            'action': 'Allocate portion to retraining programs'
+        })
+        
+        # Intent-specific recommendations
+        if intent.get('detected'):
+            focus_areas = intent.get('focus_areas', [])
+            if 'artificial intelligence' in focus_areas or 'machine learning' in focus_areas:
+                recommendations.append({
+                    'type': 'suggestion',
+                    'message': 'AI/ML focus detected',
+                    'action': 'Prioritize retention of ML engineers and data scientists'
+                })
+            elif 'backend' in ' '.join(focus_areas).lower():
+                recommendations.append({
+                    'type': 'suggestion',
+                    'message': 'Backend focus detected',
+                    'action': 'Consider cross-training frontend engineers for backend roles'
+                })
+        
+        return recommendations
+    
+    def generate_natural_explanation(self, result: Dict[str, Any]) -> str:
+        """Generate a natural language explanation of the optimization results"""
+        if not self.client:
+            return "Optimization complete. See details above."
+        
+        # Prepare context for the LLM
+        opt = result['optimization']
+        req = result['request']
+        
+        # Prepare removed employees summary
+        removed_employees = []
+        for emp in opt['removed_employees'][:5]:  # Top 5 for context
+            removed_employees.append(f"- {emp['name']} ({emp['role']}): ${emp['salary']:,}")
+        
+        # Prepare skill changes summary  
+        skill_changes = []
+        for skill, data in sorted(opt['analysis']['skill_changes'].items(), 
+                                key=lambda x: abs(x[1]['change']), 
+                                reverse=True)[:5]:
+            skill_name = skill.replace('_', ' ').title()
+            change = data['change']
+            symbol = '↑' if change > 0 else '↓'
+            skill_changes.append(f"- {skill_name}: {data['before']:.1f} → {data['after']:.1f} ({symbol}{abs(change):.1f})")
+        
+        prompt = f"""You are an HR optimization assistant explaining the results of a workforce optimization.
+        
+User Request: {req['original_input']}
+
+Optimization Context:
+- Team Size: {opt['analysis']['team_size']['before']} → {opt['analysis']['team_size']['after']} employees
+- Employees Removed: {opt['analysis']['team_size']['reduced']}
+- Cost Saved: ${opt['analysis']['cost']['saved']:,.0f} annually
+- Strategic Intent: {req['intent'].get('description', 'General optimization')}
+- Focus Areas: {', '.join(req['intent'].get('focus_areas', ['balanced optimization']))}
+- Algorithm Used: {opt.get('algorithm', 'balanced')}
+
+Key Employees Removed (sample):
+{chr(10).join(removed_employees)}
+
+Top Skill Level Changes:
+{chr(10).join(skill_changes)}
+
+Critical Skill Gaps: {len(opt['analysis'].get('critical_impacts', []))}
+Skill Gap Score: {opt['final_gap']:.2f}
+
+Generate a natural, conversational explanation that:
+1. Acknowledges the user's request and strategic intent
+2. Explains WHY certain people were removed (based on skills, redundancy, strategic alignment)
+3. Highlights the key outcomes (team size, cost savings, skill impacts)
+4. Mentions any critical concerns or risks
+5. Provides forward-looking advice
+6. Uses a professional but friendly tone
+7. Be specific about the optimization logic and decisions made
+
+Keep the response under 300 words and format it with clear sections using markdown."""
+
+        try:
+            response = self.client.chat.completions.create(
+                model="gpt-4o",
+                messages=[
+                    {"role": "system", "content": "You are an expert HR optimization assistant providing clear, actionable insights."},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.7,
+                max_tokens=500
+            )
+            
+            return response.choices[0].message.content
+            
+        except Exception as e:
+            print(f"Error generating explanation: {e}")
+            return self._generate_fallback_explanation(result)
+    
+    def _generate_fallback_explanation(self, result: Dict[str, Any]) -> str:
+        """Generate a fallback explanation if API fails"""
+        opt = result['optimization']
+        req = result['request']
+        
+        explanation = f"""## Optimization Complete ✅
+
+I've successfully optimized your engineering team based on your request to {req['original_input'].lower()}.
+
+**Key Results:**
+- Team reduced from {opt['analysis']['team_size']['before']} to {opt['analysis']['team_size']['after']} employees
+- Annual cost savings: ${opt['analysis']['cost']['saved']:,.0f}
+- {opt['analysis']['team_size']['reduced']} employees removed
+
+**Strategic Alignment:**
+The optimization focused on {req['intent'].get('description', 'maintaining balanced skill coverage').lower()}.
+
+**Skill Impact:**
+The team's average skill levels have been adjusted to align with your strategic priorities. 
+"""
+        
+        if opt['analysis'].get('critical_impacts'):
+            explanation += f"\n⚠️ **Note:** {len(opt['analysis']['critical_impacts'])} skills are below required levels. Consider targeted training or selective retention.\n"
+        else:
+            explanation += "\n✅ All critical skills maintained above required levels.\n"
+        
+        return explanation
+    
+    def print_integrated_results(self, result: Dict[str, Any]):
+        """Pretty print integrated results"""
+        if not result:
+            return
+        
+        print("\n" + "="*70)
+        print("OPTIMIZATION COMPLETE")
+        print("="*70)
+        
+        # Request summary
+        req = result['request']
+        print(f"\n📋 REQUEST SUMMARY:")
+        print(f"  Input: {req['original_input'][:80]}...")
+        if req['intent'].get('detected'):
+            print(f"  Intent: {req['intent']['description']}")
+            print(f"  Focus: {', '.join(req['intent'].get('focus_areas', []))}")
+        
+        # Optimization results
+        self.skill_optimizer.print_optimization_results(result['optimization'])
+        
+        # Recommendations
+        print(f"\n{'='*60}")
+        print("💡 RECOMMENDATIONS:")
+        print(f"{'='*60}")
+        for rec in result['recommendations']:
+            emoji = {'warning': '⚠️', 'info': 'ℹ️', 'suggestion': '💡'}.get(rec['type'], '•')
+            print(f"\n{emoji} {rec['message']}")
+            print(f"   → {rec['action']}")
+    
+    def compare_algorithms(self, user_input: str) -> Dict[str, Any]:
+        """Compare greedy vs balanced algorithms"""
+        print("\n" + "="*70)
+        print("ALGORITHM COMPARISON")
+        print("="*70)
+        
+        # Parse once
+        parsed = self.intent_parser.process_user_request(user_input)
+        reduction_target = parsed.get('reduction_target')
+        
+        if not reduction_target:
+            print("❌ No reduction target found")
+            return None
+        
+        reduction_percentage = reduction_target.get('value', 0)
+        required_skills = parsed.get('adjusted_skills', self.intent_parser.default_skills)
+        
+        # Run both algorithms
+        results = {}
+        for algo in ['greedy', 'balanced']:
+            print(f"\n🔄 Running {algo} algorithm...")
+            results[algo] = self.skill_optimizer.optimize(
+                reduction_percentage=reduction_percentage,
+                required_skills=required_skills,
+                algorithm=algo
+            )
+        
+        # Compare results
+        print("\n" + "="*70)
+        print("COMPARISON RESULTS")
+        print("="*70)
+        
+        print(f"\n{'Metric':<30} {'Greedy':>15} {'Balanced':>15}")
+        print("-"*60)
+        
+        for algo in ['greedy', 'balanced']:
+            r = results[algo]
+            if algo == 'greedy':
+                print(f"{'Skill Gap Score':<30} {r['final_gap']:>15.2f}", end='')
+            else:
+                print(f" {r['final_gap']:>15.2f}")
+        
+        for algo in ['greedy', 'balanced']:
+            r = results[algo]
+            if algo == 'greedy':
+                print(f"{'Cost Saved':<30} ${r['analysis']['cost']['saved']:>14,.0f}", end='')
+            else:
+                print(f" ${r['analysis']['cost']['saved']:>14,.0f}")
+        
+        for algo in ['greedy', 'balanced']:
+            r = results[algo]
+            critical = len(r['analysis']['critical_impacts'])
+            if algo == 'greedy':
+                print(f"{'Critical Skill Gaps':<30} {critical:>15}", end='')
+            else:
+                print(f" {critical:>15}")
+        
+        # Recommendation
+        if results['greedy']['final_gap'] < results['balanced']['final_gap']:
+            print("\n\n✅ Recommendation: Use GREEDY algorithm (lower skill gap)")
+        else:
+            print("\n\n✅ Recommendation: Use BALANCED algorithm (better overall)")
+        
+        return results
+
+
+def main():
+    """Test integrated system"""
+    
+    # Test cases
+    test_inputs = [
+        "Reduce engineering by 20% and focus heavily on AI and machine learning capabilities",
+        "Cut 25% of the team while shutting down frontend to focus on backend APIs",
+        "Optimize team by 15% to become a data platform company"
+    ]
+    
+    # Use your API key
+    api_key = "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"
+    
+    optimizer = IntegratedOptimizer(api_key=api_key)
+    
+    # Test first case
+    print("\n" + "🚀 "*20)
+    print("TESTING INTEGRATED OPTIMIZATION")
+    print("🚀 "*20)
+    
+    for test_input in test_inputs[:1]:  # Test just the first one
+        result = optimizer.process_optimization_request(test_input)
+        optimizer.print_integrated_results(result)
+        
+        # Save detailed results
+        with open('integrated_result.json', 'w') as f:
+            # Make it JSON serializable
+            output = {
+                'request': result['request'],
+                'team_changes': {
+                    'removed': [e['name'] for e in result['optimization']['removed_employees']],
+                    'remaining_count': len(result['optimization']['remaining_employees']),
+                    'cost_saved': result['optimization']['analysis']['cost']['saved']
+                },
+                'recommendations': result['recommendations']
+            }
+            json.dump(output, f, indent=2)
+        
+        print("\n💾 Detailed results saved to integrated_result.json")
+    
+    # Compare algorithms
+    print("\n\n" + "="*70)
+    print("ALGORITHM COMPARISON TEST")
+    print("="*70)
+    optimizer.compare_algorithms(test_inputs[0])
+
+
+if __name__ == "__main__":
+    main()

intent_parser.py

@@ -0,0 +1,362 @@
+"""
+Intent Parser for Workforce Optimization
+Extracts headcount reduction targets and adjusts skill requirements based on strategic intent
+"""
+
+import json
+from typing import Dict, Any, Optional, Tuple
+from openai import OpenAI
+import os
+from dotenv import load_dotenv
+from pydantic import BaseModel, Field
+from typing import List
+from typing import Literal, Union
+
+# Load environment variables
+load_dotenv()
+
+# Default engineering skills and required levels
+DEFAULT_ENGINEERING_SKILLS = {
+    # Technical Skills
+    "python": 3.0,
+    "javascript": 2.5,
+    "react": 2.5,
+    "backend_design": 3.0,
+    "system_architecture": 3.0,
+    "api_development": 3.0,
+    "database_management": 2.5,
+    "devops": 2.5,
+    "cloud_infrastructure": 2.5,
+    "security": 2.5,
+    
+    # AI/ML Skills
+    "machine_learning": 2.0,
+    "pytorch": 2.0,
+    "tensorflow": 1.5,
+    "data_engineering": 2.5,
+    "data_analysis": 2.5,
+    
+    # Soft Skills
+    "communication": 3.0,
+    "problem_solving": 3.5,
+    "team_collaboration": 3.0,
+    "leadership": 2.0,
+    "project_management": 2.0,
+    
+    # Specialized Skills
+    "frontend_optimization": 2.0,
+    "mobile_development": 1.5,
+    "testing": 2.5,
+    "documentation": 2.5,
+    "code_review": 3.0
+}
+
+# Prompt for extracting reduction target and intent
+EXTRACTION_PROMPT = """You are an expert at understanding workforce optimization requests.
+
+Given a user's input, extract TWO things:
+1. The headcount reduction target (either as a percentage or absolute number)
+2. The strategic intent or initiative (what the company is focusing on)
+
+Return a JSON with this exact structure:
+{
+    "reduction_target": {
+        "type": "percentage" or "absolute",
+        "value": <number>
+    },
+    "intent": {
+        "detected": true/false,
+        "description": "<brief description of the strategic intent>",
+        "focus_areas": ["<area1>", "<area2>", ...],
+        "de_emphasis_areas": ["<area1>", "<area2>", ...]
+    }
+}
+
+Examples:
+- Input: "Reduce headcount by 20%"
+  Output: {"reduction_target": {"type": "percentage", "value": 20}, "intent": {"detected": false}}
+
+- Input: "Cut 5 engineers and focus on AI initiatives"
+  Output: {"reduction_target": {"type": "absolute", "value": 5}, "intent": {"detected": true, "description": "Focus on AI initiatives", "focus_areas": ["artificial intelligence", "machine learning"], "de_emphasis_areas": []}}
+
+- Input: "We need to reduce costs by 15% and shift from web to mobile development"
+  Output: {"reduction_target": {"type": "percentage", "value": 15}, "intent": {"detected": true, "description": "Shift from web to mobile development", "focus_areas": ["mobile development"], "de_emphasis_areas": ["web development"]}}
+
+User Input: {user_input}
+
+Return ONLY valid JSON, no additional text."""
+
+# Prompt for adjusting skill requirements based on intent
+SKILL_ADJUSTMENT_PROMPT = """You are an expert at translating strategic business initiatives into technical skill requirements.
+
+Given:
+1. Current skill requirements for an engineering team
+2. A strategic intent/initiative
+
+Adjust the skill requirement levels to align with the strategic direction.
+
+Rules:
+- You can ONLY adjust the levels of existing skills (range: 0.0 to 5.0)
+- You CANNOT add new skills
+- Increase levels for skills critical to the initiative (max increase: +2.0)
+- Decrease levels for skills less relevant to the initiative (max decrease: -2.0)
+- Most adjustments should be in the 0.5-1.0 range
+- Keep at least a minimum level (1.0) for basic skills even if de-emphasized
+
+Current skill requirements:
+{current_skills}
+
+Strategic Intent: {intent_description}
+Focus Areas: {focus_areas}
+De-emphasis Areas: {de_emphasis_areas}
+
+Return ONLY a JSON object with the SAME skill names and adjusted levels.
+Provide brief reasoning for significant changes (>1.0 change).
+
+Example output format:
+{
+    "adjusted_skills": {
+        "python": 3.5,
+        "javascript": 2.0,
+        ...all skills must be included...
+    },
+    "adjustments_reasoning": {
+        "python": "Increased by 0.5 as it's critical for the initiative",
+        "javascript": "Decreased by 0.5 as frontend is de-emphasized"
+    }
+}"""
+
+class ReductionTarget(BaseModel):
+    type: Union[Literal["percentage"], Literal["absolute"]] = Field(description="The type of reduction target")
+    value: float = Field(description="The value of the reduction target")
+
+class Intent(BaseModel):
+    detected: bool = Field(description="Whether the intent was detected")
+    description: str = Field(description="The description of the intent")
+    focus_areas: List[str] = Field(description="The areas of focus for the intent")
+    de_emphasis_areas: List[str] = Field(description="The areas of de-emphasis for the intent")
+
+
+class ExtactionUnderstanding(BaseModel):
+    reduction_target: ReductionTarget = Field(description="The reduction target")
+    intent: Intent = Field(description="The intent")
+
+class AdjustedSkills(BaseModel):
+    skill: str = Field(description="The skill name")
+    level: float = Field(description="The level of the skill")
+
+class AdjustmentsReasoning(BaseModel):
+    skill: str = Field(description="The skill name")
+    reasoning: str = Field(description="The reasoning for the adjustment")
+
+
+class SkillAdjustment(BaseModel):
+    adjusted_skills: List[AdjustedSkills] = Field(description="The adjusted skill levels")
+    adjustments_reasoning: List[AdjustmentsReasoning] = Field(description="The reasoning for the adjustments")
+
+
+class IntentParser:
+    def __init__(self, api_key: Optional[str] = None):
+        """Initialize the Intent Parser with OpenAI client"""
+        self.api_key = api_key or os.getenv("OPENAI_API_KEY")
+        if not self.api_key:
+            raise ValueError("OpenAI API key not found. Set OPENAI_API_KEY environment variable.")
+        
+        self.client = OpenAI(api_key=self.api_key)
+        self.default_skills = DEFAULT_ENGINEERING_SKILLS.copy()
+    
+    def extract_intent_and_target(self, user_input: str) -> Dict[str, Any]:
+        """
+        Extract headcount reduction target and strategic intent from user input
+        
+        Args:
+            user_input: Natural language input from user
+            
+        Returns:
+            Dictionary with reduction target and intent information
+        """
+        prompt = EXTRACTION_PROMPT.replace("{user_input}", user_input)
+        
+        try:
+            response = self.client.responses.parse(
+                model="gpt-4o",
+                input=[
+                    {"role": "system", "content": "You are a precise JSON generator. Only output valid JSON."},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.1,  # Low temperature for consistent extraction
+                text_format=ExtactionUnderstanding
+            )
+
+            parsed: ExtactionUnderstanding = response.output_parsed
+            return parsed.model_dump()
+            
+        except json.JSONDecodeError as e:
+            print(f"Error parsing JSON response: {e}")
+            return {
+                "reduction_target": None,
+                "intent": {"detected": False},
+                "error": "Failed to parse response"
+            }
+        except Exception as e:
+            print(f"Error calling OpenAI API: {e}")
+            return {
+                "reduction_target": None,
+                "intent": {"detected": False},
+                "error": str(e)
+            }
+    
+    def adjust_skills_for_intent(self, intent_info: Dict[str, Any]) -> Dict[str, float]:
+        """
+        Adjust skill requirements based on detected intent
+        
+        Args:
+            intent_info: Intent information from extraction
+            
+        Returns:
+            Dictionary with adjusted skill levels
+        """
+        # If no intent detected, return default skills
+        if not intent_info.get("detected", False):
+            return self.default_skills.copy()
+        
+        # Prepare the prompt
+        # prompt = SKILL_ADJUSTMENT_PROMPT.format(
+        #     current_skills=json.dumps(self.default_skills, indent=2),
+        #     intent_description=intent_info.get("description", ""),
+        #     focus_areas=json.dumps(intent_info.get("focus_areas", [])),
+        #     de_emphasis_areas=json.dumps(intent_info.get("de_emphasis_areas", []))
+
+        prompt = SKILL_ADJUSTMENT_PROMPT.replace("{current_skills}", json.dumps(self.default_skills, indent=2))
+        prompt = prompt.replace("{intent_description}", intent_info.get("description", ""))
+        prompt = prompt.replace("{focus_areas}", json.dumps(intent_info.get("focus_areas", [])))
+        prompt = prompt.replace("{de_emphasis_areas}", json.dumps(intent_info.get("de_emphasis_areas", [])))
+        
+        try:
+            response = self.client.responses.parse(
+                model="gpt-4o",
+                input=[
+                    {"role": "system", "content": "You are an expert at workforce skill optimization. Adjust skill levels based on strategic initiatives."},
+                    {"role": "user", "content": prompt}
+                ],
+                temperature=0.2,
+                text_format=SkillAdjustment
+            )
+            
+            result: SkillAdjustment = response.output_parsed
+            result = result.model_dump()
+            adjusted_skills = result.get("adjusted_skills", self.default_skills)
+            adjusted_skills = {skill['skill']: skill['level'] for skill in adjusted_skills}
+            print(f"Adjusted skills: {adjusted_skills}")
+            
+            # Validate that all skills are present and within range
+            validated_skills = {}
+            for skill in self.default_skills.keys():
+                if skill in adjusted_skills:
+                    # Ensure skill level is within valid range
+                    level = max(0.0, min(5.0, float(adjusted_skills[skill])))
+                    validated_skills[skill] = level
+                else:
+                    validated_skills[skill] = self.default_skills[skill]
+            result["adjustments_reasoning"] = {skill['skill']: skill['reasoning'] for skill in result["adjustments_reasoning"]}
+            # Print reasoning if available
+            if "adjustments_reasoning" in result:
+                print("\nAdjustment Reasoning:")
+                for skill, reason in result["adjustments_reasoning"].items():
+                    print(f"  - {skill}: {reason}")
+            
+            return validated_skills
+            
+        except Exception as e:
+            print(f"Error adjusting skills: {e}")
+            return self.default_skills.copy()
+    
+    def process_user_request(self, user_input: str) -> Dict[str, Any]:
+        """
+        Complete pipeline: extract intent and adjust skills
+        
+        Args:
+            user_input: Natural language input from user
+            
+        Returns:
+            Complete optimization parameters
+        """
+        print(f"\n{'='*60}")
+        print(f"Processing: {user_input}")
+        print(f"{'='*60}")
+        
+        # Step 1: Extract intent and target
+        print("\n1. Extracting intent and target...")
+        extraction_result = self.extract_intent_and_target(user_input)
+        
+        print(f"\nExtracted:")
+        print(f"  Reduction Target: {extraction_result.get('reduction_target')}")
+        print(f"  Intent Detected: {extraction_result.get('intent', {}).get('detected')}")
+        if extraction_result.get('intent', {}).get('detected'):
+            print(f"  Intent: {extraction_result.get('intent', {}).get('description')}")
+            print(f"  Focus Areas: {extraction_result.get('intent', {}).get('focus_areas')}")
+            print(f"  De-emphasis Areas: {extraction_result.get('intent', {}).get('de_emphasis_areas')}")
+        
+        # Step 2: Adjust skills based on intent
+        print("\n2. Adjusting skill requirements...")
+        adjusted_skills = self.adjust_skills_for_intent(extraction_result.get('intent', {}))
+        
+        # Show significant changes
+        print("\nSignificant Skill Changes:")
+        for skill, new_level in adjusted_skills.items():
+            old_level = self.default_skills[skill]
+            change = new_level - old_level
+            if abs(change) >= 0.5:
+                direction = "↑" if change > 0 else "↓"
+                print(f"  {skill}: {old_level:.1f} → {new_level:.1f} ({direction}{abs(change):.1f})")
+        
+        return {
+            "user_input": user_input,
+            "reduction_target": extraction_result.get('reduction_target'),
+            "intent": extraction_result.get('intent'),
+            "default_skills": self.default_skills,
+            "adjusted_skills": adjusted_skills,
+            "skill_changes": {
+                skill: adjusted_skills[skill] - self.default_skills[skill]
+                for skill in self.default_skills.keys()
+            }
+        }
+
+
+def main():
+    """Test the intent parser with various examples"""
+    
+    # Initialize parser
+    parser = IntentParser()
+    
+    # Test cases
+    test_cases = [
+        "Reduce engineering headcount by 20%",
+        "Cut 15% of the team and focus on AI and machine learning capabilities",
+        "We need to reduce costs by 25%. We're shutting down mobile development and focusing entirely on backend infrastructure",
+        "Reduce headcount by 30% while transitioning from a product company to a services company",
+        "Cut 10 engineers. We're moving all development offshore except for architecture and security",
+        "Optimize team size by 15% and double down on our data platform initiatives"
+    ]
+    
+    results = []
+    for test_input in test_cases:
+        result = parser.process_user_request(test_input)
+        results.append(result)
+        print("\n" + "="*60)
+    
+    # Save results to file for review
+    with open("intent_parsing_results.json", "w") as f:
+        json.dump(results, f, indent=2)
+    
+    print("\n\nResults saved to intent_parsing_results.json")
+
+
+if __name__ == "__main__":
+    # For testing without API key, use mock mode
+    import sys
+    if "--mock" in sys.argv:
+        print("Running in mock mode (no actual API calls)")
+        # Add mock implementation here if needed
+    else:
+        main()

skill_optimizer.py

@@ -0,0 +1,439 @@
+"""
+Skill-Based Workforce Optimization Algorithm
+
+This module optimizes team composition based on:
+1. Maintaining required skill levels
+2. Minimizing skill gap after reduction
+3. Preserving critical capabilities
+"""
+
+import json
+from typing import Dict, List, Tuple, Any
+from itertools import combinations
+
+class SkillOptimizer:
+    def __init__(self, team_data_path='team_data.json', employees_data_path='employees_data.json'):
+        """Initialize the optimizer with team and employee data"""
+        with open(team_data_path, 'r') as f:
+            self.team_data = json.load(f)
+        
+        with open(employees_data_path, 'r') as f:
+            self.employees_data = json.load(f)
+        
+        self.employees = self.employees_data['employees']
+        self.all_skills = self._get_all_skills()
+        
+    def _get_all_skills(self) -> List[str]:
+        """Get list of all unique skills"""
+        skills = set()
+        for emp in self.employees:
+            skills.update(emp['skills'].keys())
+        return sorted(list(skills))
+    
+    def calculate_team_skill_averages(self, employee_list=None) -> Dict[str, float]:
+        """
+        Calculate average skill levels across the team
+        
+        Args:
+            employee_list: Specific list of employees to calculate for (default: all)
+            
+        Returns:
+            Dictionary of skill -> average level
+        """
+        if employee_list is None:
+            employee_list = self.employees
+        
+        skill_totals = {skill: [] for skill in self.all_skills}
+        
+        for emp in employee_list:
+            for skill, level in emp['skills'].items():
+                skill_totals[skill].append(level)
+        
+        skill_averages = {}
+        for skill, levels in skill_totals.items():
+            if levels:
+                skill_averages[skill] = sum(levels) / len(levels)
+            else:
+                skill_averages[skill] = 0.0
+        
+        return skill_averages
+    
+    def calculate_skill_coverage(self, employee_list=None, threshold=3.0) -> Dict[str, Dict]:
+        """
+        Calculate skill coverage metrics
+        
+        Returns dict with:
+        - count: number of people with skill >= threshold
+        - average: average level
+        - max: highest level
+        - experts: people with level >= 4.0
+        """
+        if employee_list is None:
+            employee_list = self.employees
+        
+        coverage = {}
+        for skill in self.all_skills:
+            levels = []
+            experts = 0
+            qualified = 0
+            
+            for emp in employee_list:
+                level = emp['skills'].get(skill, 0)
+                levels.append(level)
+                if level >= 4.0:
+                    experts += 1
+                if level >= threshold:
+                    qualified += 1
+            
+            coverage[skill] = {
+                'average': sum(levels) / len(levels) if levels else 0,
+                'max': max(levels) if levels else 0,
+                'qualified_count': qualified,
+                'expert_count': experts,
+                'total_with_skill': sum(1 for l in levels if l > 1.0)
+            }
+        
+        return coverage
+    
+    def calculate_employee_value(self, employee: Dict, required_skills: Dict[str, float]) -> float:
+        """
+        Calculate value score for an employee based on:
+        1. How well they meet required skills
+        2. Their unique/rare skills
+        3. Their overall skill level
+        """
+        value = 0.0
+        
+        # Factor 1: Meeting required skills
+        for skill, required_level in required_skills.items():
+            emp_level = employee['skills'].get(skill, 0)
+            if emp_level >= required_level:
+                value += (emp_level - required_level + 1) * 2  # Bonus for exceeding
+            else:
+                value -= (required_level - emp_level) * 3  # Penalty for not meeting
+        
+        # Factor 2: Unique skills (check rarity)
+        current_coverage = self.calculate_skill_coverage()
+        for skill, level in employee['skills'].items():
+            if level >= 4.0:  # Expert level
+                # More valuable if few experts
+                expert_count = current_coverage[skill]['expert_count']
+                if expert_count <= 2:
+                    value += 10 / expert_count
+                elif expert_count <= 4:
+                    value += 5 / expert_count
+        
+        # Factor 3: Overall competency
+        avg_skill = sum(employee['skills'].values()) / len(employee['skills'])
+        value += avg_skill * 2
+        
+        # Factor 4: Role criticality
+        if employee['role_id'] in ['tech_lead', 'engineering_manager']:
+            value += 20  # Leadership bonus
+        elif employee['role_id'] in ['ml_engineer', 'senior_backend', 'senior_frontend']:
+            value += 10  # Senior specialist bonus
+        
+        return value
+    
+    def identify_critical_employees(self, required_skills: Dict[str, float], top_n=10) -> List[Dict]:
+        """
+        Identify employees critical for maintaining skill requirements
+        """
+        employee_values = []
+        
+        for emp in self.employees:
+            value = self.calculate_employee_value(emp, required_skills)
+            employee_values.append((value, emp))
+        
+        # Sort by value (highest first)
+        employee_values.sort(key=lambda x: x[0], reverse=True)
+        
+        return [emp for _, emp in employee_values[:top_n]]
+    
+    def calculate_skill_gap(self, remaining_employees: List[Dict], 
+                           required_skills: Dict[str, float]) -> float:
+        """
+        Calculate total skill gap for a team configuration
+        Lower is better (0 = all requirements met)
+        """
+        team_skills = self.calculate_team_skill_averages(remaining_employees)
+        
+        total_gap = 0.0
+        for skill, required_level in required_skills.items():
+            actual_level = team_skills.get(skill, 0)
+            if actual_level < required_level:
+                # Weight critical gaps more heavily
+                gap = required_level - actual_level
+                weight = 2.0 if required_level >= 4.0 else 1.0
+                total_gap += gap * weight
+        
+        return total_gap
+    
+    def greedy_optimization(self, reduction_count: int, 
+                          required_skills: Dict[str, float]) -> Dict[str, Any]:
+        """
+        Greedy algorithm: Remove employees with least impact on skill gaps
+        """
+        remaining = self.employees.copy()
+        removed = []
+        
+        for _ in range(reduction_count):
+            best_removal = None
+            best_gap = float('inf')
+            
+            for i, emp in enumerate(remaining):
+                # Skip critical roles
+                if emp['role_id'] in ['engineering_manager']:
+                    continue
+                
+                # Test removing this employee
+                test_remaining = remaining[:i] + remaining[i+1:]
+                gap = self.calculate_skill_gap(test_remaining, required_skills)
+                
+                if gap < best_gap:
+                    best_gap = gap
+                    best_removal = i
+            
+            if best_removal is not None:
+                removed.append(remaining.pop(best_removal))
+        
+        return {
+            'remaining_employees': remaining,
+            'removed_employees': removed,
+            'final_gap': self.calculate_skill_gap(remaining, required_skills),
+            'algorithm': 'greedy'
+        }
+    
+    def balanced_optimization(self, reduction_count: int,
+                            required_skills: Dict[str, float]) -> Dict[str, Any]:
+        """
+        Balanced algorithm: Considers multiple factors including skill diversity
+        """
+        # Calculate employee scores
+        scores = []
+        for emp in self.employees:
+            # Protect critical roles
+            if emp['role_id'] in ['engineering_manager', 'tech_lead']:
+                score = float('inf')  # Never remove
+            else:
+                value = self.calculate_employee_value(emp, required_skills)
+                redundancy = self._calculate_redundancy(emp)
+                salary_factor = emp['salary'] / 100000  # Normalize salary
+                
+                # Combined score (lower is more likely to be removed)
+                score = value - redundancy * 2 + salary_factor
+            
+            scores.append((score, emp))
+        
+        # Sort by score (keep high scores)
+        scores.sort(key=lambda x: x[0], reverse=True)
+        
+        # Select employees to keep
+        keep_count = len(self.employees) - reduction_count
+        remaining = [emp for _, emp in scores[:keep_count]]
+        removed = [emp for _, emp in scores[keep_count:]]
+        
+        return {
+            'remaining_employees': remaining,
+            'removed_employees': removed,
+            'final_gap': self.calculate_skill_gap(remaining, required_skills),
+            'algorithm': 'balanced'
+        }
+    
+    def _calculate_redundancy(self, employee: Dict) -> float:
+        """Calculate how redundant an employee's skills are"""
+        redundancy = 0.0
+        coverage = self.calculate_skill_coverage()
+        
+        for skill, level in employee['skills'].items():
+            if level >= 3.0:
+                # More redundant if many people have this skill
+                count = coverage[skill]['qualified_count']
+                if count > 5:
+                    redundancy += 2.0
+                elif count > 3:
+                    redundancy += 1.0
+        
+        return redundancy
+    
+    def optimize(self, reduction_percentage: float, 
+                required_skills: Dict[str, float],
+                algorithm='balanced') -> Dict[str, Any]:
+        """
+        Main optimization function
+        
+        Args:
+            reduction_percentage: Percentage to reduce (0-100)
+            required_skills: Skill requirements (adjusted from intent parser)
+            algorithm: 'greedy' or 'balanced'
+        
+        Returns:
+            Optimization results
+        """
+        reduction_count = int(len(self.employees) * (reduction_percentage / 100))
+        
+        print(f"\n{'='*60}")
+        print(f"OPTIMIZATION PARAMETERS")
+        print(f"{'='*60}")
+        print(f"Current team size: {len(self.employees)}")
+        print(f"Reduction target: {reduction_percentage}% ({reduction_count} employees)")
+        print(f"Algorithm: {algorithm}")
+        
+        # Calculate current state
+        current_skills = self.calculate_team_skill_averages()
+        current_coverage = self.calculate_skill_coverage()
+        
+        # Run optimization
+        if algorithm == 'greedy':
+            result = self.greedy_optimization(reduction_count, required_skills)
+        else:
+            result = self.balanced_optimization(reduction_count, required_skills)
+        
+        # Calculate new state
+        new_skills = self.calculate_team_skill_averages(result['remaining_employees'])
+        new_coverage = self.calculate_skill_coverage(result['remaining_employees'])
+        
+        # Add analysis to result
+        result['algorithm'] = algorithm
+        result['analysis'] = {
+            'team_size': {
+                'before': len(self.employees),
+                'after': len(result['remaining_employees']),
+                'reduced': len(result['removed_employees'])
+            },
+            'cost': {
+                'before': sum(e['salary'] for e in self.employees),
+                'after': sum(e['salary'] for e in result['remaining_employees']),
+                'saved': sum(e['salary'] for e in result['removed_employees'])
+            },
+            'skill_changes': {},
+            'critical_impacts': []
+        }
+        
+        # Analyze skill impacts
+        for skill in self.all_skills:
+            before = current_skills.get(skill, 0)
+            after = new_skills.get(skill, 0)
+            required = required_skills.get(skill, 0)
+            
+            change = after - before
+            if abs(change) > 0.1:
+                result['analysis']['skill_changes'][skill] = {
+                    'before': round(before, 2),
+                    'after': round(after, 2),
+                    'change': round(change, 2),
+                    'required': required,
+                    'meets_requirement': after >= required
+                }
+                
+                if after < required and before >= required:
+                    result['analysis']['critical_impacts'].append(
+                        f"{skill}: Dropped below required level ({after:.1f} < {required:.1f})"
+                    )
+        
+        return result
+    
+    def print_optimization_results(self, result: Dict[str, Any]):
+        """Pretty print optimization results"""
+        print(f"\n{'='*60}")
+        print(f"OPTIMIZATION RESULTS")
+        print(f"{'='*60}")
+        
+        analysis = result['analysis']
+        
+        # Team changes
+        print(f"\nTeam Size: {analysis['team_size']['before']} → {analysis['team_size']['after']} "
+              f"(-{analysis['team_size']['reduced']})")
+        print(f"Cost Savings: ${analysis['cost']['saved']:,.0f}/year")
+        print(f"Skill Gap Score: {result['final_gap']:.2f}")
+        
+        # Removed employees
+        print(f"\n{'='*60}")
+        print("REMOVED EMPLOYEES:")
+        print(f"{'='*60}")
+        for emp in sorted(result['removed_employees'], key=lambda x: x['salary'], reverse=True):
+            print(f"  - {emp['name']:<20} ({emp['role']:<25}) ${emp['salary']:,}")
+        
+        # Skill impacts
+        print(f"\n{'='*60}")
+        print("SKILL IMPACTS:")
+        print(f"{'='*60}")
+        
+        if analysis['critical_impacts']:
+            print("\n🚨 CRITICAL IMPACTS:")
+            for impact in analysis['critical_impacts']:
+                print(f"  - {impact}")
+        
+        print("\nSIGNIFICANT SKILL CHANGES:")
+        for skill, data in sorted(analysis['skill_changes'].items(), 
+                                 key=lambda x: abs(x[1]['change']), reverse=True)[:10]:
+            symbol = "✓" if data['meets_requirement'] else "✗"
+            arrow = "↑" if data['change'] > 0 else "↓"
+            print(f"  {symbol} {skill:<25} {data['before']:.1f} → {data['after']:.1f} "
+                  f"({arrow}{abs(data['change']):.1f}) Required: {data['required']:.1f}")
+
+
+def main():
+    """Test the optimization algorithm"""
+    optimizer = SkillOptimizer()
+    
+    # Example: Focus on backend and AI, reduce frontend
+    required_skills = {
+        "python": 3.5,
+        "javascript": 2.0,
+        "react": 2.0,
+        "backend_design": 3.5,
+        "system_architecture": 3.5,
+        "api_development": 3.5,
+        "database_management": 3.0,
+        "devops": 3.0,
+        "cloud_infrastructure": 3.0,
+        "security": 2.5,
+        "machine_learning": 3.0,
+        "pytorch": 2.5,
+        "tensorflow": 2.0,
+        "data_engineering": 3.0,
+        "data_analysis": 3.0,
+        "communication": 3.0,
+        "problem_solving": 3.5,
+        "team_collaboration": 3.0,
+        "leadership": 2.0,
+        "project_management": 2.0,
+        "frontend_optimization": 1.5,
+        "mobile_development": 1.0,
+        "testing": 2.5,
+        "documentation": 2.5,
+        "code_review": 3.0
+    }
+    
+    # Run optimization
+    result = optimizer.optimize(
+        reduction_percentage=20,
+        required_skills=required_skills,
+        algorithm='balanced'
+    )
+    
+    optimizer.print_optimization_results(result)
+    
+    # Save results
+    with open('optimization_result.json', 'w') as f:
+        # Convert employee objects to serializable format
+        output = {
+            'parameters': {
+                'reduction_percentage': 20,
+                'algorithm': 'balanced'
+            },
+            'results': {
+                'removed_employees': [e['name'] for e in result['removed_employees']],
+                'remaining_count': len(result['remaining_employees']),
+                'skill_gap': result['final_gap'],
+                'cost_saved': result['analysis']['cost']['saved']
+            }
+        }
+        json.dump(output, f, indent=2)
+    
+    print("\n✅ Results saved to optimization_result.json")
+
+
+if __name__ == "__main__":
+    main()